This PDF file contains the front matter associated with SPIE Proceedings Volume 13251, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The diaphragm pressure sensor is simple and easy to manufacture. In order to apply it to test the pressure of flow fields such as shock waves, the design method of the diaphragm pressure sensor has been improved. Through material selection, the sensitivity and dynamic characteristics of the sensor have been improved. By optimizing the sensor structure, the surface environment of the sensor-sensitive diaphragm is improved to reduce interference with the pressure field. Theoretical calculation and simulation analysis were conducted on the strain distribution, dynamic characteristics, and fatigue life of the sensor. The results showed that the strain distribution of the diaphragm pressure sensor conforms to the theoretical model. It has a high strain response, and the fatigue life meets the requirements of engineering applications.

The gearbox used in the tractor and double trailer combination has more gears, which is complicated and not easy to design. In order to improve the overall vehicle power performance, this paper jointly builds a virtual simulation platform with MATLAB and TruckSim and carries out transmission optimization design based on the Monte Carlo method. The design combines the original scheme of an 18-speed gearbox and the two-parameter optimal power shifting strategy to form a new scheme and a new shifting strategy by randomly casting points in the region. The study shows that the iterative convergence curve of the Monte Carlo design for transmission gears is continuously increasing; for the fixed driver shifting strategy, the power performance of the manual transmission is effectively improved after optimization, and the improvement effect is more significant in the middle and high speed; the power performance of the vehicle generally decreases after the change of the two-parameter optimal power shifting strategy; the improvement effect of the automatic transmission is relatively limited after optimization on the basis of the original scheme. This kind of virtual simulation platform can help the teaching and practice of the content related to powertrain design in vehicle engineering.

This paper introduces a new type of magnetic bearing structure based on flux extrusion effect. The structure consists of a square stator, a circular rotor, permanent magnets and exciting coils. By generating permanent magnet flux on the four sides of the square stator and electromagnetic flux at the four corners, and forming mixed flux in the sections of maximum area, the restriction of the structure size on the magnetic pole area is broken through, so as to reduce the maximum power consumption of the magnetic bearing and effectively improve the bearing capacity of the magnetic bearing. On this basis, the formulas of electromagnetic force, current stiffness and displacement stiffness are established according to the equivalent magnetic circuit method. The effectiveness of the formulas is verified by the FEA analysis.

In the power system, tower heights vary with different voltage levels. During operation, the suspension clamp in overhead transmission lines may loosen or fall off because the pins are easily affected by external forces. For suspension clamps with different voltage levels, this article designs an intelligent installation tool for live working pins that can communicate wirelessly by studying the R-pinhole positioning system, which can achieve the installation of pins with different orientations. Firstly, the mechanical structure for pin installation was designed; secondly, precise positioning of the pinhole was achieved by using ball-head screws and photoelectric sensors; then, by designing a wireless communication module, precise control of its various functions was achieved; finally, basic functionality and on-site testing were conducted on the designed intelligent pin installation tool. The results show that the average installation time of the designed tool is about 2 minutes. Compared with manual equipotential operation, it greatly improves work efficiency, reduces the labor intensity of operators, and improves the safety factor introduction.

This study aims to address the issues of low integration and incomplete primary and secondary fusion design of intelligent high-voltage switches. Taking ZF11Z-252 kV intelligent high-voltage switches as the research object, a high integration design method is proposed. This method can optimize network structure, be applied to intelligent institutions, and establish multi-dimensional state perception systems. This study is based on the state monitoring and evaluation technology of the SVM algorithm, completing the design and development of the system, and conducting mechanical characteristic monitoring and evaluation experiments. Through the application of comprehensive monitoring and evaluation technology, early hidden dangers of high-voltage switchgear can be identified in a timely manner, avoiding fault deterioration, thus greatly improving the level of intelligence and digitization, meeting the requirements of the new generation of intelligent substations for high-voltage switchgear, and having good promotion and application value.

In the construction of deep-sea submarine optical cable laying, the impact of changes in seabed stability leads to higher construction costs. To this end, the construction margin control technology for deep-sea submarine optical cable laying was studied. The control parameters of the construction margin in the submarine optical cable laying area are calculated, the turning point of the construction margin control of the submarine optical cable laying is determined, and the construction margin control process of the submarine optical cable laying is generated, thus completing the construction margin control of the submarine optical cable laying. The experimental results show that the designed control technology has a good control effect on the construction allowance of submarine optical cable laying, and the laying cost of each layer after construction is low, which has certain economic value.

Owing to significant deflection and insufficient stability during extended operation of live working tools, this study presents an inner lining design which features a hollow structure for the insulation pipe. In place of the former glass fibre reinforcement, basalt fibre has been chosen. Several insulation pipes lined with basalt fibre-reinforced composite (BFRP) and featuring differing outer diameters and wall thicknesses are designed, processed, and tested alongside hollow insulation pipes reinforced with glass fibre-reinforced composite material (GFRP). The test results demonstrate a significant improvement in the mechanical and electrical properties of the new BFRP insulation pipes when compared to the GFRP insulated pipes. The basalt fibre insulation pipe produces just one-third of the leakage current witnessed in the electric evaluation when measured up against the glass fibre pipe. Furthermore, the load-bearing bending ability of the insulation pipe drops with an increase in the diameter-thickness ratio. Compared to traditional glass fibre pipes, the novel BFRP insulation pipe displays roughly 56% more maximum compressive power and approximately 70% better bending ability.

The hydraulic cylinder of a shear-fork high-altitude working vehicle needs a large thrust, a large rate of speed change and a high dead weight in the lifting stage. In view of these problems, the multi-objective optimization of its structure was studied. Firstly, the force analysis and lifting speed analysis of the shear-fork high-altitude working vehicle were carried out to obtain the maximum thrust expression of the hydraulic cylinder and the lifting speed change rate of the working platform, the strength and stability of the shear-fork arm were analyzed, and then the multi-objective optimization model of the structure was established. Then, the multi-objective optimization of the structure was carried out by using the combination of NSGA-II and fuzzy approach degree method, and the optimal solution was obtained. Finally, the results before and after optimization were analyzed and compared. The results show that the maximum thrust of the hydraulic cylinder is reduced by 3.8%, the maximum lifting speed change rate of the working platform is reduced by 9.6%, and the weight of the shear-fork arm is reduced by 17.3%. The force conditions of each hinge point are improved to varying degrees.

During the long-term operation of transmission lines, the contact resistance increases due to insufficient crimping force, poor contact, and oxidation and corrosion of the contact surface at the connection points of wires such as clamps, which in turn leads to heating problems during operation. Heating in transmission lines usually occurs at the clamping position of the line. By monitoring the clamping status of the prone hot wire line, accidents such as wire breakage caused by heating defects can be effectively avoided. At present, the use of infrared monitoring and other heating defect monitoring methods for transmission lines has problems such as low efficiency and inability to monitor the heating status in real time; In addition, currently, online monitoring temperature sensors for transmission line clamps are generally powered by batteries, which poses problems such as difficulty in later operation and maintenance, and complex installation methods. Based on this, this article conducts research on intelligent temperature measurement screw cap technology, develops a passive wireless temperature measurement sensor module based on micro energy harvesting technology and Internet of Things communication technology, integrates the mechanical and electrical integration design of the sensing key module and screw cap structure, develops an intelligent screw cap with passive wireless temperature measurement function that can be quickly installed, realizes real-time monitoring and warning of temperature anomalies, effectively reduces the workload of maintenance personnel, and reduces direct economic losses caused by equipment safety accidents.

This paper optimizes the production process of T12 based on the theory of system simulation, mainly from the balance of production rhythm to simulate the assembly line of the debugging section. The model is established according to the actual situation and the model is analyzed to find out the bottleneck of the model. The causes of the bottleneck are analyzed and the production process is optimized to get better simulation results.

Sport utility vehicles (SUVs) often suffer first-order vibration when the heat engine is running at idle and the air conditioning is off. In the present work, a “source-path-response” fishbone analysis framework was designed to analyze this problem. It was found that the excessive first-order dynamic unbalance of the clutch is the major source of vibration, and the mounting system is the key transfer path of vibration. The vibration can be controlled by regulating the source and transfer path of vibration. Specifically, in terms of the source, the powertrain clutch dynamic unbalance value was set below 1200 g.mm, and the idle speed was increased to 50 rpm to limit the source of excitation; in terms of the transfer path, the size and rigidity of the mounting system were reconfigured to make the difference between the rigid-body mode frequency of the mounting system and the first-order excitation frequency of the engine larger than 1 Hz, so that the frequency coupling problem in the mounting system transfer path can be avoided, and the first-order idle vibration amplitude is reduced by above 50%. The research outcome is of great importance to avoid vibration problems in SUV design.

With the advancement of domestic ocean development initiatives, the requisites for electrical power in large-scale marine development operations surged. Consequently, there was an escalating demand for the laying of marine power cables. In response to this imperative, the study focused on the design and investigation of a 50-ton submarine cable laying A-frame structure. To uphold the comprehensive stability of the mechanism, the A-frame structure’s design adopted an integrated connection approach for each constituent mechanism. The methodology involved employing simulation analysis to assess performance, scrutinize deformation effects, and analyze the vibration modes of the A-frame. The findings indicated that the proposed A-frame structure exhibited commendable mechanical performance and possessed robust anti-vibration capabilities. This outcome served to further substantiate the reliability of the A-frame structure.

In this paper, a new type of dual-channel triangular planar capacitive sensor is proposed, which can overcome the problems of small dynamic detection range and low sensitivity in nondestructive testing (NDT) applications. By establishing a three-dimensional simulation model of the sensor, the influence of structural parameters such as excitation electrode, shielding electrode, protective electrode, and thickness of substrate on its various performance indicators was systematically analyzed using ANSOFT finite element analysis (FEA) software. By studying the corresponding relationships between different performance indicators and structural parameters, a mathematical relationship between penetration depth and electrode structural parameters was established, and the general design principles and methods for dual-channel triangular planar capacitive sensors were determined. Finally, through experimental verification, it was proven that the theoretical analysis results were consistent with the experimental results, providing a theoretical basis for the optimization design and application of the dual-channel triangular planar capacitance sensor.

In this paper, an ESD protection device for high-speed I/O interface circuits is proposed, and an SCR protection device utilizing trap resistance triggering and reduced parasitic capacitance is designed and investigated, and verified in a 0.18 μm CMOS process. The design has a low overshoot voltage under ESD stress, a high failure current, a sufficiently low parasitic capacitance, and a low leakage current, and is therefore suitable for low-voltage ESD protection for high-speed circuits in CMOS processes.

The 0.18 um CMOS process is used to develop a high-precision power-adjustable LED driver circuit for the hardware circuit of a fluorescent fiber-optic temperature sensor. The driver circuit uses the microcontroller system's VDAC conversion output voltage to create a controlled constant-current source, and it is based on the operational amplifier to create the topology of current series negative feedback. The conversion output voltage VDAC is adaptively adjusted to display the fluorescent signal produced by the fluorescent material excited by the LED driver circuit, correctly. The accurate temperature value can be output by the sensor. This circuit has finished the tape out and testing. The findings indicate that the output current may attain a current regulation range of 12 mA under various supply voltage changes, with VDAC varying from 0 to 3.3 V. The output current is barely impacted by temperature, and the change in output current with VDAC at various temperatures is investigated at 4.5 V supply voltage. The circuit shutdown time at 20 Hz frequency is 21.35 us. Good circuit performance makes it suitable for use in fluorescent fiber-optic temperature sensors.

Traffic network design is the core field in transportation planning, and it is also the key point and difficulty. The traditional traffic network design model assume that demand and supply are deterministic which can result in unreasonable planning scheme. This paper assumes that the traffic supply and trip production-attraction are random variables which aims to build new transport network design method with uncertainty theory. A bi-level model is established with combined trip distribution/traffic assignment model to improve the accuracy of predictions. The essence of combinatorial optimization for network design problems has been solved with Monte Carlo simulation, genetic algorithm, and traffic assignment algorithm. The results of the Nguyen Dupuis network show that the parameter of the combined trip distribution/traffic assignment model and degrees of uncertainty in trip production-attraction have an important impact on the decision-making of network design.

Giant magnetoresistive sensors are based on the giant magnetoresistive effect, thanks to the contemporary high-speed, low-cost electronic circuits, new signal processing methods and advanced manufacturing technology, so that its development is very rapid, widely used in various industries. Compared with Hall sensors, anisotropic magnetoresistive sensors and a series of sensors on the market, giant magnetoresistive sensors have obvious advantages in terms of sensitivity, size, power consumption and resolution. This paper firstly compares the giant magnetoresistive sensors with other magnetic sensors, then introduces the principle of giant magnetoresistive effect and the structure of giant magnetoresistive sensors, describes in detail the typical process of making giant magnetoresistive sensors, and finally elaborates on the development history of giant magnetoresistive sensors and looks forward to its development prospect.

Based on GB 12021.4-2013 electric washing machine energy efficiency and water efficiency limit value and grade, it is required to measure the water consumption of the washing machine in the whole process of specific washing. In view of the above, this paper studies the application of PID constant pressure water supply technology in washing machine water efficiency detection equipment, and proposes a Siemens 200 smart based on the research and analysis of the needs and background of washing machine water efficiency detection, and combined with the problems existing in the current detection of washing machine water efficiency. The design of PID constant pressure water supply for washing machine water efficiency detection system in PLC proposes a method to use PID constant pressure water supply technology to improve the performance and accuracy of water efficiency detection equipment. It includes the design of system hardware, the design of control logic, the detailed analysis of the process and control requirements of the detection and control system, and the control and PID monitoring and self-adjustment of PLC are realized through the final online debugging. The use value of the detection system is large, and it has a good detection application prospect.

As a key step of the purification of balanced charged oil, the charging process of impurity particles affects the final purification effect of the balanced charge oil purification device. To enhance the charging effect of impurity particles, a rectangular fin electrode is designed. The numerical simulation shows that the particle charge performance is excellent, compared with the traditional linear electrode. The ideal structure, size, torsion angle and parameter range of ideal working conditions (voltage and flow rate) of the rectangular fin electrode are obtained. The experiment shows that the experimental device equipped with rectangular fin electrode has better lubricating oil filtering effect. The best experimental operating parameters are voltage 14 kV and purification flow 3 L/min. After 120 minutes of purification, the contamination degree of lubricating oil is NAS6, and the removal rate of impurity particles reaches 97%, which meets the purification standard.

In this paper, we propose a Low Voltage Trigger Silicon Controlled Rectifier (LVTSCR) device based on LVTSCR with Deep N-type well (DNW) isolation, realized by embedding reverse diode into the device, operating an additional parasitic PNP bipolar junction transistor (BJT) in the anode terminal and adding an extra N-well(NW)region. The proposed electrostatic discharge (ESD) protection device was developed through a 0.18μm Bipolar CMOS-DMOS (BCD) process. According to the analysis of TCAD simulation and measurement results of transmission line pulse (TLP) testing system, the LVTSCR embedded reverse diode reduces the slope of the reverse TLP I-V curve, which can effectively reduce the reverse conduction resistance. On the basis of the LVTSCR embedded reverse diode, when the additional parasitic PNP BJT operates in the anode terminal, the performance of this device is basically not affected. But the holding voltage of the LVTSCR embedded reverse diode can be improved by inserting additional NW with different widths in the P-well.

In order to increase the heavy-duty AGV load capacity to 60t, this paper proposes a design scheme of AGV frame structure based on the working demand of AGV workshop; based on the designed heavy-duty AGV frame structure, a finite element model of the frame structure is established, and the static simulation of the AGV frame is carried out to get the stress distribution and deformation under the full-loaded working condition. Through the dynamic performance analysis of the AGV frame, the 7th to 12th order modal frequencies and vibration pattern diagrams of the frame are obtained, and the results show that the dynamic performance of the AGV is good, and the frequencies of each order are greater than the road excitation, and no resonance damage will occur.

In this paper, the noise generation mechanism of space station exhaust process is analyzed, and the control schemes of small hole jet and diaphragm type are put forward. A simulated gas release system was constructed to test the influence of various parameters on noise control. It is found that the smaller the aperture, the better the noise reduction effect, the better the noise reduction effect when the hole spacing is 7-12 times the aperture, and the noise control requirements can be met when the series is ≥2. The higher the density of the baffle muffler, the greater the thickness of the substrate, the better the noise reduction effect, and the composite sound-absorbing material inside the substrate can further enhance the noise control effect. It has important guiding significance for the noise control design of the future manned spacecraft exhaust equipment in China.

At present, there is a serious dependence on foreign products in the field of industrial robot servo drives, which restricts the independent development of China's industrial robot industry. Based on this situation, research and development goals for industrial robot servo drives are proposed, and core technologies of servo drives are carried out, including driver hardware circuit design technology, EtherCAT bus communication technology, and high-power power supply circuit design based on IPM, the design and research of the driver core algorithm. The designed servo drive is compatible with mainstream motor feedback interfaces, has a complete fault alarm and protection mechanism, and has the characteristics of high precision, high speed, and high performance, which can meet the application requirements of industrial robot servo drives.

In plateau areas with altitudes exceeding 4000 meters, where air density is only 67% of that at sea level, the lift of unmanned aerial vehicles (UAVs) decreases directly by one-third. The output power and thrust of propulsion systems such as engines and propellers, which rely on air density, are significantly reduced. Consequently, UAVs designed for low-altitude regions are unable to operate in plateau areas. This paper proposes a low-pressure experimental method suitable for UAVs operating in plateau environments, addressing the operational scenarios of a specific type of UAV. This method serves as a reference for performance research on UAVs with similar operational environments.

Currently, during the operation and maintenance of the power system, maintenance personnel often use large vehiclemounted lifting and hoisting equipment to work at heights. However, this method has several issues, including expanding the scope of substation outages, high working difficulty, and posing a high risk to personnel and equipment. To address these problems, this paper proposes the design of special mechanical equipment for operating in the narrow spaces of substations. The equipment uses a crawler walking system and remote control operation, which reduces working difficulty, ensures personnel safety, and enables high-efficiency overhauling without extensive power outages.

With the advent of the information age, the frequency of meetings in the railway industry, including video phone conferences, special office meetings, and report presentations, has reached thousands of times, which implies a large demand for conference content organization and output. Currently, there are various smart conference systems on the market, but for railway scenarios, there are still problems such as high error rates in identifying railway-specific terms, difficulties in separating multiple speakers in the meeting records, and low record confidentiality. This article analyzes the needs of the smart conference recording system for railway scenarios, designs the system architecture and functions, and studies the key technologies of the railway smart conference recording system, which is of great significance for promoting the sharing and exchange of railway conference content, improving the organizational efficiency of railway enterprises, and promoting the intelligent upgrade of railway conferences.

In order to reduce the weight of the rotating carrying device of the tower garage to improve the stability performance of the lifting system, the lightweighting method of the center turntable of the tower garage based on differential evolution algorithm is proposed. Taking the center turntable of a tower garage as the research object, the center turntable is subjected to static analysis, and 160 sets of data are obtained by using the optimal Latin superlattice method to optimize the support vector machine through the particle swarm optimization algorithm to carry out the model training; finally, the predicted data of the model is used in combination with the differential evolution algorithm to find out the best combination of the design variables and round up to obtain the optimal solution. The results show that the maximum deformation and the maximum stress after optimization are within the specified range, and the weight reduction of the final model is 5.26%, which is of great significance for the further research of tower garage.

This paper proposes a method of using machine vision technology related to deep learning to automatically determine the posture of scissor type isolation switches based on photos of the operation area of the power grid where the scissor type isolation switch is located. Firstly, by constructing an image dataset of scissor type isolation switches, opening and closing posture style images were selected, and feature vectors describing landmarks and features were extracted. Next, by using the method of marker point pairing, the pairing points are selected to determine the scissor style isolation switch posture of the image to be paired. The innovation of this paper lies in proposing a new method for generating descriptive feature vectors and pairing feature points. By establishing the scale space of the image and extracting features, it can more accurately determine the posture of the scissor type isolation switch, improving the efficiency and reliability of automatic recognition. This research achievement is of great significance in power grid operation and maintenance, which can improve the safety and efficiency of power grid equipment maintenance, and contribute new technological means and methods to the development of the power industry.

This article uses sensitivity analysis method to analyze the design parameters of the key load-bearing components of heavy-duty robots. Stress and deformation are used as constraint conditions, and the total weight of the robot is used as the objective function to optimize the structure, verify the strength, and perform dynamic analysis of the key components of heavy-duty robots. The results show that, under the constraints of design strength, stiffness, and maximum deformation, the mass of the boom is reduced by 10%.

This study investigates the efficacy of pre-tunnel shading structures in mitigating the "black hole effect" at tunnel entrances to reduce traffic accidents. Utilizing modeling techniques, light reduction performance analyses, 3D printing, and artificial sky simulations, data on the daylight factor of a scaled tunnel model under standard overcast sky conditions were obtained. The analysis reveals that shading structures effectively attenuate the rapid decrease in illumination at tunnel entrances, with top-line symmetry gradient shading pattern exhibiting optimal performance. Furthermore, the implementation of shading structures in tunnel entrances yields substantial energy savings, potentially up to 75%. The study concludes by outlining future research directions, including the integration of shading structures with photovoltaic systems and the refinement of structures for tunnels with specific orientations. More shading structure design and application could be developed, offering improved safety and sustainability in tunnel infrastructure.

In the field of aerospace testing, it is critical for pressure sensors to maintain high sensitivity at high temperatures for pressure testing. Based on indium tin oxide (ITO) piezoresistive effect, a high temperature high sensitivity pressure sensor is designed. Firstly, by employing the theory of small deflection bending of circular thin plates and utilizing COMSOL Multiphysics software to model and simulate sensitive components, the influence of sensitive diaphragm size on sensor performance is analyzed, and the optimal parameters are determined. Then, the chip of 30um line width piezoresistors are prepared by photolithographic sputtering stripping process. Finally, the sensor is packaged using an inverted flush structure and tested. The results show that the sensitivity of the sensor is 31.3μV/kPa/mA at 600℃ ambient temperature and 0.5MPa pressure range, which is higher than most of the high temperature piezoresistive pressure sensors. This research provides valuable insights for future development endeavors aimed at enhancing high temperature piezoresistive pressure sensors.

The widespread application of OTA technology has promoted the process of software-defined vehicles. In order to achieve the development of OTA cloud platform and vehicle terminal communication links and meet the authenticity and integrity requirements of foreign ECE-R156 regulations. This paper designs the four-layer hierarchical architecture of the cloud platform and deploys the cloud platform through front-end and back-end offloading. Vehicle-side architecture design, and definition of version detection, software package download, and update installation during the update process. An OTA system security architecture scheme is proposed, the cloud platform signs the uploaded software package, uses HTTPS encrypted channel transmission, and the vehicle terminal verifies and decrypts the software package. Finally, the OTA test environment is built, and the software upgdate test of the automotive VCU is carried out to verify the effectiveness of the OTA system architecture and the system security scheme.

In order to optimize the design of air flow field and temperature field in the shelter, an accurate method based on CFD is proposed to calculate the air flow field in different configurations. By simulating the different configurations of air conditioning and fresh air system in the shelter, the distribution uniformity of air flow field, temperature field and particulate pollutants in the shelter are calculated, so that the air flow uniformity and air supply rationalization are realized, which provides theoretical support and reference for the design of man-machine environment in the shelter.

The data acquisition board is mainly used to collect the signals of the motor's resolver and synchronizer, it is an interface module of the standard 6U CPCI architecture. The data acquisition board is based on the CPCI bus architecture and uses TI's DSP device TMS320LF2407APGEA as the processing core. The core components include the PCI9052 bridge and dual port RAM, bus driver circuit, CPLD, angle measurement module 1, and angle measurement module 2. The CPLD chip controls the angle measurement module to output rotation data, which is transmitted from the bus driver circuit to the DSP chip. After processing the rotation data, the DSP sends it to the dual port RAM. The CPCI bus uploads the rotation data from the dual port RAM to the main module of the control computer.

The lifespan of a subway system is closely tied to the condition of its tracks. Prompt detection and repair of track issues are pivotal in enhancing the subway's longevity. An in-depth analysis of the fatigue strength characteristics of a subway vehicle track detection device structure was conducted. This involved integrating the EN 13749 and IEC 61373 standards. Subsequently, a finite element model of the subway vehicle track detection device was established, and fatigue simulation design was carried out using HyperMesh.

According to the influence of stopper and drawing speed on the online control of continuous casting crystallizer liquid level, an optimization algorithm combining BP neural network and fuzzy PID has been proposed. Considering the control model, BP neural network is used to quantify the influencing factors of the molten metal inflow to the position of the stopper, and then fuzzy PID is used to correlate the liquid level in the crystallizer with the correction amount of the stopper position, the drawing speed, and other parameters. PID parameters can be adjusted online to achieve optimal control of the liquid level in the crystallizer. Simulation shows that the BP neural network and fuzzy PID can avoid the overshoot of the continuous casting crystallizer level control, make the level fluctuation stabilize in a short time, and reduce the deviation and the influence of uncertain disturbances such as stopper and drawing speed.

In recent years, the noise control technology for sound source equipment has received significant research attention, especially in the field of power grids. Due to the influence of low-frequency noise from transformers, many substations face the risk of exceeding factory boundary noise standards. This article develops a sound absorption and insulation device for transformer noise control. Firstly, a sound absorption component with a cavity in the middle is designed by combining micro-perforated plates and aluminum silicate fiber felt. Subsequently, constrained damping sound insulation components are prepared. Finally, the sound absorption and insulation components were combined, and its noise reduction effect is tested. The results show that after the installation of the device, the sound pressure level of transformer decreases from 65dB (A) to 56dB (A).

Addressing the issue of insufficient real-vehicle validation in the current research on fault-tolerant control of steer-by-wire (SBW) system sensors, an experimental platform for fault-tolerant control of unmanned vehicle SBW sensors was designed based on the Robot Operating System (ROS), Controller Area Network (CAN) communication, and serial communication. The platform was utilized to validate the effectiveness of the fault-tolerant control system for autonomous vehicle SBW sensors through experimental testing and analysis of the results. The outcomes demonstrated that the designed platform successfully accomplished various functions, including the installation and configuration of analytical redundant sensors, software fault injection, actuation of SBW, the implementation of analytical redundancy and fault-tolerant control systems.

This design is a somatosensory controlled vehicle implemented on the STM32F103C8T6 hardware platform. The design consists of a control terminal and a controlled terminal, which can be used to control the vehicle on the controlled terminal to realize the basic motion functions through the handheld control terminal. The control terminal uses MPU6050 as the data collector, which is used by Digital Motion Processor (DMP) for data solving and processing, and calls the relevant functions to complete the control; the whole system utilizes the HC-05 Bluetooth module to realize wireless transmission of data; and the Pulse Width Modulation (PWM) speed regulation is completed by setting the duty cycle. In addition, the overall assembly of the vehicle is realized by circuit construction of each module and software programming.

This paper addresses the Flexible Job Shop Scheduling Problem (FJSP) by developing a mathematical model with the goal of minimizing the maximum makespan, and presenting a solution approach using an improved genetic algorithm. The proposed algorithm incorporates optimized encoding and decoding techniques, applies multiple combinatorial crossover operations for process coding and machine coding, and integrates a taboo search algorithm to enhance local search effectiveness. The method is validated using a set of benchmark algorithms for testing. The experimental results indicate that this enhanced genetic algorithm shows considerable improvement in solving FJSP.

At present, many articles have studied the three-phase PWM bidirectional converter, which is one of the research focuses of grid connected converter. It can be used as SVG or APF to control reactive power and harmonic of power grid. In a DC charging station for electric vehicles, when multiple PWM bidirectional converter modules are connected in parallel to work on the same AC bus, some modules may have DC loads, while others may be unloaded. The traditional approach is to let the unloaded modules exit operation, while the loaded modules only undergo rectification. This approach does not make good use of bidirectional converters for power quality management, nor does it utilize parallel multiplication to improve output characteristics. Therefore, this article studies a system in which all modules, regardless of whether they are loaded or not, do not need to exit operation. When there is a load, the inverter module rectifies to provide energy to the DC side. When there is no load, the inverter module continues to maintain voltage. The presence or absence of a load does not affect the module's participation in managing the reactive current and harmonic current of the power grid. Modules with the same load state use CPS-SPWM to reduce output harmonics. This paper presents the control method of this parallel multiple converters, and the simulation experiment proves the feasibility of the method.

Aiming at the question of whether the static and dynamic properties of door hood could meet the requirements. Firstly, the grid model of the hood was established based on finite element technology, all degrees of freedom of its mounting point were constrainsed, its natural frequency were 35.1Hz, 42.8Hz and 58.4Hz based on modal properties simulation, it didn't overlap with the excitation frequency range, the resonance and noise was avoided, so it could meet modal properties requirements. Secondly, the corresponding load were applied to four typical contact areas, the deformation of each position were obtained by quasi-static stiffness simulation, its maximum deformation was 4.764mm, it was less than the actual engineering requirement value, so it could meet stiffness properties requirements. Thirdly, the frequency response strength was simulated according to vibration excitation, so its displacement and stress distribution were obtained, its maximum stress was 172.0 MPa, it was less than the material yield value, the fatigue strength cracking would be avoided, so it could meet stiffness properties requirements. Lastly, its thickness was collaborative optimized based on multi-properties and multi-objective integration method by adopting non-dominated sorting genetic algorithm, its optimal structural parameters were obtained, the first order mode was 33.2 Hz, the maximum deformation of stiffness was 5.907 mm, its maximum vibration strength stress was 208.3 MPa, its static and dynamic properties could meet design requirements after optimization. Its weight was reduced by 8.42%, the lightweight were achieved.

The basic principles of deep learning face recognition are described, the concepts of deep learning in vivo detection and face recognition are introduced, and the Bottleneck features extracted by the VGG16 deep neural network are inputted into the fully connected network for face classification, and the employee face images are collected to construct a training dataset to train the deep learning model. Based on the Keras framework, the deep learning algorithm program is implemented, MySQL is used to establish the employee information, and the face recognition smart charging cabinet based on deep learning is applied and designed. This intelligent charging cabinet can realize face recognition, unified maintenance of equipment, and condition monitoring, which improves the informatization level of marketing mobile operation terminal equipment management.

Distribution network single-phase grounding fault fault point may ignite the grounding arc, the current use of arccanceling coil arc-canceling traditional arc-canceling method of arc-canceling effect is not ideal, and the current active arc-canceling method because of the arc-canceling path of the arc-canceling effect of the theoretical existence of certain differences. In view of the above problems, this topic uses the hybrid arc elimination method combining active inverters and grounding transformers, and according to the different arc elimination ideas to develop two arc elimination schemes, Scheme 1 for the use of active arc elimination devices to force the neutral voltage to change opposite of target phase voltage, Scheme 2 for the measurement of the parameters of the ground, to the neutral point of the injection of the compensation current of the hybrid current calculation Because the control objective of both schemes is that the bus voltage of the faulted phase is zero, they are all active voltage arc extinction in nature. Using Matlab to simulate the difference between the traditional arc elimination method and the hybrid arc elimination method under different fault conditions and compare the arc elimination effect of the two hybrid arc elimination schemes, the simulation results show that the hybrid arc elimination method can eliminate the arc quickly compared with the traditional arc elimination method, and the arc elimination effect of the hybrid voltage arc elimination scheme is better than that of the hybrid current arc elimination scheme under different fault conditions.

In the process of accelerating a certain UAV to take off, the elevation angle of the launcher needs to be adjusted according to the aerodynamic characteristics of the UAV. The aerodynamic characteristics of the UAV affect the kinetic analysis of the launching process, and in order to ensure that the UAV can take off smoothly and at a predetermined speed, it is necessary to find the optimal launching angle of the launcher to provide data support for the launching kinetic simulation. In this paper, a simple aerodynamic estimation of a certain UAV is carried out based on CFD software, and the amount of calculation is reduced by simplifying the model and the calculation process, and the aerodynamic performance parameters of the UAV are finally obtained through simulation.

The Remaining useful life (RUL) estimation of batteries like lithium batteries have been studied using the Convolutional Neural-networks (CNNs) and Necurrent NN (RNN). Concurrently the transformers-based architectures have been built to predict the Natural Language Processing (NLPs). Utilization of transformers in predicting the text especially the RUL estimation has not been attempted. The research proposed is a novel approach that uses deep learning based custom model of six layers that utilizes feed-forward NN with sequential layers. Data is obtained from Hawaii Natural Energy Institute (HNEI), where 14 lithium batteries of type NMC-LCO 18650 are adopted. The Adam optimizer is used for increasing the model performance. For evaluating the model, the RMSE, MAE and R2 scores are used. The model obtained 99.21% accuracy with R2 score and the obtained MAE is with RMSE as. The model is found significant and efficient in estimating the lithium batteries’ RUL.

A new method for calculating time-varying mesh stiffness (TVMS) has been proposed. This method takes into account the coupling effect between each sliced spur gear on the basis of the traditional slicing method, and accurately calculates the time-varying meshing stiffness of the helical gear. By comparing the calculation results with finite element method and traditional slicing method, the correctness of the proposed method is proven. Subsequently, to investigate the impact of cracks on the gear system, 10 cracks with different parameters were set up, and the variation trends of six statistical indicators such as kurtosis were studied under different degrees of crack propagation. The results showed that kurtosis is more sensitive to crack propagation in helical gears.

With the continuous expansion of the power grid scale and structure, the workload of scheduling and operation has become heavier, while also increasing the risk of being affected by different factors in the power grid. While many factors such as wind, lightning, bird damage, and ice covering have been studied extensively, their combined impact on transmission line operation has received little attention. This paper analyzes the factors that influence the operation of transmission lines and identifies four key factors. Subsequently, a transmission line operation status assessment model is constructed using the Markov chain. Finally, the transmission line operation state prediction model is established based on the improved support vector machine, which includes power supply stability and reliability in the line operation state prediction, and inputs the key influencing factors for model validation, and the results show that the prediction accuracy rate is 84.96%, which proves the practicability of the model.

Microchannel evaporator has a great effect on the heat transfer efficiency, and has broad application prospects in the field of air conditioning refrigeration, electronic components, etc., but the problem of uneven refrigerant flow distribution seriously affects the performance of two-pass microchannel evaporator. Based on the two-pass microchannel evaporator, a new gas ducting device is designed with R134a as the simulation medium to compare the flow distribution and heat transfer of each flat tube with different depth parameters of the gas ducting device by means of numerical simulation. The results show that the two-phase length of the microchannel evaporator increases by 5% on average after adding the gas ducting device, and the greater the inlet flow is, the more obvious the improvement is. The uneven distribution of the total flow after the improvement is reduced by 31% ~ 48%, and the heat transfer is increased by 21%.

Rolling bearings, as key components in the gearbox of high-speed train running gear, are crucial for the establishment of degradation assessment models and the formulation of maintenance strategies. Appropriate bearing degradation models can effectively reduce the cascading failures caused by bearing failures. Addressing the issues of delayed judgment and low accuracy in single reconstruction data-driven models, this paper compares single-layer autoencoders with K-SVD, analyzing their advantages and disadvantages. It proposes an AE-KSVD dual-layer data reconstruction model that combines Auto-Encoders (AE) with Dictionary Learning (K-Singular Value Decomposition, K-SVD). The first layer reconstructs the input signal through autoencoding, and the second layer reconstructs the hidden layer of the autoencoder using K-SVD. This model combines the accuracy of dictionary learning models with the efficiency of autoencoder models and introduces the Particle Swarm Optimization algorithm to optimize the hyperparameters of the autoencoder and dictionary learning, achieving optimal feature representation capabilities for automatic extraction of vibration data. The model's adaptability is significantly enhanced while ensuring superior feature learning capabilities. The model's superiority is validated by comparing the PSO optimization results of the AE-KSVD and the dual-layer autoencoder models using bearing data from Xi'an Jiaotong University and full-life fatigue test data of bearings from Cincinnati.

In response to randomness and fuzziness issues of ship navigation safety evaluation indicators in bridge group water, extension cloud model is applied to evaluation of ship navigation safety in bridge group water which has been providing decision support for improving ship navigation safety, and transformation between qualitative indicators and quantitative indicators is achieved. Firstly, 10 indicators are selected from natural environment, waterway environment, and traffic conditions to establish evaluation indicator system for ship navigation safety in bridge group water. Secondly, Analytic Hierarchy Process (AHP) is used to determine the weights of each indicator. Finally, evaluation model for ship navigation safety in bridge group water based on extension cloud is constructed to analyze the safety status of ship navigation in bridge group water area at the exit of Dongting Lake and determine safety level. The results show that evaluation level of ship traffic flow is higher, the evaluation level of bridge layout and safety facilities is high, the evaluation level of wind and flow is average, and the evaluation level of other indicators is low. Countermeasures and suggestions are proposed for these indicators. The feasibility and applicability of the extension cloud model in evaluation of ship navigation safety in bridge group water are demonstrated through case analysis.

To enhance the safety of maritime transportation, a risk analysis method is conducted for fire and explosion accidents involving dangerous goods at sea. It begins by categorizing dangerous goods based on their distinct characteristics and then identifies five key risk factors: human, ship, cargo, environment, and management. Then, risk evaluation is made based on fuzzy-AHP, the evaluation index system of primary and secondary risk factors is established, and the weight and risk evaluation value of each risk factor are calculated by integrating expert scores. Finally, the qualitative and quantitative risk analysis is summarized, and the corresponding countermeasures and suggestions are put forward from five aspects according to the existing safety management problems.

In response to the high installation requirements and inability to directly measure rotating components in traditional vibration testing technology for condition monitoring and fault diagnosis of ship platform equipment, a studying on the application of laser vibration measurement in mobile platform equipment has been done based on the basic principle of laser vibration measurement. A traditional vibration testing equipment and a laser vibration measurement equipment to conduct vibration testing on a marine refrigerant water pump respectively. The results of test show that laser vibration meters can directly and accurately measure the vibration speed of such equipment; Due to the influence of the platform's own vibration, the measurement of vibration displacement introduces significant errors, which require further processing to obtain accurate displacement signals.

The common faults encountered during the operation and maintenance of the Fuxing train sets are summarized and classified into refrigeration faults, ventilation faults, control and power supply faults, and heating faults. Through the analysis of the composition and working principle of the air conditioning system of the train set, the causes of these failures are summarized, and corresponding treatment methods are proposed for different types of failures to provide important reference for the daily operation and maintenance of the air conditioning system of the Fuxing train set and emergency disposal.

In response to the issues of prolonged execution time and susceptibility to local optima in traditional Particle Swarm Optimization (PSO) algorithms for robotic arm trajectory planning, this paper proposes a 3-5-3 polynomial interpolation trajectory planning method based on an improved PSO algorithm. The algorithm dynamically optimizes the inertia weight and learning factor in the PSO algorithm and uses the sum of interpolation point time as the fitness function. Under velocity constraints, it achieves optimal time trajectory planning. The Elite EC66 robotic arm is selected as the research object, and simulations and data analysis are conducted using Matlab. Experimental results indicate that the improved PSO algorithm exhibits significantly enhanced convergence speed and optimization accuracy compared to traditional PSO algorithms, effectively reducing the time required for the robotic arm to complete target tasks.

Discrete integrated chips are widely used in data acquisition systems due to their small size and large number of channels. In order to effectively collect discrete signals, it is necessary to test the integrated acquisition chip to check whether its function is normal. This article mainly analyzes the occasional discrete self-test anomaly of an embedded computer, analyzes the principle of the cause of the anomaly, investigates and locates the abnormal factors, and conducts chip testing and simulation analysis on the mechanism of the anomaly. Finally, by adding more testing items and screening vectors, the chip was rigorously screened and successfully solved the problem. This improvement measure provides important usage and testing support for the large-scale application of the discrete integrated chip

The compact crane plays a crucial role in equipment replacement and transportation operations within substations, necessitating the resolution of issues pertaining to supporting stability and operational reliability during its use. To address these concerns, a specific compact crane tailored for high-voltage substations is designed, incorporating specially engineered folding legs. Subsequently, theoretical analysis is conducted to ensure force and torque balance across all four legs, enabling calculation of the maximum supporting force under various working conditions. Finally, AMESim software is employed to simulate and analyze the hydraulic system governing the crane's legs while also designing a visual interface for parameter observation and optimization purposes. The results obtained from both theoretical analysis and simulation demonstrate that the proposed hydraulic leg system effectively supports crane operation while ensuring operational stability.

In this paper, the multi-rigid body dynamic analysis of the piston mechanism is carried out for 265 diesel engine. Combing with the acoustic-solid interaction theory, the obtained data are adopted to gain the noise sound pressure change curves and sound pressure level distribution laws. According to the results, the noise will peak at the excitation of a certain frequency band, which provides a theoretical basis for the noise analysis in the working process of 265 diesel engine.

In view of the lack of abnormal samples in the abnormal detection of bearing vibration and the difficulty in improving the accuracy of abnormal detection due to insufficient consideration of working conditionsIn this paper, a multi-condition bearing vibration unsupervised anomaly detection method based on variational autoencoder is proposed. In this method, the correlation analysis between working condition data and vibration data is realized by constructing bearing state characteristic vectors, and the probability distribution model parameters of bearing vibration data are used as vibration characteristics to reduce the calculation and complexity of anomaly detection. The variational autoencoder is trained by using the normal bearing state eigenvector sample to learn the internal law between the vibration features and the working condition features in the normal bearing state eigenvector sample. Based on the similarity of the probability distribution of the vibration data of the two bearings represented by the vibration characteristics in the eigenvector before and after reconstruction, the anomaly detection index anomaly is designed to measure the difference between the internal law between the working condition characteristics and vibration characteristics of the sample to be tested and the normal sample, so as to realize the unsupervised anomaly detection of bearing vibration under multiple working conditions. Simulation results show that the proposed method can fully consider the influence of working conditions on the bearing vibration signal, and realize the unsupervised anomaly detection of bearing vibration under multiple working conditions based on the unlabeled normal bearing state data, which has good anomaly detection accuracy and anti-interference ability.

The main content of this paper is to realize non-contact and visual contamination detection and analysis in substation equipment. Firstly, the experimental platform is built, and the visible light of the incident spectrometer is arranged into the spectral database. Then the database is reduced and screened by convolutional neural network. The filtered new data are classified by YOLOv5s algorithm. When different types of insulators are background substrates, The influence of spectral contamination on classification recognition is different, It is mainly affected by the coverage of the dirty layer and the morphology of the dirty particles.

Limited research has been conducted on the interaction forces between the clamping and lifting mechanism and the front landing gear wheel during the overall turning of towbarless aircraft tractors and the aircraft. To address this issue, Adams software was utilized to model the clamping and lifting mechanism along with the front landing gear for dynamic simulation. Subsequently, an analysis of the simulation results was conducted. The results indicate that during turning, shifts in the aircraft's center of gravity result in fluctuating horizontal forces acting on the clamping mechanism. Furthermore, the angle between the clamping mechanism and the horizontal plane is much more significantly influenced by the turning radius than the turning speed. To ensure that the angle remains within a safe range, it is necessary to utilize the angle-adjusting function of the front landing gear and keep the clamping device as stable as possible. Through this analysis and research, a foundation is provided for the design and improvement of the clamping and lifting mechanism for towbarless aircraft tractors.

Conducting research on the impact of heavy train speed on the characteristics of the wind flow field at the tunnel entrance is crucial for minimizing the waste of coal dust and coal resources resulting from the "piston wind" effect upon the train's entry into the tunnel. Liangjiashan Tunnel uplink as the object of study, based on FLUENT software to establish a dynamic grid model, simulation and analysis of heavy trains at different speeds, different locations at the wind speed and wind pressure change rules, and on-site wind speed and wind pressure real-time monitoring, the alignment between the measured outcomes and simulation findings demonstrates the high precision of the simulation process. The analysis reveals a direct correlation between variations in wind pressure and wind speed with the speed of the heavy train. Notably, the maximum piston wind pressure is observed at a distance of 20 meters from the tunnel entrance, while the maximum piston wind speed occurs at the tunnel's immediate vicinity, at 0 meters. the flow field wind pressure changes compared to the position of the train has a preemptive, the flow field wind speed changes have a lagging; when heavy trains enter the tunnel at speeds of 60 km/h or lower, the train speed can be efficiently mitigated within the tunnel entrance region, minimizing its impact on the wind flow dynamics.

A gas turbine health status evaluation method based on genetic algorithm optimization of BP neural network parameters is proposed to address the instability and singularity of data obtained from sensors in natural gas station combustion driven compressor units, and the low prediction accuracy caused by weak algorithm feature extraction ability. Based on the machine structure and working principle of gas turbines, a health indicator system is constructed and evaluated. Through genetic algorithm selection, crossover, and mutation operations, a new BP neural network parameter combination is generated and its performance is evaluated. Input the preprocessed gas turbine dataset into the BP neural network model, and use the BP algorithm to train the neural network corresponding to each parameter combination. Gradually adjust the parameters of the neural network to better fit the training data and complete state evaluation. According to the experimental results, it can be sure that the GA-BP model has the highest accuracy in health assessment of combustion driven compressor units, reaching 93%, which can effectively solve the problems of dataset instability and difficult health status assessment.

As maritime traffic increases, the sailing environment becomes increasingly complex, necessitating the enhancement of maritime traffic safety. The precise real-time quantification of ship navigation risk is essential for the intelligent management of water traffic, helping to improve the ability to handle emergencies. This study seeks to analyze regional ship navigation risk comprehensively using complex network theory. Firstly, the ship conflict relationship is calculated using DCPA and TCPA to construct a conflict connection topology diagram. Then, using the ship as the node and the conflict relationship as the edge, a dynamic quantification network of regional navigation risk is built, and the regional network risk value is calculated. Through the node deletion method, key ships and key communities are identified, and the total risk is quantified. The results of the study assist in optimizing route planning and enhancing sea traffic safety and efficiency.

Technological advances and changing climatic conditions have ushered in a new era of "Silk Road on Ice" (SRI). However, the growing popularity of SRI and the isolated, pristine environment of the polar ice regions have exacerbated the risks for conventional ice shipping. As shipping risk, especially in the Arctic ice region, involves environmental, economic, technological and managerial aspects, most existing risk assessments and methodologies do not incorporate specific Arctic risk factors to estimate shipping risk from a systemic perspective. This paper proposes a System Dynamics (SD) model to simultaneously incorporate these combined factors into the risk assessment of ice-area shipping, focusing on elucidating the factors shaping the risk of ice-area shipping and the various interlinked responses and feedbacks among the factors. The results show that the proposed risk assessment tool for shipping in icy regions can provide decision-making for advance planning of voyages and thus further improve the reliability of shipping route planning through ice.

Intelligent driving technology is developing rapidly on the fast lane, and autonomous vehicle must be fully tested and verified to be truly applied on a large scale. Safety Of The Intended Functionality (SOTIF) performance and reliability testing are crucial. However, most of the testing for SOTIF both domestically and internationally revolves around three major methods: simulation testing, closed field testing, and public road testing. These three methods all have their drawbacks, resulting in significant time and economic costs for complete testing. To solve this problem, this paper designs a test method for SOTIF performance and reliability with high coverage scenarios based on multi-level element simulation system. By building a scenario simulation system that covers four major scenario levels: road layer, environment layer, dynamic interaction layer, and information layer, high coverage reproduction of the test scenario is carried out in the laboratory environment. The system can not only achieve SOTIF performance test, but also carry out all-weather efficient verification of the reliability of automatic vehicle. The experimental results show that this method has the advantages of three traditional methods and can significantly improve testing efficiency.

Aiming at the problem of lane changing in expressway ramp area to avoid the influence of the driving of this vehicle when the merging vehicles converge into the mainline, a cooperative lane changing guidance method for the mainline of expressway under the V2X (vehicle to everything) environment is innovatively proposed. Firstly, using RSU (Road Side Unit) and OBU (On Board Unit), real-time information such as vehicle speed, acceleration and position is acquired. On this basis, prediction effects and correction coefficients are put in place to construct the safety evaluation coefficients of lane-changing to analyze the safety of the lane-changing process. Finally, a fifth-degree polynomial trajectory model is introduced for lane change trajectory planning with the goal of lane change efficiency and comfort. Simulation experiments commit the model. The experiment shows that the vehicle has excellent smoothness based on the fifthdegree polynomial lane changing trajectory model during the cooperative lane changing process in the main line of the expressway, and the states of displacement and velocity meet the requirements of smoothness and comfort. This in turn validates the feasibility of the proposed collaborative lane changing guidance method outlined in this paper and provides a modeling basis for the study of safe vehicle lane-changing in a V2X environment.

Aiming at the difficulty of measuring the contact load of tapered roller in the main bearing of wind turbine, the smart roller and strain gauge holder for measuring the contact load of the bearing are designed. Taking single-row tapered roller bearing as an example, firstly, a single roller-circle three-dimensional model is established, and a suitable hollow degree is selected. Then, the structure of the strain gauge bracket is designed according to the finite element analysis, and the measuring circuit inside the smart roller is designed. Finally, calibration experiments and static loading simulation experiments are carried out. The results show that the equivalent force change of the hollow roller with a hollow degree of 35% is small; the average measurement error of the designed smart roller is not more than 10% in the range of 0-3t.

The regenerative braking system is one of the core technologies to reduce the energy consumption of electric motorcycles and improve their driving range. In this study, the performance test principle and method for the regenerative braking system of electric motorcycles were introduced, including such test items as steady-state regenerative braking efficiency and transient-state regenerative braking efficiency. The steady-state and transient-state regenerative braking tests were carried out at different speeds. The findings revealed that the steady-state regenerative braking efficiency could reach a maximum of 80.45% and a minimum of 40.11%. Additionally, it was observed that the transient-state regenerative braking efficiency at high speeds is higher than that at low speeds. Subsequently, the factors affecting the regenerative braking performance were analyzed, and the influence laws of speed and braking torque on regenerative braking efficiency were revealed. It was observed that beyond 50% of the rated speed, higher torque correlated with increased efficiency. Conversely, at 100% of the rated speed, lower speeds were associated with decreased regenerative braking efficiency. These findings were elucidated by considering the operational principles of electrical appliances and the flow of energy.

In order to study the influence of wind loads with different time-domain characteristics on the operational safety of highspeed trains, an aerodynamic model of high-speed trains is established by STAR-CCM+, which simulates wind loads at different speeds and outputs aerodynamic forces and torques that are loaded onto the high-speed train dynamics model established by SIMPACK. In different speed intervals, certain working conditions are simulated by controlling the loading time, duration time, and introducing various track irregularities to carry out fluid-structure coupling offline simulation. The results of analyzing the safety indexes of high-speed trains under different working conditions show that: under the condition of constant wind speed, the maximum derailment coefficient is mainly affected by the loading time of wind load, and the shorter the loading time is, the larger the maximum derailment coefficient is; the effect of duration is smaller, mainly due to the effect of local track irregularity within the duration; the changes of other safety indexes are similar to the maximum derailment coefficient, and the shortest loading time of each safety index is not the same; track irregularity will make the maximum safety indexes larger, which puts the high-speed train in a more dangerous state. The higher the car speed, the larger the contribution rate of track irregularity.

In order to analyze the real-time risk of the confrontation between a law enforcement boat and a suspected vessel, the method of simulation confrontation on simulator was used to carry out experimental research. 3122 sampling points in the experiment was collected to observe the changes of rudder angle, heading and ship speed when two ships confronted each other. The simulation model of ship hull collision is established on the ship incurring extrusion, and the risk of extrusion of ship hull structure is analyzed by numerical analysis, which provides theoretical support for commanders to evaluate the risk of collision damage and command decision-making.

A mandatory lane changing decision model for self-driving vehicles is proposed for forced lane changing constrained by objective conditions. First, the NGSIM dataset is analysed to extract the trajectories of completed mandatory lane changing and the trajectories of uncompleted mandatory lane changing as the training data of the lane changing decisionmaking model, and for the problem that it is difficult to obtain the trajectory data of the trajectories that have the intention of mandatory lane changing but have not succeeded in changing lanes, this paper puts forward a method of extracting the target trajectories through the transverse acceleration thresholds, and the lateral acceleration thresholds are obtained from the vehicle's mandatory lane changing through the DBSCAN clustering. The transverse acceleration threshold is obtained by DBSCAN clustering, and the unsuccessful vehicle trajectory is extracted from the trajectory of the completed forced lane change according to this threshold. Finally, based on the Transformer encoder unit, the decision-making model of forced lane changing for self-driving vehicles is constructed. The model is verified to be able to achieve accurate decision-making for forced lane changing, which illustrates the reasonableness of filtering the unfinished trajectories of forced lane changing based on the transverse acceleration threshold, and the accuracy of the forced lane changing model reaches 95.6%, which is better than that of the long and short-term memory LSTM network model and the gated recurrent unit GRU model.

To address the issues of relying on manual maintenance and insufficient digitization in the management of highway electromechanical equipment. This paper introduces the design and development process of the highway electromechanical Digital Twin platform from the perspective of full-lifecycle asset management and digital maintenance. The platform's functions focus on equipment asset management, providing a digital solution for real-time monitoring, fault alarm and diagnosis. The platform collects electromechanical equipment data and highway elements to construct a digital twin system integrated with map services, facilitating high-performance 3D visualization for equipment management. The system has designed a series of core modules to enhance platform performance, including scenario rapid construction, equipment layout schemes simulation, and equipment spatial correlation display. Through the application of a platform at the test site, the platform demonstrated the advantages of digital maintenance, boosting maintenance efficiency, and enhancing user interaction experience. The research has promoted the development of highway digitization.

System identification theory is a impactful tool for modelling vehicle dynamics. Aiming at the situation that the UAV flight test conditions are limited and complete flight data cannot be obtained, a method to estimate the aerodynamic angle and aerodynamic coefficients based on measurable data is proposed. Taking a flying-wing UAV as an example, the longitudinal and transverse-lateral related aerodynamic parameters are identified and calculated by the least squares method, respectively. The comparison between the test data and simulation results verifies the correctness and practicality of the methods. Considering the mathematical nature of the discriminative modelling, the method is generally applicable to fixed-wing UAVs of all aerodynamic layouts.

Using finite element software, using the single beam method and the beam lattice method, the finite element model of a large-width span special-shaped single-box multi-chamber PC cast-in-place box girder and the ANSYS threedimensional solid refined finite element model are established, and the stress calculation results of the single beam model and the beam lattice model are compared with the stress results of the ANSYS solid model from the structural stress, firstly, the accuracy of the finite element calculation methods of the two rod systems is discussed, and then the three finite element models are used to analyze the transverse stress distribution law of each mid-span section under lane loadto explore the variation law of shear hysteresis coefficient under different width span ratios. The results show that compared with the ANSYS solid element calculation results, the calculation results of the beam lattice method are in good agreement with the ANSYS results. The single-beam method cannot reflect the real stress state of a single-box multi-chamber wide-body box girder, nor can it reflect its shear hysteresis effect. Under the action of lane load, comparing the shear hysteresis coefficient under different width-span ratios, it can be seen that the larger the width-span ratio, the more obvious the shear lag effect. For single-box multi-chamber wide bridges, the beam lattice method can directly give the axial force, shear force and bending moment required for design review compared with the ANSYS solid model, and has good calculation accuracy. In the extreme case, i.e., when the uplink line is fully loaded and the downline is unloaded, the maximum deviation rate between the shear hysteresis coefficient of the beam lattice model and the shear hysteresis coefficient of the single-beam model is 91.9%, and it can be concluded that for the wide bridge, the load calculation cannot be carried out by the single-girder model. The research methods and conclusions obtained in this paper can provide a reference for the design review of similar bridges.

In order to investigate the hand-borne vibration transfer characteristics of overhead slap picking operations, reduce the hand-borne vibration hazards of slap picking operators and prevent the occurrence of hand-arm vibration diseases, this study took overhead slap picking operators as the research object, set up the mechanics model of the upper limb of the picking operation, carried out experimental research on the picking rod modes, and analyzed the hand-borne vibration characteristics of the picking operators. It was found that the overhead slap picking operator has a higher risk of triggering hand-arm vibration disease. The axial vibration of the operating rod is much larger than the tangential and radial vibration, which is the main factor triggering hand-transmitted vibration; the radial vibration from the back of the hand through the wrist to the small arm is absorbed in large quantities, and the tangential vibration from the small arm through the elbow joint to the large arm is absorbed in large quantities. In addition, the difference in the body size of the aerial beat picking operator also leads to the difference in the transmission of vibration from the back of the hand to the small arm. Therefore, not only the radial protection of the wrist but also the tangential protection of the elbow joint should be strengthened for overhead slap pickers. The results of this research can not only provide a reference basis for the optimization of the tapping mechanism, handle and carrying device, but also provide an important reference for the early diagnosis and protection of hand-arm vibration disease.

In this study, a thermal runway test method for power batteries of electric bicycles was introduced, including the trigger mode for the thermal runway of batteries and the layout of sensors such as visual sensors, infrared sensors, and smoke sensors. Subsequently, relevant fire tests were carried out to validate the functional reliability of the battery management system (BMS). Additionally, the combustion characteristics, temperature characteristics, smoke, and debris status of electric bicycle fire triggered by the thermal runaway of power batteries were explored. Then, the typical phenomena (e.g., smoke emission, fire breakout, explosion, and deflagration) in the process of fire were analyzed through two lenses of time interval and temperature. It was found through tests that fire broke out 36 s after the thermal runway of the power battery of the electric bicycle, and explosion and deflagration took place 1 min and 23 s after the thermal runaway, which occurred 6 times during the whole process with an impact scope of 4.48 m. During the combustion, the maximum height of flame combustion was greater than 2.5 m, the maximum width of flame combustion was about 1.3 m, and the highest infrared temperature could reach 956.5℃. Analyzing the change laws of fire temperature of electric bicycles and the flame propagation characteristics based on test results will be of great reference significance to the fire safety design and protection of electric bicycles.

Cooling water channel is the core structure of diesel engine cooling, and its flow characteristics have an important impact on the reliability and cooling effect of the diesel engine, a motorcycle tricycle high-pressure common rail single-cylinder diesel engine as a research object, in order to understand the change of the engine's water jacket flow characteristics and the rationality of the design of the water jacket structure, this paper utilizes the CFD method of the engine's cooling water channel inside the coolant flow characteristics of the simulation. Through the analysis of the cooling water channel speed, pressure, turbulent kinetic energy flow characteristics, it is found that the average flow speed of the coolant is lower than 0.5m/s, and the coolant flow cyclicity does not meet the design requirements; the pressure inside the cooling water cavity, the turbulent kinetic energy distribution are in line with the design requirements. Adopting increase in inlet flow rate and structure optimization scheme, the average flow rate of the overall coolant is increased by 165.62% compared with the original scheme, and the overall coolant fluidity is improved.

Starting from functional safety requirements and the current development status of automotive diagnostics, this article elaborates on the deficiencies in the existing system level diagnostic design work and diagnostic development process in the automotive industry. By analyzing the development paths of diagnostic technology in the automotive and equipment industries, and summarizing the process of equipment testability development, a forward development process for automotive diagnosis based on testability theory was formulated in combination with the development process of equipment testability.

At present, the presentation of the knowledge graph of train-controlled on-board equipment is only one model in most cases, the entity extraction process is more complicated, and there is a lack of Q&A system and other related practical applications. In view of the above problems, the paper proposes to apply the deep learning model to the entity recognition in order to improve the accuracy rate, F₁, and increase the faults of the other four models to make the presentation of the graph more complete, and ultimately combine the knowledge graph, graph database, natural language processing, knowledge quiz and other technologies are combined with this field. This paper adopts entity extraction,relationship extraction, entity alignment and knowledge storage to create a knowledge map of train-controlled on-board equipment faults, and accordingly realises the construction of a Q&A system to provide auxiliary decision-making for railway maintenance personnel.

In order to improve the vibration characteristics of the electronic water pump, a radial electromagnetic force distribution model is established. The mechanism of the impact of harmonic currents on the vibration characteristics of the electronic water pump is studied. By using the harmonic voltage compensation method, a new synchronous rotating coordinate system is established in the control system of the electronic water pump. A PI controller is used to achieve tracking control of specific-order harmonic currents and output harmonic compensation voltages, thus suppressing specific-order harmonic currents. Experimental results show that the suppression effect of this method on harmonic currents can reach up to 83%, and the suppression effect on the vibration acceleration at the corresponding frequency can reach up to 75%.

To improve the synchronization control accuracy of the lifting double cylinders of the segment erector under the influence of unbalanced load in the actual assembling pipe piece task and to accelerate the efficiency of the pipe piece assembling, this paper proposes a cross-coupled synchronization position control strategy of the double cylinders based on the back-stepping sliding mode position control. The mathematical model of the lifting double-cylinder system is established by MATLAB/Simulink, the back-stepping sliding mode position controller is designed according to the state space equation of the deduced system, and the hydraulic cylinder speed control is realized by combining with the speed feed-forward compensation. Finally, the cross-coupling control strategy is added for the double-cylinder error compensation. The effectiveness of the control strategy designed in this paper is verified by simulation analysis.

Explosion proof lithium batteries, as a power source for vehicles, have significant advantages in working environments underground coal mines, such as zero emissions and low noise, which are crucial for improving working conditions and increasing production efficiency in mines. However, in practical applications, this type of vehicle faces problems such as overheating when going uphill, rapid battery performance degradation, and frequent braking when going downhill. This article studies the design parameters of power batteries under complex working conditions in coal mines, such as temperature, current, voltage, and management unit, and further tests in actual environments to analyze and evaluate the impact of power battery characteristics on the overall power and economic performance of the vehicle. It proposes solutions to improve the performance of power batteries for coal mine vehicles, such as improving battery structure, optimizing charging strategies, upgrading battery management systems, etc., to enhance their adaptability under special working conditions.

Stepper motors are susceptible to a variety of faults during operation, which can compromise their performance and potentially lead to system shutdown or damage. Such issues can result in production interruptions and increased maintenance costs. This paper introduces a novel method for detecting stepper motor faults, utilizing the K-means clustering algorithm in conjunction with vibration sensors. By capturing vibration signals from the stepper motor and processing them via a microcontroller, we achieve real-time monitoring and classification of potential faults. Experimental results demonstrate that this method yields effective clustering outcomes, offering a promising solution for stepper motor fault detection.

With the rapid advancement of the times and technology, the updating of automobiles is very swift. The popularization of automobiles brings convenience to life, but also numerous problems. Safety accidents caused by being mistakenly left in the car have become a significant proportion of non-traffic accident deaths inside vehicles. This paper focuses on a vehicle detection device aimed at preventing accidental human occupancy. By utilizing the central control function of a microcontroller, it achieves data collection, display, judgment, and corresponding operation functions of various devices. It uses human and sound sensors to detect the presence of humans in the vehicle, preventing dangerous situations such as children or the elderly being trapped inside.

In order to understand the design of electrical automation mechanical control system, a research on the design of electrical automation mechanical control system based on mechatronics is put forward. Firstly, this paper analyzes the integrated design of automatic control technology of electromechanical control system, and combines the design method to understand its application and enhance the understanding of electromechanical control system. Secondly, the integrated design of the system is analyzed, and the hydraulic impactor is taken as an example to illustrate that the quality of mechanical work can be improved through the integrated design of machinery. Finally, it shows that mechatronics is a product based on the combination of automatic control technology and electromechanical control system. Applying mechatronics to the production process of enterprises can further improve the production quality and efficiency of enterprises, make the investment and income more coordinated, and finally let enterprises take the road of intelligent development.

To locate high-risk areas of ice-dancing disasters on transmission lines and maintain the normal operation of the power grid, an automated positioning method for high-risk areas of ice-dancing disasters on 220 kV high-voltage transmission lines was studied. The three-axis acceleration and angular velocity signals of high-voltage transmission lines during icing and dancing were collected, and a wavelet packet transform method was used to preprocess the signals for noise removal. A time-varying measurement matrix was established to extract high-voltage transmission lines’ amplitude and frequency characteristics during icing and galloping disasters. A wavelet neural network was constructed to determine the positional relationship between the transmission line’s characteristics and the high-risk area affected by icing and galloping disasters. The motion characteristics of the high-voltage transmission line were determined using this network. The automatic location of high-risk areas was accomplished by applying a particle swarm optimization wavelet neural network. The experimental results show that the denoising effect of this method is good. When the number of nodes in the hidden layer of the wavelet neural network is 150, the positioning error is the smallest, and it can accurately and accurately locate high-risk areas where ice-dancing disasters occur.

In order to effectively utilize energy storage characteristics, balance energy fluctuations and supply and demand of renewable energy, optimize economic and environmental benefits, and promote sustainable development of energy systems. This article proposes a multi-objective dispatching method for pumped storage power stations that considers the volatility of renewable energy. Based on the analysis of the volatility of renewable energy, the objective function is defined as the minimum remaining load and the peak-valley difference, and an energy dispatching model is constructed. The flower pollination algorithm is used to solve the multi-objective scheduling model by obtaining the optimal fitness function value, and the multi-objective scheduling of pumped storage power station is completed. Experimental results show that the average remaining load and peak-valley difference of this method are both low.

In order to enhance the efficiency of power plant risk management and control, this study focuses on the allocation of risk control tasks and the planning of risk inspection routes for the first time. We propose a dual-objective optimization model based on NSGA-II, which takes into account both fairness in allocation and control costs. In the design of the model, we consider various practical aspects of the enterprise, including different types of risk points, average speeds on different road sections, and the possibility of multiple personnel passing through the same risk point. Taking the risk distribution network of 28 risk points and 40 road sections in two regions of a certain enterprise as an example, there are a total of 3 available inspection personnel for allocation. In this case, we obtained 22 sets of Pareto solutions, including schemes with the shortest time, the most fair allocation, and those that balance both factors. This model has generality across various types of enterprises and can provide better decision-making references for the allocation of risk control tasks and the planning of inspection routes.

This paper investigates high type control system with the LuGre friction compensation in Optical-electric Tracking System. Firstly, it introduces the basic principles and application scenarios of the high type control and LuGre friction model and analyzes its advantages and shortcomings in high-performance motion control systems. Afterwards, this paper proposes an improved LuGre friction compensation method based on high type control, which tunes the high type control system using multi-objective optimization algorithms, fits the LuGre friction model, and adds a friction compensation loop to compensate for the friction force during low-speed motion of the platform. To verify the effectiveness of the proposed method, tracking experiments were conducted on a single-axis horizontal photoelectric tracking platform. The experimental results show that compared with the traditional type-I and pure type-III control methods, the proposed LuGre friction compensation method based on high-type control has better control performance and higher robustness, effectively reducing the tracking error of the system and improving the accuracy and stability of the motion control system.

In this section, a differential countermeasure guidance law for target active defence is proposed for the "three-body" game confrontation situation, in which the target can release a defence missile to assist itself in breaking through the defence when it is manoeuvring to avoid the interceptor missile. Firstly, based on the zero-lag dynamics model, the relative motion model of the target, interceptor missile and target is established. Then, based on the linear-quadratic differential countermeasure theory, the optimal control strategy of each vehicle is designed. Finally, the confrontation scenarios under different cooperation situations between the target and the defence missile are compared and analyzed in the simulation. The results show that when the target acts as a lure to cooperate with the defence missile with a small manoeuvre in the attack on the interceptor, the survival ability of the target can be effectively improved. The results show that when the target acts as a lure and attacks the interceptor with a smaller manoeuvre in cooperation with the defence bomb, the target's survivability can be effectively improved.

The inversion of nuclear magnetic logging data is used to obtain nuclear magnetic parameters such as the lateral relaxation time of fluids in reservoir pores by inversion calculation of spin echo strings observed from nuclear magnetic logging. The nuclear magnetic parameters can be used to calculate the porosity, permeability, oil, gas, and water saturation of the rock. The mathematical model for the inversion of nuclear magnetic parameters of pore fluids with spin echo strings is the first type of Fredholm integral equation. In this paper, an extended singular cost decomposition algorithm and the linear truncation algorithm are proposed by analyzing the relationship between the most fulfilling quantity of retained singular values and the signal-to-noise ratio. This algorithm can recognize the spectral inversion shortly and effectively, and it is steadier than the standard singular price decomposition algorithm, i.e., the solution does not change a lot when the signal-to-noise ratio modifies a lot. The linear truncation algorithm can be applied to the spectral inversion with low SNR (SNR>10), and it can maintain the realism of the relaxation spectral distribution at very low SNR.

In the electric power system, transmission lines perform a vital role. This article provides a comprehensive analysis of the current situation and existing problems in the maintenance of transmission and transformation lines through the practical summary of maintenance work, in order to provide a reference for improving the safe operation level of transmission and transformation lines. This article first provides a detailed introduction to the Back Propagation neural network (BPNN) and intelligent optimization of transmission lines. Then, it explores the design of an intelligent optimization system for transmission lines based on BPNN. At the same time, it discusses the design of an optimization system for transmission lines based on the algorithm proposed in this article. Finally, experiments were conducted to verify that the intelligent optimization system for transmission lines based on BPNNs has better accuracy and accuracy in detecting line faults than systems based on random forest models and convolutional block attention models. The fault detection rate of the algorithm in this article is 16% and 21% higher than that of the random forest model and convolutional block attention model systems. The fault location accuracy of the algorithm in this article is 10.88% and 16.11% higher than that of the systems using random forest models and convolutional block attention models. The accuracy of fault type recognition in this algorithm is higher than that of the random forest model and convolutional block attention model systems by 7% and 17.12%, respectively.

With the development of aviation technology, improving flight safety has become a focus. The introduction of artificial intelligence (AI) technology is a key method to improve aircraft landing safety. It can effectively reduce human errors, especially in harsh weather conditions, and its real-time data processing and response capabilities greatly exceed traditional methods. Through experimental data, it can be seen that the landing accuracy of the two aircraft in the AI automatic landing control system experiment is 98% and 97%, the data transmission accuracy is 96% and 97%, the navigation environment judgment accuracy is 96% and 98%, and the route accuracy is both 97%. It is not difficult to find that AI not only improves the punctuality of flights but also helps reduce aircraft wear and tear, extend service life, and reduce maintenance costs for airlines. The advanced data analysis capabilities of AI enable aircraft to safely land in more complex and challenging environments, improving the adaptability of flight missions. Finally, the application of AI in automatic landing systems can help reduce the environmental impact of the aviation industry and promote its sustainable development by reducing unnecessary fuel consumption.

To better support the research of counter UAV systems on swarm UAV targets and meet the needs of dynamic deduction and detection modeling of swarm UAVs, a swarm UAV scene simulation method based on differential flatness and swarm intelligence is proposed. Based on the trajectory generation model of cluster algorithm, dynamic algorithm and spatial division technology, this method implements group strategies such as exclusion, attraction and velocity alignment and ensures that the trajectory generation meets point-to-point navigation while having advanced group behavior. Through simulation experiments, the superiority of this technology in generating complex swarm UAV scenarios can be proved, which can effectively support the dynamic deduction requirements of counter UAV systems for swarm UAV targets.

In the process of sharing power data through online channels, there exists a potential risk of data leakage, necessitating effective data traceability of leaked information. This paper proposes a novel method for tracing semi-structured pow-er data based on robust zero-watermarking, which enables traceability even when the leaked data undergoes local modifications. Initially, relevant key-value pairs are extracted from the semi-structured power data to create a feature sequence as the carrier for the zero-watermark. Subsequently, the feature sequence and the watermark body are divided into equal-sized blocks. By integrating erasure codes and redundant error correction codes theory, an intermediate sequence is obtained through a transfer matrix, and then encoded using error correction codes. Finally, the error-corrected watermark information is embedded into the feature sequence, generating a robust zero-watermark for the power data. During the tracing process, the robust zero-watermark of the tracked data is extracted and analyzed to effectively identify and locate data anomalies. Experimental and simulation results demonstrate that the proposed method achieves a watermark extraction success rate of over 98%, while ensuring data security. The presented approach holds significant application value in the monitoring and anomaly tracing of power systems' data.

Aiming to solve the problems of slow convergence speed and poor positioning accuracy of the position servo system, we presents a novel second-order adaptive non-singular fast terminal sliding mode control method for the translation mechanism. First, we analyze a mathematical model of the translation mechanism that includes the uncertainty of other models. Then, we design a backstepping 2th-order adaptive non-singular terminal sliding mode controller (2ABNFTSMC) using the backstepping principle, which updates the control gain by establishing adaptive rules, resulting in high control accuracy. We theoretically demonstrate the stability of the method. By comparing with the traditional PI and sliding mode controllers, we verified the performance of the designed and developed controllers. The simulation results verify the effectiveness of the proposed 2ABFNTSMC in accelerating convergence and reducing steady-state error.

In view of the shortcomings of current wireless charging technology for electric vehicles, this article provides a new wireless charging solution that achieves high power and high efficiency through multi-channel concurrency and multi-channel reception. First, the design scheme was analyzed; then a multi-channel circuit that complied with the wireless charging standards of electric vehicles was designed; finally, the scheme was tested by making a model on an electric moped and found that the scheme was feasible. It provides new solutions and solutions for the application of wireless charging technology in the field of electric vehicles.

The COVID-19 pandemic led to disruptions in the agricultural supply chain. This paper adopts a multi-agent-based modelling approach and proposes three supply chain strategies: alternative suppliers, emergency inventory, and mobilization of inventory from other logistics center. It simulates the impact of implementing these strategies on the agricultural supply chain resilience and key economic indicators during disruptions. The simulation results indicate that all three strategies enhance the agricultural supply chain resilience, contributing to meeting market demands.