The method for achieving non-collinear phase matching in stimulated polariton scattering (SPS) using cross-pumping is proposed. Under sub-nanosecond pulse laser pumping conditions, tunable narrowband Stokes light output is obtained. The cross-pumping configuration is established by total internal reflection of the pump light on the side of the crystal. By rotating the crystal, altering the total internal reflection angle of the pump light, thereby changing the phase matching condition and tuning the frequency of the output Stokes light. The output Stokes light is with a linewidth of 0.2 nm. The wavelength tuning range is 1068 nm to 1075 nm. At a pump energy of 8 mJ, the output energy is 1.1 mJ, corresponding to a conversion efficiency of 13.8%. This study provides a reference for phase matching methods in nonlinear parametric conversion processes.
This paper presents a study on continuous wave tunable CO2 lasers, which could be used in good pumping source of optically pumped gas THz laser. A series of comparative experiments to investigate the effects of cavity structure and discharge current on laser tunability and output power was conducted. The wavelength selection of CO2 lasers primarily achieved by altering the angle of incidence of the blazing grating. Two enclosed CO2 laser tubes were designed and customized, one was used for external cavity structure and the other was used for semi-external cavity structure research. Using the semi-external cavity structure, 78 lines was obtained at the range of 9-11μm, and achieved 60.7 W of laser output power at a wavelength of 10.59 μm. A beam quality factor of M2 = 1.68 was measured using the traveling 90/10 knife-edge method.
With the improvement of the level of social and economic development and the improvement of living standards, the amount of garbage output in China is increasing year by year, and the problem of garbage disposal is becoming more and more serious. In view of this situation, China began to carry out garbage classification. Garbage classification can give full play to the utilization value of resources and means of production and reduce the impact of human economic activities on nature. Therefore, aiming at the problems of difficult garbage classification and low efficiency of manual sorting, this paper designed and developed an intelligent garbage sorting vehicle. The system combines the background replacement algorithm to enhance the garbage data set, detects and classifies the garbage targets through the improved YOLOv5 network model, and finally realizes the garbage sorting work through the all-terrain intelligent vehicle equipped with mechanical arm. Through the test of garbage sorting in the real environment, the success rate of garbage identification by the intelligent garbage truck is 85%, which can meet the needs of garbage sorting in real life.
The existing fire alarm system has strict distance and installation requirements between the fire point and the detector, and is easy to be interfered by environmental factors. It is not suitable for places with large space and many interference factors such as Climbazole production line. This paper proposed a flame image detection technology based on RGB+HSI color model and the detection system is designed and developed. The experimental results show that the flame image detection system based on RGB+HSI color model has the better recognition efficiency, which meets the real-time and accuracy requirements for early flame image detection in Climbazole production line.
Non-destructive testing technology for large grinding wheel geometry is getting more and more attention from the industry. A device based on machine vision technology for intelligent measurement of large grinding wheel size is introduced. After calibrating and measuring the inside and outside radius of the grinding wheel and the thickness of the grinding wheel, intelligent detection is realized through a series of operations such as binarization of the original map, filling, expanding, outline extraction and outline coordinate extraction through hardware design and software programming. The hardware requirements of this design are simple. When measuring the radius of a grinding wheel, the method described in this paper gives the results of radius and height measurements with accuracy up to 5mm and 1mm, respectively. Finally, through repeated measurement experiments, the intelligent detection device of large grinding wheel size established in this paper can effectively solve the problems of field calibration of large grinding wheel and fast detection of inside and outside diameters.
The train wheelset is a crucial part of railway vehicles, and its damage may lead to serious safety accidents. Therefore, it is imperative to detect tread damage timely and accurately. With the rapid development of deep learning, the image detection method based on a convolutional neural network (CNN) has played an important role. Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the target detection field. The algorithm has achieved excellent results in target detection, but there is a low recognition rate for small targets. Therefore, we propose an improved SSD target detection algorithm. The Original SSD algorithm is ineffective in detecting small targets with pits and cracks, so conv3-3 is selected to join the detection. We optimize convolution kernel parameters; the convolution layer contains more small target details. Compared with the original SSD, the Mean Average Precision (MAP) of tread defect is improved by 4.38%, and the MAP of small target detection is enhanced by 7.24%. This algorithm has a better performance in detection accuracy.
Foreign fibers in cotton have serious adverse effects on the quality of textile products, so its effective identification and elimination has important significance and social value. To solve the above problems, we propose a fusion image pretreatment method based on limited contrast adaptive histogram equalization ( CLAHE ) and wavelet analysis ( WT ), The collected cotton polarization images were processed by WT & CLAHE, which effectively improved the contrast of anisotropic fibers in cotton images, and laid the foundation for the rapid and accurate identification of various anisotropic fibers in cotton in the later stage, It laid a foundation for the rapid and accurate identification of all kinds of anisotropic fibers in cotton in the later stage. Compared with manual and systematic detection, the results showed that technical personnel and detection system could accurately detect and identify dead leaves, white paper and color paper without interference from external environment and foreign fiber size. For white wool, hair and mulch film due to similar color or shape is small, technical personnel in the detection is easy to miss, and the detection system in WT & CLAHE image pretreatment, white wool, hair and mulch film detection accuracy is obviously due to artificial detection, especially for the mulch film this is not easy to detect foreign fiber has good recognition effect.
With rapid development of rail transport in our country, more and more people choose because of on time, fast and convenient. Safety of the subway is urgent with passenger increasing, and it's very important to inspire hidden danger. The paper proposed The auto-inspection method based on Infrared Laser Imaging and Deep Learning to detect foreign objects between subway doors and the platform screen doors(PSDs). Fast-RCNN Algorithm based on TensorFlow Deep Learning frame was adopted and the image information were fused with classification model, vgg16. The detecting system was built and experiments were made and analyzed. The experimental results showed that this system and method was robust to The illumination variations and focussing. The system is simple and cost-effective and The algorithm is promising for detecting accuracy. The method and technology can be potentially applied for The subway safety detection.
Rolling quality is a key index of the cotton quality, which directly influences the quality of the lint and textiles, however, it is mainly decided through visual classification by skilled personnel. In order to realize the intelligent rapid classification of cotton quality, this paper proposed a decision-level fusion recognition method for the cotton quality grade based on colored-image information. After the preprocessing of images, RGB and HSV features were calculated, respectively. The features are normalization processed and principal component analysis (PCA) is employed to extract the greater contribution features of RGB and HSV images, which are adopted as BP neural network (BPNN) input parameters to identify the quality grade recognition of cotton, respectively, and then output parameters of BPNN are used as independent evidence to construct Basic Probability Assignment (BPA). Finally, D-S Theory is used to obtain the decision fusion and realize the high accuracy the recognition of cotton quality grades. The compared experimental results show that the precision of proposed method is significantly superior to classification using RGB and HSV features respectively. The method provided in this paper can realize the intelligent rapid classification of cotton quality, and proves the feasibility of cotton-graded artificial intelligent classification.
Ginned cotton’s quality is one significant factor to evaluate the cotton grade and influence the yarn qualities. Ginned cotton is always mixed with contaminants during picking, storing, drying, transporting, purchasing, and processing. Manual evaluation is time consuming, labor intensive, and unreliable. This paper proposed a fast feature extraction algorithm is presented for the measurement of cotton defects in ginned cotton within a complex background. The edge of cotton defects are extracted from fusion of three channel image of color image. A criterion based on areas is proposed to achieve fast morphological analysis. The different defects can be inspected automatically based on different thresholds. The comparison experiments between measuring system and technician were done and analyzed. The costing time of measuring system was less than 30 seconds, and accuracy was 89.5%. The measuring results show the method can meet with the requirement of grade determination of ginned cottons.
Train wheel tread will produce scrapes, peelings and other defects due to the friction between wheel and rail surface for its long-running process. Tread defects not only have a bad affect for the stability and security of the operation of the vehicle, but reduce the service life of the bearing and rail facilities and do harm for the safety and efficiency of rail transport. Among them tread scrapes and peelings are the two main defects of train tread. In order to achieve the detection and classification of tread scrapes and peelings, a method based on image processing and BP Neural Networks model was presented for detection and classification of scrapes and peelings in train wheel tread. First we preprocess the acquired images, and extract the defects. Next calculate four characteristic parameters including energy, entropy, moment of inertia and correlation, and eventually we calculate the mean and standard deviation of those characteristic parameters as the 8 texture parameters. Then we adopt principal component analysis method to turn 8 texture characteristic parameters of these two types of defects into three unrelated comprehensive variables. Finally by extracting and analysis the texture features of tread defects, the recognition correct rate reaches to 93.3%. The result shows that the method can meet the requirement of train wheel tread defects online-measurement.
The triangulation measurement is a kind of active vision measurement. The laser triangulation displacement is widely used with advantages of non-contact, high precision, high sensitivity. The measuring error will increase with the nonlinear and noise disturbance when sensors work in large distance. The paper introduces the principle of laser triangulation measurement and analyzes the measuring error and establishes the compensation error. Spot centroid is extracted with digital image processing technology to increase noise-signal ratio. Results of simulation and experiment show the method can meet requirement of large distance and high precision.
Laser collimation technology is widely applied in the positioning and measurement. The accuracy is affected by the laser beam drift, so laser beam drift compensation is necessary. Effective compensation depends on the characteristics analysis of laser beam drifts. Spectrums and values of noise signals caused by electronic noise, laser source, and environment, are analyzed in detail. The characteristics of various types of noise signals are gained and the effectiveness of low-pass filter and mean process are verified and compared. This study will provide support for separation of various types of signals and compensation of beam drifts.
The geometric parameters of wheelsets, such as flange thickness, and rim width, and rim inside distance, are key
parameters that influence the wheel-rail contact. The online measurement techniques of these parameters are important to
ensure the safety of train vehicle and increase the reliability and efficiency of maintaining. The paper purposed the
measurement system based on the optoelectronic techniques. The measuring system is composed of the trigger sensor
and the laser displacement sensors fixed on the rails and the system can measure the wheelset's parameters when trains
pass through. The measuring results are improved by the wavelet analysis denoised. The average value difference is
between 0-0.3mm comparing the system and the manual that shows two methods are coincided. When trains pass
through the measuring system under the speed of 10km/h, measuring results shows that the system can meet with the
measuring requirement on line.
Improvement of absolute phase calibration in phase-measuring profilometry is presented. In order to calculate the absolute phase of calibration plane, linear interpolation of unwrapping phase of reference point is introduced. Accuracy of height calibration and measurement is improved. A novel 3D surface shape measurement system is designed. Experiments of given height plane are presented. The mean of measurement error of conventional algorithm is about 0.5 mm. That of the novel algorithm introduced in this paper is reduced to about 0.2 mm.
A novel method to measure the roll angle using a quarter wave plate and a Wollaston prism is presented in this paper. The setup and principle of the measurement system are introduced. According to the optical structure, the mathematical model was established by Jones matrix method. The output voltage from an optical detector located after the Wollaston prism can be used to determine the roll angle. Theoretical analysis and experimental results show that the sensitivity of roll measurement can reach arc-second. The chief advantages of the sensor are simply, stable and compact. This system can be applied to dynamic roll angle measurement. Experimental results have shown that the angular resolution is 0.04 arc-minute in a ±40 are-minute range.
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