Multiple LED-based spectral synthesis technology has been widely used in the fields of solar simulator, color mixing, and artificial lighting of plant factory and so on. Generally, amounts of LEDs are spatially arranged with compact layout to obtain the high power density output. Mutual thermal spreading among LEDs will produce the coupled thermal effect which will additionally increase the junction temperature of LED. Affected by the Photoelectric thermal coupling effect of LED, the spectrum of LED will shift and luminous efficiency will decrease. Correspondingly, the spectral synthesis result will mismatch. Therefore, thermal management of LED spatial layout plays an important role for multi-LEDs light source system. In the paper, the thermal dissipation network topology model considering the mutual thermal spreading effect among the LEDs is proposed for multi-LEDs system with various types of power. The junction temperature increment cased by the thermal coupling has the great relation with the spatial arrangement. To minimize the thermal coupling effect, an optimized method of LED spatial layout for the specific light source structure is presented and analyzed. The results showed that layout of LED with high-power are arranged in the corner and low-power in the center. Finally, according to this method, it is convenient to determine the spatial layout of LEDs in a system having any kind of light source structure, and has the advantages of being universally applicable to facilitate adjustment.
Hardness, as one kind of tactile sensing, plays an important role in the field of intelligent robot application such as gripping, agricultural harvesting, prosthetic hand and so on. Recently, with the rapid development of magnetic field sensing technology with high performance, a number of magnetic sensors have been developed for intelligent application. The tunnel Magnetoresistance(TMR) based on magnetoresistance principal works as the sensitive element to detect the magnetic field and it has proven its excellent ability of weak magnetic detection. In the paper, a new method based on magnetic anomaly detection was proposed to detect the hardness in the tactile way. The sensor is composed of elastic body, ferrous probe, TMR element, permanent magnet. When the elastic body embedded with ferrous probe touches the object under the certain size of force, deformation of elastic body will produce. Correspondingly, the ferrous probe will be forced to displace and the background magnetic field will be distorted. The distorted magnetic field was detected by TMR elements and the output signal at different time can be sampled. The slope of magnetic signal with the sampling time is different for object with different hardness. The result indicated that the magnetic anomaly sensor can recognize the hardness rapidly within 150ms after the tactile moment. The hardness sensor based on magnetic anomaly detection principal proposed in the paper has the advantages of simple structure, low cost, rapid response and it has shown great application potential in the field of intelligent robot.
To solve the heat dissipation problem of LED, a radiator structure based on strip fins is designed and the method to optimize the structure parameters of strip fins is proposed in this paper. The combination of RBF neural networks and particle swarm optimization (PSO) algorithm is used for modeling and optimization respectively. During the experiment, the 150 datasets of LED junction temperature when structure parameters of number of strip fins, length, width and height of the fins have different values are obtained by ANSYS software. Then RBF neural network is applied to build the non-linear regression model and the parameters optimization of structure based on particle swarm optimization algorithm is performed with this model. The experimental results show that the lowest LED junction temperature reaches 43.88 degrees when the number of hidden layer nodes in RBF neural network is 10, the two learning factors in particle swarm optimization algorithm are 0.5, 0.5 respectively, the inertia factor is 1 and the maximum number of iterations is 100, and now the number of fins is 64, the distribution structure is 8*8, and the length, width and height of fins are 4.3mm, 4.48mm and 55.3mm respectively. To compare the modeling and optimization results, LED junction temperature at the optimized structure parameters was simulated and the result is 43.592°C which approximately equals to the optimal result. Compared with the ordinary plate-fin-type radiator structure whose temperature is 56.38°C, the structure greatly enhances heat dissipation performance of the structure.