Abstract:The propagation of shock wave pressure in the tunnel is greatly affected by the tunnel structure, shape, material and other factors, and there are great differences in the propagation law of shock wave pressure in different kinds of tunnels. In order to study the propagation law of shock wave pressure in tunnels with different mate-rials, taking the long straight tunnel with the square section as an example, the AUTODYN software is used to simulate the explosion of TNT in the concrete, steel and granite tunnel, and study on the variation law of shock wave pressure in tunnels with different materials. By using dimensional analysis and combined with the results of numerical simulation, a mathematical model of the propagation law of shock wave pressure in the tunnel is established, and the effectiveness of the mathematical model is verified by making the explosion test of the warhead in the reinforce concrete tunnel. The results show that the same mass of TNT explodes in the tunnel with different materials, and the shock wave overpressure peak at the same measuring point is approximate in the near field. However, there is a significant difference in the middle-far fields from the explosion center, the shock wave overpressure peak in the steel tunnel is 20.76% and 34.82% higher than that of the concrete and the granite tunnel respectively, and the shock wave overpressure peak in the concrete tunnel is 24.91% higher than that in the granite tunnel. Through the experimental verification, getting the result that the maximum relative deviation between the measured value and the calculated value of the shock wave overpressure peak is 11.85%. Therefore, it is proved that the mathematical model can be used to predict the shock wave overpressure peak in the tunnel with different materials, and it can provide some reference for the power evaluation of warhead explosion in the tunnel.

Abstract:A local path optimization model and obstacle avoidance strategy based on Actor-Critic algorithm is proposed for the local obstacle avoidance problem of automatic guided vehicles in a complex workshop environment. In the complex working environment of the production workshop, we analyze the automatic obstacle avoidance problem of AGV trolley, establish the front and both sides of the AGV tentacle model and Markov decision process, and describe the local obstacle avoidance path in the form of virtual tentacles. And based on deep reinforcement learning to solve the path obstacle avoidance strategy, it is applied to the AGV self-navigation system. The dynamic obstacle avoidance performance of AGV is tested through simulation experiments, and the effectiveness of the proposed algorithm is verified by completing local obstacle avoidance path planning under global path guidance.

Abstract:For the current robotic grasping scenario, the market for single gripper grippers to grasp items is limited, ex-pensive, difficult to use, after-sales cumbersome, and other problems. This paper designs a multi-functional gripper, integrating electro-pneumatic functions and designing multi-functional flanges, which can be used for all kinds of robots and multi-angle mounting, and designs multiple suction cups on the basis of the electric gripper to solve the problem that some items cannot be grasped, and designs various finger grippers at the end of the gripper to solve the problem of grasping items of different shapes. In this paper, the jaws are analyzed using the ANSYS Workbench for static simulations and also tested for gripping stability with a dozen terms. The versatile gripper has the advantages of compact design, reliable grip, easy maintenance, high-cost performance, and multi-scene use.

Abstract:Deep stochastic configuration networks (DSCNs) produce redundant hidden nodes and connections during training, which complicates their model structures. Aiming at the above problems, this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance. During the training process, the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner, the node pruning rate of each layer is set according to the depth of the DSCN at the current time, the nodes that contribute little to the model are deleted, and the network-related parameters are updated. When the model completes the configuration procedure, the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections; then, the network parameters are updated after pruning is completed. The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed; the model accuracy loss is small, and fine-tuning for accuracy restoration is not needed. The obtained DSCN model has certain application value in the field of regression analysis.

Abstract:Metal substance detection plays an extremely important role in daily life, industrial manufacturing and even industrial security. The traditional methods include optical detection, X-ray detection, microwave detection and ultrasonic detection. These methods, playing a vital role in the field of non-destructive testing, can not only judge the presence or absence of metal, but also accurately detect the type and size of metal defects. For mi-crowave detection, the detection efficiency of metal materials is limited by the response sensitivity of the de-tector to microwaves. In recent years, scientists have discovered a quantum sensing system based on the dia-mond nitrogen-vacancy (NV) color center. The system obtains optical detection magnetic resonance (ODMR) fluorescence spectra under the combined action of a 532nm laser and a certain frequency band of microwaves, and the signal contrast changes significantly with the microwave power. Based on the NV color center quantum sensing system, this paper studies its application in the field of metal detection, and takes steel detection as an example to detect the size of steel bars according to the changes in the spectral line, providing a new method for non-destructive testing such as metal substance detection.

Abstract:Discrete Cosine Transform (DCT) is the most widely used technique in image and video compression. In this paper, the structure of DCT and Inverse DCT (IDCT) algorithm is split in the form of COordinate Rotation DIgital Computer (CORDIC) rotation matrix. The two-dimensional (2-D) 88 DCT/IDCT units based on the improved rotation CORDIC algorithm is proposed. The shift and addition operations of the CORDIC algorithm are used to replace the cosine multiplication operations in the algorithm. The design does not contain any mul-tiplier unit, which reduces the complexity of the hardware unit. The row-column transform unit composed of register arrays connects two 1-D 8-point DCT units to complete the calculation of 2-D 88 DCT. The pipeline latency of proposed architecture is 28 clock cycles. The proposed efficient two-dimensional DCT architecture has been synthesized on the Xilinx’s Kintex-7 FPGA. The resource utilization is 17.36 % for Slice LUTs, 3.49 % for Slice Registers, and the maximum operating frequency is 172 MHz. It takes only 0.161s to complete a process of block of 88 samples. A frame of image is processed by the designed DCT unit and then recon-structed by the IDCT unit to verify the function. The Peak Signal to Noise Ratio (PSNR) can reach 51.99 dB.

Abstract:On account of the traditional multiple signal classification (MUSIC) algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR. In this paper, the traditional MUSIC algorithm is improved. The algorithm combines the idea of spatial smoothing, constructs a new covariance matrix using the covariance information of the measurement data, and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum. Simulation results show that the im-proved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation. The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.