Abstract:The in-wheel motor drive can greatly simplify the chassis mechanical structure and improve the control accuracy of the vehicle. An 11-degrees-of-freedom hub-driven vehicle modelis first established in Catia software, and then a layered control strategy of the improved sliding mode control is designed in the MATLAB/Simulink environment., and the control strategy of the upper and lower controllers is analyzed. The improved nonlinear sliding mode control method is integrated, which not only improved the robustness and response speed, but also has strong anti-interference ability. The working conditions and the serpentine working conditions are verified. The siimulation and test results show that the strategy can significantly improve the stability and power transmission efficiency of the in-wheel drive axle vehicle motion.

Abstract:Aiming at the problems of large volume, poor insertion loss and isolation of MEMS SP7T switch at present, a kind of SP7T MEMS switch based on "meter" power divider is designed. RF MEMS switch unit and "meter" power divider are designed by HFSS software, and the S parameter and standing wave ratio of the switch are simulated and studied. The mechanical performance of RF MEMS switch unit is analyzed by COMSOL software. The results show that the insertion loss of each 端口 of SP7T MEMS switch is better than 0.45dB@40GHz, the isolation is better than 29.5dB@40GHz, the standing wave ratio is less than 1.45, and the volume is only 0.65 mm× 0.5 mm× 0.5 mm. This design can be integrated with filters, attenuation resistors, antennas, etc. It can be used in multi-channel tunable MEMS devices, and has certain application value in satellite communication, radar and microwave test systems.

Abstract:Aiming at the problem that the parameter selection of LCL grid-connected filter is complicated and how the parameter selection can directly affect thyine filtering effect, a parameter optimization strategy of LCL grid-connected inverter based on improved butterfly algorithm is put forward. Modeling in MATLAB simulation，the choice in port series damping resistance capacitance, maximizing and minimizing ripple current, harmonic attenuation, low power loss into as the optimization goal, and using the linear weighted method is changed into single objective model, using the entropy weight method to determine the weight coefficient of each target, finally using the improved butterfly algorithm for solving the objective function. Simulation results show that the improved butterfly algorithm, butterfly algorithm and genetic algorithm are compared with the function convergence curve, and the improved butterfly algorithm has outstanding optimization ability. Compared with traditional graphic method and genetic algorithm, the optimization strategy designed in this paper has stronger ability to reduce harmonic distortion rate and power loss under certain filtering conditions.

Abstract:For the gas calibrator with dynamic dilution ratio of gas, the change of temperature will affect the density of gas, thus affecting the mass flow rate through the mass flow controller. If the temperature dilution ratio cannot be collected in real time, there will be a large error. In this paper, a four-wire temperature sensor PT100, an external excitation constant-current source, an anti-mixing filter circuit and a 24-bit DELta-sigma ADC are used to measure the temperature in a gas chamber. The constant-current source excitation PT100 converts the temperature signal into a voltage signal, and then into a digital signal through the Delta-sigma ADC. Finally, the data is collected by FPGA and stored in Nor Flash chip. The experimental results show that the temperature resolution of the designed system can reach 0.005℃, the repeatability is good, and the problems of lead resistance, self-heating effect and small temperature measurement range are solved, which has certain engineering application value.

Abstract:Proton exchange membrane fuel cell (PEMFC) output performance is particularly important. The existing PEMFC model is complex and the internal description is not detailed. Based on Srinivasan model, ohmic polarization voltage, concentration difference polarization voltage, activation polarization voltage, their detailed models and output voltage models are established to analyze their relationship with current density. The stack temperature, hydrogen pressure, oxygen pressure and limit electricity are studied by control variable method Influence of key parameters such as current on cell voltage. Considering the practical application of PEMFC, a buck converter with voltage and current double closed-loop control is designed. Based on the state space average method, the s-domain small signal model of the converter is established, the transfer function of the controlled object is obtained, and the compensation controller is added to improve the output voltage performance. PSCAD / EMTDC simulation is used to verify the effectiveness and correctness of the model and control strategy. The results show that the output voltage increases with the increase of gas (H2, O2) pressure, electric push temperature and limit current. When the rated voltage (70V) is input, the adjustment time is 5.5ms to reach the target value (35V), when the load changes from 30 Ω to 15 Ω, the peak of disturbance voltage is 5.4v, and the time to stabilize at the target value is 64ms. The strategy has good stable / dynamic characteristics, correct and effective, and has certain reference significance for engineering design.

Abstract:A novel PMSM speed control strategy based on nonlinear active disturbance rejection control was proposed to solve the problems of poor anti-load disturbance ability and speed overshoot in PMSM servo system. By analyzing the disturbance mechanism of the servo system, the traditional PI controller is replaced by a nonlinear active disturbance rejection controller in the speed loop. The contradiction between response speed and overshoot is overcome by smoothing the given speed with tracking-differentiator, and the response capability of the system is improved. The second order extended state observer is introduced to estimate and compensate the external disturbance and improve the anti-interference ability of the system. Through nonlinear state error feedback control law, the nonlinear control of "small error with large gain, large error with small gain" is used to improve the control precision of the system. Simulation results show that the system has the characteristics of fast response, no overshoot, strong ability to resist load disturbance, and strong robustness to load change and speed change, which verifies the effectiveness of the strategy

Abstract:Aiming at the two key links of loudspeaker fault diagnosis and fault recognition in the process of loudspeaker rub & buzz automatic classification, an automatic classification method of loudspeaker rub & buzz based on Variational Mode Decomposition (VMD) multiscale entropy (MSE) and Grey Wolf Optimizer-Support Vector Machines is proposed. First, the radiated acoustical signals of loudspeaker units were decomposed by VMD, and calculate the correlation coefficient of each intrinsic mode function (IMF) with the original signal. Then, select the IMF component with high correlation coefficient to extract the multi-scale entropy as the feature vector. Finally, the loudspeaker rub & buzz was judged by GWO-SVM. The experimental results show that, compared with the EMD (EMD) multi-scale entropy, VMD multiscale dispersion entropy (MDE), and EMD multiscale dispersion entropy, VMD multi-scale entropy has a higher recognition rate，The recognition accuracy rate is 99.3%.VMD multi-scale entropy can more accurately characterize the loudspeaker rub & buzz characteristics of the loudspeaker unit .

Abstract:Commutation deflection angle adjustment is a feasible way to improve PMDC performance, but its research is relatively simple. Combined with the convenient characteristics of RMxprt simulation PMDC, this paper carries out the simulation research on the impact of commutation deflection angle adjustment on PMDC performance. Firstly, the PMDC model is established in rmxprt through the example parameters of the research object, and then the commutation deflection angle parameters are modified based on the model to obtain the rated speed, energy conversion efficiency, working current and cogging torque under different deflection angles. The influence law of commutation deflection angle on PMDC performance is summarized, and the influence of commutation deflection angle on speed and working current is verified through test experiments. The simulation and test results show that the commutation deflection angle can adjust PMDC speed and working current, improve PMDC energy conversion efficiency and reduce PMDC cogging torque. Compared with other optimization methods, the commutation deflection angle adjustment method has the advantages of flexible application and high cost performance.

Abstract:To acquire the optimum value of the phase noise of signal source which is changing with the deviation of frequency from the carrier, area equivalent algorithmic is proposed to replace single-point method. The average power of phase noise is calculated by area equivalent algorithmic which utilize integration of area assuming the acquisition interval as a rectangle. Simulation was carried out using the average power of phase noise as only inference in the PCM/BPSK demodulation process, and the difference between the results of experiment in the same condition and simulation is no more than 0.7dB termed as Eb/N0 in the input, which is within 0.3dB compare to the demodulation loss acquired in white Gaussian noise. Experiments results prove that it is a practical method in test.

Abstract:Terahertz (THz) non-destructive testing technology has the advantages of non-destructive, non-ionizing and non-contact, and has been rapidly developed and applied in the non-destructive testing of fiber-reinforced composite materials in the aeronautics and astronautics field. In this paper, PTFE was inserted into 4 different depths (0.225 mm, 0.450 mm, 0.675 mm, 0.900 mm) of carbon fiber composite laminates as artificial defects, and the terahertz time-domain spectroscopy and imaging system was used to image and spectrally analyze it, discuss the imaging effects and spectral properties of defects under terahertz radiation. The results of the study show that in the frequency band of 0.25 ~ 2.0 THz, terahertz reflection imaging can successfully detect different depths defects in carbon fiber composites, the terahertz frequency imaging signal and spectral signal linear increased with the defect depth increment, the absorption coefficient imaging signal and spectral signal linear decreased with the defect depth increment, and with the increase of frequency, the power spectral density of the defect firstly increment and then decrement, and the absorption coefficient slowly increases. The results can provide a reference for the visualization and quantitative analysis of unknown depth defects in carbon fiber composites.

Abstract:In order to improve the sampling accuracy and rate of the negative temperature coefficient thermistor temperature sampling circuit, in view of the large fluctuation of hardware interference, low software execution efficiency and inability to eliminate the performance differences of the negative temperature coefficient thermistor of different individuals in the traditional temperature sampling scheme, a method based on optimizing the performance of software and hardware and eliminating the overall deviation of the temperature sampling circuit is presented. The common-cause interference is eliminated by optimizing the hardware sampling circuit, combined with the software- oversampling algorithm and using the Steinhart-Hart equation to establish an accurate mathematical model, and then the temperature calibration of the temperature-sampling module is performed to further eliminate the overall deviation. The experimental results show that using this method improves the temperature sampling accuracy from ±1.624℃ to ±0.030℃, which greatly improves the temperature sampling accuracy.

Abstract:In view of the different influence of different steady-state features on the identification results, and considering the misjudgment of minority classes caused by unbalanced data sets, a non-invasive load identification method based on feature weighted KNN is proposed in this paper. Firstly, the feature weight is calculated by entropy weight method, and it is used to improved feature distance calculation. Secondly, the voting weight is calculated according to the number of samples and the k value of algorithm, which is brought into the voting process to increase the classification accuracy of minority classes. The experimental results show that the average recognition accuracy of algorithm in this paper is 93.4%, which is 2.8% higher than that of KNN algorithm; For public data sets, the average accuracy and F1 score of algorithm in this paper are 86.8% and 81.6%, which are better than the other four classification algorithms.

Abstract:In order to obtain the optimal capacity ratio of the micro power sources in the microgrid, and satisfy the output demand of the load, this paper establishes a capacity optimization configuration model for the grid-connected microgrid with wind, solar, diesel and battery, and takes the lowest comprehensive operating cost as the objective function, and the distributed power output and pollutant emissions as constraints. Using refracted opposition-based learning strategy, differential mutation, cross selection strategies and the dynamic step factor to improve the standard sparrow search algorithm to solve the model, and comparing with whale optimization algorithm, differential evolution, gray wolf optimizer, and sparrow search algorithm. Two typical days in Ningxia are selected for analysising of calculation examples, the required cost is 3.05%, 4.12%, 8.46% and 1.13% lower than the other four algorithms respectively. The simulation results show that the proposed model is reasonable, and the improved sparrow search algorithm has better optimization ability.

Abstract:In order effectively verify the diagnosability of discrete event system after failure, a method of boundary diagnosability verification based on Petri net was designed. Firstly, according to the structural characteristics of Petri net, the concept of visible reachable graph and its construction algorithm are proposed. Secondly, a verifier based on visible reachability graph and its construction algorithm are proposed to analyze whether the system is diagnosable after the occurrence of faults and calculate the boundary values that meet the requirement of system diagnosable. Finally, an example is given to verify the proposed method. The experimental results show that the design method can effectively verify whether the system has diagnosable ability after failure and give diagnosable boundary value. The calculation process is relatively simple, which can be used for reference to the problem of fault diagnosable ability of discrete event system in practical industrial applications.

Abstract:Landing gear device is an important component of aircraft with both load-bearing and maneuver ability. It undertakes an extremely important mission in the process of safe takeoff and landing of aircraft. Landing gear is a necessary support system for aircraft take-off, landing, taxiing, ground movement and parking. It is one of the main components of the aircraft. Its performance is directly related to the safety of the aircraft. This paper start with the failure phenomenon that the landing gear is not retracted during flight, combined with the working principle of landing gear retraction and retraction, establish a fault tree to analyze and troubleshoot the fault, finally, locate the fault point accurately. Through the analysis and processing method of establishing fault tree, it has certain guiding significance for relevant personnel to deeply understand the main components and function realization process of landing gear and expand the troubleshooting ideas of technicians.

Abstract:Aiming at the weak fault characteristics and susceptibility to external noise when a sing-phase ground occurs in the distribution network, a fault section location method based on the combination of parameter optimization optimization variational mode decomposition(VMD) and energy relative entropy is proposed. Firstly, the fault zero-sequence current is decomposed by the VMD optimized by the whale optimization algorithm, and several intrinsic mode functions(IMF) reflecting the local signal characteristics are obtained. Then, the Hilbert marginal spectrum of each IMF is obtained, the transient main frequency component of the fault zero sequence current is selected as the largest energy component, the energy difference between the adjacent detection points is represented by energy relative entroy. Through calculation and comparison, the section of maximum entropy is fault section. The simulation results show that the method is not affected by different fault conditions such as fault location, fault initial phase angle, etc, and in the presence of noise interference, high accuracy fault section location can still be achieve.

Abstract:In view of the problems of ambiguous and poor effect of vehicle image segmentation methods in actual traffic scenarios, this paper proposes a MSSA-UNet model that integrates multi-scale modules and spatial attention mechanism based on the UNet neural network model. In the encoding and decoding stage, dilated convolution is used to build a multi-scale module to improve the limited receptive field size of the convolutional layer while the output contains multi-scale feature information. Before up-sampling, a spatial attention mechanism is introduced to compensate for the problem of local information loss during the sampling process and improve the feature restoration ability. Combined with cross entropy loss and Dice loss, the network learning and training process is optimized, and the segmentation accuracy of the model is improved. The experimental results show that the MSSA-UNet model proposed in this paper achieves 83.48% in the IoU evaluation index for vehicle image segmentation tasks, which is 2.28% higher than the accuracy before improvement, the predicted value of the model is closer to the real value, and the segmentation effect is better, which effectively improves the segmentation performance of the model.

Abstract:In order to solve the problems of low detection accuracy, high false alarm rate and complex calculation of saliency map based on single-class prior knowledge of human visual system detection method in the field of infrared small target detection, a detection method that fuses various characteristics of infrared small targets under complex background conditions is proposed. By fusing the three characteristics of infrared small targets that the local gray value is large, its own gray information conforms to the two-dimensional Gaussian distribution, and the similarity with the neighborhood is low, the saliency map is calculated by covariance detection and similarity comparison . And then threshold segmentation of the saliency map to get the real target. The small target detection experiments are carried out on infrared source images with different complex backgrounds and different data types. The results show that: compared with the baseline algorithm, the detection results of the proposed algorithm in this paper increase the background suppression factor and the signal-clutter ratio gain by 2-3 times , the intersection of union is the best in the HVS method, and the ROC curve obtains the highest detection accuracy at a lower false alarm rate. The method in this paper effectively fuses multiple characteristics of small targets in the infrared source image, improves the detection accuracy and reduces the complexity of the algorithm, and can still achieve good target positioning and background suppression in the case of different complex backgrounds and clutter interference.

Abstract:Aiming at the problem of low accuracy of small target detection in traffic sign recognition tasks,which caused by that most of traffic signs in actual scene are small and dense,this paper proposes an improved YOLOv5 algorithm.firstly,embedding the CBAM into the Backbone and Neck of YOLOv5 network to improve the network feature extraction ability.and in order to solve the problem of slow network converge caused by GIOU Loss, DIoU Loss was used as the regression Loss function of the network. Experimental results show that the improved algorithm reaches 96.40% mAP in traffic sign recognition task,which is 6.83% higher than the original YOLOv5 algorithm. Finally, sending the improved network into TX2 embedded system to recognize traffic signs in real video,the result shows that the improved algorithm can run smoothly in embedded system.

Abstract:AbstractBoth false detection and missed detection are frequent for most edge detection algorithm, since the picture acquired in bad weather, the complexity of the image content itself, and the edge cues become vague especially when it is close to the background. Due to the design defects of the model or the imbalance between the edge pixels and non-edge pixels in the training samples, the edge detection results of most algorithms generally have the problem of thick lines and low quality. A multi-scale convolutional neural network is proposed, which is composed of three sub-structures and each one accepts one scale of an image. The algorithm learns the knowledge under different scale vision, extracts the edge of the image after the process of fusing gradually the edges from coarse to fine. Except for the advantages of multi-scale technology in image processing, a self-attention mechanism is introduced to improve the ability to capture the internal relevance of convolutional features. A new loss function, which is composed of the cross-entropy loss function and the L1 norm term, is proposed to train the network, and avoid the impact of the imbalance of training samples. Indices: Optimal Dataset Scale (ODS), Optimal Image Scale (OIS), Average Precision (AP) are used to measure the quality of edge detection. The scores of three indicators are 0.845, 0.856, 0.886 respectively when tested on the BIPED dataset. The algorithm scored 0.826 on the F-measure indicator, tested on BSDS500 dataset. The experimental results show that the algorithm can generate more delicate image edge results.

Abstract:Aiming at the shortcomings of traffic sign detection algorithm, such as high network complexity, large amount of calculation and difficult to be applied at the edge. A lightweight traffic sign target detection algorithm based on YOLOv5 is proposed. By increasing the attention mechanism and using the fusion of CBAM and CA, the anti-interference ability of the detection model is strengthened; Through FPGM pruning, the model is compressed to reduce the amount of calculation and improve the reasoning speed; Through the integration design of software and hardware, YOLOv5s model and hardware are integrated to form a complete set of mobile intelligent traffic sign target detection system; The results show that the accuracy of the model is improved by 2.8% after adding multiple attention mechanisms. In the case of extreme pruning, the model is only 0.54MB. Under the environment of Jetson Nano (20W), the detection speed is up to 21 frames / s, which meets the real-time traffic sign detection.

Abstract:Aiming at the situation that CFNet, the back propagation filter tracking algorithm based on the siamese network, is likely to cause the model drift tracking effect to decrease when it encounters the interference of similar objects or the background information is similar to the foreground target, a siamese network target tracking algorithm fused with semantic feature network is proposed. In image processing, through the deep network of deep convolutional neural network, rich semantic information can be extracted. These semantic information can cause similar interference, motion blur, severe target deformation, etc. In situations, it is very useful to identify the target. In the proposed algorithm, a semantic feature network is added to the original network structure of CFNet, which is complementary to the appearance feature network of CFNet. The training of the two feature networks is independent to maintain the heterogeneity of the two features and obtain their respective response maps. Later, the fusion is performed by calculating the confidence of the two response graphs, which improves the discriminative ability of algorithm. Tests show that, the algorithm in this paper achieves the optimum and can track the target effectively compared with other 5 commonly used algorithms.

Abstract:Aiming at the temperature drift phenomenon of strain gauge torque sensor, the temperature compensation model based on piecewise linear interpolation method was established, and the two-dimensional calibration data of torque and temperature were embedded into the data fusion software algorithm. According to the accuracy requirement, the data was divided into multiple different intervals and the calibration points in the intervals were replaced into the linear expression. Then the predicted torque value was output. In this way, the temperature drift of the torque sensor was corrected in real time, so that the sensor has the function of temperature self-compensation. The results show that after compensation by piecewise linear interpolation, the sensitivity temperature coefficient of the sensor is increased from to , and the relative value of temperature additional error is increased from 2.67% to 0.5%, both of which are increased by more than 5 times. The method is simple and effective. The real-time temperature compensation of torque sensor has been realized, and the temperature stability of torque sensor has been improved.

Abstract:Aiming at the problem that the telecentric lens cannot adjust the field of view due to its own characteristics, and it is difficult to adapt to the problem of obtaining the complete image of the part at one time when measuring the linear bearing, the HALCON image processing software is used to measure the length of the linear bearing through the image stitching method. Considering the effect of feature point extraction and edge contour acquisition, using the combination of backlight and forward lighting, a linear bearing measurement system based on combined lighting is established; in image stitching, a method of using images in the approximate overlap area of the image is proposed. The method of pyramid hierarchical search performs feature point detection and matching to improve the efficiency of image stitching; finally, the edge location of the image is performed by the Canny operator and the least square fitting method, and the length measurement is completed. The experimental results show that: the combined light source can better take into account the effect of image surface feature points and edge contour extraction; the detection time of the proposed stitching method is about 0.2S, which is 88% less than the traditional method; The visual measurement error based on image stitching is less than 0.1mm. In terms of measurement repeatability, it is more stable than traditional methods, and the maximum standard deviation of measurement is 0.005. Therefore, the detection efficiency, accuracy and stability of the vision system are guaranteed, and it has a certain theoretical basis and practical value in industrial automation detection.

Abstract:Wireless transmission has always been difficult for unattended transmission line monitoring in remote mountainous areas without 4G signal wiring, and it is even more difficult for image data with large data volume. This paper designs and implements an intelligent wireless image sensor network transmission system for high voltage transmission line monitoring, using the research and development of embedded technology and wireless sensor network technology to realize image transmission .on the basis of the traditional wireless sensor network, aiming at the large image data and easy to cause interference in the communication process, an adaptive data subcontracting protocol is designed according to the RSSI value of the receiving end signal strength; In order to optimize the transmission routing and increase the redundancy of the transmission system, an intelligent routing transmission protocol is designed. Experimental test and simulation results show that the adaptive data subcontracting protocol can adjust the subcontracting number adaptively according to different bit error rates, which reduces the probability of retransmission and improves transmission efficiency; The intelligent dynamic routing transmission protocol can quickly find the nearest 4G signal node, saving transmission time and energy loss. The hopping function reduces the transmission time and increases the redundancy of the transmission system, so as to achieve the design purpose.

Abstract:In order to make better use of the advantages of RIS to save transmit power and improve system energy efficiency, the problem of how to choose the most suitable surface in multi-RIS scenarios is solved in this paper. This paper proposes a "minimum path product" selection scheme based on the characteristics of the RIS-assisted communication cascaded signal path loss model, and uses transmit power and energy efficiency as evaluation indicators. D2D communication user scenarios are simulated. The simulation results show that the "minimum path product" option is slightly better than the "minimum path sum" option in terms of saving transmit power and improving energy efficiency. Therefore, in the general RIS application scenario, according to the characteristics of the far-field path loss channel, using the "minimum path product" is the optimal choice.

Abstract:Continuous phase modulation spread signal is gradually applied to satellite communication because the spread spectrum sequence changes randomly and can be adapted to multi-user communication. Random access channel of VDE system uses QPSK-CPM spread modulation scheme. A new multiuser detection algorithm based on interference cancellation detection is presented for QPSK-CPM spread spectrum system. The algorithm detects multi-user information by cascaded one-iteration elimination of successive interference cancellation for receiving user information. The algorithm can effectively eliminate the user's doppler frequency offset and reduce the performance loss caused by user power difference. The simulation results show that the new algorithm has better detection performance than the traditional interference cancellation detection algorithm. When the number of real-time communication ships reaches 800, the deframe rate of overlapping information within 8 users can reach 90%.

Abstract:Aiming at the challenge to subsequent transmission and storage caused by large amount of data in array guided wave detection, a segmental hybrid compression algorithm is proposed in this paper. Based on the high amplitude and correlation characteristics of damage scattering signal, the detection data is divided into high and low fidelity segments. To consider both compression ratio and the fidelity of damaged signal, LOPCM lossless compression method is designed for the high-fidelity segment, and JPEG lossy compression algorithm is applied for the low fidelity segment. After constructing experimental platform, the measured signals are obtained and compressed. The effectiveness of LOPCM method was verified by comparison. The influence of the piecewise hybrid compression algorithm on the imaging accuracy is evaluated by investigating the overall error index of the signal and combining with the reconstructed data imaging results. The results show that the proposed algorithm can effectively reduce the amount of data while ensuring the accuracy of damage imaging, and it can better meet the requirements of data compression in array guided wave detection than only using lossless or lossy compression.