Table of Content

    28 February 2022, Volume 56 Issue 2 Previous Issue    Next Issue
    Design of a Virtual Assembly Gesture Library and Optimization of Ergonomics Evaluation
    GUO Jiawei, XU Zhijie, HE Qichang
    2022, 56 (2):  127-133.  doi: 10.16183/j.cnki.jsjtu.2021.095
    Abstract ( 1795 )   HTML ( 354 )   PDF (7083KB) ( 544 )   Save

    In view of the low efficient of virtual human upper limb simulation in the virtual assembly environment and the inaccurate evaluation of ergonomics, this paper analyzes hand assembly action, defines hand joint structure, arm and hand size, establishes a parametric assembly gesture model, and forms a gesture library. The fuzzy algorithm is used to improve the rapid upper limb assessment (RULA) method. The trapezoidal function is used to optimize the evaluation score when the joint angle is at the critical value. The final evaluation result is obtained through the rule base. Strain index(SI) is used to evaluate the risk of musculoskeletal operations such as hands and wrists, and the comprehensive score is weighted with fuzzy RULA evaluation. The ergonomics of the virtual hand assembly process is continuously evaluated to capture the risk posture in the assembly operation. Finally, the above methods are integrated based on the 3D Experience platform and verified by the assembly of mobile communication components.

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    Underwater Image Enhancement Based on Generative Adversarial Networks
    LI Yu, YANG Daoyong, LIU Lingya, WANG Yiyin
    2022, 56 (2):  134-142.  doi: 10.16183/j.cnki.jsjtu.2021.075
    Abstract ( 1916 )   HTML ( 56 )   PDF (22386KB) ( 790 )   Save

    This paper proposes an underwater image correction and enhancement algorithm based on generative adversarial networks. In this algorithm, the multi-scale kernel is applied to the improved residual module to construct a generator, which realizes the extraction and fusion of multiple receptive fields feature information. The discriminator design considers the relationship between global information and local details, and establishes a global-region dual discriminator structure, which can ensure the consistency of overall style and edge texture. An unsupervised loss function based on human visual sensory system is proposed. Reference image constraints are not required, and the confrontation loss and the content loss are jointly optimized to obtain better color and structure performance. Experimental evaluations on multiple data sets show that the proposed algorithm can better correct color deviation and contrast, protect details from loss, and is superior to typical algorithms in subjective and objective indexes.

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    A Multi-Feature Particle Filter Vehicle Tracking Algorithm Based on Adaptive Interpolation Moth-Flame Optimization
    HUANG He, WU Kun, LI Xinrui, WANG Jun, WANG Huifeng, RU Feng
    2022, 56 (2):  143-155.  doi: 10.16183/j.cnki.jsjtu.2021.037
    Abstract ( 1528 )   HTML ( 21 )   PDF (33546KB) ( 366 )   Save

    In order to solve the problem of low accuracy and the poor global searching ability of the moth-flame optimization algorithm, an improved adaptive interpolation moth-flame optimization algorithm is proposed, which is embedded into multi-feature particle filter to optimize. Besides, a multi-feature particle filter vehicle tracking algorithm based on adaptive interpolation moth-flame optimization is constructed. First, adaptive weights are added to the moths’ position updating mechanism to improve the global searching ability of the proposed algorithm. Next, the adaptive interpolation moth-flame optimization algorithm is used to optimize the sampling process. Then, in combination with the multi-feature adaptive fusion particle filter vehicle tracking algorithm, the particle distribution according to the latest observation information is continuously adjusted, so that the particles in the low weight layer can move to the area with higher weight to enhance the particle quality and avoid sample degradation. The experimental results show that the proposed algorithm can effectively reduce the number of sample particles required for state prediction, improve the tracking performance of the algorithm, and track the target vehicle accurately and stably under the interferences of occlusion, illumination, attitude, and scale changes.

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    Static Output Feedback Control of Vehicle Active Front Steering Considering Multiple Performance Constraints
    MAO Yingzhong, FENG Zhiyong, GUO Huiru
    2022, 56 (2):  156-164.  doi: 10.16183/j.cnki.jsjtu.2021.073
    Abstract ( 1227 )   HTML ( 13 )   PDF (1328KB) ( 454 )   Save

    To enable the vehicle to accurately track the ideal yaw rate, thereby improving the vehicle path tracking ability, this paper proposes a static output feedback (SOF) control method for active front steering (AFS) considering multiple performance constraints. Since the cornering stiffness of vehicle tire is a strong nonlinear parameter, the cornering stiffness is taken as the uncertainty parameter of the model. In addition, the two-degree-of-freedom poly-topic model of the vehicle dynamics is established based on the saturated linear tire model to deal with the parameter uncertainty. Moreover, the design of robust SOF controllers with regional pole configuration constraints and H performance constraints are considered for this type of uncertain system. Furthermore, the linear matrix inequality (LMI) sufficient conditions for this type of uncertain system are given, and a coordinate transformation matrix(CTM) optimization method is used to iteratively solve the obtained LMI conditions for the first time. Thus, the robust optimal H SOF controller for this type of uncertain system is obtained. Co-simulation results of MATLAB/Simulink and CarSim show that the designed SOF controller can significantly improve the tracking performance of the desired yaw rate and improve the vehicle path tracking ability. In addition, the controller has a good robustness to the uncertainties of vehicle model parameters.

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    Neural-Network-Based Adaptive Feedback Linearization Control for 6-DOF Wave Compensation Platform
    DING Ming, MENG Shuai, WANG Shuheng, XIA Xi
    2022, 56 (2):  165-172.  doi: 10.16183/j.cnki.jsjtu.2020.424
    Abstract ( 1458 )   HTML ( 31 )   PDF (1175KB) ( 523 )   Save

    Ocean resource exploration expands into deep and ultra-deep waters, which has posed great challenges to the 6-DOF parallel platform that requires to finish the long-span and high-velocity wave compensation task with high precision and anti-interference ability. The control strategy employed in the asymmetric hydraulic system of large aspect ratio requires more careful considerations when operating in the harsh and severe environment. An adaptive feedback linearization control strategy was proposed by employing the radial basis function neural network (RBFNN) for identification. First, a nonlinear model of the asymmetric hydraulic system was established. Then, an adaptive controller was designed based on RBFNN and feedback linearization. Finally, simulations were performed by using MATLAB/Simulink under the five-stage wave environment at a 90° wave encounter angle and under the external interference condition. The result shows that this method has a good traceability and robustness compared to classic PID and sliding mode control methods, which is more suitable in control of the wave compensation platform in complex sea conditions. The new controller can significantly increase the compensation accuracy and anti-interference ability, and provide a workbench for the 6-DOF parallel platform operation in deep waters.

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    Shift Strategy of Electric Drive Loader with Compound Control of Motor and Clutch
    REN Haoling, CAI Shaole, CHEN Qihuai, LIN Tianliang, LANG Bin
    2022, 56 (2):  173-181.  doi: 10.16183/j.cnki.jsjtu.2020.409
    Abstract ( 1298 )   HTML ( 23 )   PDF (6683KB) ( 584 )   Save

    To give full play to the advantages of pure electric drive, and aimed at the transmission system of electric loader, the low efficiency hydraulic torque converter and reverse direction clutch of traditional models are cancelled. By analyzing the shift law of the pure electric drive system, the electro-hydraulic shift control system is used to control the wet clutch, and based on the feedback of pressure and speed, combined with the driving motor active working in the speed and torque mode, the matching of torque and speed in the process of clutch engagement and disengagement is realized. For a 50-type pure electric drive loader, a shift control strategy of pure electric drive loader based on composite control of drive motor and clutch pressure is proposed. The result of vehicle test shows that the proposed control strategy can give full play to the advantages of pure electric drive. The shift time is reduced by about 50%, the sliding friction work is greatly reduced, and the maximum shift impact is 14.08 m/s3, which is within the recommended limit value of 17.64 m/s3 for Chinese vehicles.

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    A Fault Diagnosis Method Based on Feature Pyramid CRNN Network
    LIU Xiuli, XU Xiaoli
    2022, 56 (2):  182-190.  doi: 10.16183/j.cnki.jsjtu.2021.001
    Abstract ( 1293 )   HTML ( 29 )   PDF (7606KB) ( 485 )   Save

    Aimed at the problems that the proportion and position of different fault characteristics of equipment components under variable working conditions and variable load in the signal are not fixed, and include the multi-scale complexity of the original vibration signal in a large number of different scenarios, a convolutional recurrent neural network (CRNN) rolling bearing fault diagnosis method based on feature pyramid network (FPN) was proposed. Using the convolution neural network (CNN) framework, the convolution layer of CNN and the long and short-term memory (LSTM) layer of recurrent neural network (RNN) were connected in parallel to form a new CRNN, so as to make full use of the learning ability of CNN to spatial domain information and RNN to time domain information. The weights were shared in each layer to reduce network parameters. A novel feature map was constructed using FPN, and one-dimensional signal and two-dimensional signal formed after stacking were input to extract the feature of the signal collected by the sensor, and realize fault diagnosis. The average diagnostic accuracy of this method is 99.20%, which is at least 3.62% higher than that of the basic neural network model, indicating that this method has a high diagnostic accuracy and a strong robustness. Using the bearing data set of Case Western Reserve University, it is proved that the method has a good universality. The t-SNE method was used to visually analyze the feature learning effect of the model. The results show that different fault category features have good clustering effect.

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    Stability of Orthogonal Cutting System Considering Nonlinear Stiffness
    SHI Huirong, WANG Haixing, LI Zonggang
    2022, 56 (2):  191-200.  doi: 10.16183/j.cnki.jsjtu.2020.413
    Abstract ( 969 )   HTML ( 13 )   PDF (2857KB) ( 316 )   Save

    In order to accurately predict the stability of orthogonal cutting of cylindrical workpiece, a nonlinear orthogonal cutting system model is established, which includes the nonlinear stiffness caused by the surface wave of work as well as the deformation of the tool and work. The multi-scale method is used to solve the system. The effect of machining parameters and system parameters on the stability of the primary resonance and 1/2 subresonance is analyzed to gain the overall stability cloud map compared with the lobe diagrams of linear approximation system. The results show that the instability of primary resonance, 1/2, 1/3, and 1/4 subresonance occur in the orthogonal cutting system with the quadratic nonlinearity and cubic nonlinearities stiffness, which makes the system have period-doubling, quasi-periodic, and chaotic operation behavior. The comparison indicates that the dynamics model of nonlinear orthogonal cutting can accurately predict the stability of the system.

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    A Modified Migrating Birds Optimization for Multi-Objective Lot Streaming Hybrid Flowshop Scheduling
    TANG Hongtao, WANG Dannan, SHAO Yiping, ZHAO Wenbin, JIANG Weiguang, CHEN Qingfeng
    2022, 56 (2):  201-213.  doi: 10.16183/j.cnki.jsjtu.2020.435
    Abstract ( 1221 )   HTML ( 19 )   PDF (1462KB) ( 488 )   Save

    This paper proposes an adaptive migrating birds optimization (AMBO) method based on variable neighborhood search to solve the inequal lot streaming hybrid flowshop scheduling problem (ILS-HFSP) for a 2+1+1 hybrid flowshop, which realizes multi-objective optimization of minimizing makespan and minimum average work in process. Compared with the original migrating birds optimization, the AMBO algorithm adopts the variable neighborhood search strategy with an adaptive selection probability of neighborhood operator that is adaptively adjusted with the number of iterations. Besides, a time-window operator is adopted to improve the search performance of exchange operators and convergence rate. Several orders of different scales generated randomly are studied, and the results show that the AMBO algorithm has a higher solution quality and a better convergence performance than the migrating birds optimization algorithm and the genetic algorithm, thereby verifying the effectiveness of the AMBO algorithm.

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    Decoupling of Vibration and Temperature Signals of Fiber Bragg Grating Sensor
    LI Han, ZHANG Botao, WANG Junjie, SUN Yunda, GONG Shengjie
    2022, 56 (2):  214-222.  doi: 10.16183/j.cnki.jsjtu.2020.313
    Abstract ( 1102 )   HTML ( 16 )   PDF (3613KB) ( 522 )   Save

    This paper uses a single fiber bragg grating (FBG) sensor to implement an experiment to measure vibration and temperature signals at the same time, and proposes a MATLAB-based decoupling method to separate vibration and temperature signals. The experimental results show that under the condition of single signal measurement, the static temperature measurement error of the FBG sensor is within ±0.4 ℃ and the relative error of the dynamic measurement of the main frequency of vibration is 0.5%. The FBG sensor measures the composite signal of vibration and temperature. The relative error of the main vibration frequency obtained by the decoupling method proposed in this experiment is 0.65%, the relative error of the vibration amplitude is 7.14%, and the temperature signal error is within ±3.3 ℃.

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    An Accuracy Dynamically Configurable FFT Processor Based on Approximate Computing
    MA Liping, ZHANG Xiaoyu, BAI Yuxin, CHEN Xin, ZHANG Ying
    2022, 56 (2):  223-230.  doi: 10.16183/j.cnki.jsjtu.2020.430
    Abstract ( 1041 )   HTML ( 11 )   PDF (5962KB) ( 616 )   Save

    In order to meet the different requirements of circuit targets in various scenarios, an accuracy configurable fast Fourier transform (FFT) processor based on the concept of approximate circuit is proposed. A configurable approximate butterfly unit which can truncate the carry chain is proposed at the butterfly node and a bit-width configurable multiplier is proposed at the rotation factor multiplication node. MATLAB is adopted to develop an error analysis platform. After analyzing the sensitivity of each butterfly node and rotation factor node to approximate calculations, five calculation modes of the accuracy configurable FFT processor are determined, which can achieve dynamic balance among performance, power consumption, and accuracy. Finally, based on the 180 nm complementary metal oxide semiconductor (CMOS) technology of Taiwan Semiconductor Manufacturing Company (TSMC), the proposed processor is implemented with the standard procedure of ultra-large-scale digital integrated circuits. The performance results are obtained by professional electronic design automation (EDA) tools. Compared with the precise mode, the maximum operating frequency of the processor in the approximate mode is increased by 14.33%, and the power consumption is reduced by 15.61% when the operating frequency is 60 MHz.

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    A High Quality Algorithm of Time-Frequency Analysis and Its Application in Radar Signal Target Detection via LMSCT
    HAO Guocheng, ZHANG Bichao, GUO Juan, ZHANG Yabing, SHI Guangyao, WANG Panpan, ZHANG Wei
    2022, 56 (2):  231-241.  doi: 10.16183/j.cnki.jsjtu.2020.432
    Abstract ( 969 )   HTML ( 16 )   PDF (8925KB) ( 437 )   Save

    Aimed at the fact that the chirplet rate parameter of the chirplet transform (CT) cannot match the instantaneous frequency of the signal completely, and that the anti-noise performance of the algorithm is poor, this paper proposes a high-quality local maximum synchrosqueezing chirplet transform (LMSCT) algorithm to improve the deviation of energy diffusion amplitude in CT time-frequency (TF)distribution. The main idea of this algorithm is to reallocate CT frequency points by local maximum synchrosqueezing operation. The experiment results show that the LMSCT algorithm has a higher TF concentration and a strong ability to suppress the interference of noise. The method can maintain a better resolution of TF representation at a low signal-to-noise ratio. In the application analysis of IPIX processing radar signals, the LMSCT algorithm can clearly describe the TF joint distribution characteristic of target signal and determine the distance unit of target, which provides the judgement basis for small target detection of IPIX radar signal in the background of sea clutter.

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    Weekly Physician Scheduling for Emergency Departments with Time-Varying Demands of Patients with Revisits
    WANG Zixiang, WU Zerui, LIU Ran
    2022, 56 (2):  242-252.  doi: 10.16183/j.cnki.jsjtu.2020.328
    Abstract ( 1113 )   HTML ( 10 )   PDF (1266KB) ( 498 )   Save

    To solve the flexible scheduling problem of emergency departments, a method based on the queuing theory and the fluid model for approximating the patient waiting length of a time-varying queuing system with returns for a given scheduling plan is proposed. A mixed-integer programming model, which considers the real constraints of physician scheduling, is then proposed and solved by using a tabu search algorithm. Numerical experiments show that the proposed method can effectively approximate the waiting queue length of patients and the scheduling plan computed by the proposed algorithm can effectively reduce the total waiting queue length of patients.

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