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Hydrogen Purification Performance of Pressure Swing Adsorption Based on Cu-BTC/zeolite 5A Layered Bed

Journal of Wuhan University of technology(Materials Science) 2022年第5期37卷 815-822页

作者： SUN Kang YANG Tianqi MA Shuo YE Feng XIAO Jinsheng Hubei Research Center for New Energy&Intelligent Connected Vehicle and Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive EngineeringWuhan University of TechnologyWuhan430070China Intelligent Manufacturing and Automobile School Chongqing College of Electronic EngineeringChongqing401331China School of Artificial Intelligence Jianghan UniversityWuhan430056China

A pressure swing adsorption (PSA) hydrogen purification model for the four-component gas (H_(2)/CO_(2)/CH_(4)/CO=73/16/8/3 mol%) in a layered bed packed with Cu-BTC and zeolite 5A was established to achieve better hydrogen purification *** comparing its simulation results with the experimental data,the adsorption isotherm model was validated and could be used to accurately describe the adsorption process of the gas mixture on the two *** breakthrough curves of the mixed gas on the layered bed were studied to verify the correctness of the established simulation *** on the validated model,the performance of the PSA system based on the layered bed was carried out,including the hydrogen purity and *** simulation results show that the hydrogen purification system based on the layered bed model can achieve hydrogen purity of 95.469% and hydrogen recovery of 83.219%.Moreover,a parametric study was carried out and its results show that reductions in feed flow rate and adsorption time result in an increase in hydrogen purity and a decrease in hydrogen recovery.A longer equalization time between the two adsorption beds can simultaneously increase the hydrogen purity and recovery.

关键词： hydrogen purification pressure swing adsorption layered bed Cu-BTC zeolite 5A

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Deep sparse representation via deep dictionary learning for reinforcement learning 41

Deep sparse representation via deep dictionary learning for ...

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第41届中国控制会议

作者： Jianhao Tang Zhenni Li Shengli Xie Shuxue Ding Shaolong Zheng Xueni Chen School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Technology (GDUT) 111 Center for Intelligent Batch Manufacturing Based on IoT Technology(GDUT) The Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing (GDUT) School of Artificial Intelligence Guilin University of Electronic Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Reinforcement Learning(RL) currently has been widely applied for various fields of decision-making,which relies on the function approximation for tackling complicated ***,most RL models including Deep Reinforcement Learning(DRL) suffer from the catastrophic interference,resulting in poor control ***,the sparse representation has been proven effective for avoiding the interference and is easy to train,but shallow sparse representations have limited representation *** this paper,we proposed to employ the deep dictionary learning(DDL) model for reinforcement learning as a joint optimization problem over the deep sparse representation and the function approximation,which considers a multi-layer dictionary for obtaining a deeper latent sparse representation with better data representation ***,to address the difficulties in optimization of DDL,we employ the decomposition method that we can decompose the multilayer problem into a set of single-layer ***,to solve the joint optimization problem efficiently,we apply the alternating update scheme for updating the sparse representation,multi-layer dictionary,and weight for value prediction ***,we can directly obtain the closed-form solution of the sparse representation by the Proximal Operator(PO),multi-layer dictionary and weight are trained by the gradient descent ***,the agent utilizes the pre-trained multilayer dictionary and weight for RL control,and the weight is updated dynamically at each *** experimental results on two benchmark environments demonstrate that the performance of the RL control training based on the DDL model can be significantly improved.

关键词： Reinforcement Learning Deep sparse representation Deep dictionary learning Proximal operator

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua Xie Kan Xie Frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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Research on Electromagnetic Immunity Hardware-In-The-Loop Simulation Test of ADAS in intelligent Vehicle 3rd

Research on Electromagnetic Immunity Hardware-In-The-Loop Si...

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3rd International Conference on Information Control, Electrical Engineering, and Rail Transit, ICEERT 2023

作者： Jia, Jia Zhou, Shiying Cui, Maoyuan Zhang, Dongbo Jia, Jin Beijing Jiaotong University Electronic and Information Engineering College Beijing100044 China China Faw Group CO. LTD Intelligent Internet Connection Development Institute Changchun130000 China Chongqing University of Technology Key Laboratory of Advanced Manufacturing Technology for Automobile Parts Ministry of Education Chongqing404100 China

ISBN: (纸本)9789819788903

The EMC testing of the ADAS of intelligent vehicles is greatly affected by weather, road conditions, and other factors in actual road testing. Therefore, this article conducts EMC simulation testing research on the ADAS system of intelligent vehicles based on a semi-anechoic chamber. This article conducts a joint simulation of typical ADAS operating conditions using PreScan and Simulink. By simulating typical test conditions of the ACC system, the effectiveness and reliability of the condition in real road scenarios were evaluated, and the vehicle hardware-in-the-loop test was conducted. The test showed the ACC activation results of the vehicle, indicating that the testing method proposed in this article is feasible and provides a certain reference value for actual road testing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware-in-the-loop simulation

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The On-Load Simulation Model of Electric Drive System of Electric Vehicle Under Complex Operating Conditions 3rd

The On-Load Simulation Model of Electric Drive System of Ele...

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3rd International Conference on Information Control, Electrical Engineering, and Rail Transit, ICEERT 2023

作者： Wang, Huihui Xie, Jun Liu, Bin Jia, Jin Beijing Jiaotong University Electronic and Information Engineering College Beijing100044 China China Faw Group CO. LTD Intelligent Internet Connection Development Institute Changchun130000 China Chongqing University of Technology Key Laboratory of Advanced Manufacturing Technology for Automobile Parts Ministry of Education Chongqing404100 China

ISBN: (纸本)9789819788903

The on–off of high voltage inverter of the motor drive system is the main source of electromagnetic interference in new energy vehicles. According to GB/T 18,655-2018, GB/T 36,282-2018 and related enterprise standards, the electromagnetic compatibility performance of the electric drive system must be strictly controlled within the specified range. To solve this problem, a simulation and development technology of a new energy vehicle electric drive system under load conditions is proposed. This paper mainly introduces the simulation modeling method of IGBT and establishes the on-load simulation platform based on an integrated electric drive system. The accuracy of the model is verified by simulation and test. In this paper, parameters analysis and electromagnetic interference performance optimization are carried out to form a closed-loop electromagnetic compatibility performance control system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric drive system EMI High power IGBT module

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on intelligent robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Research on the Method of on-Load Testing for Electric Drive System of New Energy Vehicles 3rd

Research on the Method of on-Load Testing for Electric Drive...

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3rd International Conference on Information Control, Electrical Engineering, and Rail Transit, ICEERT 2023

作者： Du, Sijie Wang, Shiwei Cao, Lijun Jia, Jin Beijing Jiaotong University Electronic and Information Engineering College Beijing100044 China China Faw Group CO. LTD. Intelligent Internet Connection Development Institute Changchun130000 China Key Laboratory of Advanced Manufacturing Technology for Automobile Parts Ministry of Education Chongqing University of Technology Chongqing404100 China

ISBN: (纸本)9789819788903

The electromagnetic compatibility (EMC) testing standard requires that the electric drive system conduct EMC testing on a test bench under a simulated load. The shaft current generated by the test bench enters the interior of the anechoic chamber through a through-wall shaft, affecting the background noise of the anechoic chamber and the EMC testing results of the electric drive system. At the same time, the through-wall shaft passing through the shielding wall of the anechoic chamber can cause serious electromagnetic leakage. In this paper, the electromagnetic simulation model of the test bench and the semi-anechoic chamber is built, and the interference path of the shaft current and the shielding effectiveness of the system are studied. Through simulation, it is found that installing the shielding cover and grounding the test system can improve the shielding effectiveness of the test system. According to the standard, the actual load testing system is subjected to background noise testing, and the load testing system is optimized and designed based on electromagnetic simulation results. Ultimately, the background noise of the semi-anechoic chamber is lower than the GB/T 18,655-2018 5-level limit of 35 dBμV/m, verifying the effectiveness of the rectification and optimization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric shielding

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A single-stream adaptive scene layout modeling method for scene recognition

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Neural Computing and Applications 2024年第22期36卷 13703-13714页

作者： Wang, Qun Zhu, Feng Lin, Zhiyuan Wang, Jianyu Li, Xiang Zhao, Pengfei Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Robot Science and Engineering Northeastern University Shenyang110016 China Faculty of Computer Science and Engineering Northeastern University Shenyang110016 China

Scene recognition has been the foundation of research in computer vision fields. Because scene images typically are composed of specific regions distributed in some layout, so modeling layouts of various scenes is a key clue for scene recognition. Existing methods usually require an additional stream to detect regions for subsequent modeling, which accumulate errors and may miss important information. Meanwhile, they use manual features to model relations between regions, which weakens the representation ability of layouts. In this paper, we propose a single-stream adaptive scene layout modeling approach based on a layout modeling module (LMM), which constructs layouts without additional detection streams and adaptively captures the relations to take advantage of graph attention network. LMM is directly concatenated to a convolutional neural network, where each pixel of the activation maps of the last convolutional layer is defined as a region that is the initial input node of the LMM. LMM first models the layout of each region, and then uses all regions with layout information to model the entire scene. Layout relations are encoded as edges, which are automatically analyzed according to region co-occurrence and relative position. Our work can be understood as optimizing features of the activation maps from a scene layout modeling perspective for scene recognition. Experimental results on MIT67, SUN397, and Places365 show that our single-stream model achieves competitive performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZATION UNDER RAINY CONDITION

FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZA...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Xinda Cheng, Xu Ju, Xinjie Wang, Xuesong Li, Jianbing College of Electronic Science and Technology National University of Defense Technology Hunan Changsha410073 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Hunan Changsha410073 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Intelligent Robot Wuhan430205 China

ISBN: (纸本)9781839539954

Polarization and waveform optimization can significantly improve the detection ability of fully polarized radar. However, in practical scenarios, atmospheric particles frequently induce depolarization effects on radar waves, thereby influencing precise target detection, a phenomenon predominantly observed with raindrop particles. This paper establishes a target detection model for fully polarized radar in rainy condition. Subsequently, a joint transceiver iterative optimization algorithm is devised, utilizing the maximum output signal-to-interference plus noise ratio (SINR) of the system as the benchmark, and imposing dual constraints of energy and resemblance on the transmitting waveform. Ultimately, the effectiveness of the proposed method under rainy condition is validated through simulation, and recommendations for engineering applications are provided based on the analysis. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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Oracle Bone Inscriptions Image Recognition and Classification Method Based on Deep Learning 7

Oracle Bone Inscriptions Image Recognition and Classificatio...

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7th IEEE Information technology, Networking, electronic and Automation Control Conference, ITNEC 2024

作者： Zhu, Mingzhi Wu, Di Xu, Binbin Cao, Shunhua Cao, Jie Wang, Kaiwei Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China College of Civil and Transportation Engineering Shenzhen University Shenzhen China College of Economics Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology Guangzhou China China Electronic Product Reliability and Environmental Testing Research Institute GuangZhou China

ISBN: (纸本)9798350370782

Applying advanced image processing and deep learning techniques to the segmentation and recognition of oracle bone inscriptions can help accelerate the research process and improve the efficiency of cultural relic protection and historical and cultural research. Firstly, mathematical morphology methods are used to denoise the original oracle bone rubbings, which can better identify and process interfering elements. Subsequently, by applying one of the most advanced visual algorithm models, YOLOv8, to image recognition and segmentation of raw oracle bone rubbings, the recognition and segmentation tasks can be quickly and accurately completed. Finally, an improved ConvNeXt, which represents the most advanced convolutional neural network, was firstly applied to the classification experiment of oracle bone character images, achieving a validation accuracy of 94.5%. Through the aforementioned steps, a complete and effective method for pre-processing, image recognition, image segmentation, and image classification of oracle bone inscriptions is constructed in this paper. © 2024 IEEE.

关键词： Convolutional neural networks

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