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A Novel Three-Phase Passive Auxiliary Resonant Pole Soft-Switching Inverter With Symmetrical Auxiliary Networks and Electric Energy Feedback Function

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International Journal of Circuit Theory and Applications 2025年第0期

作者： Wang, Shuyu Liu, Hongchen Gao, Fei Wheeler, Patrick School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Department of Engineering Science Shanghai Jiao Tong University Shanghai China Power Electronics Machines and Control Research Institute University of Nottingham Santiago Chile

To solve the problems of high cost, large additional component size, large circulating loss, and complex control in existing auxiliary resonant soft-switching inverters, a three-phase passive auxiliary resonant pole inverter (ARPI) with symmetrical auxiliary networks and electric energy feedback function is proposed. On each phase bridge arm of the inverter, a set of passive auxiliary networks (PANs) without large electrolytic capacitors, transformers, and other devices is provided, which is capable of enhancing the power density of the inverter. Additionally, as the auxiliary commutation circuit does not employ auxiliary switches, it not only lowers the hardware cost but also simplifies the control complexity and enhances the reliability of the soft-switching inverter operation. The proposed topology is capable of enhancing the utilization ratio of DC voltage and reducing the harmonic content of the output waveform. According to the equivalent circuit diagrams in different operating modes, the soft-switching operating principle and realization conditions of soft-switching for the proposed ARPI are analyzed. The parameters of passive components are designed with the aim of minimizing the PAN loss. A 3-kW experimental prototype is constructed. The experimental results indicate that the switching tubes can realize the soft-switching action and suppress the rate of change of voltage and current. The efficiency of the prototype can reach 98.30% at the rated output power, which is a remarkable improvement compared with the same type of ARPIs. © 2025 John Wiley & Sons Ltd.

关键词： Poles

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Multi-Channel Based on Attention Network for Infrared Small Target Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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Distance Estimation of Fixed Objects in Driving Environments 9

Distance Estimation of Fixed Objects in Driving Environments

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Leporoni, Giorgio Ponzi, Valerio Pro, Francesco Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

Autonomous driving is a highly relevant topic today, particularly among major car manufacturers attempting to lead in technological innovation and enhance driving safety. An autonomous vehicle must possess the capability to sense its environment and navigate without human intervention. Thus, it serves as both a driver support system and, in some cases, a substitute. A crucial aspect involves identifying the positions of pedestrians, traffic signs, traffic lights, and other vehicles while computing distances from them. This enables the vehicle to emit alerts to the driver in potentially dangerous situations, such as impending obstacles due to external factors or driver distraction. In this paper, we introduce an approach for identifying traffic signs and determining the distance from them. Our method utilizes the YOLOv4 network for identification and a customized network for distance computation. This integration of AI technologies facilitates the timely detection of hazards and enables proactive alert mechanisms, thereby advancing the capabilities of autonomous vehicles and enhancing driving safety. © 2023 Copyright for this paper by its authors.

关键词： Autonomous vehicles

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AGV Path Planning Using Curiosity-Driven Deep Reinforcement Learning 19

AGV Path Planning Using Curiosity-Driven Deep Reinforcement ...

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19th IEEE International Conference on automation Science and engineering, CASE 2023

作者： Yin, Huilin Lin, Yinjia Yan, Jun Meng, Qian Festl, Karin Schichler, Lukas Watzenig, Daniel School of Electronic and Information Engineering Tongji University Shanghai China University of South-eastern Norway Mis Research Group Vestfold3603 Norway Virtual Vehicle Research GmbH Graz Austria Institute of Automation and Control Graz University of Technology Graz8010 Austria

ISBN: (纸本)9798350320695

In the field of intelligent manufacturing, automated guided vehicles (AGV s) are required to plan an optimal path to a destination quickly and safely in complex environments. Several research papers have shown that Deep Reinforcement Learning (DRL) can plan a reasonable path for AGVs. However, almost all studies do not consider the influence of sparse external rewards. In addition, most research still uses an unrealistic 2D grid for their path planning environments. To alleviate these problems, we propose a novel AGV path planning method and provide the virtual environments of AGV s that approximate the real physical world. Moreover, the curiosity mechanism helps to enhance the Proximal Policy Optimization (PPO) method to provide additional intrinsic rewards to AGV agents in the sparse reward scenario. As for the environments, we no longer divide them into a 2D grid. Thus, the AGV has a continuous position in our environments. In addition, dynamic obstacles are added to make our environments more realistic. Based on our experiments, we make qualitative and quantitative analyses of the performance of the proposed method and the baseline DRL method. The empirical results show that our proposed method can make AGVs plan a reasonable and safe path with considerable speed during the training process in our virtual environments. © 2023 IEEE.

关键词： Reinforcement learning

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A One Stage Non-motorized Vehicle Detection Network

A One Stage Non-motorized Vehicle Detection Network

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2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022

作者： Fu, Xiaofeng Yuan, Xia Ye, Jialin Bo, Yuming School of Automation Nanjing University of Sicence and Technology Nanjing China Chengdu Southwest Information Control Research Institute Co. Ltd Chendu China School of Computer Science and Engineering Nanjing University of Sicence and Technology Nanjing China

ISBN: (数字)9781665498357

ISBN: (纸本)9781665498357

With the growth of non-motorized vehicles, low-carbon and environmentally friendly travel has gradually become the mainstream of people all over the world. The wide distribution and small proportion of non-motorized vehicles in the image make it difficult for traffic regulators to detection them, which increases the work of urban managers. Focusing on the task of non-motorized vehicle detection, we improve the YOLOv5s. The symmetric revolution module is used to construct the spatial attention mechanism, so as to improve the robustness of the network model. We design a spatial attention module to reduce the parameters. The proposed dimension decoupling module decouples the dimensions of the width and height of the feature map respectively, so that the network can adapt to the width and height of non-motorized vehicle targets. Through multi-scale receptive field fusion module, residual connection is used to improve the detection ability of the network to small targets. Finally, above modules are integrated into the YOLOv5s. Experiments on real road traffic images show that the proposed model has achieved good results in non-motorized vehicle detection. © 2022 IEEE.

关键词： Vehicles

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Lumbar spine localisation method based on feature fusion

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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Adaptive Uniform Performance control of Strict-Feedback Nonlinear Systems With Time-Varying control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces 46

MSDAC: A multi-source domain adversarial framework for motio...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Liu, Haozhou Yang, Banghua Guan, Shouliang Rong, Fenqi Guo, Mengao Fang, Ying Liu, Bingyu Gao, Yan Gu, Yong Shanghai University School of Mechatronic Engineering and Automation Research Center of Brain-Computer Engineering Shanghai200444 China Lingang Laboratory Shanghai200031 China National Center for Nanoscience and Technology Beijing100190 China Institute of Neurosciences Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9798350371499

Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems. © 2024 IEEE.

关键词： Brain computer interface

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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