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43rd Chinese Control Conference, CCC 2024

作者： Liu, Shuqi Wang, Jinhuan Wang, Lin Hebei University of Technology Institute of Mathematics School of Science Tianjin300401 China Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581581

This paper investigates the pricing problem of transportation based on a framework of network congestion game with both player and resource failures. Firstly, the traffic pricing problem is modeled as a network congestion game by redefining the player's cost function in the event of player and resource failures. Secondly, by using the method of semi-tensor product of matrices, the game with player and resource failures is algebraized and verified to be a weighted potential game. The weighted potential function is also obtained. Then, the impact of player and resource failures on Nash equilibria (NEs) is studied, and the condition on keeping the NE of the original game unchanged is discussed. Finally, the theoretical results are verified by numerical examples. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Tensors

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Accelerated SCUC Solution: Integrating Cutting Planes, Interior Points, and Rounding Heuristics 5

Accelerated SCUC Solution: Integrating Cutting Planes, Inter...

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5th International Conference on Power Engineering, ICPE 2024

作者： Cai, Zhi Hu, Xiaojing Chen, Yanguang Xu, Shengnan Automation Technology China Electric Power Research Institute Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Beijing China Co. Ltd R&Development Department Beijing China

ISBN: (纸本)9798331531140

In this research contribution, we present a novel approach aimed at accelerating online mixed-integer optimization by leveraging heuristic ROUNDING techniques and interior point methods. The cornerstone of our method lies in the construction of a Cardinality cutting plane. This Cardinality cutting plane method effectively harnesses the solution of linear programming (LP) without incorporating the CROSSOVER procedure within the interior point method. It ingeniously forms a feasible area for the segmentation of cutting planes while meticulously configuring the relevant parameters to ensure their viability. Notably, our method seamlessly integrates into any Mixed-Integer Programming (MIP) solver. Our empirical findings decisively demonstrate the superior performance of the proposed algorithm compared to conventional direct solution methods, particularly in terms of its agility in rapidly exploring feasible regions. Additionally, our algorithm exhibits a remarkable acceleration in improving the lower bound. This enhancement significantly enhances the efficiency of solving optimization problems, offering a promising avenue for effectively addressing the complexities of energy system scheduling. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Research on Data Classification Based on LNDS-SVDD 7

Research on Data Classification Based on LNDS-SVDD

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7th International Symposium on Autonomous systems, ISAS 2024

作者： Yuan, Yiwei Xue, Qiming Cao, Yuan Yi, Yingmin Bai, Lei Xi'an University of Technology Faculty of Automation and Information Engineering Shaanxi Xian710048 China Xianyang Key Laboratory of New Energy and Microgrid System Department of Electrical Engineering Shanxi Polytechnic Institute Xianyang712000 China

ISBN: (纸本)9798350363173

Due to the high dimension of industrial process data, there are aliasing areas when building hyper-spheres of different types of data by the SVDD algorithm, which will lead to inaccurate results in condition identification. A data classification method based on Local Neighbor Density Similarity Support Vector Data Description (LNDS-SVDD) is proposed to address this issue. Utilizing the local similar density in discriminating data similarity, the LNDS-SVDD can further judge the classification of samples distributed in the aliasing area using the density information. In this paper, a randomly generated synthetic data set and a real industrial process data set is used for simulating the proposed data classification algorithm. The results indicate that the classification accuracy is high, which is an effective condition identification method for the industrial process. © 2024 IEEE.

关键词： Classification (of information)

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Variable Frequency Phase-Shift Modulation Technique for Single Stage Dual-Active-Bridge AC-DC Converter 11

Variable Frequency Phase-Shift Modulation Technique for Sing...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Guo, Dongxin Wang, Panbao Ren, Chunguang Guerrero, Josep M. Harbin Institute of Technology School of Electrical Engineering and Automation Harbin China Taiyuan University of Technology Shanxi Key Lab. of Power System Operation and Control Taiyuan China Aalborg University Department of Energy Technology Aalborg Denmark

ISBN: (纸本)9788957083505

This article proposes a variable frequency phase-shift modulation technique for single stage dual-active-bridge (DAB) AC-DC converter. This isolated single-stage single-phase converter consists of a DAB and a full bridge (FB) inverter. The proposed strategy applies a composite control strategy combining frequency modulation and extended phase shift (EPS) method to overcome the drawback of the non-linear power control of traditional phase-shift control scheme, which significantly reduces the complexity of the controller algorithm. Moreover, the synchronous inverter control applied to the back-end FB inverter is designed, which can realize the energy conversions in a single-stage, and a high frequency (HF) filter capacitor can replace the electrolytic capacitor at the output side DC-link. Thus, the safety of system operation improves and the power density of the system increases. Finally, simulation results confirm the validity and practical feasibility of the proposed control strategy. © 2023 The Korean Institute of Power Electronics.

关键词： Electrolytic capacitors

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Bibliometric and Content Analysis of Large Language Models Research in Software Engineering: The Potential and Limitation in Software Engineering

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of Science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings International Conference on Software Engineering and ACM International Conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (Science and Information Organization). All Rights Res

关键词： Software design

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A ResNet-based approach for accurate radiographic diagnosis of knee osteoarthritis

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CAAI Transactions on Intelligence technology 2022年第3期7卷 512-521页

作者： Yu Wang Shibo Li Baoliang Zhao Jianwei Zhang Yuanyuan Yang Bing Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhenChina Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina TAMS Department of InformaticsUniversity of HamburgHamburgGermany

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence(KL)scoring ***,individual scoring is subjective and error *** study proposes an approach for automated knee osteoarthritis classification based on deep neural *** knee X-ray images are first preprocessed with frequency-domain filtering and histogram normalisation,making the trabecular bone texture more obvious and benefiting the subsequent classification ***,a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the ResNet-50 *** addition,a rebalance operation is proposed to deal with the dataset unbalance problem,and a quick search technique is proposed to improve the iterative search efficiency for the joint *** all of these techniques,a classification accuracy of 81.41%is obtained,which is higher compared to the state-of-the-art approaches.

关键词： AMSGrad knee osteoarthritis recognition ResNet VGG

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Self-Supervised Learning with Min-Max Distance Approach for Few-Shot Steel Surface Defects Recognition 43

Self-Supervised Learning with Min-Max Distance Approach for ...

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43rd Chinese Control Conference, CCC 2024

作者： Wang, Tianlei Chen, Xinyao Li, Zeliang Zhou, Zhencai Zhou, Zhiheng Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Province Kejie Machinery Automation Company Technical Department Jiangmen China

ISBN: (纸本)9789887581581

Surface defects in steel are crucial for quality inspection in the manufacturing of industrial metal products. While deep learning methods have been extensively studied in this field, the lack of large-scale annotated defect samples imposes significant labeling pressure, making training fully supervised deep models challenging. In response to this issue, we employ self-supervised learning for few-shot recognition, effectively learning representations from limited unlabeled data and achieving defect recognition with a small amount of labeled samples. In particular, we construct Other encoder with multi-view information and Base encoder with single view information based on the distillation self-supervised learning framework through the multi-crops mechanism, and the Other encoders and Base encoders perform information share. Our main contribution is that, based on this framework, we propose a new regularization method to solve the problem of instances deviating from the center of self-supervised clustering by minimizing the maximum instance distance (MMD). In testing, fine-tuning the MMD pre-trained model with a small number of labeled data achieves superior recognition performance on the steel surface defect dataset NEU, significantly outperforming other testing methods. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Self-supervised learning

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A Survey of Output Feedback Robust MPC for Linear Parameter Varying systems

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1717-1751页

作者： Xubin Ping Jianchen Hu Tingyu Lin Baocang Ding Peng Wang Zhiwu Li the Department of Automation School of Electro-Mech-anical EngineeringXidian UniversityXi’an 710071China the Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringFaculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China the State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China the College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China the Information and Navigation College Air Force Engineering UniversityXi’an 710077China IEEE the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071 the Institute of System Engineering Macao University of Science and TechnologyMacao SARChina

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related *** to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and *** extensions of OFRMPC for LPV systems to other related uncertain systems are also *** methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are *** issues on OFRMPC optimizations for LPV systems are ***,the future research directions on OFRMPC for LPV systems are suggested.

关键词： Linear parameter varying(LPV)systems model predictive control(MPC) output feedback robust control

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Resilient Event-Triggered Control of Vehicle Platoon Under DoS Attacks and Parameter Uncertainty

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-9页

作者： Han, Qiaoni Ma, Jianguo Zuo, Zhiqiang Wang, Xiaocheng Yang, Bo Guan, Xinping Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Tianjin China Department of Automation Shanghai Jiao Tong University Shanghai China

This paper investigates the problem of dynamic event-triggered platoon control for intelligent vehicles (IVs) under denial of service (DoS) attacks and parameter uncertainty. DoS attacks disrupt vehicle-to-vehicle (V2V) communications, leading to the destabilization of vehicle formations. To alleviate the burden of the V2V communication network and enhance the tracking performance in the presence of DoS attacks and parameter uncertainty, a resilient and dynamic event-triggered mechanism is proposed. In contrast to the static event-triggering mechanism (STEM), this approach leverages the internal dynamic variable to further save communication resources. Subsequently, a method is developed for designing the desired triggering mechanism. Following this, a co-design framework is constructed to guarantee robust and resilient control against DoS attacks, with the analysis of eliminating Zeno behavior. Lastly, extensive simulations are presented to show the superiority of the proposed method in terms of enhancing platoon resilience and robustness and improving communication efficiency. IEEE

关键词： Vehicular ad hoc networks

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A New Technique of Soluble NPK Nutrient Detection system Using Deep Learning-Based Recurrent Neural Networks (RNN) 12

A New Technique of Soluble NPK Nutrient Detection System Usi...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University Of Technology Krungthep Department Of Electrical And Telecommunication Engineering Bangkok Thailand Thammasat School Of Engineering Thammasat University Faculty Of Engineering Department Of Electrical And Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department Of Robotic Engineering And Automation System Bangkok Thailand

ISBN: (纸本)9798350383591

Modern agriculture relies heavily on effective process monitoring, control, and remote data collection, making accurate and cost-effective sensors crucial for innovation. This research focuses on the development of a spectroscopy-based device for real-time and remote monitoring of nutrient levels in hydroponic systems. The device utilizes IoT architecture, incorporating principles of light absorbance to quantify concentrations of essential nutrients during crop growth. Three deep learning techniques, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) were adopted for data analysis. Results demonstrate that tailored implementation of deep learning can significantly enhance the performance of sensor network system (SNS). Specifically, the RNN format achieved an impressive 98% model accuracy during both training and testing phases. The technological advancements in addressing contract variation and photometric levels contribute to increased accuracy as more sensors become available. The findings serve as a comprehensive guide for collecting extensive agricultural data, encompassing environmental factors throughout the entire cultivation period, using state-of-the-art remote control sensor equipment. This work provides a practical, efficient, and cost-effective solution for precise nutrient control and continuous data monitoring in agricultural production. © 2024 IEEE.

关键词： Cost effectiveness

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