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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Huang, Changchuang Kou, Baoqaun Zhao, Xiaokun Harbin Institute of Technology Electrical Engineering and Automation Heilongjiang China Northeast Forestry University Computer and Control Engineering Heilongjiang China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency, high power factor and small volume. In this paper, a segmented permanent magnet interior permanent magnet synchronous motor (IPMSM) is proposed and optimized. First, the topology of the IPMSM is introduced. Second, multi-objective optimization is performed by non-dominated sorting genetic algorithm-II (NSGA-II) using rotor structure parameters as optimization variables, saliency ratio as constraint, electromagnetic torque and constant-power speed range as objective functions. Then, the electromagnetic characteristics of the initial IPMSM and the optimized IPMSM are analyzed and compared. The results show that the torque and constant-power speed range of the optimized motor are improved, which verifies the validity of the optimization method. The proposed motor is suitable for electric vehicles, which require large torque at low speeds and wide constant-power speed range at high speeds. © 2023 IEEE.

关键词： Multiobjective optimization

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Rethinking Distribution Alignment for Inter-class Fairness 1

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Ye, Jinhuang Wu, Jiawei Li, Zuoyong Zheng, Xianghan College of Computer and Big Data Fuzhou University Fuzhou350108 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning. However, the excellent performance brought by SSL does not transfer well to the task of class imbalance. The reason is that the class bias of pseudo-labelling further misleads the decision boundary. To solve this problem, we propose a new plug-and-play approach to handle the class imbalance problem based on a theoretical extension and analysis of distribution alignment. The method, called Basis Transformation Based Distribution Alignment (BTDA), efficiently aligns class distributions while taking into account inter-class *** implements the basis transformation through a learnable transfer matrix, thereby reducing the performance loss caused by pseudo-labelling biases. Extensive experiments show that our proposed BTDA approach can significantly improve performance in class imbalance tasks in terms of both accuracy and recall metrics when integrated with advanced SSL algorithms. Although the idea of BTDA is not complex, it can show advanced performance on datasets such as CIFAR and SVHN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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A Dual-Stage Optimal controller Design for controlling AVR System 27

A Dual-Stage Optimal Controller Design for Controlling AVR S...

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27th International computer Science and engineering Conference, ICSEC 2023

作者： Abood, Layla H. Mohamed, Mohamed J. Abood, May H. University of Technology Department of Control and System Engineering Baghdad Iraq Al-Iraqia University Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9798350342109

In this study a cascade controller is proposed to maintain the Automatic Voltage Regulator (AVR) system, its comprises from two stage the first one is a conventional Proportional and Derivative (PD) controller and the second one is a Fractional Order PID (FOPID) controller is combined together for regulating the terminal voltage in AVR system, the gains of the proposed controller is tuned used a smart optimization algorithm called Squirrel Search algorithm (SSA) and adopt an Integral Time Absolute Error (ITAE) for monitoring for monitoring desired response to obtain a stable and robust response, the system response is tested using Matlab 2019 and the transient analysis is used to calculate overshoot, settling time, rise time, and peak time then compared these values selected classical controllers(PID,FOPID) to verify the robust response of the proposed controller. A robustness analysis verified the system response when there is an external disturbances, positive and negative disturbances signals are added, the controller try to face these signals, it takes 0.15 sec in negative disturbance and 0.1 sec in positive disturbance then in both states force the response to return to its desired response. © 2023 IEEE.

关键词： controllers

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SSVEP Based BCI control of an Exoskeleton 18th

SSVEP Based BCI Control of an Exoskeleton

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18th International Conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Sibișanu, Remus Constantin Olar, Marius Leonard Roșca, Sebastian Daniel Leba, Monica Computer and System Control Engineering Department University of Petrosani 20 Universitatii Petrosani332006 Romania

ISBN: (纸本)9783031816840

This paper explores recent innovation in the field of robotic teleoperation, presenting a state-of-the-art system for a robotic arm, configurable as an exoskeleton or prosthetic limb. Based on noninvasive neural headset technology, the system captures movement intentions directly from the user’s neural activity. By precisely interpreting neural signals and translating them into robotic arm movements, the system bridges the gap between human intention and robotic action. Its uniqueness lies in its ability to interpret the subtleties of human intent through non-invasive means, providing intuitive and fluid control over the robotic arm. The system is distinguished by the efficient use of rich neural activity, thus improving usability and opening up new possibilities for people with mobility impairments. The paper analyzes technical complexities, detailing the functionality of neural headsets, signal decoding algorithms, and the command formation process for the robotic arm. It also presents empirical evidence of the effectiveness of the system and discusses possible applications and future improvements. This research not only contributes to robotic teleoperation, but also paves the way for more human and empathetic interactions with machines in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Exoskeleton (Robotics)

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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Unmanned Aerial Vehicle for Sparse Sampling in Wireless Sensor Networks 9

Unmanned Aerial Vehicle for Sparse Sampling in Wireless Sens...

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9th International Conference on Electrical engineering, control and Robotics, EECR 2023

作者： Lv, Cuicui Wang, Peijin Ma, Guoxin Chi, Haokun Du, Zhenbin Li, Xiangming YanTai University School of Computer and Control Engineering YanTai China YanTai University School of Environment and Materials Engineering YanTai China

ISBN: (纸本)9781665491204

In Wireless Sensor Networks, sensory data among adjacent sensor nodes have strong spatial correlation. If all sensor nodes collect and transmit data, energy consumption is very large and the network life is shortened. To address this issue, we put forward an Unmanned Aerial Vehicle (UAV)-assisted sparse sampling algorithm. In this algorithm, Compressive Sensing is applied for sparse sampling, and the UAV is utilized as a mobile relay to collect the sensory data of partial nodes. To reduce energy consumption for data transmission, we combine greedy algorithm with the rules of genetic algorithms to plan the route of the UAV. The numerical results given by the simulations indicate that the proposed algorithm has less energy consumption than other baseline algorithms, and can better recover a great number of raw data by the fewer measurements. © 2023 IEEE.

关键词： Compressed sensing

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Research of STF-CKF-SLAM algorithm using Variational Bayes 23

Research of STF-CKF-SLAM algorithm using Variational Bayes

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6th International Conference on Electronics, Communications and control engineering, ICECC 2023

作者： Yuan, Shuai Bai, Yueyan Wu, Jian Li, Aichun Guo, Pengcheng School of Computer Science and Engineering Shenyang Jianzhu University China School of Electrical and Control Engineering Shenyang Jianzhu University China

ISBN: (纸本)9798400700002

Aiming at the problem of poor robustness of the driving model and divergence of the filtering process due to time-varying or abrupt-varying noise of the driving model during simultaneous localization and mapping of mobile robots, this paper proposes a strong tracking cubature kalman based SLAM algorithm using Variational Bayes, which uses the variational bayes algorithm to iteratively estimate the prediction covariance of the cubature kalman filter. Then the strong tracking cubature kalman algorithm is used to filter and fuse the observation data and the driving model, and the optimized pose data is obtained to further construct a more accurate feature map. Finally, the effectiveness of the algorithm is illustrated by simulation and practical experiments using a mobile robot equipped with lidar sensors. The simulation results represent that the simultaneous localization and mapping errors of this algorithm are reduced by 58.43% compared with the traditional CKF algorithm. The experimental results in real environment show that the average error of acquiring environmental features is reduced by 44.46%, which further verifies the feasibility and effectiveness of the proposed algorithm. This algorithm provides a new idea for simultaneous localization and mapping of mobile robots. © 2023 ACM.

关键词： Mobile robots

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Novel 1-$\varphi$ High-Voltage Boosting Transformerless Inverter Topology with Optimal Power Components and Negligible Leakage Currents

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IEEE Transactions on Industry Applications 2023年第5期59卷 1-14页

作者： Chamarthi P.K. Muduli U.R. Moursi M.S.E. Al-Durra A. Khadkikar V. Hosani K.A. EL-Fouly T.H.M. Real-Time Control and Optimization Laboratory (RT-COOL) in Holcombe Department of Electrical and Computer Engineering Clemson University USA APEC Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

Inverter topologies for integrating a rooftop photovoltaic (PV) unit into a microgrid are becoming increasingly complex. This paper proposes a high-voltage boosting transformerless inverter (HVBTI) topology for enhancing such applications. The coupled inductor-based high voltage gain feature of the HVBTI configuration allows power to be delivered into the grid from a lower voltage PV source without using higher duties. In addition, HVBTI suppresses leakage current as the common connection shared between the

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terminal of the PV source and the grid neutral point. In comparison to existing topologies, the HVBTI topology uses a compact pulse width modulation strategy to control only six controllable switching devices. Again, using lower-rated switching devices is more cost-effective while increasing reliability and efficiency. In a laboratory prototype of a 1 kVA grid integrated system, the proposed HVBTI configuration is validated, and the maximum efficiency of the HVBTI is estimated at approximately 95%. Author

关键词： Boosting Inverter Inductors Inverters Leakage currents Low Switch Count Microgrid Rooftop Photovoltaics Switches Topology Transformers Voltage Voltage Gain

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing

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Digital Communications and Networks 2022年第4期8卷 516-530页

作者： Qi Zhang Yingjie Wang Zhipeng Cai Xiangrong Tong The School of Computer and Control Engineering Yantai UniversityYantai264005China Yantai Key Laboratory of High-end Ocean Engineering Equipment and Intelligent Technology Yantai264005China The Department of Computer Science Georgia State UniversityAtlantaGA30303USA

In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical *** a part of the IoT ecosystem,task assignment has become an important goal of the research *** task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment *** the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is *** is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task *** first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph *** second stage uses a bipartite graph to complete the online task assignment *** paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment *** encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’*** avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase ***,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.

关键词： Spatiotemporal crowdsourcing Cross-regional Edge cloud Offline prediction Oline task assignment

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