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Novel Cooperative Particle Swarm Optimization with Interactive Learning Strategy 1

8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Qiliang Liu, Yiheng Xu, Hao Zhang, Pinggai Wu, Qinlin Wei, Lisheng Anhui Key Laboratory of Electric Drive and Control School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Industrial Process Control Optimization and Automation Engineering Research Center School of Electronic Engineering Chaohu University Anhui Chaohu238024 China School of Computer Science and Artificial Intelligence Chaohu University Anhui Chaohu238024 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

Particle Swarm Optimization (PSO) is an optimization method grounded in swarm intelligence, designed to mimic the social behaviors of bird flocks during hunting. By sharing information about their positions, the entire swarm can explore and search diverse areas within a given space. This method features rapid convergence, minimal parameters, and straightforward implementation, making it a potent and efficient metaheuristic technique. Nevertheless, conventional PSO algorithms, despite their simplicity and efficiency, often become trapped in local optima when tackling complex multimodal problems, which limits their global search capability. To mitigate these limitations, a new Cooperative Particle Swarm Optimization algorithm with Interactive Learning (CPSOIL) is introduced. This approach incorporates an interactive learning strategy, allowing particles to learn from their neighbors by perceiving the surrounding particles during updates. Moreover, a mechanism has been added to modify the particle velocity update when the algorithm encounters local optima, thereby preserving population diversity, enhancing the likelihood of escaping local optima, and improving algorithm accuracy. Comparisons with other leading algorithms on the CEC2005 benchmark functions indicate that, in most cases, the enhanced algorithm outperforms its counterparts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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An Anti-Overlap Object Feature Weighted Fusion Detector for X-Ray Prohibited Item Detection

An Anti-Overlap Object Feature Weighted Fusion Detector for ...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Tong Jia Wanying Jia Yixin Tian Bowen Ma Hao Wang Shiyi Guo State Key Laboratory of Synthetical Automation for Process Industries Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Security checkpoints equipped with X-ray scanners play a crucial role in ensuring public safety by inspecting luggage for prohibited items. However, even trained professionals can easily make errors when manually inspecting X-ray images. To assist in manual inspection, direct application of object detection methods to X-rays can lead to false positives and false negatives due to overlapping objects. To address this issue, this paper proposes an object feature weighted fusion detector (OFWF-Detector) for X-ray prohibited item detection. The OFWF-Detector utilizes an object confidence mask selection (OCMS) mechanism to filter overlapping features, improving detection accuracy. Experimental results on the PIX ray dataset demonstrate that the proposed method effectively reduces false positives and false negatives, achieving superior performance compared to traditional methods. Specifically, the OFWF-Detector enhances the detection of small objects in overlapping scenarios by 12.9 % in terms of APs from Tab. I, showcasing its effectiveness in all the challenges posed by overlapping objects in X-ray images. These findings highlight the potential of the OFWF-Detector to enhance the efficiency and accuracy of security screening systems, contributing to improved public safety in high-traffic areas.

关键词： Accuracy Detectors Object detection Manuals Inspection Feature extraction Public security Security Artificial intelligence X-ray imaging

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Generative Adversarial Nets Model Based on Spatio-Temporal Graph Attention Network for Multi-Step Prediction

Generative Adversarial Nets Model Based on Spatio-Temporal G...

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Chinese automation Congress (CAC)

作者： Ling Wang Rongxian Wang Gaofeng Jia Key Laboratory of Knowledge Automation of Industrial Process of Ministry of education School of Automation and Electrical Engineering Shunde Innovation School University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation of Industrial Process of Ministry of education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

The precise forecasting of traffic data holds significant importance for numerous practical applications in the real world. The challenge lies in the nonlinear spatio-temporal dependencies of the data, as well as rapid error accumulations for multi-step predictions. Therefore, we propose a Generative Adversarial Nets model based on a Spatio-Temporal Graph Attention Network (STGAT-GAN). Utilizing spatio-temporal graph attention proves to be an effective method for capturing the spatio-temporal dependencies, while GANs can learn a rich distribution over spatio-temporal data implicitly to produce realistic data adversarially. Experiments are carried out on two public traffic datasets. Comparative experimental analysis reveals that the proposed STGAT-GAN has better performance.

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A Multi-objective Bayesian Optimization Algorithm with Online Knowledge Transfer

A Multi-objective Bayesian Optimization Algorithm with Onlin...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Huiting Lil Yaochu Jin State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Engineering Westlake University Hangzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Since transfer learning can learn effective knowledge from the source task to help solve the target task, it offers a powerful tool for assisting multi-objective evolutionary Bayesian optimization in solving expensive multi-objective problems in the presence of data paucity using. Most existing optimization algorithms use genetic operators or construct Gaussian process models to achieve knowledge transfer and much less work have been reported that make use of transfer learning methods for knowledge transfer. To fill the gap, this paper proposes an online knowledge transfer-based multi-objective Bayesian optimization algorithm. The algorithm first classifies the data of cheap and expensive problems based on the dominance relationship and trains a classifier between cheap and expensive problems using the balanced distribution adaptation method. Then, this classifier is employed to generate synthetic data for training the surrogates for expensive optimization. In addition, to enhance the prediction accuracy for the classifier, an online transfer approach is adopted to retrain the classifier using the newly evaluated data in each iteration of optimization. Finally, the proposed algorithm is examined in comparison with two popular algorithms on two benchmark test problems to verify its performance.

关键词： Training Transfer learning Gaussian processes Benchmark testing Prediction algorithms Classification algorithms Bayes methods Optimization Knowledge transfer Synthetic data

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Integrated Stabilizing Control for NCSs with Intermittent Observation and Multiple Random Transmission Delays 42

Integrated Stabilizing Control for NCSs with Intermittent Ob...

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42nd Chinese Control Conference, CCC 2023

作者： Sui, Haoting Tan, Cheng Zhang, Qinglong Li, Yuzhe Wong, Wing Shing College of Engineering Qufu Normal University Rizhao276800 China Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China The Chinese University of Hong Kong The Department of Information Engineering Shatin N. T. Hong Kong

ISBN: (纸本)9789887581543

In this paper, we mainly investigate sampled-data networked control systems (NCSs) operating over wireless communication networks featuring multi-path channels. Affected by limited bandwidth power, the transmission of the observed state signals from the sensor to the controller may suffer packet loss leading to intermittent observation. And due to an unreliable multi-path routing network, the conveyance of control signals from the controller to the actuator is subject to random transmission delays. By proposing an idle-time-based scheduling policy with a pre-defined deadline constraint for signal reception on the actuator side, we transform the integrated system into a stochastic one with constant input delay. Based on this, we derive a set of Riccati-type stabilization conditions, which are necessary and sufficient, and design the stabilizing control policy in terms of the feedback of the optimal estimate with minimum mean square error. Additionally, for the scalar case, we study the existence and uniqueness properties of the maximum sampling period, and develop an algorithm based on system parameters, service quality and packet loss rate. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Stabilization

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Research on Fiber Optic Surface Plasmon R ***:A Review

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Photonic Sensors 2024年第2期14卷 1-30页

作者： Qi WANG Dianyun ZHANG Yizhuo QIAN Xiangyu YIN Lei WANG Shushuai ZHANG Yuyang WANG State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China College of Information Science and Engineering Northeastern UniversiyShenyang 110819China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004China State Key Laboratory of Chemo/Biosensing and Chemometrics Hunan UniversityChangsha 410082China

Due to the benefits of the high sensitivity,real-time response,no labeling requirement,and good selectivity,fiber optic sensors based on surface plasmon resonance(SPR)have gained popularity in biochemical sensing in recent *** current research on such sensors is hot in enhancing sensitivity,improving detection accuracy,and achieving the detection of biochemical *** goal of this work is to present a thorough overview of recent developments in the optical fiber SPR biosensor ***,it explores the basic principles and sensing structures of optical fiber SPR biosensors,focusing on four ***,this paper introduces three fiber optic surface plasmon biosensors:SPR,localized surface plasmon resonance(LSPR),and long-range surface plasmon resonance(LRSPR).Each concept is explained from the perspective of the basic principles of fiber optic SPR ***,a classification of fiber optic SPR biosensors in health monitoring,food safety,environmental monitoring,marine detection,and other applications is introduced and ***,this paper summarizes the current research directions of SPR ***,it provides a prospective outlook on how fiber optic SPR sensors will develop in the future.

关键词： Surface plasmon resonance biosensors optical fiber sensors sensitivity

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Simulation Based Robustness Evaluation of Train Rescheduling Plan Under Uncertainties 28

Simulation Based Robustness Evaluation of Train Rescheduling...

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28th International Conference on automation and Computing, ICAC 2023

作者： Zhao, Dezhao Cui, Dongliang Yang, Pengxin Liu, Ruiguang Dai, Xuewu Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Shenyang China Hebei Petroleum University of Technology Department of Automotive Engineering Chengde China Northumbria University Faculty of Engineering and Environment NewcastleNE1 8ST United Kingdom

ISBN: (纸本)9798350335859

When a train deviates from its planned operational schedule due to unexpected events causing delays, it becomes necessary to reschedule the train in order to obtain a revised operation plan and minimize the delays. However, evaluating the robustness of the actual train schedule under various uncertainties is a critical aspect of train rescheduling. This paper presents the development of a simulation system for high-speed railway operation scheduling and proposes a method to evaluate the robustness of train rescheduling plans considering uncertain disturbances in the simulation process. The simulation system incorporates a train operation model based on both mechanisms and data, as well as an uncertain interference model for train delays based on data. By introducing various disturbances generated by the uncertain interference model, the robustness of the train rescheduling plan can be simulated and evaluated. Using the Jinan West-Shanghai Hongqiao high-speed railway network as a case study, the rescheduling plans generated by two scheduling algorithms are simulated. The results validate the reliability of the developed simulation system for verifying and evaluating train rescheduling plans. Furthermore, it provides valuable data and technical support for intelligent scheduling of high-speed trains and performance verification of scheduling algorithms. © 2023 IEEE.

关键词： Scheduling algorithms

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Resilient Learning-Based Control Under Denial-of-Service Attacks 63

Resilient Learning-Based Control Under Denial-of-Service Att...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Chakraborty, Sayan Gao, Weinan Vamvoudakis, Kyriakos G. Jiang, Zhong-Ping New York University Can Lab BrooklynNY11201 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Georgia Institute of Technology The Daniel Guggenheim School of Aerospace Engineering GA30332-0150 United States

ISBN: (纸本)9798350316339

In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart. © 2024 IEEE.

关键词： Inverted pendulum

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High-sensitivity intensity-interrogated temperature sensor based on phase-modulated grating in chalcogenide fiber 28

High-sensitivity intensity-interrogated temperature sensor b...

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28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Zhu, Chengliang Wang, Lei Zhao, Yong Li, Hongpu College of Information Science and Engineering Northeastern University Shenyang110004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Graduate School of the Engineering Shizuoka University Hamamatsu432-8561 Japan

ISBN: (纸本)9781957171302

A high-sensitivity intensity-interrogated temperature sensor is proposed and numerically demonstrated. The sensor is realized by the optical fiber edge-filter (OFEF) based on the phase-modulated long period fiber grating (PMLPG) in chalcogenide fiber. The obtained sensitivity is ~30 times larger than that of the conventional LPGs in silica fibers. © Optica Publishing Group 2023, © 2023 The Author(s)

关键词： Temperature sensors

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Sensor and Filter Attacks Against Distributed State Estimation with Dual Stealthiness

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IEEE Transactions on Automatic Control 2024年

作者： Jin, Kaijing Ye, Dan The College of Information Science and Engineering Northeastern University Shenyang110819 China The State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China

To reveal the security vulnerabilities of distributed estimation systems, we investigate the existence of false data injection attacks in this paper, where attacks can corrupt sensor data and filter data. Considering the innovation-based and consensus-based strict stealthiness, a successful attack should ensure the divergence of each local estimation and not affect innovation and consensus signals. By analyzing the null space of system matrices, the necessary and sufficient conditions for the existence of attacks with such dual strict stealthiness are derived. Furthermore, if attacks can only destroy filter or sensor data, we discuss the existence of filter attacks and obtain that there is no strictly stealthy sensor attack to cause divergent estimation. By introducing a relaxed dual stealthiness constraint, the corresponding existence condition of sensor attacks is developed. Based on the given attack existence conditions, some data protection strategies are offered to defend against the threat of attacks. Simulations are conducted to demonstrate the effectiveness of the theoretical results. ©1963-2012 IEEE.

关键词： consensus-based stealthiness Distributed state estimation false data injection attacks innovation-based stealthiness

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