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5th International Conference on Artificial Intelligence and computer science, AICS 2023

作者： Gao, Han Li, Xu Cao, Jianzhao Luan, Feng Zhu, Xiaoyan Zhang, Dianhua The State Key Laboratory of Rolling and Automation Northeastern University Liaoning Shenyang110819 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110169 China College of Engineering Training and Innovation Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9781510668621

This paper proposes a low-latency method, based on FPGA, for edge extraction of hot-rolled strip. The method uses a global shutter camera module to capture strip images and detects the strip edge position by analyzing the intensity profile and the gradient of image gray values. The method has the advantages of low latency, strong robustness, and accurate results. The feasibility and effectiveness of the method are verified by FPGA simulation experiments, and a foundation is laid for the subsequent strip deviation automatic control algorithm. © 2023 SPIE.

关键词： Field programmable gate arrays (FPGA)

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Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification

Decomposition-based Multi-objective Genetic Programming for ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Zhang, Tuo Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University China State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical and Information Engineering Zhengzhou University Luoyang China School of Electrical and Information Engineering Zhengzhou University School of Electrical Engineering and Automation Henan Institute of Technology State Key Laboratory of Intelligent Agricultural Power Equipment China School of Engineering and Computer Science Centre for Data Science and Artificial Intelligence Victoria University of Wellington New Zealand

ISBN: (纸本)9798400704956

Image classification is challenging due to the high dimensionality and large variations of the image data. As an effective method in image classification, feature learning can be considered a multiobjective problem of maximizing the classification accuracy and minimizing the number of learned features. Existing multi-objective genetic programming (MOGP) methods directly apply the multiobjective techniques to GP without considering the characteristics of feature learning tasks. Therefore, this paper proposes a decomposition-based MOGP approach with a global replacement strategy to feature learning in image classification. The proposed approach is compared with existing MOGP methods and the experimental results demonstrate the effectiveness of the proposed approach. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Time-Synchronized Safety-Critical Attitude Control for Spacecraft with Hardware-in-Loop Validation

Time-Synchronized Safety-Critical Attitude Control for Space...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Tong, Shang Yang, Haoyang Zhang, Rongchuan Hu, Qinglei Sam Ge, Shuzhi Li, Dongyu Tianmushan Laboratory Hangzhou China School of Cyber Science and Technology Beihang University Beijing China School of Engineering University of Warwick Coventry United Kingdom Institute Beihang University Hangzhou International Innovation Hangzhou China School of Automation Science and Electrical Engineering Beihang University Beijing China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798331504755

A time-synchronized controller is proposed to solve the time-varying constrained reorientation problem. Time-Synchronized Stability (TSS) that considers the interrelationships among different state errors plays an important role in our method, enabling synchronized convergence of individual error components while ensuring compliance with state constraints. Compared to traditional sliding mode controllers, the efficiency of our safety-critical attitude controller is imporoved without adding computational burden, as evidenced by improved control energy consumption and reduced convergence time. A hardware-in-the-loop (HiL) experiment confirms its practical deployment capabilities. © 2024 IEEE.

关键词： Sliding mode control

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging Control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning 27th

UAD-DPL: An Unknown Encrypted Attack Detection Method Based ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031781681

In the field of cybersecurity, a significant concern is the potential for unknown attacks to occur at any moment. The primary obstacle faced by intrusion detection systems lies in the identification of unknown attacks and the differentiation of such threats from benign network traffic and known attack patterns. Many current deep learning approaches struggle to discern the underlying features of these new attack types, especially in the case of encrypted attacks, resulting in inherent inaccuracies. To tackle these challenges, a novel unknown attack detection method based on deep prototype learning (UAD-DPL) is proposed to improve the accuracy of unknown encrypted attack detection. Firstly, a spatiotemporal fusion feature extraction network for encrypted traffic is employed to enhance the feature representation ability. Subsequently, an innovative prototype-based deep feature space learning model is proposed, which utilizes discriminative loss and open loss training to ensure that the learned encrypted traffic features meet the predetermined prior distribution. Finally, an unknown attack recognition approach is designed to effectively identify both known and unknown attacks, and a three-stage training strategy is proposed to train the model. Experimental results indicate that the UAD-DPL method is highly proficient in detecting both known and unknown attacks in encrypted traffic. Moreover, UAD-DPL exhibits promising application potential in open-set network intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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DecGAN: Decoupling Generative Adversarial Network for Detecting Abnormal Neural Circuits in Alzheimer's Disease

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5050-5063页

作者： Pan, Junren Zuo, Qiankun Wang, Bingchuan Chen, C.L. Philip Lei, Baiying Wang, Shuqiang Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China Hubei University of Economics Hubei Key Laboratory of Digital Finance Innovation School of Information Engineering Hubei Internet Finance Information Engineering Technology Research Center Hubei Wuhan430205 China Central South University School of Automation Changsha410083 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China

One of the main reasons for Alzheimer's disease (AD) is the disorder of some neural circuits. Existing methods for AD prediction have achieved great success, however, detecting abnormal neural circuits from the perspective of brain networks is still a big challenge. In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD. Concretely, a decoupling module is designed to decompose a brain network into two parts: one part is composed of a few sparse graphs that represent the neural circuits largely determining the development of AD;the other part is a supplement graph, whose influence on AD can be ignored. Furthermore, the adversarial strategy is utilized to guide the decoupling module to extract the feature more related to AD. Meanwhile, by encoding the detected neural circuits to hypergraph data, an analytic module associated with the hyperedge neurons algorithm is designed to identify the neural circuits. More importantly, a novel sparse capacity loss based on the spatial-spectral hypergraph similarity is developed to minimize the intrinsic topological distribution of neural circuits, which can significantly improve the accuracy and robustness of the proposed model. Experimental results demonstrate that the proposed model can effectively detect the abnormal neural circuits at different stages of AD, which is helpful for pathological study and early treatment. © 2020 IEEE.

关键词： Brain

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A DL Intrusion Detection Method for Low-Rate Denial of Service Attacks

A DL Intrusion Detection Method for Low-Rate Denial of Servi...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Wu, Yuhong Lv, Yunshan Hu, Xiangdong School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Public Big Data Security Technology Chongqing Chongqing China School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665457033

Denial of service (DoS) attacks is among the most harmful network threats. Low-rate DoS (LDoS) attacks feature good concealment and are likely to evade detection by conventional signal analysis-based methods. A LDoS attack detection method that combines the convolutional neural network and the gated recurrent unit is proposed in the present work. To evaluate the proposed method, we collect real user traffic from a university's website, label the traffic with LDoS attacks as normal traffic. In order to get the real attack traffic, this paper has collected some new LDoS attack programs, such as slowloris, slowhttptest, pwnlloris, torhammer and httplog, which can be obtained from public channels, and constructed a LAN environment that conformed to the working scenario of the program. By running the attack program, the attack traffic could be obtained. In a large sample scenario, the average detection rate of our proposed method can reach 98.68%. This article focuses on the research: the average detection rate of the detection method proposed in this article in the case that the security agency can only intercept a small number of attack samples. That is, in the small sample scenario, the detection of the collected traffic shows that the average detection rate of the proposed detection method can reach 89.54% in a small sample scenario. Its effective detection rate is obviously superior to the existing similar detection methods. © 2023 IEEE.

关键词： Denial-of-service attack

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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