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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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Neighborhood Combination Search for Single-Machine Scheduling with Sequence-Dependent Setup Time

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Journal of computer Science & Technology 2024年第3期39卷 737-752页

作者：刘晓路徐宏云陈嘉铭苏宙行吕志鹏丁俊文 College of System Engineering National University of Defense TechnologyChangsha 410015China School of Artificial Intelligence Jianghan UniversityWuhan 430056China Xi’an Satellite Control Center Xi’an 710043China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China CCF

In a local search algorithm,one of its most important features is the definition of its neighborhood which is crucial to the algorithm's *** this paper,we present an analysis of neighborhood combination search for solv-ing the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness(SMSWT).First,We propose a new neighborhood structure named Block Swap(B1)which can be con-sidered as an extension of the previously widely used Block Move(B2)neighborhood,and a fast incremental evaluation technique to enhance its evaluation ***,based on the Block Swap and Block Move neighborhoods,we present two kinds of neighborhood structures:neighborhood union(denoted by B1UB2)and token-ring search(denoted by B1→B2),both of which are combinations of B1 and ***,we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms:the Iterated Local Search Algorithm(ILSnew)and the Hybrid Evolutionary Algorithm(HEA_(new))to investigate the performance of the neighborhood union and token-ring ***-sive experiments show the competitiveness of the token-ring search combination mechanism of the two *** on the 120 public benchmark instances,our HEA_(new)has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent *** have also tested the HEA,new algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup *** is able to match the optimal or the best known results for all the 64 *** particular,the computational time for reaching the best well-known results for five chal-lenging instances is reduced by at least 61.25%.

关键词： single-machine scheduling sequence-dependent setup time neighborhood combination search token-ring search hybrid evolutionary algorithm

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Attention Based Temporal Convolutional Networks for Short-Term Wind Power Forecasting 19th

Attention Based Temporal Convolutional Networks for Short-T...

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Li, Qingwei Hou, Guolian Yu, Qi School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819968855

As wind is characterized by its inherent stochasticity and volatility, accurate wind power forecasting has become increasingly crucial for efficient operation of the power system. In this paper, we propose a deep learning method combining temporal convolutional networks (TCNs) and attention mechanism for wind power forecasting. Temporal convolutional networks are employed for extracting features, and then attention mechanism is utilized to concentrate on the important timesteps. In data preprocessing part, we adopt sliding windows approach to generate training samples from the normalized data, which facilitates processing and learning for the model. The comparison results demonstrate that the proposed model outperforms the comparison models, indicating the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Short-term Load Prediction of Integrated Energy System Based on MCNN-LSTM Neural Network 6

Short-term Load Prediction of Integrated Energy System Based...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Lv, You Shi, Yijun Tian, Helu School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9798350346671

The high coupling characteristics between various energy of integrated energy system (IES) and the uncertainty of each link of power-grid-load make the dynamic process of IES more complex than that of the traditional single-load system, which brings great difficulties to the effective operation of IES. To solve the above problems, this paper proposes a method for short term multivariate load prediction based on deep neural network. Firstly, the correlation between each single load is used to divide the categories, and the load data at the same time is reorganized into an image mode to maximize the use of correlation;then, the multi-channel convolutional neural networks (MCNN) are used to extract and fuse the features of the images, and the fused feature data is input into the long short-term memory (LSTM) neural network to realize the prediction of multi-load data from the time level;finally, the feasibility of the proposed method is verified by an actual example analysis. © 2023 IEEE.

关键词： Long short-term memory

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QoI-Aware Configuration Adaptation and Heterogeneous Resource Allocation for Edge Video Analytics

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IEEE Internet of Things Journal 2025年第12期12卷 19215-19230页

作者： He, Zixuan Sun, Yanjing Zhang, Beibei Dong, Kaiwen Wang, Bowen Xu, Hongli China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology IoT Perception Mine Research Center Xuzhou221116 China School of Information and Control Engineering China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China University of Science and Technology of China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive Deep Neural Network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this paper, we propose an edge-enabled multi-video analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov Decision Process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion deep reinforcement learning based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes. © 2025 IEEE.

关键词： Markov processes

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Assessment and Enhancement of FRC of Power Systems Considering Thermal Power Dynamic Conditions

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1371-1383页

作者： Feng Hong Yalei Pang Weiming Ji Lu Liang Fang Fang Junhong Hao Jizhen Liu State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources and School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources School of Energy Power and Mechanical EngineeringNorth China Electric Power UniversityBeijing 102206China

Frequency stability and security have been a vital challenge as large-scale renewable energy is integrated into power *** contrast,the proportion of traditional thermal power units decreases during the decarbonization transformation process,resulting in poor frequency *** paper aims to explore the potential of frequency regulation support,dynamic assessment,and capacity promotion of thermal power plants in the transition *** the dynamic characteristics of the main steam working fluid under different working conditions,a nonlinear observer is constructed by extracting the main steam pressure and valve opening degree *** real-time frequency modulation capacity of thermal power units can provide a dynamic state for the power grid.A dynamic adaptive modification for primary frequency control(PFC)of power systems,including wind power and thermal power,is proposed and *** power dynamic allocation factor is adaptively optimized by predicting the speed droop ratio,and the frequency modulation capability of the system is improved by more than 11%under extreme ***,through the Monte Carlo simulation of unit states of the system under various working conditions,the promotion of the frequency regulation capacity with high wind power penetration(WPP)is verified.

关键词： Dynamic evaluation frequency regulation power system predictive modeling wind penetration

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A Surface Defect Detection Method based on Information Entropy 26

A Surface Defect Detection Method based on Information Entro...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Guoqing Sun, Hongbo Liu, Xin Liu, Lei Yantai University School of Computer and Control Engineering Shandong China Shandong University Shandong China

ISBN: (纸本)9798350331684

Surface defect detection is important in the industrial field. Most factories use the difference method to solve the problem of defect detection. However, difference method can't solve misjudgments caused by shooting angle and location. In this paper, using the information entropy to solve the problem of the misjudgment for qualified products caused by product position and camera Angle. At the same time, 30 experiments were carried out to determine the threshold, and then 100 experiments were carried out to compare the accuracy of information entropy and difference methods, including the images of qualified products and unqualified products. Finally, the information entropy method is better than the difference method, and its detection accuracy is 97%. © 2023 IEEE.

关键词： Image processing

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Research on Fine-grained Access control Technology for Energy Data Based on Blockchain

Research on Fine-grained Access Control Technology for Energ...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Liang, Yin Li, Jianbin Li, Shike North China Electric Power University School of Control and Computer Engineering Beijing102206 China

ISBN: (纸本)9798350357677

In data-sharing scenarios in the energy sector, it is essential to establish a sound and reliable access control mechanism to protect energy resources and make informed decisions. However, traditional access control has flaws such as centralization, low transparency, and low flexibility, making it challenging to meet the current security needs of energy data sharing. In addition, due to the distributed, dynamic, and sensitive nature of energy data, it is necessary to ensure its security and auditability. To solve the above problems, this paper proposes an auditable access control model for energy data based on blockchain. The model adopts the attribute-based access control (ABAC) authorization model, which uses attributes as the adjudication element to build access control policies while adding data and user hierarchy attributes to improve adjudication efficiency and meet the need for fine-grained authorization. Based on this authorization model, blockchain technology ensures the data-sharing process is decentralized and transparent. The access control process is automated through four types of smart contracts, and the traceability of blockchain is used to store the access control operation records to monitor the illegal behavior of data requesters. The experimental results show that the model can control the access rights of data requesters with different attributes and ensure the security and controllability of data. © 2023 IEEE.

关键词： Authorization

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A Machine Vision-based Deep Learning Method in Hemodialysis Filter Defects Detection 26

A Machine Vision-based Deep Learning Method in Hemodialysis ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Xin Sun, Hongbo Zhang, Guoqing Liu, Lei Yantai University School of Computer and Control Engineering Shandong China Shandong University Shandong China

ISBN: (纸本)9798350331684

Hemodialysis filters are widely used in the treatment of kidney diseases. In order to reduce the occurrence of medical accidents, they need to go through a strict inspection process before being put into use to avoid defective products from entering the market, so it is crucial to locate and classify hemodialysis filters defects. In this paper, a deep learning method based on machine vision is proposed to solve the problem that the background of the inspected products is very similar to the defects and difficult to distinguish them. First, image denoising is performed, then the center of the product is located and the circle is expanded into a rectangle, the edge is extracted using a firstorder difference operator to distinguish the background from the defects, and finally the image after the extended channel is provided to the lightweight U-Net for training by online sample sampling. The test results show that the proposed method have achieved 0.78 mean IoU, 4.67% false detection rate and 0.67% missing rate on the hemodialysis filter dataset, which demonstrates the effectiveness of the proposed method. © 2023 IEEE.

关键词： Defects

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Robust Car-Following control of Connected and Autonomous Vehicles: A Stochastic Model Predictive control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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