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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4620-4634页

作者： Wang, Yan Sun, Xiaoyan Zhang, Yong Gong, Dunwei Hu, Hejuan Zuo, Mingcheng China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Shandong Qingdao266061 China China University of Mining and Technology Artificial Intelligence Research Institute Jiangsu Xuzhou221116 China

It is very challenging to autonomously generate algorithms suitable for constrained multiobjective optimization problems due to the diverse performance of existing algorithms. In this article, we propose a linear regression (LR)-based autonomous intelligent optimization method. It first extracts typical features of a constrained multiobjective optimization problem by focused sampling to form a feature vector. Then, a LR model is designed to learn the relationship between optimization problems and intelligent optimization algorithms (IOAs). Finally, the trained model autonomously generates a suitable IOA by inputting the feature vector. The proposed method is applied to six constrained multiobjective benchmark test sets with various characteristics and compared with seven popular optimization algorithms. The experimental results verify the effectiveness of the proposed method. In addition, the proposed method is used to solve the operation optimization problems of an integrated coal mine energy system, and the experimental results show its practicability. © 2020 IEEE.

关键词： Feature extraction

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Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation 7

Med-Tuning: A New Parameter-Efficient Tuning Framework for M...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shen, Jiachen Wang, Wenxuan Chen, Chen Jiao, Jianbo Liu, Jing Zhang, Yan Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States School of Computer Science University of Birmingham United Kingdom Institute of Automation Chinese Academy of Sciences China

The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines’s precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4×, with even better segmentation performance. Our project webpage is at https://***/Med-Tuning/. © 2024 CC-BY 4.0, J. Shen, W. Wang, C. Chen, J. Jiao, J. Liu, Y. Zhang, S. Song & J. Li.

关键词： Image enhancement

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An Efficient Privacy-Preserving Asynchronous Federated Approach for Intelligent Decision Making in Equipment Maintenance 7

An Efficient Privacy-Preserving Asynchronous Federated Appro...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Dou, Kangkang He, Fan Shang, Fanshu Li, Xingyi Dong, Yuhao Liu, Jiayu Jiangsu Automation Research Institute Nanjing University of Aeronautics and Astronautics Department of Information Technology Research Lianyungang China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350352009

Federated learning enables collaborative training of a high-quality global model across multiple participants without sharing raw local data. Its advantages, such as decentralization, data isolation, and high computational performance, have made it a popular research direction in various fields. However, federated learning faces privacy risks during model parameter transmission. Additionally, reducing communication overhead and computational resource consumption while ensuring data privacy protection remains a critical challenge. To address these issues, this paper proposes an efficient privacy-preserving asynchronous federated learning method tailored for equipment maintenance scenarios. Leveraging asynchronous federated learning techniques, this method enables efficient collaborative training among multiple participants. It also employs homomorphic encryption to protect local data information. By utilizing time-series models, we construct a predictive model for equipment spare parts consumption, providing valuable insights for intelligent maintenance decision-making. Experimental results demonstrate that our proposed method has faster convergence. More importantly, the root mean square error decreases by at least 0.004, the mean square error decreases by at least 0.024, and the relative error decreases by at least 0.008 compared to the baseline. © 2024 IEEE.

关键词： Federated learning

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research and Analysis of the Method for Measuring the Moisture Content of Cottonmaterials at Ultrahigh Frequencies

Research and Analysis of the Method for Measuring the Moistu...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Turkmenov, Khasan E. Ubaydullayeva, Shakhnoza R. Nikolov, Nikola N. Alexandrova, Mariela I. 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria

ISBN: (纸本)9798350312911

The article provides a theoretical analysis of the ultrahigh frequency (microwave) method of humidity measurement, discusses the problems of instrumentation of the method in question, and proposes a scheme of an experimental installation. Bulk cotton materials were selected as the objects of research. The electromagnetic wave radiation is analyzed during measurement and the evaluation of electrophysical characteristics requires the study of the functional dependence of the amplitude and phase shift on the mass ratio of humidity, mathematical models for various bulk cotton materials describing the joint effect of humidity, material density on the attenuation and phase shift of the passing wave are obtained. © 2023 IEEE.

关键词： Humidity control

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1-Hour Ahead Wind Power Prediction Based on Multi-model Fusion Strategy 19th

1-Hour Ahead Wind Power Prediction Based on Multi-model Fusi...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zhang, Tengyu Di, Shuai Wang, Xu Zhu, Deyu Ren, Xiaochen School of Electrical Engineering and Automation Harbin Institute of Technology Heilongjiang Harbin China Institute of Thermal Control Technology Datang Northeast Electric Power Test and Research Institute Co. Ltd. Jilin Changchun China

ISBN: (纸本)9789819613861

The accuracy of wind power prediction is of great significance for optimizing power system scheduling and improving power output stability. In order to solve the established difficulties in wind prediction, a prediction method combining multiple models is proposed with the aim of realizing a high-precision wind prediction strategy up to 1 hour in advance. Firstly, the raw wind power data is preprocessed. Secondly, the comprehensive feature se-lection (CFS) algorithm is used to calculate the sum of the importance of each feature variable, and the input set of asynchronous eldest child models corresponding to the target feature is selected. Then, the adaptive noise complete set empirical mode decomposition algorithm (CEEMDAN) is used to decompose the wind power sequence, and each component after decomposition is modeled separately. The comparative analysis of a series of experimental methods shows that the method proposed in this study demonstrates excellent prediction accuracy and stable performance in the field of multi-step wind energy prediction. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Wind forecasting

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Fault diagnosis for wasterwater treatment plant based on an improved support vector data description method

Fault diagnosis for wasterwater treatment plant based on an ...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Zhou, Meng Song, Weiwei Wang, Jing Wang, Chang North China University of Technology School of Electrical and Control Engineering Beijing China Beijing Aerospace Automation Research Institute Beijing China

ISBN: (纸本)9798350337754

In this paper, a fault detection method based on improved support vector data description is proposed for wastewater treatment plants. First, an improved Multi-Kernel Support Vector Data Description (MKSVDD) method is presented in which multicore-kernel functions are combined with the traditional support vector data description method. This can make the model more adaptable to different input samples, allowing it to have both learning and generalization capabilities. Then, the parameters of the MKSVDD are optimizted by Sparrow Search Algorithm (SSA), which overcomes randomness and uncertainty of human manual adjustment parameters. Finally, simulation results are given to demonstrate the effectiveness of the proposed method. Nine types of fault data sets generated by the Benchmark Simulation Model 1 (BSM1) are used and simulations results show that the model is more adaptable to the samples and has better indicators for anomaly detection compared to the original algorithm. © 2023 IEEE.

关键词： Wastewater treatment

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automation of Technological Processes for controlling Grain Moisture in the Flowu

Automation of Technological Processes for Controlling Grain ...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Ubaydullayeva, Shakhnoza R. Gaziyeva, Rano T. Nikolov, Nikola N. Alexandrova, Mariela I. 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria

ISBN: (纸本)9798350312911

The paper deals with the problems of grain wounding requires large energy costs, requiring a multi-stage process of grain processing, installation and perfect and reliable automation systems for the storage and processing of raw materials, theoretical and experimental studies and the results of the use of the microwave method in measuring the moisture content of grain and grain materials. The method under consideration is based on the principle of selective absorption of microwave radiation energy by water in materials and its derivatives. By the degree of absorption of radiation, the concentration of moisture is judged. The paper investigates the amplitude and phase methods of the passing wave in free space. The influence of the elastic properties of the material under study on the electrical characteristics of the microwave wave, suggesting fundamentally new ways of measuring the attenuation and phase shift of the electromagnetic wave, including discrete conversion of the signal of the primary converter, which does not require rigid metrological support and the use of a phase shift, is substantiated. Information on the values of the thickness of the material is analyzed in order to correct the influence of this value, consisting of two parameters: the signal of the passing wave, the attenuation and phase shift which characterizes the humidity and the additional signal - the phase wave reflected from the front surface of the materials and carrying information about its thickness. © 2023 IEEE.

关键词： automation

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems Science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

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computers, Materials & Continua 2023年第4期75卷 1157-1177页

作者： Guo-Wen Sun Gang Xiang Wei He Kai Tang Zi-Yi Wang Hai-Long Zhu School of Computer Science and Information Engineering Harbin Normal UniversitySongbei DistrictHarbin150025China Beihang University School of Automation Science and Electrical Engineering Beijing100191China Beijing Aerospace Automatic Control Institute Beijing100074China Rocket Force University of Engineering Xi’anShaanxi Province710025China

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

关键词： Self-adaptive quality factor belief rule base wireless sensor networks fault diagnosis

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Configuration Optimization of Hybrid Electrolyzer for Wind-Based Green Hydrogen Production System 7

Configuration Optimization of Hybrid Electrolyzer for Wind-B...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Lin, Jiahu Hao, Wenbo Ban, Mingfei Northeast Forestry University College of Computer and Control Engineering Harbin China State Grid Heilongjiang Electric Power Research Institute Harbin China

ISBN: (纸本)9798350359558

As the development of renewable energy progresses, the prospects for the application of green hydrogen are gradually expanding, with countries strengthening the construction of industries related to green hydrogen. This paper investigates the hybrid electrolytic hydrogen production system and its configuration optimization problem, considering wind power decomposition and multiple types of electrolyzers. Firstly, a model for wind power redistribution is established based on the characteristics of different types of electrolyzers to determine the power loads of each type. Subsequently, based on the solution results of the redistribution model, a hybrid electrolyzer system configuration optimization model with the objective of maximizing annual revenue is proposed. Finally, case studies show that Hybrid electrolyzer system offers advantages in terms of energy efficiency, reliability and economy. Compared to alkaline electrolyzer system, hybrid system can improve the efficiency and stability of energy utilization and better cope with the uncertainty of renewable energy sources. The hybrid system can significantly reduce the initial capital investment and number of configurations compared to proton exchange membrane electrolyzer system, thereby lowering the operating cost of the overall energy system. © 2024 IEEE.

关键词： Wind power

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