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Detection of Oscillations in Process control Loops From Visual Image Space Using Deep Convolutional Networks

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 982-995页

作者： Tao Wang Qiming Chen Xun Lang Lei Xie Peng Li Hongye Su the Department of Electronic Engineering the School of InformationYunnan UniversityKunming 650091China DAMO Academy Alibaba GroupHangzhou 311100China the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant *** numerous automatic detection techniques have been proposed,most of them can only address part of the practical *** oscillation is heuristically defined as a visually apparent periodic ***,manual visual inspection is labor-intensive and prone to missed *** neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction *** this work,an exploration of the typical CNN models for visual oscillation detection is ***,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation *** feasibility and validity of this framework are verified utilizing extensive numerical and industrial *** with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and *** addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

关键词： Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries

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Anti-Windup Active Power control for Wind Turbines Considering Loads Reduction 13

Anti-Windup Active Power Control for Wind Turbines Consideri...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Guozhong Luo, Hao Jiao, Xuguo Yang, Zhibo Zhao, Xinxin School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

In recent years, the proportion of wind power in electricity contribution is increasing, and wind power generation technology has developed rapidly. How to enhance the stability of wind turbine systems and extend their service life are still challenging problems due to uncertain and intermittent characteristics of natural wind. In order to ensure that variable speed wind turbines track the active power instructions released by the power system, an improved anti-windup rotor speed control strategy is designed to switch different control strategies according to the wind turbine rotor speed value, with the aim of optimizing the mechanical loads of pitch systems. Firstly, in the low wind speed region, the maximum wind energy capture is achieved by optimal torque control. When the rotor speed increases to a critic value, torque control determined by the turbine's rated output power and rotor speed is adopted. In the high wind speed region, the pitch controller is used to adjust the wind energy capture coefficient to maintain the rotor speed at its rated value. Moreover, in order to solve the problem of integral saturation in traditional rotor speed control methods, this paper proposes a bounded constraints assisted Proportional-Integral (PI) pitch angle control scheme to optimize the operation of pitch system. This control method can effectively eliminates integral saturation, mitigate fatigue loads of wind turbines, reduces overshoot of rotor speed and ensures safe and stable operation of wind turbines. Finally, the proposed method is simulated on OpenFAST platform to verify its advancement. © 2024 IEEE.

关键词： Wind turbines

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Research on Fault Diagnosis Algorithm of Air Compressor Based on Low-Rank Matrix Recovery

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第12期51卷 2010-2016页

作者： Yu, Tianming Long, Cheng Feng, Guoliang School of Automation Engineering Northeast Electric Power University Jilin Jilin 132012 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin 100190 China

Air compressors are crucial in industrial production, but issues such as equipment aging and improper operation can lead to faults, severely impacting efficiency and safety. Traditional MFCC spectrogram-based diagnostic methods are challenged by background noise interference, making accurate fault feature extraction difficult. To address this, this study proposes an air compressor fault identification algorithm based on low-rank matrix recovery. By applying low-rank background modeling to the MFCC spectrogram to eliminate background noise, the algorithm effectively extracts primary features. The MobileNetV2 model is then used to extract features from the MFCC foreground images, followed by dimensionality reduction using Principal Component Analysis (PCA). The reduced-dimensional fault features are analyzed using k-means clustering. Experimental results show that for Data 2, the VGG19 model achieved an accuracy of 95.90%, an F1-score of 0.91, and a recall of 0.93, with a processing time of 1916 seconds. In comparison, the ResNet50 model attained an accuracy of 94.88%, an F1-score of 0.91, and a recall of 0.93, with a processing time of 826 seconds. These results demonstrate that the foreground features obtained through low-rank background modeling exhibit superior performance in fault identification and clustering, enhancing the accuracy and stability of fault diagnosis. Thus, the proposed algorithm shows promising potential for diagnosing and repairing air compressor faults. © (2024), (International Association of Engineers). All rights reserved.

关键词： K-means clustering

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An anonymous authentication and secure data transmission scheme for the Internet of Things based on blockchain

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Frontiers of Computer Science 2024年第3期18卷 183-197页

作者： Xingxing CHEN Qingfeng CHENG Weidong YANG Xiangyang LUO State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of TechnologyZhengzhou 450001China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China

With the widespread use of network infrastructures such as 5G and low-power wide-area networks,a large number of the Internet of Things(IoT)device nodes are connected to the network,generating massive amounts of ***,it is a great challenge to achieve anonymous authentication of IoT nodes and secure data *** present,blockchain technology is widely used in authentication and s data storage due to its decentralization and ***,Fan et *** a secure and efficient blockchain-based IoT authentication and data sharing *** studied it as one of the state-of-the-art protocols and found that this scheme does not consider the resistance to ephemeral secret compromise attacks and the anonymity of IoT *** overcome these security flaws,this paper proposes an enhanced authentication and data transmission scheme,which is verified by formal security proofs and informal security ***,Scyther is applied to prove the security of the proposed ***,it is demonstrated that the proposed scheme achieves better performance in terms of communication and computational cost compared to other related schemes.

关键词： Internet of Things blockchain authentication data transmission

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Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1944-1953页

作者： Kailong Liu Qiao Peng Yuhang Liu Naxin Cui Chenghui Zhang School of Control Science and Engineering Shandong UniversityJinan 250061China Information Technology Analytics&Operations groupQueen’s University BelfastBelfast BT71NNUK

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery *** battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently *** paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven *** be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and *** experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating *** addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural *** to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

关键词： Battery management battery manufacturing data science explainable artificial intelligence sensitivity analysis

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Question Answering System of Traditional Chinese Medicine based on BERT and Knowledge Graph

Question Answering System of Traditional Chinese Medicine ba...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Wang, Renyuan Wang, Zhongce Li, Qi Jilin Institute of Chemical Technology College of Information and Control Engineering Jilin China Jilin Agricultural Science and Technology University Electrical and Information Engineering College Jilin China

ISBN: (纸本)9798350375442

With the continuous growth in demand for health information and medical services, there is a large number of medical question-answering systems available in the market. The system has made progress in the realm of general medicine, but still faces challenges in the field TCM (Traditional Chinese Medicine). For example, problems such as inaccurate information and lack of personalization of answers may arise. There is a vast amount of TCM knowledge available online for medical diagnosis, but traditional search engines are unable to make judgments based on the actual condition of patients, failing to meet user needs. Therefore, this paper proposes a TCM question and answer system based on BERT and knowledge graph. The experimental results show that both the BERT+BiLSTM+ CRF model and the BiLSTM+Attention model have achieved improvements, enhancing the precision, recall, and F1-score of TCM information recognition. This ensures the question-answering system's responses are more closely aligned with user needs. © 2024 IEEE.

关键词： Question answering

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FreeLesion: Synthetic Image-Mask Pairs for Fundus Lesion Segmentation via Curriculum Learning and Feature-Loss Guided Filtering

FreeLesion: Synthetic Image-Mask Pairs for Fundus Lesion Seg...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Fu, Peilei Guo, Song College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China

ISBN: (纸本)9798350368741

Fundus lesion segmentation plays a crucial role in the automated diagnosis of diabetic retinopathy. While recent advances in lesion segmentation networks have shown promising results, they are often constrained by limited pixel-level annotations, leading to suboptimal model training. In this paper, we introduce FreeLesion, a deep generative model-based approach for synthesizing image-mask pairs to mitigate the issue of data scarcity. Specifically, we employ a curriculum learning strategy that progressively enables the generative model to learn from easy to hard lesion categories. This approach enhances the model's ability to generate smaller, more morphologically complex lesions. Additionally, we propose a feature-loss guided filtering method to remove noisy synthetic regions, improving the alignment between generated images and their masks. Experimental results demonstrate that our synthetic fundus images significantly boost lesion segmentation model performance, reaching new state-of-the-art performance. Notably, training the segmentation model using only images generated by our approach yields better performance than using real images alone (36.2 vs. 35.6 in mIoU on IDRiD dataset). Our code will be released at https://***/guomugong/FreeLesion. © 2025 IEEE.

关键词： curriculum learning fundus lesion segmentation noise filtering semantic image generation

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Prediction Method of Axial Compression Capacity of CCFST Columns Based on Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第3期51卷 243-251页

作者： Cheng, Yong Tao, Huawei Lu, Xiangying Xu, Zhijun Fu, Hongliang Zhu, Chunhua Key Laboratory of Food Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China Key Laboratory of Food Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China School of Civil Engineering Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China

Accurate prediction of axial compression capacity (ACC) is crucial for evaluating the performance of circular concrete-filled steel tubes (CCFST). Traditional methods face challenges in dealing with the intricate nonlinear relationship inherent in the structure. This paper presents a deep learning (DL) method to predict ACC. The proposed method standardizes parameters, including steel yield strength, column length, and other relevant characteristics of CCFST. Subsequently, a three-layer Long Short-Term Memory (LSTM) network is constructed to capture the complex relationships among these parameters. To enhance the model’s capability for nonlinear fitting, the Rectified Linear Unit (ReLU) activation function is applied between network layers. Additionally, an attention mechanism is introduced to improve feature extraction capabilities. Comprehensive simulations demonstrate that the proposed method surpasses state-of-the-art techniques, achieving optimal values for Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2). The simulation results validate the efficacy of the proposed method. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

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Adaptive Tracking control for Output-Constrained Switched MIMO Pure-Feedback Nonlinear Systems with Input Saturation

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Journal of Systems Science & Complexity 2023年第3期36卷 960-984页

作者： ZHANG Haoyan ZHAO Xudong WANG Huanqing NIU Ben XU Ning College of Control Science and Engineering Bohai UniversityJinzhou 121013China Faculty of Electronic Information and Electrical Engineering Dalian University of TechnologyDalian 116024China College of Mathematical Science Bohai UniversityJinzhou 121013China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China College of Information Science and Technology Bohai UniversityJinzhou 121013China

In this paper,an adaptive neural tracking control scheme for a class of uncertain switched multi-input multi-output(MIMO)pure-feedback nonlinear systems is *** considered MIMO pure-feedback nonlinear system contains input and output constraints,completely unknown nonlinear functions and time-varying external *** unknown nonlinear functions representing system uncertainties are identified via radial basis function neural networks(RBFNNs).Then,the Nussbaum function is utilized to deal with the nonlinearity issue caused by the input *** prevent system outputs from violating prescribed constraints,the barrier Lyapunov functions(BLFs)are ***,a switched disturbance observer is designed to make the time-varying external disturbances *** on the backstepping recursive design technique and the Lyapunov stability theory,the developed control method is verified applicable to ensure the boundedness of all signals in the closed-loop system and make the system output track given reference signals ***,a numerical simulation is given to demonstrate the effectiveness of the proposed control method.

关键词： Adaptive control input saturation neural networks output constraints switched MIMO pure-feedback nonlinear systems

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Frequency Agile Strategy Design of Radar Network for Wideband Sweep Jamming via Coalition Formation Game

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 7906-7919页

作者： Liao, Maosen Xiong, Kui Zhang, Long Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100089 China School of Information and Communication Engineering Chengdu611731 China UESTC Shenzhen Institute for Advanced Study Chengdu611731 China

This article proposes an intelligent frequency agile design strategy for radar network using coalition game theory to counter wideband sweep jamming, which is formulated as a two-stage decision-making problem. In the first stage, the entire frequency agile range is divided into several non-overlapping agile subrange, and each radar selects a frequency agile subrange to join, which is solved by the coalition formation algorithm. Then, after achieving a stable coalition partition, a distributed multi-agent reinforcement learning algorithm is developed to address the specific carrier frequency selection problem within each coalition. By combining these two stages of the frequency agile design strategy, a joint coalition formation game and distributed Q-learning algorithm (CFDQ) is proposed. Finally, numerical simulations are conducted to illustrate the superiority of the proposed algorithm over several typical frequency agile strategies. © 1965-2011 IEEE.

关键词： Jamming

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