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63rd IEEE Conference on Decision and control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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H∞Asynchronous control for Discrete-Time Semi-Markov Jump Systems 16th

H∞Asynchronous Control for Discrete-Time Semi-Markov Jump S...

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16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022

作者： Jin, Liqiang Yin, Yanyan Liu, Yanqing Teo, Kok Lay Liu, Fei National Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur47500 Malaysia Tianjin University of Finance and Economics Tianjin China

This paper investigates the problem of H∞asynchronous control for discrete-time semi-Markov jump systems. In practice, it is difficult to ensure that controller modes and system modes are updated synchronously. Therefore, a hidden semi-Markov model is introduced to characterize such an asynchronous phenomenon between system modes and controller modes. Meanwhile, the switching signal of the controller is generated by a given conditional probability related to the system mode. Based on the mode-dependent and elapsed-time-dependent Lyapunov function, together with the semi-Markov kernel (SMK) method, sufficient conditions constructed by linear matrix inequalities (LMIs) are derived to ensure the existence of an asynchronous controller such that the resulting system is σ-error mean square stable and satisfies an H∞performance index. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the method proposed. © 2022 Elsevier B.V.. All rights reserved.

关键词： controllers

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Enhanced removal of bisphenol A by cyclodextrin in photocatalytic systems:Degradation intermediates and toxicity evaluation

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Chinese Chemical Letters 2020年第10期31卷 2623-2626页

作者： Yi Zhou Jie He Jian Lu Yongdi Liu Yanbo Zhou State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and TechnologyShanghai 200237China National Engineering Laboratory for Industrial Wastewater Treatment East China University of Science and TechnologyShanghai 200237China

At present,the assessment of photooxidation system mainly focuses on the photodegradation efficie ncy of target pollutant,lacking of the toxicity assessment in the photocatalysis ***,photodecomposition of bisphenol A(BPA)was used to investigate the performance of several cyclodextrin modified ***,the comprehensive toxicity changes of BPA under different photocatalytic oxidation conditions were ***β-cyclodextrin(β-CD)modified photocatalyst,including titanium dioxide(CM-β-CD-TiO_(2)),carbon nitride(CM-β-CD-C_(3)N_(4))and cadmium sulfide(SH-β-CD-AM/CdS)exhibit high degradation rate and mineralization efficiency of *** highest total organic carbon(TOC)removal of BPA observed in the oxidation system of SH-β-CD-AM/CdS nano reactor(73.4%).The main oxidation intermediates in these systems were detected,and the comprehension toxicity of BPA and its oxidation intermediates in different system were compared by toxicity estimation software tool(T.E.S.T.)based on quantitative structure-activity relationship(QSAR)*** results show thatβ-CD can facilitate the photodecomposition of the target *** r,many oxidation intermediates with high compre hensive toxicity,eve n in the oxidation system with high B PA removal,can still be *** re,not only decomposition of target contaminant but also the comprehensive toxicity of oxidation intermediates should be regarded as index to evaluate a photocatalysis technology.

关键词： Cyclodextrin Bisphenol A Photocatalysis Toxicity Carbon nitride

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Research on Cardiac Medical Image Segmentation Based on an Improved U-Net Model

Research on Cardiac Medical Image Segmentation Based on an I...

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Image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yuqing Wu Jun Wang Rui Wang Jian Wang Kecheng Zhou Xiaoxin Zhang School of Computer Science and Technology Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Emergency Zhongyi Northeast International Hospital Co. Ltd. Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This paper presents a cardiac MRI image segmentation model based on an improved U-Net architecture. Accurate segmentation of cardiac MRI images is critical for the diagnosis and treatment of cardiovascular diseases, yet existing U-Net models exhibit limitations in handling complex cardiac structures and multi-scale features. To address these challenges, this paper proposes two key enhancements. First, we propose a Multi-Dimensional Context Attention module, designed to improve the integration of global and local information within the skip connections, thereby enhancing segmentation accuracy for intricate cardiac structures. Furthermore, we propose a Reverse Feature Modulation module, which generates reverse masks and dynamically adjusts feature weights across different classes using adaptive weighting, effectively mitigating class imbalance issues in multi-class segmentation tasks and improving focus on difficult-to-segment regions. Experimental results demonstrate that the proposed model outperforms the standard U-Net on the ACDC dataset, achieving significant improvements in evaluation metrics such as the Dice coefficient. These enhancements underscore the model's efficacy and robustness in complex cardiac image segmentation tasks, offering new technical support for automated cardiovascular disease diagnosis.

关键词： Image segmentation Accuracy Magnetic resonance imaging Computational modeling Modulation Context awareness Robustness Medical diagnosis Cardiovascular diseases Context modeling

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Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites

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Polymers 2024年第11期16卷 1561页

作者： Liu, Yi Yu, Qing Liu, Kaixin Zhu, Ningtao Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China The State Key Laboratory for Electronic Testing Technology North University of China Taiyuan030051 China Xi’an Zhanshi Testing & Engineering Co. Ltd. Xi’an710000 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

Ultrasonic testing is widely used for defect detection in polymer composites owing to advantages such as fast processing speed, simple operation, high reliability, and real-time monitoring. However, defect information in ultrasound images is not easily detectable because of the influence of ultrasound echoes and noise. In this study, a stable three-dimensional deep convolutional autoencoder (3D-DCA) was developed to identify defects in polymer composites. Through 3D convolutional operations, it can synchronously learn the spatiotemporal properties of the data volume. Subsequently, the depth receptive field (RF) of the hidden layer in the autoencoder maps the defect information to the original depth location, thereby mitigating the effects of the defect surface and bottom echoes. In addition, a dual-layer encoder was designed to improve the hidden layer visualization results. Consequently, the size, shape, and depth of the defects can be accurately determined. The feasibility of the method was demonstrated through its application to defect detection in carbon-fiber-reinforced polymers. © 2024 by the authors.

关键词： Convolution

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Enhanced Soft Sensor with Qualified Augmented Data Using Centroid Measurement Criterion

Enhanced Soft Sensor with Qualified Augmented Data Using Cen...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Dai, Yun Yu, Qing Yang, Tao Yao, Yuan Liu, Yi Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

ISBN: (纸本)9781665402453

Development of reliable soft sensors using limited labeled samples is not an easy task in industrial processes. A selective generative adversarial network (SGAN)-based support vector regression (SGAN-SVR) soft sensor is proposed for quality prediction using limited labeled training data. Specifically, SVR is considered as a base prediction model. The Wasserstein GAN (WGAN) is adopted to capture the distribution of available labeled data and generate virtual candidates. Subsequently, using a proposed similarity measurement strategy, those synthetic data with more information are selected and introduced into the training set. Using the designed data augmentation approach, the SGAN-SVR model can achieve better prediction performance compared with the SVR soft sensor. The quality prediction results on an industrial polyethylene process demonstrate the effectiveness and advantages of the proposed method. © 2021 IEEE.

关键词： Generative adversarial networks

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Robust optimization for quantum reinforcement learning control using partial observations

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Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

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Deep Graph Network for process Soft Sensor Development

Deep Graph Network for Process Soft Sensor Development

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Jia, Mingwei Dai, Yun Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

ISBN: (纸本)9781665402453

In the (bio)chemical processes, traditional hardware sensors are difficult to directly measure the quality of critical products due to their time-varying, non-linear, and dynamic characteristics. This makes process soft sensor modeling methods important. Since the process variables can be regarded as natural graph data, this work introduces graphs in the soft sensor modeling area. A soft sensor model based on the graph neural network (GNN) is proposed. The model can learn the topological structure of graph data between each unit variable. Moreover, it characterizes variable relationships from the spatial and temporal dimensions to the output prediction by introducing the spatial-temporal convolutional layer. The effectiveness and advantages of the GNN-based soft sensor model are verified using a simulated fermentation process. © 2021 IEEE.

关键词： Graph neural networks

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Five-Tiered Route Planner for Multi-AUV Accessing Fixed Nodes in Uncertain Ocean Environments

arXiv

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arXiv 2023年

作者： Zhang, Jiaxin Liu, Meiqin Zhang, Senlin Zheng, Ronghao Dong, Shanling The College of Electrical Engineering Zhejiang University Hangzhou China The National Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China The Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

This article introduces a five-tiered route planner for accessing multiple nodes with multiple autonomous underwater vehicles (AUVs) that enables efficient task completion in stochastic ocean environments. First, the pre-planning tier solves the single-AUV routing problem to find the optimal giant route (GR), estimates the number of required AUVs based on GR segmentation, and allocates nodes for each AUV to access. Second, the route planning tier plans individual routes for each AUV. During navigation, the path planning tier provides each AUV with physical paths between any two points, while the actuation tier is responsible for path tracking and obstacle avoidance. Finally, in the stochastic ocean environment, deviations from the initial plan may occur, thus, an auction-based coordination tier drives online task coordination among AUVs in a distributed manner. Simulation experiments are conducted in multiple different scenarios to test the performance of the proposed planner, and the promising results show that the proposed method reduces AUV usage by 7.5% compared with the existing methods. When using the same number of AUVs, the fleet equipped with the proposed planner achieves a 6.2% improvement in average task completion rate. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles

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Multilevel filtering image denoising algorithm based on edge information fusion 16

Multilevel filtering image denoising algorithm based on edge...

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16th national Conference on Laser technology and Optoelectronics

作者： Zhang, Xin Sun, Jianfeng Jiang, Peng Zhou, Xin Qiu, Changrui Zhang, Yinbo Zhang, Hailong National Key Laboratory of Science and Technology on Tunable Laser Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Industrial Technology Innovation Research Institute Co. Ltd Beijing101312 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9781510646643

ISBN: (纸本)9781510646636

Edges are critical important for the visual appearance of images. The traditional denoising algorithms are difficult to preserve the edges of the image while removing the noise of ICCD sensing image. At the same time, it is difficult to eliminate the problems of image darkness and low resolution caused by uneven illumination. This paper proposes a multilevel filtering image denoising algorithm based on edge information fusion. The target edges detection of the image after non-local means (NL-means) filtering is carried out based on the eight-direction Sobel operator. In order to filter the false edge points and residual noise, an adaptive threshold is determined according to the mean and variance of the eight neighborhood pixels of the detected pixel. Meanwhile, homomorphic filtering is used to enhance the image contrast and uniformity. By comparing the pixel values of the edge image and the homomorphic filtered image, the final denoised image is obtained by fusing the two images. The results indicate that, compared with the traditional algorithms, the edge preserving ability of the proposed algorithm is improved by more than 20%, and the denoising ability is improved by 63.5% for building target. For specific targets (vehicle), the results demonstrate that the proposed algorithm have the maximum edge preserving index and contrast, and the minimum non-uniformity. This algorithm lays a foundation for target segmentation and recognition. © 2021 SPIE.

关键词： Image fusion

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