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作者： Yang, Zeyu He, Liang Yu, Hua Zhao, Chengcheng Cheng, Peng Chen, Jiming Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China University of Colorado Department of Computer Science and Engineering DenverCO80204 United States

Programmable logic controllers (PLCs), i.e., the core of control systems, are well-known to be vulnerable to a variety of cyber attacks. To mitigate this issue, we design PLC-Sleuth, a novel noninvasive intrusion detection/localization system for PLCs, which is built on a set of control invariants - i.e., the correlations between sensor readings and the concomitantly triggered PLC commands - that exist pervasively in all control systems. Specifically, taking the system's supervisory control and data acquisition log as input, PLC-Sleuth abstracts/identifies the system's control invariants as a control graph using data-driven structure learning, and then monitors the weights of graph edges to detect anomalies thereof, which is in turn, a sign of intrusion. We have implemented and evaluated PLC-Sleuth using both a platform of ethanol distillation system (EDS) and a realistically simulated Tennessee Eastman (TE) process. The results show that PLC-Sleuth can: 1) identify control invariants with 100%/98.11% accuracy for EDS/TE;2) detect PLC intrusions with 98.33%/0.85 ‰ true/false positives (TPs/FPs) for EDS and 100%/0% TP/FP for TE;and 3) localize intrusions with 93.22%/96.76% accuracy for EDS/TE. © 2014 IEEE.

关键词： Ethanol

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An Improved Model Predictive control of Wave Energy Converters Based on Weighted Optimization of Objective Function

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SSRN 2023年

作者： Wang, Zhenchun Wang, Nianguo School of Electrical Engineering Yanshan University Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

This paper investigates the model predictive control (MPC) of a point-absorption sea wave energy converter (WEC). The dynamic model and information about upcoming waves serve as the foundation for MPC implementation, but building the plant model and forecasting waves will invariably introduce errors. As a result of this fact and the multi-step predictive optimization feature of MPC, a novel objective function based on the weighting factor is adopted in the MPC design, which has the following benefits: First, the weighting factor penalizes the objective function with errors at various steps in time, which can limit the adverse effects of uncertainties on the controller. Second, the weighting factor can better constrain the float displacement, ensuring the device’s safe operation. In addition, the weighting factor is optimized offline using a genetic algorithm, and its value changes depending on the sea state. The simulation results show the effectiveness of the proposed MPC strategy. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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A Novel A-ESO Composited Non-Recursive control Methodology for Complex High-Order Disturbed Nonlinear Systems

A Novel A-ESO Composited Non-Recursive Control Methodology f...

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Chinese control Conference (CCC)

作者： Cong Zhang Shiji Liu Shuang Liu School of Electrical Engineering Yanshan University Qinhuangdao Hebei China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao Hebei China

This paper mainly focuses on a disturbance rejection control methodology for a series of mismatched and matched disturbed complex systems. Firstly, a novel finite-time arbitrary-order extended state observer (A-ESO) is proposed to accurately estimate the system states, disturbances, and their corresponding order derivatives. And then, a novel concise nonrecursive control ideology utilizing all the estimations of A-ESO is proposed to strictly guarantee finite-time stability and convergence of the whole closed-loop system. Significantly, compared with the existing nonrecursive control methods, the essential characteristic is that all the estimation information is utilized to construct the composite controller. This will be tremendously conducive to cost-saving and promoting practical implementations.

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TPGP: Temporal-Parametric Optimization with Deep Grasp Prior for Dexterous Motion Planning

TPGP: Temporal-Parametric Optimization with Deep Grasp Prior...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Haoming Li Qi Ye Yuchi Huo Qingtao Liu Shijian Jiang Tao Zhou Xiang Li Yang Zhou Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineering the State Key Laboratory of Industrial Control Technology Zhejiang University The Key Key Lab of CS&AUS of Zhejiang Province State Key Lab of CAD&CG Zhejiang University Zhejiang Lab OPPO US Research Center

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Grasping motion planning aims to find a feasible grasping trajectory in the configuration space given an input target grasp. While optimizing grasp motion with two or three-fingered grippers has been well studied, the study on natural grasp motion planning with a dexterous hand remains a very challenging problem due to the high dimensional working space. In this work, we propose a novel temporal-parametric grasp prior (TPGP) optimization method to simplify the difficulty of grasping trajectory optimization for the dexterous hand while maintaining smooth and natural properties of the grasping motion. Specifically, we formulate the discrete trajectory parameters into a temporal-based parameterization, where the prior constraint provided by a hand poser network, is introduced to ensure that hand pose is natural and reasonable throughout the trajectory. Finally, we present a joint target optimization strategy to enhance the target pose for more feasible trajectories. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp motion on various metrics.

关键词： Measurement Employment Optimization methods Grasping Planning Robotics and automation Grippers

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Branch-Yolo：Efficient Object Detector for Thin Structure Objects Like Pantograph

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SSRN 2024年

作者： Li, Yaqian Han, Jiaqi Li, Haibin Zhang, Wenming College of Electrical Engineering Yanshan University Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China

The pantograph is the key structure in the railway Pantograph-OCS system, acting as the switch for the railway traction power supply system. So it’s crucial to detect the pantograph's lifting and lowing states for production efficiently and safely. However, due to the pantograph’s special slender structure and complex application scenarios, it occupies few pixels and is susceptible to background interference, making accurate real-time detection a challenging task. In this work, we aim to propose a generalized model, designed for detecting, branch-like objects with thin structure and multi-scale size such as pantographs. To this end, we propose the Branch-YOLO based on YOLOv8, which is improved in two stages, feature extraction and feature fusion, to simultaneously enhance perception. First, we introduce BrLayer is consist of BranchConv and EFAC(Extend receptive field attention Convolution), BranchConv can adaptively capture features of thin and tortuous local structures,EFAC is able to assist in expanding the effective receptive field. Subsequently, we propose a feature fusion network (BranchNet) to integrate multilevel semantic information and multi-scale features, which significantly reduces background interference in detecting slender structure objects and enhances the ability to perceive changes in object scale. Besides, we propose the Lightweight Cross-Kernel Convolution layer (LckLayer) and introduce the Feature Decomposition module (FdM) in BranchNet for lightweight design, reducing the computational overhead and enhancing model efficiency. Our proposed Branch-YOLO not only achieves the best performance on the multi-scale pantograph datasets but also attains an outstanding 42.9% AP on the COCO val2017 datasets, with 5.9M parameters and 16.8 GFLOPs. © 2024, The Authors. All rights reserved.

关键词： Religious buildings

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Defect Detection in Solar Panels Based on PV-Flow

Defect Detection in Solar Panels Based on PV-Flow

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Minghao Niu Jiaqi Li Mengyu Li Yifan Zhao Ying Liu Shuhuan Wen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Yingli Energy Development Co. Baoding China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

With technological advancements, resource utilization has increased, making renewable energy sources like solar power essential. Effective defect detection for solar panels is crucial. However, manual inspection is currently inefficient and machine vision lacks accuracy. In order to improve defect detection in high-resolution images and overcome the limitations of existing methods, we propose an unsupervised deep learning model called PV-Flow, which combines a hybrid attentional mechanism and a multiscale feature extraction module of the flow model. Before defect detection we need to categorize the defects and make a dataset, after observing the features we classify the defects into black spots, broken grids, hidden cracks and fragmentations. The performance of the model while performing defect detection is evaluated by metrics of reconstructed images and visual comparisons. Experiments on real solar panel datasets show that our proposed PV-Flow model is able to accurately identify the location of broken gate defects and show more details, with even the smallest defects clearly labeled and magnified. This suggests that our proposed model is able to detect potential defects earlier, thus reducing production risks and costs. We also conducted comparative experiments to further validate the reliability of the model.

关键词： Deep learning Visualization Accuracy Costs Sensitivity Production Feature extraction

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Research and Treatment of Abnormal Steam Generator Level control in Transient Test 6th

Research and Treatment of Abnormal Steam Generator Level Con...

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6th International Symposium on Software Reliability, industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, ISNPP 2021

作者： Liu, Peng Song, Fei Che, Junxia Luan, Zhen-Hua Qiao, Jian-Feng Liu, Shuang -Jin State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Guangdong Shenzhen518172 China College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518000 China State Key Lab of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789811911804

Steam generator level control is the most difficult control system in nuclear power plant. It is a typical nonlinear and large delay control system. It is affected by many factors, such as feed water flow, steam flow, feed water temperature, average temperature of primary circuit and so on. Over the years, nuclear power unit shutdown events caused by improper control of steam generator level are common. The abnormal level response occurred in the turbine trip without reactor trip test of a power plant, which was caused by the deviation of steam and feed water flow measurement and the accumulated deviation through calculation. This paper analyzes the influence of steam and feed water flow measurement deviation on the steam generator level control in detail, and gives the correction scheme, which has achieved good results after application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steam

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Distributed Fault-Tolerant Secondary control For Dc Microgrids Against False Data Injection Attacks

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SSRN 2022年

作者： Xie, Zongkui Wu, Zhongqiang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

This paper focuses on the issue of unknown false data injection attacks (FDIA) of controllers in DC microgrid, and proposes a distributed fault-tolerant secondary controller for DC microgrid to ensure that the control objectives of voltage regulation and current sharing can be achieved when the system is subject to FDIA. Firstly, the secondary control problem in DC microgrid with FDIA is transformed into a first-order leader-following multi-agent system fault-tolerant consensus problem, and the impact of FDIA signals on the microgrid system is analyzed. Then, a distributed extended state observer (DESO) is designed based on the derived multi-agent system, which observes the injected FDIA signals using the relative output error of each distributed generators and its neighbours. Further, based on the observed FDIA signals, a fault-tolerant secondary controller is designed to eliminate the adverse effects of the attack signals on the system. Particularly, the stability analyses for the designed DESO and secondary controller prove that the control system is global asymptotically stable. Finally, the effectiveness and superiority of the proposed fault-tolerant secondary control method for DC microgrids with FDIA are verified by simulation analyses of four cases. © 2022, The Authors. All rights reserved.

关键词： Network security

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Energy Efficiency Maximization for Hybrid Access Femtocell Networks via Incentive Mechanism and Power control Strategy

Energy Efficiency Maximization for Hybrid Access Femtocell N...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Xuelin Liu Weimin Wang Zhixin Liu School of control science and engineering Dalian University of Technology Dalian China School of electrical engineering Yanshan University Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao Hebei Province China

Femtocell network has a great development space in increasing coverage and capacity of wireless network. In order to meet the quality of service (QoS) requirements of MUEs in MBS coverage, we propose a seed channel incentive mechanism in the two-tier femtocell network, where the MBS provides sub-channels to FBSs to motivate them to serve for the MUEs within the MBS coverage. To maximize the energy efficiency of femtocell layer after hybrid access, the optimization problem is modeled. The combined method of worst-case, Dinkelbach and continuous convex approximation is developed to make the optimization problem solvable. In addition, in order to realize the practical application of the proposed scheme, an optimization algorithm combining sub-channel and power allocation is developed. The numerical results show that the proposed algorithm is effective in improving the energy efficiency of the femtocell layer.

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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