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arXiv 2024年

作者： Sun, Haili Huang, Yan Han, Lansheng Fu, Cai Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of industrial cyber-physical systems (CPS). However, existing researches pay little attention to the logical dependencies among system working states, and have difficulties in explaining the evolution mechanisms of abnormal signals. To reveal the spatio-temporal association relationships and evolution mechanisms of the working states of industrial CPS, this paper proposes a fine-grained adaptive anomaly diagnosis method (i.e. MAD-Transformer) to identify and diagnose anomalies in MTS. MAD-Transformer first constructs a temporal state matrix to characterize and estimate the change patterns of the system states in the temporal dimension. Then, to better locate the anomalies, a spatial state matrix is also constructed to capture the inter-sensor state correlation relationships within the system. Subsequently, based on these two types of state matrices, a three-branch structure of series-temporal-spatial attention module is designed to simultaneously capture the series, temporal, and space dependencies among MTS. Afterwards, three associated alignment loss functions and a reconstruction loss are constructed to jointly optimize the model. Finally, anomalies are determined and diagnosed by comparing the residual matrices with the original matrices. We conducted comparative experiments on five publicly datasets spanning three application domains (service monitoring, spatial and earth exploration, and water treatment), along with a petroleum refining simulation dataset collected by ourselves. The results demonstrate that MAD-Transformer can adaptively detect fine-grained anomalies with short duration, and outperforms the state-of-the-art baselines in terms of noise robustness and localization performance. © 2024, CC BY.

关键词： Cyber Physical system

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A Novel Two-Stage Method for Texture Defect Detection

A Novel Two-Stage Method for Texture Defect Detection

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Lu Liu Min Jiang Lifa Xu School of Computer Science and Technology Wuhan University of Science and Technology WuHan Hubei P.R. China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei China

Texture defect detection has wide applications in both textiles and industry, where traditional defect detection methods focus on costly manual labeling and defect samples are complex to collect in real-world industrial scenarios. Furthermore, labeling errors are common in datasets from industrial scenarios, and the problem still needs to be explored. Therefore, we propose a novel texture defect detection method. It has two stages; the defective samples are synthesized in the first stage. In the second stage, we represent normal samples using a self-supervised learning method. Then, we construct a single classifier based on this learned representation. Subsequently, the model is trained to distinguish between normal and defective samples within the embedding space. To increase the robustness of our method, we adopt a novel strategy to construct defective samples and compute the feature similarity through a similarity-weighted aggregation method to achieve defect detection, which is more robust to noise. Experimental results on the DAGM2007 dataset show that our method performs comparably to the leading texture defect detection methods without label noise. In contrast, in the presence of noise, our method can effectively minimize the impact of noise on model performance.

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Graph Pattern Matching through Model Checking

Graph Pattern Matching through Model Checking

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International Conference on Database Theory and Application (DTA)

作者： Rui Qiao Xiaolei Zhong Ling Zhang Heng He College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Software Huazhong University of Science Wuhan China College of Management Wuhan University of Science and Technology Wuhan China

Graph pattern matching is a hot spot in the big data era, which is to find answer graphs matching a given query graph in a data graph of graph databases. “Matching” means two graphs satisfy some relation, such as isomorphism, simulation, bisimulation, etc. Since there are seldom algorithms for the subgraph bisimulation, our work commits to solve the graph pattern matching problem involving bisimulation relations through the model checking technology. We characterize query graphs by modal formulas. By model checking the formulas in the data graphs, the answer graphs bisimilar to the query graphs can be discovered. We add * to basic modal logic language resulting in ML + * language, and add r* to form ML + * formulas. Then a theorem which states that ML + * formulas characterize finite directed graphs modulo bisimulation is put forward. Furthermore, we list steps to find answer graphs bisimilar to a query graph.

关键词： Model checking Pattern matching Databases Big data Topology Arrays Computer science

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Radar imaging simulation for typical urban structures based on analytical models

Radar imaging simulation for typical urban structures based ...

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2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

作者： Hongzhen chen yueting zhang kan tang wenchang xiong chibiao ding Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences NO.9 Beiyitiao Zhongguancun Beijing 100190 China The National Key Laboratory of Microwave Imaging Technology，NO.19 Beisihuan Xilu Beijing 100190 China Institute of Electronics Chinese Academy of Sciences NO.19 Beisihuan Xilu Beijing 100190 China

This paper proposes a novel radar imaging simulator based on analytical electromagnetic and geometric models for a common typical kind of urban structure. The analytical models are presented not only for improving th... 详细信息

ISBN: (纸本)9781424484447

关键词： imaging simulator urban structure SAR image electromagnetic and geometric models.

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Prediction of hot spots in protein-protein interaction by Nine-Pipeline & Ensemble Learning strategy

Prediction of hot spots in protein-protein interaction by Ni...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Hu Zonghao Li Xiaolong Zhang Nansheng Chen School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Molecular Biology and Biochemistry Simon Fraser University BC Canada

ISBN: (纸本)9781665429825

This paper proposes a NPEL (Nine-Pipeline & Ensemble Learning) strategy based on machine Learning algorithm to predict protein-protein interaction hotspots by training amino acid composition, surface area, amino acid chains and other complex/interface-related structural information. We applied Random Forest, Linear Svm, KNN, Gaussian Naive Bayes, Multi-layer Perceptron Neural Network, Adaboost, XGBoost etc. nine machine learning algorithms combination into an independent pipeline to predict protein hot spots, and the final results are optimized through voting and stacking scheme. In the stacking result of XGBoost and Logistic Regression, the highest accuracy is 0.8462 and improve the indicators of the pipeline results greatly.

关键词： Proteins Drugs Training Protein engineering Machine learning algorithms Three-dimensional displays Pipelines

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Robust edge Detection Based on Anisotropic Mathematical Morphology and Scale Multiplication in NSCT Domain

Robust edge Detection Based on Anisotropic Mathematical Morp...

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2014 2nd International Conference on Materials Science and Mechanical Engineering(2nd ICMSME 2014)

作者： Yimian Dai Yiquan Wu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization Ministry of Agriculture Freshwater Fisheries Research Center Chinese Academy of Fishery Sciences Key laboratory of Fishery Equipment and Engineering Ministry of Agriculture of the People’s Republic of China Jiangsu Key Laboratory of Quality Control and Further Processing of Cereals and Oils Nanjing University of Finance Economics

A novel edge detection method based on anisotropic mathematical morphology and scale multiplication in nonsubsampled contourlet transform(NSCT) domain is proposed to obtain a superior and robust performance under heavy noise. One preliminary result is obtained using anisotropic morphological gradient of the low-frequency component, yielding a single-pixel response with few pseudo edges. Due to the great ability of NSCT to localize distributed discontinuities such as edges, scale multiplication results of high-frequency components can get rid of a large amount of noise and produce well-localized edge candidates. The final result is a fusion of the detection results of low-frequency component and high-frequency components. Detailed experiments compared with other state-of-the-art methods demonstrate that the proposed method has a superior performance of edge detection and is quite robust even under heavy noise.

关键词： Image processing Edge detection Nonsubsampled contourlet transform Anisotropic mathematical morphology Scale multiplication

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Study of Non-Conventional Instrument Transformer and IEC61850 impact on current differential protection

Study of Non-Conventional Instrument Transformer and IEC6185...

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IEEE/PES Transmission and Distribution Conference and Exposition: Latin America

作者： K. L. S. Shenxing X. Dong Z. Bo State Key Lab of Control and Simulation of Power System and Generation Equipments Department of Electrical Engineering Tsinghua University Beijing 100084 China Tsinghua University Beijing Beijing CN State Key Laboratory of Control and Simulation of Power System and Generation Equipments Department of Electrical Engineering Tsinghua University Beijing China AREVA T&D Automation슠and Information Systems Stafford UK

Non conventional instrument transformer (NCIT) and IEC 61850 are crucial technologies in digital substations. But they bring some new uncertainties to protection IEDs, especially the time synchronization issue in current differential protection. The paper analyzes the time delay of transferring sampled values from NCIT to current differential relay in NCIT and IEC61850 based digital substation, then finds out the delay-non-deterministic problem, at last presents a novel current differential protection scheme by using phase-locked loops (PLL ) technology.

关键词： Substations Delay effects Ethernet networks Phase locked loops IEC Synchronization Data communication

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Robust fault tolerant manoeuvring and rudder control of marine vehicles with state saturation

Robust fault tolerant manoeuvring and rudder control of mari...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiaozheng Jin Qiuhong Liu Yu Kang Jiahu Qin School of Electrical Engineering and Automation HeFei University of Technology HeFei AnHui China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467380768

In this paper, the problem of rudder-roll damping control for a class of cruise keeping ships subject to both actuator fault and state saturation is investigated. A manoeuvring and rudder control model of marine vessels is firstly reviewed based on the existing literature. Actuator faults which including partial loss of rudder effectiveness and actuator-bias faults are considered in the model. Then a switching control strategy is developed to compensate for the actuator faults and to guarantee the stability of the rudder-roll damping control system. Moreover, some relationship regarding state saturation bounds, actuator fault limits and control parameters are analyzed in this paper. Finally, simulation results are given to illustrate the proposed procedures and their effectiveness.

关键词： Actuators Vehicles Damping Mathematical model Marine vehicles Vehicle dynamics

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A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on Space Payload

A Comparative Evaluation of AutoEncoder-Based Unsupervised A...

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International Conference on Dependable systems and Their Applications (DSA)

作者： Junrong Du Lei Song Taisheng Zheng Lili Guo Chao Ma Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Science Beijing China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Beijing key Laboratory of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781728160573

ISBN: (纸本)9781728160580

The anomaly detection technology is the basis for ensuring the safe and stable operation of the on-rail payload. The traditional threshold-based anomaly detection method has low accuracy and poor flexibility, and cannot detect abnormalities in real time. In addition, due to the lack of abnormal samples, the distribution of positive and negative samples is extremely imbalanced, which increases the difficulty of abnormal detection. Therefore, this paper proposes an unsupervised learning method based on AutoEncoder and its variants, the Basic AutoEncoder, Deep AutoEncoder and Sparse AutoEncoder are used to verify the algorithm on three public datasets. And using the above three algorithms to carry out the case application on the real load dataset. The experiments show whether in the public dataset or the real data of the payload, the three methods of AutoEncoder have achieved good results, proving the AutoEncoder and its variants have a good application in anomaly detection. At the same time, it is verified that the three algorithms have different effects on different datasets, which proves that the AutoEncoder with different characteristics need to be selected in different scenarios.

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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