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Abnormal Detection of Industrial control System Based on LSTM and GSK Algorithm Customized by Taguchi Method 3

Abnormal Detection of Industrial Control System Based on LST...

引用

3rd ieee international conference on Computer Communication and Artificial Intelligence, CCAI 2023

作者： Zhao, Huiqi Lei, Rui Fan, Fang Guo, Yulong Li, Ying Shandong University of Science and Technology College of Intelligent Equipment Tai'an271000 China

ISBN: (纸本)9798350335262

Industrial control systems are at the core of critical national infrastructures such as petroleum, chemical, natural gas, power and metallurgy. However, with the integration of industrial control systems with the Internet, the Internet of Things and other network fields, industrial control systems have been penetrated by various security threats. At present, the existing anomaly detection methods have many limitations and cannot effectively identify various attacks. Therefore, in this paper, we propose an effective anomaly detection model for industrial control systems that combines Gaining-sharing knowledge based algorithm (GSK) and the LSTM network. Specifically, we first use the GSK algorithm for feature selection to eliminate redundant features, improve algorithm accuracy and reduce running time, and then use an LSTM classifier to classify different categories of attacks. Secondly, we used Taguchi method to customize the optimal solution for the GSK algorithm applied to the feature selection problem, which improves the efficiency and robustness of the algorithm. Furthermore, we experimentally validate the model using a real gas pipeline dataset. The experimental results show that the proposed TBGSK-LSTM model outperforms other traditional methods in terms of accuracy, precision, recall, F-score, average fitness function value and average number of selected features. © 2023 ieee.

关键词： Anomaly detection

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Solving the Problem of Convergence of the Results of Analog Signals Conversion in the Process of Aircraft control 3

Solving the Problem of Convergence of the Results of Analog ...

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3rd ieee international conference on System Analysis and intelligent Computing, SAIC 2022

作者： Trystan, Andrii Chystov, Valerii Zakharchenko, Iryna Laptiev, Oleksandr Yevseiev, Serhii Matiushchenko, Olha Ivan Kozhedub Kharkiv National Air Force University Department Scientific Center Kharkiv Ukraine Ivan Kozhedub Kharkiv National Air Force University Flight Faculty Kharkiv Ukraine Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Taras Shevchenko National University Department of Cyber Security and Information Protection Kyiv Ukraine National Technical University «kharkiv Polytechnic Institute» Department of Cyber Security Kharkiv Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

The development of robotic systems, artificial intelligence systems, and information technology is changing the paradigm of search management for both moving and stationary objects (targets). The task of finding targets in a given limited space is not new and is solved by performing monitoring tasks, search and rescue operations, assessing the state of critical infrastructure, and planning and performing special tasks. The task is complicated by the need to organize group search, coordinate between groups, search autonomy by eliminating the role of the operator, and the introduction of elements of the artificial element. The presented research has shown the efficiency of the use of cellular automatic machines for the decision of problems of search of a route at the search of the purposes in various conditions. Improving the efficiency of the cellular automaton is achieved by clustering search objects and bypassing targets in the cluster. This reduces the number of search steps. The proposed approach to the implementation of cellular automata will create an effective system for monitoring the condition of observation objects, which will minimize the number and composition of monitoring groups. © 2022 ieee.

关键词： Efficiency

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Data-Driven Approach for Accurate Wheel Slip Ratio Estimation in Electric Vehicles: Performance Comparison and Validation Analysis 27

Data-Driven Approach for Accurate Wheel Slip Ratio Estimatio...

引用

27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Ikram, Mubasher Liu, Li Shen, Qiguang Zhang, Qingguang Liu, Jia Xu, Kun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China University of Chinese Academy of Sciences Beijing China Changsha University of Science and Technology Hunan China School of Automotive Technology Liuzhou Railway Vocational and Technical College Liuzhou China

ISBN: (纸本)9798331505929

Data-driven techniques advance accurate vehicle state estimation for electric vehicles (EVs), especially for distributed drive electric vehicles (DDEVs) that require precise slip ratio estimation for safe motion control. This study proposes advanced data-driven estimation models of wheel slip ratio using machine learning (ML) techniques, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and extreme learning machine (ELM), to manage the dynamic complexities of EVs effectively. The proposed models process measurable input parameters and are trained on a comprehensive dataset comprising both synthetic and real-vehicle datasets. Comparative and sensitivity analyses evaluate each model's accuracy, robustness, and generalization capabilities. The results reveal highly accurate slip ratio estimation across diverse driving conditions. The LSTM and GRU models demonstrate exceptional performance even under low friction conditions, achieving variance accounted for (VAF) scores of 99% and 98% respectively. Meanwhile, the ELM model proves effective on high adhesion surfaces with an average VAF around 95%. Validation results on real-world data show the GRU model outperforms the others, achieving a VAF of 90%. This study advances vehicle state estimation by offering a robust, data-driven approach incorporating comparative and validation analyses. © 2024 ieee.

关键词： Vehicle wheels

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Federated-Edge Computing Based cyber-Physical systems Framework for Enhanced Diabetes Management 15

Federated-Edge Computing Based Cyber-Physical Systems Framew...

引用

15th international conference on Innovations in Information technology, IIT 2023

作者： Khater, Heba M. Tariq, Asadullah Sallabi, Farag Serhani, Mohamed Adel Barka, Ezedin United Arab Emirates University College of Information Technology Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

This paper presents an intelligent cyber-physical system framework for detecting and managing type 2 diabetes. The framework leverages the benefits of cloud and federated-edge computing while addressing concerns of security, delay, and communication costs. It enables the seamless integration of knowledge extracted from machine learning (ML) models trained at the edges into a CNN model using distillation and ensemble techniques. To identify the effective model for detecting diabetes, we performed a comparative study using four different ML classifiers: Support Vector Machine (SVM), Artificial Neural Network (ANN), Case-Based Reasoning with Fuzzy K-Nearest Neighbor (CBR-FKNN), and K-means with Logistic Regression (K-Means-LR) at a single edge. We employed various preprocessing techniques and feature selection algorithms to identify the optimal features for the four models. An experimental evaluation was conducted using two diabetes datasets, the PIMA dataset and the Diabetes dataset 2019. The experiments were mainly conducted to assess our proposed framework but not the datasets. The results demonstrated that the SVM model outperforms other models in diabetes detection. The results also showed that the Diabetes dataset 2019 provided better accuracy and F1 scores than the PIMA dataset. © 2023 ieee.

关键词： Edge computing

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Research on optimization of highway tunnel outside brightness detection device and algorithm 8

Research on optimization of highway tunnel outside brightnes...

引用

8th international conference on intelligent Computing and Signal Processing, ICSP 2023

作者： Wang, Fucheng Xin, Song Xu, Wanrong Wang, Dinuo Shandong University of Science and Technology College of Safety and Environmental Engineering Qingdao China Shandong University of Science and Technology College of Transportation Qingdao China North China Electric Power University Technology College Department of Electrical Engineering Baoding China

ISBN: (纸本)9798350302455

Tunnel lighting control systems that do not accurately sense changes in the tunnel's external lighting environment not only increase tunnel operating costs, but also may exacerbate the "black hole"and "white hole"effects that cause traffic safety accidents. In this paper, we use STM32F103RCT6 chip to design an illuminance detection device consisting of a light level acquisition module, STM32 development board and upper computer software to accurately detect the illuminance outside the tunnel, and it can be successfully applied to the tunnel lighting control system to send data to the control system. Based on the accurate acquisition of illuminance data outside the tunnel, the software designs an accurate processing algorithm to reject abnormal data and a multi-sensor data fitting algorithm, and finally verifies through experimental testing that the hardware system designed in this paper with the data processing algorithm can accurately reject the abnormal values of illuminance data outside the tunnel and provide accurate external light data for the tunnel lighting control system. © 2023 ieee.

关键词： Operating costs

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DC-DC Single Switch High Gain Converter with Voltage Multiplier

DC-DC Single Switch High Gain Converter with Voltage Multipl...

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2024 international conference on Advancement in Renewable Energy and intelligent systems, AREIS 2024

作者： Manukrishna, M.A. Krishnan, Geethu Sunny, Anu Roshni Rajan, K. Balakrishnan, Akhil A. Shijoh, V. Jyothi Engg. College Kerala Cheruthuruthy India Carmel College of Engg. and Technology Kerala India

ISBN: (纸本)9798350387230

Solar PV and other renewable energy systems increasingly use DC-DC converters with high gains. The converter for these applications must have a convenient steady-state and energetic performance, as well as high efficiency. This paper proposes a DC-DC boost non-inverting converter. The proposed converter features a single switch, ensuring an uninterrupted current at the input side and reducing voltage stress. The duty cycle offers a wide control range, with higher gain achieved at lower duty cycle values. Moreover, the device proposed is more efficient when the input current is continuous with a lower duty cycle. The input current without interruption is a sensible characteristic of dc-dc converters, molding them capable of use in solar PV systems. The operation of the proposed device has been described in specific and elongated to incorporate actual circuit parameters for a feasible performance assessment. Comparative performance analysis is also was carried out with the converter topologies existing in literature. Loss analysis has also been performed for the proposed converter. Finally, simulations were validated by comparing them with experimental results. © 2024 ieee.

关键词： Converter efficiency DC-DC converters Fuel cells High voltage gain Hybrid converters Renewable energy Reverse recovery issues Solar PV systems

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Lightweight Traffic Accident Detection Algorithm Based on Attention Mechanism 2

Lightweight Traffic Accident Detection Algorithm Based on At...

引用

2nd international conference on intelligent control and Computing, IC-C 2024

作者： Hou, XiaoYan Wen, ShiHuan Chen, ShengFu Li, JianHe Xu, Kai Wang, ZhenYu Wu, WenHan South China University of Technology GuangZhou China PSBC Consumer Finance Company Limited GuangZhou China Tianjin University of Science & Technology TianJin China

ISBN: (纸本)9798350351873

Traffic accident detection is a challenging task in intelligent transportation systems. Rapid and accurate detection of traffic accidents can ensure the safety of people's lives and property to the greatest extent. However, the existing accident detection models have some limitations such as slow speed or low precision, which limit the practical application. Aiming at the current low accuracy of rapid traffic accident detection, this paper proposes a traffic accident detection algorithm based on attentional feature fusion, which combines the feature extraction and fusion process with the attention mechanism. Specifically, the MHSA module is integrated into the Backbone stage of the YOLOv5 network, and the CBAM module is integrated into the Neck stage to refine and capture image features and enhance network feature extraction and fusion capabilities. At the same time, in view of the lack of traffic accident image data, this paper establishes a traffic accident image data set. Experiments were carried out on the traffic accident image data set and the results show that, under the condition that the detection speed of each image is guaranteed to be 9.7ms, the accuracy of the proposed algorithm to identify traffic accidents is 4% higher than that of YOLOv5s. It achieves a balance between speed and accuracy of traffic accident detection under limited computational cost. © 2024 ieee.

关键词： Convolutional neural networks

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Relaxed Graph Semi-Supervised Contrastive Learning for Node Classification 29

Relaxed Graph Semi-Supervised Contrastive Learning for Node ...

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29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Li, Qiyu Li, Xianxian Qian, Haodong Li, De Wang, Jinyan Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin China Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350330717

Graph Neural Networks (GNNs) have emerged as promising tools in graph semi-supervised learning. They acquire low-dimensional node embeddings for downstream tasks by aggregating and updating features from neighboring nodes. However, in a semi-supervised setting, the availability of labeled node information is limited, resulting in the underutilization of vast amounts of unlabeled node information. Current research integrates contrastive learning with graph semi-supervised learning to better use this unlabeled node information. Nevertheless, the differences between cross-entropy and self-supervised contrastive loss are often overlooked. To address this issue, we propose a novel relaxed graph contrastive semi-supervised learning method. We analyze the difference between nodes correctly classified under cross-entropy loss and self-supervised contrastive loss. Inspired by this, we design a relaxed contrastive loss that considers the node features optimized under these two loss functions, thereby expanding the positive sample set. Furthermore, to ensure the quality of the positive sample set, we introduce a threshold constraint based on feature similarity and prediction results to select more reliable positive samples. The experimental results indicate that our method exhibits competitive performance across most datasets. © 2023 ieee.

关键词： Contrastive learning Graph Neural Networks Semi-supervised learning

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Cutting Tool Surface Defect Detection based on Chaos Difference Method in Manufacturing Process

Cutting Tool Surface Defect Detection based on Chaos Differe...

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ieee international conference on automation Science and Engineering (CASE)

作者： Ziqin Wu Weilong Niu Xiaoguang Sun Li Chen School of Intelligent Systems Science and Engineering Jinan University Zhuhai P.R. China School of Rail Transportation Soochow University Suzhou P.R. China Suzhou Ahno Precision Tangent Technology Co. Suzhou P.R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

CPMS(cyber-Physical Manufacturing systems) plays a pivotal role in Industry 4.0, driving the automation and intelligence of modern manufacturing. In industrial quality control, however, manual visual inspection persists for detecting surface defects in small products, incurring high labor and time costs detrimental to automation. The cutting tools commonly utilized in machine tools typically have diameters ranging from 8mm to 15mm, making their surface defects even more minute. This paper introduces an MDC (Modeling Differences in Chaos) method enhancing deep learning models for detecting tiny surface defects in CPMS, which improving contrast in regions with defects by leveraging the similarity relationships among cutting tools that share the same type, achieving 86.08% Precision , 76.95% mIoU and 84.04% Recall in experiments, outperforming classical semantic segmentation algorithms.

关键词： Deep learning Chaos Cutting tools Visualization Attention mechanisms Semantic segmentation Machine tools Calibration Surface treatment Defect detection

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A Traffic Prediction Model Based on Multi Stream Feature Fusion 1

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6th international conference on Communications and cyber-Physical Engineering, ICCCE 2023

作者： Musike, Mahipal Reddy Tiwari, Rajesh Shrivastava, Rajeev CMR Engineering College Hyderabad India Princeton Institute of Engineering and Technology for Women Hyderabad India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

Traffic is constantly changing, traffic supposition is extremely crucial in urban planning congestion control as well as management systems. Long-term traffic predictions help with relevant factors and smart city current traffic reduction. Traffic flow is an important topic because it transports regional factors and patterns regional spatial structure. Traffic prediction is gaining popularity due to its widespread real-world applications in traffic management, urban computing, public safety, and other fields. For intelligent transportation systems, timely and accurate traffic flow prediction is critical. A MSFF-Multi-Stream-Feature-Fusion method is proposed in this paper to remove and incorporate rich features after traffic data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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