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Using Kolmogorov–Arnold network for cyber–physical system security: A fast and efficient approach

International Journal of Critical Infrastructure Protection 2025年 50卷

作者： Mohammadmahdi Ghorbani Alimohammad Ghassemi Mohammad Alikhani Hamid Khaloozadeh Amirhossein Nikoofard Department of Control and System Engineering Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

A cyber–physical system (CPS) is the foundation of modern industrial infrastructures but is vulnerable to cyber attacks due to its connectivity. Detecting these attacks is crucial, driving research into machine learning and deep learning-based models for intrusion detection systems. Many of these models, though effective, suffer from high computational complexity and large parameter counts, limiting their practicality for real-time deployment. Additionally, extensive data preprocessing, commonly used in attack detection, can introduce drawbacks such as loss of critical information, reduced interpretability, and increased latency. This paper employs the Kolmogorov–Arnold network (KAN) as a lightweight and efficient alternative to conventional models for attack detection in CPSs. With a compact architecture and significantly fewer parameters, KAN achieves high classification accuracy while minimizing computational overhead. It eliminates the need for complex feature extraction and preprocessing, preserving data integrity and enabling faster decision-making. Evaluated on the SWaT, WADI, and ICS-Flow datasets, KAN demonstrates superior performance in detecting cyber attacks across binary and multi-class tasks on both physical and network data. Its low inference time and minimal resource requirements make it a practical solution for real-time CPS security.

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Traffic Flow Estimation using Graph Neural Network with Aggregation of Traffic Features

Traffic Flow Estimation using Graph Neural Network with Aggr...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Adiyana Putri Ferio Brahmana Endra Joelianto Herman Y. Sutarto Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Artificial Intelligence Control and Automation Laboratory Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia

ISBN: (纸本)9781665476881

The increasing vehicle volume every year affects the prediction of the traffic system. The purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions due to connection disruptions or ages. In estimating traffic flow, obtaining and explaining the historical data from the nearest segment sensor is necessary. The graph model can describe the spatial relationship between segments by connecting nodes with edges. Graph Neural Network (GNN) has been used as a learning method to predict the traffic flow in the segment sensor. GNN gets input in the form of spatial data built through a graph model, and the features are traffic density, average vehicle speed, and delay time on the road network. This paper shows that GNN's capability performs better in extracting spatial features and predicting the traffic flow by aggregating the features through connectivity nodes and edges.

关键词： Learning systems Roads Estimation Feature extraction Robot sensing systems Graph neural networks Spatial databases

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Simplistic refinement of self-supervised feature representations for classification of Brain strokes using contrastive learning on CT Images

Simplistic refinement of self-supervised feature representat...

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control instrumentation system Conference (CISCON)

作者： Mahesh Anil Inamdar Anjan Gudigar Raghavendra U Raja Rizal Azman Bin Raja Aman Nadia Fareeda Binti Muhammad Gowdh Izzah Amirah Banti Mohd Ahir Ajay Hegde Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Instrumentation & Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Biomedical Imaging Faculty of Medicine Universiti Malaya Malaysia Department of Biomedical Imaging Universiti Malaya Malaysia Consultant Neurosurgeon Manipal Hospitals

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

The procedure for determining the cause of strokes makes heavy use of Computed Tomographic (CT) images. The existing methods try different Machine Learning (ML) based methods but haven’t so far explored the self-supervised learning approach. In this study we have showcased a simple technique for improving the feature representation generated using Deep Learning (DL) models through self-supervised technique. We utilized (triplet) loss for generating different embeddings for normal and acute stroke cases and developed a simple strategy to improve the same for better differentiability. We showcase this refining technique, which improves discriminability and able to classify using simple ML model. The results are tested against various DL models for better generality with the best begin ResNet101 with 95.16% accuracy. We also compared the results based on different levels of refinement based on their architecture. Hence, the proposed system can be used in hospitals to analyze brain CT images.

关键词： Deep learning Hospitals Computed tomography Instruments Computational modeling Refining Contrastive learning Computer architecture Brain modeling control systems

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Modeling and control of a half Car Active Suspension system using Sliding Mode controller and Linear Quadratic Regulator controller

Modeling and Control of a half Car Active Suspension System ...

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Renewable Energy & Sustainability (ieCRES), International engineering Conference on

作者： Abdussalam Ali Ahmed Hisham.F. A. Ramadhan Mohamed El Malki Yasser Fathi Nassar Mechanical Engineering Department Bani Waleed University Bani Waleed Libya Control System Engineering College of Electronic Technology Bani Walid Libya Department of Physics Faculty of Sciences Mohammed First University Oujda Morocco Mechanical and Renewable Energy Eng. Dept. Wadi Alshatti University Brack Libya

This paper illustrates the use of two distinct control strategies. Namely, the Sliding Mode control (SMC) and a Linear Quadratic Regulator (LQR) controller to monitor and handle the suspension system vibrations that occurred. We used a half car suspension model with (4DOF). The necessary suspension performance attributes, such as ride comfort, suspension deflection and car handling require a comprehensive control system. Three components make up this control system, which is carried out using the MATLAB application: the controller, the suspension system model, and input signals (road profile and actuator force). A comparison of the half car active suspension system LQR and SMC controllers as well as the uncontrolled suspension system is presented.

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Reviewing Fault Diagnosis Methods in Electric Drives: Power Subsystem and Electrical Machine

Reviewing Fault Diagnosis Methods in Electric Drives: Power ...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Galina Demidova Hao Chen Yibin Li Dmitry Lukichev Alecksey Anuchin Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China Institute of Marine Science and Technology Shandong University Shandong China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

This paper provides a review of popular fault diagnosis methods in the power subsystem and electrical machine of electric drives. The review aims to explore the different approaches used to detect faults in these components and the most common types of faults encountered in electric drives. The paper first provides an overview of the power subsystem and electrical machine and their respective functions within the electric drive system. It then proceeds to discuss the different types of faults that can occur in these components, including overvoltage, undervoltage, overcurrent, and short circuits. The review evaluates the various fault diagnosis methods available for these components, including model-based, signal-based, and artificial intelligence-based approaches. Model-based approaches use mathematical models of the system to detect and diagnose faults. Signal-based approaches rely on the analysis of measured signals to detect changes in system behavior that indicate a fault. Artificial intelligence-based approaches use machine learning algorithms to identify patterns in data and detect faults. Model-based approaches are highly accurate but can be computationally intensive. Signal-based approaches are less computationally intensive but may be less accurate in certain situations. Artificial intelligence-based approaches are versatile but require significant amounts of data for training. This review provides a comprehensive overview of the current state-of-the-art in fault diagnosis methods for the power subsystem and electrical machine of electric drives. The review concludes by suggesting potential areas for future research to further improve the accuracy and efficiency of fault diagnosis methods for electric drives.

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FDP-GAMLP: New Financial Distress Prediction Technique based on Genetic Algorithm and Multi-Layer Perceptron 19

FDP-GAMLP: New Financial Distress Prediction Technique based...

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19th IEEE International Multi-Conference on systems, Signals and Devices, SSD 2022

作者： Khedr, Ahmed M. Bannany, Magdi El Saif, Abdul-Wahid A. University of Sharjah Computer Science Dept. Sharjah27272 United Arab Emirates College of Business Administration Umm Al Quwain University Umm Al Quwain356 United Arab Emirates Ain Shams University Faculty of Business Cairo11566 Egypt King Fahd University of Petroleum & Minerals Control and Instrumentation Engineering Department Dhahran Saudi Arabia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Developing a reliable financial distress prediction model has been a long-standing research area. Recently, machine learning algorithms have been increasingly popular in the area. In this paper, a hybrid approach of Genetic Algorithm (GA) and Multi-Layer Perceptron (MLP) for Financial Distress Prediction (FDP) (FDP-GAMLP) is proposed. FDP-GAMLP emphasizes on GA-based tuning of the four major hyper-parameters, namely network width, network depth, network optimizer, and dense layer activation function, which can influence on whether the algorithm explodes or converges. An improved GA is utilized to optimize the MLP model's hyper-parameters for better prediction. The prediction performance is evaluated using real data set with samples of companies from countries in Middle East region. The resampling technique using k-fold evaluation metrics is adopted to get unbiased and most accurate results. The simulation results show that FDP-GAMLP outperforms the classical machine learning models in terms of predictive accuracy. © 2022 IEEE.

关键词： Genetic algorithms

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Analysis of stiction fault in pneumatic control valves 16th

Analysis of stiction fault in pneumatic control valves

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16th control instrumentation system Conference, CISCON 2019

作者： Navada, Bhagya R. Santhosh, K.V. Department of Instrumentation and Control Engineering Centre for Cyber Physical System Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

ISBN: (纸本)9789811546754

To cater to the ever-increasing needs of customers, industries have to give more importance toward maintenance of health of industrial process equipments through better monitoring system. Thus, fault detection and diagnosis techniques have a vital role in maintaining industrial process health. Stiction is one of the commonly occurring control valve faults in most of the flow process industries. In this paper, an attempt is made to synthesis the Choudhury’s stiction model to analyze the behavior of the control valve with stiction fault. Behavior of control valve is analyzed under different stages like linear, deadband, undershoot, overshoot and no offset conditions. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Fault detection

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Multistability Analysis and Adaptive Feedback control on a New Financial Risk system

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International Journal of Applied and Computational Mathematics 2023年第5期9卷 1-16页

作者： Johansyah, Muhamad Deni Sambas, Aceng Vaidyanathan, Sundarapandian Benkouider, Khaled Sulaiman, Ibrahim Mohammed Department of Mathematics Universitas Padjadjaran Jatinangor Sumedang 45363 Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu Besut 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Center for Control System Vel Tech University Avaddi Chennai 60062 India Department of Non-Destructive Automatic Testing Laboratory Jijel University Rue Rouikha Mokhta Jijel 1800 Algeria School of Quantitative Science University Utara Malaysia Sintok Kedah Bukit Kayu Hitam 06010 Malaysia

This study presents the findings of a new financial risk system with one absolute function and two quadratic nonlinearities. The rich dynamic behaviors of the new financial risk system are discussed analytically and numerically through stability analysis, phase portrait, bifurcation diagram, Lyapunov exponent, multistability and coexisting attractors. In addition, a new financial risk system with two quadratic nonlinearities and one absolute function shows multistability and coexistence of chaotic attractors. Using adaptive feedback control, we established a new control law for fully synchronising the proposed chaotic financial systems with itself. To conclude, some numerical simulation was presented using MATLAB to illustrate the efficiency of the designed method. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Backstepping control Chaos Financial chaotic system Synchronisation

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Novel Classification Algorithm for Complex Class Structures

SSRN

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SSRN 2024年

作者： Melhaoui, Ouafae El Baghaz, E. Benchaou, S. Research Center High Studies of Engineering School EHEI Electronic and system Laboratory faculty of science Mohamed first university Oujda Morocco Laboratory of Electronics Instrumentation and Energetic Department of physics Faculty of Sciences Chouaïb Doukkali University El Jadida24010 Morocco Labo MATSI EST. University Mohammed 1. Oujda Morocco. Oujda Morocco

In recent years, with the rapid development of information technology, the research on complex data classification is full of opportunities and challenges. The present paper proposes a new complex data classification technique that addresses the challenge of classification accuracy, time consuming. This method is also designed to be more straightforward for programming unlike more intricate methods such as the fuzzy min-max *** novel approach is structured around two main phases: the initial phase focuses on prototypes creation and noise elimination, while the subsequent phase involves merging prototypes into classes. The merging phase, iteratively combines the closest prototypes until the desired number of classes is achieved. The proposed algorithm is compared to the K-means, the fuzzy C-means, the competitive neural networks and the fuzzy min-max classifier methods through a number of simulations. The proposed approach demonstrates promising results, outperforming established methods in terms of classification accuracy and computational efficiency.. © 2024, The Authors. All rights reserved.

关键词： Merging

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A K-Means Clustering-Based Optimization of IT2FL for Improved Regenerative Braking Performance

A K-Means Clustering-Based Optimization of IT2FL for Improve...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Muhammad Rizalul Wahid Endra Joelianto Bentang Arief Budiman Manida Tongroon Mulia Pratama Mahmudah Salwa Gianti Muhammad Fauzan Hernadi Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia Department of Mechanical Engineering ATAE Research Unit Kasetsart University Sriracha Thailand Pusat Riset Mekatronika Cerdas Organisasi Riset Elektronika dan Informatika-BRIN Bandung Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Optimizing regenerative braking systems in electric vehicles presents a complex challenge requiring precise and adaptable control. This research proposes employing K-Means Clustering to the Interval Type-2 Fuzzy Logic (IT2FL) membership functions, thereby improving overall regenerative braking efficiency and enhancing driving range. This research demonstrates that implementing a K-Means in the IT2FL control system in electric vehicles effectively recovers 35.84% of energy. By incorporating a K-means-based approach, the IT2FL controller further enhances vehicle range by 43 kilometers, achieving a total range of 108 kilometers. In comparison, an IT2FL system without K-means recovered only 26.67% of energy and achieved a total range of 93 kilometers. Notably, the IT2FL controller with K-means enabled the electric vehicle to attain an energy efficiency of 22 kilometers per kilowatt-hour. These findings underscore the significant improvement in maximum range offered by the IT2FL controller with K-means compared to alternative methods.

关键词： Fuzzy logic Energy consumption Electric potential Mechatronics Electric vehicles control systems Energy efficiency Optimization

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