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A Fused Machine Learning Approach for Intrusion Detection System

computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Extended Polymorphism Semantics for fUML Models

Extended Polymorphism Semantics for fUML Models

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Maximilian Hammer Ralph Maschotta Armin Zimmermann Computer Science and Automation Department Systems and Software Engineering Group Technische Universitat Ilmenau Ilmenau Germany

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

Polymorphism is a foundational concept within the object-oriented paradigm and is a feature of any mainstream object-oriented language that supports reusability and abstraction in software designs. With fUML being a standard specification of execution semantics of UML activity diagrams, it defines functionalities to simulate polymorphic behavior within such model executions. fUML only provides a simple standard implementation for simulating dynamic polymorphism at runtime. This default implementation does not meet the criteria of polymorphism as it is well-known and established by most mainstream object-oriented programming languages over the last decades, nor does it comply with the constraints for method overriding imposed by the original UML specification. However, fUML offers extension capabilities to add user-defined behavior for semantic variation points like polymorphism handling. This paper presents an extension of fUML execution semantics, enabling refined and more sophisticated simulation of polymorphism for executable UML models to comply with UML and the general understanding and functioning principles of polymorphic behavior within the object-oriented paradigm.

关键词： Hands computer languages Runtime Software design Object oriented modeling Unified modeling language Semantics Object oriented programming Standards Software development management

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ALIEN-YOLO: Low-Light Object Detection with Edge Extraction and Convolutional Attention

ALIEN-YOLO: Low-Light Object Detection with Edge Extraction ...

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International Conference on computer and Communications (ICCC)

作者： Xinpeng Yang Yongxiong Wang Department of Automation School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In the field of security monitoring, the detection of targets in low-light nighttime images is essential for identifying potential security threats. However, this task is challenged by factors such as increased noise, loss of detail, and reduced contrast, which often result in the suboptimal performance of existing detection algorithms under these conditions. To address these challenges, this study introduces an enhanced model based on YOLOv8, termed ALIEN-YOLO. This model incorporates the Adaptive Edge Extraction Low-light Image Enhancement Network (ALIEN), which is specifically designed to mitigate noise and enhance contrast. Additionally, it integrates the Head Depthwise Convolution Attention (HDCA) module to improve the detection of small and occluded objects, and the C2F-SH module, which is optimized with a Shrinkage mechanism to enhance robustness and accuracy through denoising. Experimental evaluations conducted on the ExDark and DARK FACE datasets demonstrated that the improved model achieved a mean Average Precision (mAP) of 81.2% and 54.3%, respectively, indicating a significant enhancement over the baseline YOLOv8. Notably, despite the integration of multiple modules, the model maintains a real-time processing capability of 60 frames per second. These findings underscore the effectiveness of the proposed ALIEN-YOLO model in enhancing target detection performance in low-light conditions, thereby underscoring its potential value for practical applications.

关键词： Adaptation models Image edge detection Noise Object detection Real-time systems Robustness Security Monitoring Image enhancement Faces

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LLM-Powered Multi-Actor System for Intelligent Analysis and Visualization of IEC 61499 Control Systems

LLM-Powered Multi-Actor System for Intelligent Analysis and ...

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Annual Conference of Industrial Electronics Society

作者： Midhun Xavier Tatiana Laikh Sandeep Patil Valeriy Vyatkin Department of Computer Science Electrical and Space Engineering Lulea Tekniska Universitet Sweden Department of Electrical Engineering and Automation Aalto University Espoo Finland

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper introduces an innovative multiactor framework that harnesses the potential of LLMs to augment the functionalities of ICS. By integrating conversational AI technologies, this framework significantly improves human-machine interactions, enabling sophisticated analysis and visualization of intricate data sets. The core of the system comprises specialized LLM actors that interact through a LangGraph-based multiactor framework, addressing various aspects of IEC 61499 control systems including PLC code analysis, SQL query execution, and data visualization. This integration enables operators to interact with the control system using natural language, significantly reducing technical barriers and enhancing the accessibility and usability of complex industrial systems.

关键词： Industrial electronics Human computer interaction Structured Query Language Conversational artificial intelligence Codes Natural languages Data visualization Control systems IEC Standards Usability

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Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias

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IEEE Transactions on Automatic Control 2025年

作者： Pachalyl, Soumen Bhatnagarz, Shalabh Prashanth, L.A. TCS Research Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Indian Institute of Science Department of Computer Science and Automation The Robert Bosch Centre for Cyber Physical Systems Bangalore India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. © 1963-2012 IEEE.

关键词： Stochastic systems

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A swarm optimizer with attention-based particle sampling and learning for large scale optimization

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9329-9341页

作者： Sheng, Mengmeng Wang, Zidong Liu, Weibo Wang, Xi Chen, Shengyong Liu, Xiaohui School of Computer Science and Technology Zhejiang University of Technology Hangzhou300023 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom Zhejiang Police College Hangzhou310053 China School of Computer Science and Technology Tianjin University of Technology Tianjin300382 China

Attention mechanism, which is a cognitive process of selectively concentrating on certain information while ignoring others, has been successfully employed in deep learning. In this paper, we introduce the attention mechanism into a particle swarm optimizer and propose an attention-based particle swarm optimizer (APSO) for large scale optimization. In the proposed method, the attention mechanism is introduced such that activating different particles to participate in evolution at different stages of evolution. Further, an attention-based particle learning is devised to randomly select three particles from a predominant sub-swarm, which is activated by the attention mechanism, to guide the learning of particles. The cooperation of these two strategies could be employed to achieve a balanced evolution search, thus appropriately searching the space of large-scale optimization problems. Extensive experiments have been carried out on CEC’2010 and CEC’2013 large scale optimization benchmark functions to evaluate the performance of proposed method and to compare with related methods. The results show the superiority of proposed method. © 2022, The Author(s).

关键词： Particle swarm optimization (PSO)

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Variational Bayesian Kalman filter using natural gradient

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Chinese Journal of Aeronautics 2022年第5期35卷 1-10页

作者： Yumei HU Xuezhi WANG Quan PAN Zhentao HU Bill MORAN School of Automation Northwestern Polytechnical UniversityXi’an 710072China The Key Laboratory of Information Fusion Technology Ministry of EducationXi’an 710072China Department of Electrical and Electronic Engineering The University of MelbourneMelbourneVIC 3010Australia School of Engineering RMIT UniversityMelbourneVIC 3000Australia School of Computer and Information Engineering Henan UniversityKaifeng 475001China

We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman *** natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of *** a Gaussian assumption for the parametrized variational distribution,we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization,producing estimates of the variational hyper-parameters of state estimation and the associated error *** results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented,showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.

关键词： Kullback-Leibler divergence Natural gradient Nonlinear Kalman filter Target tracking Variational Bayesian optimization

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Auto-Adaptive Stator Ground Fault Protection for Synchronous Generators in Diesel-Electric Locomotives 14

Auto-Adaptive Stator Ground Fault Protection for Synchronous...

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14th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Mahtani, Kumar Guerrero, Jose M. Beites, Luis F. Platero, Carlos A. Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Dept. Madrid Spain Mondragon Unibertsitatea Electronics and Computer Science Dept. Arrasate-Mondragon Spain Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Department Madrid Spain

ISBN: (纸本)9798350320770

Diesel-electric locomotives are equipped with one or more synchronous generators driven by diesel engines, that provide the necessary electrical energy for their operation. These generators are usually provided with some protection relays, depending on their rated power and their type of service. Standard stator-ground fault protection has a setting for rated conditions. In case of variable speed and voltage operation, the protection does not cover all the operation range of the machine. In addition, unwanted tripping may occur due to incorrect filtering of the third harmonic. In this paper, an auto-adaptative stator ground protection function is presented, for the operation of synchronous machines at variable voltage and frequency. A specific case study is tackled, focused on the main generators of a diesel-electric locomotive. Numerous simulations are carried out to verify the proposed protection method. The obtained results are promising, and they can be used in other synchronous machines operating at variable speed. © 2023 IEEE.

关键词： Stators

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Energy Consumption Optimisation for Horticultural Facilities

Energy Consumption Optimisation for Horticultural Facilities

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Kirill Zhukovskii Polina Ovsiannikova Pranay Jhunjhunwala Paolo Scarabaggio Raffaele Carli Mariagrazia Dotoli Valeriy Vyatkin Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Computer and Space Engineering Lulea Tekniska Universitet Sweden Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

This paper proposes a framework designed to optimise energy consumption in vertical farming. It aims to maximise cost efficiency by balancing between minimising system operations during the electricity price peaks and the ability to trade capacity on the FCR market while also fulfilling constraints on the internal growing process. We consider that the vertical farming system has distributed control with a series of actuators controlled by various spatially distributed PLCs that we refer to as agents, to underline their independence. The paper conducts two experiments for a lO-agent system with a Pareto controller and a lOO-agent system with a Lagrangian approach and shows the balance between more cost-efficient momentary energy consumption control.

关键词： Energy consumption Actuators Costs Systems operation Electricity Decentralized control Optimization Manufacturing automation Farming

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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