Automation and Remote Control最新文献_第5页

Advancing Connected Vehicle Security Using Advanced Deep Learning and Optimized Feature Selection for Intrusion Detection 利用先进的深度学习和优化的入侵检测特征选择提高联网车辆的安全性

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600703

M. Udaya Prakash Reddy, Yugandhar Garapati

{"title":"Advancing Connected Vehicle Security Using Advanced Deep Learning and Optimized Feature Selection for Intrusion Detection","authors":"M. Udaya Prakash Reddy, Yugandhar Garapati","doi":"10.1134/S0005117925600703","DOIUrl":"10.1134/S0005117925600703","url":null,"abstract":"Vehicle network data is being created in massive quantities due to the Internet of Vehicles (IoV) rapid expansion. Network communication security is challenged by the volume of data. The significant amount of data created within the vehicle network presents time-consuming detection issues, even while intrusion detection technologies can help protect the system from unwanted attacks. In this manuscript, advancing connected vehicle security using advanced deep learning and optimized feature selection for intrusion detection (ACVS-OFSID-NCGNN) is proposed. The CIC-IDS-2017 dataset is where the data is first gathered. The gathered information is then sent to preprocessing. In prior to processing, to employ unsharp mask guided filtering (UMGF), identify the missing values, clean the data and standardization are carried out. Next, the previously processed data are provided to superb fairy-wren optimization algorithm (SFOA) for feature selection. SFOA selected 10 optimum features features from CIC-IDS-2017 information. The node-level capsule graph neural network (NCGNN) is then fed the chosen features to detecting the intrusion and classify as benign, brute force, DoS, portscan, web attack, bot, and infiltration. generally speaking, NCGNN doesn’t articulate how to modify optimization techniques to identify the best parameters to guarantee Internet of Vehicles. Therefore, the purpose of the house swallow optimizer (HSO) is to optimize the node-level capsule graph neural network, which correctly classifies intrusion detection. The proposed EID-IoV-NCGNN Python is used to implement this strategy. Using performance criteria such as accuracy, recall, FPR, precision, F1-score, and detection time, the effectiveness of the suggested approach was evaluated. The proposed NCGNN-HSO approach.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"425 - 441"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithm for Determining the Set of Graph Centers 图中心集的确定算法

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925601381

V. V. Vorobiev

引用次数: 0

Online Reinforcement Learning and Sliding Mode Cooperative Control Strategy for Overhead Cranes 桥式起重机在线强化学习与滑模协同控制策略

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600673

Weiqiang Tang, Jiazhen Zhang, Rui Ma

{"title":"Online Reinforcement Learning and Sliding Mode Cooperative Control Strategy for Overhead Cranes","authors":"Weiqiang Tang, Jiazhen Zhang, Rui Ma","doi":"10.1134/S0005117925600673","DOIUrl":"10.1134/S0005117925600673","url":null,"abstract":"This study proposes a control strategy combining online reinforcement learning and sliding mode for trolley positioning and payload swing suppression of overhead cranes. First, an actor–critic reinforcement learning framework is built. And then an adaptive algorithm is designed to address three goals: trolley positioning, swing suppression, and energy consumption reduction. Next, a robust discrete-time sliding mode control law is designed in accordance with reaching law. Finally, the above two parts of control are integrated to form a composite control structure. In addition, the stability of the crane system under the composite control effect is guaranteed. The composite control system fully leverages the advantages of reinforcement learning and sliding mode control, endowing it with strong robustness and adaptability. Extensive validation and comparisons across multiple scenarios demonstrate that the proposed control strategy achieves up to 48% reduction in payload swing compared to sliding mode control. Simultaneously, it enables overshoot-free positioning and exhibits enhanced robustness against online reinforcement learning.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"442 - 456"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized Hybrid Approach for Assessing Pollution Severity in Polymer Insulators Using Random Forest and Fuzzy Logic 基于随机森林和模糊逻辑的聚合物绝缘体污染程度评估优化混合方法

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600971

Kannan Kandavelu, Sivakumar Sivagnanam, Senthilkumar Angappan, Manikumar Thangaraj, Sangeetha Balashanmugam, Meenakshi Sundaram Padamanabhan, Moniya Pathrakalimuthu

{"title":"Optimized Hybrid Approach for Assessing Pollution Severity in Polymer Insulators Using Random Forest and Fuzzy Logic","authors":"Kannan Kandavelu, Sivakumar Sivagnanam, Senthilkumar Angappan, Manikumar Thangaraj, Sangeetha Balashanmugam, Meenakshi Sundaram Padamanabhan, Moniya Pathrakalimuthu","doi":"10.1134/S0005117925600971","DOIUrl":"10.1134/S0005117925600971","url":null,"abstract":"An insulator is one of the crucial parts of the high-voltage overhead cables. Leakage currents may circulate on the insulator’s surface as a result of external factors and contaminants adhered to the surface. Large leakage currents (LC) have the potential to produce flashover, heat losses and surface damage to the insulator. In order to prevent early flashover, this research offers a method that measures the severity of the insulator surface using harmonic measurements of leakage currents. In this work, on 11 kV polymer insulators, the leakage currents were assessed at various equivalent soluble deposit density (ESDD) levels. Discrete Wavelet transform topology is incorporated to extract features from the LC signal. Random forest (RF) with fuzzy inference system (FIS) using a diverse range of LC signals collected over an extended period is incorporated. From results, it is evident that the suggested pollution severity classifier, is highly effective with an accuracy about 95%. Its implementation holds great promise for electrical utilities.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"375 - 387"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Performance DC Motor Speed Control via Fractional Modeling and Algorithmic FOPID Tuning 基于分数建模和FOPID算法整定的高性能直流电机速度控制

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600752

Bilel Kanzari, Adel Taieb, Achraf Jabeur Telmoudi, Abdelkader Chaari

{"title":"High-Performance DC Motor Speed Control via Fractional Modeling and Algorithmic FOPID Tuning","authors":"Bilel Kanzari, Adel Taieb, Achraf Jabeur Telmoudi, Abdelkader Chaari","doi":"10.1134/S0005117925600752","DOIUrl":"10.1134/S0005117925600752","url":null,"abstract":"This study introduces an innovative control strategy for a DC motor speed control system, referred to as a black box approach. The methodology involves system identification through two models: a fractional-order model, increasingly prevalent in identification processes, and a conventional integer-order model. The identification phase employs advanced heuristic optimization techniques, including particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO), and genetic algorithms (GA). The subsequent control phase utilizes a fractional-order proportional-integral-derivative (FOPID) controller, widely recognized as a leading fractional-order control solution. The FOPID controller is characterized by five parameters: proportional gain (({{K}_{p}})), integral gain (({{K}_{I}})), derivative gain (({{K}_{d}})), integral order (({{lambda }})), and derivative order (({{mu }})). These parameters are optimized using the aforementioned algorithms to enhance the performance of the fractional-order model, particularly in terms of energy efficiency during the stabilization phase, compared to the traditional integer-order model. Performance is evaluated using the integral of time and absolute error (ITAE) criterion, alongside standard metrics such as overshoot and settling time. A robustness test, involving parameter variations, is conducted to validate the effectiveness of the proposed approach. Experimental validation is performed using Matlab/Simulink interfaced with an Arduino Uno board to determine the parameters of a real-world DC motor. The results demonstrate that fractional calculus significantly enhances the efficacy of DC motor speed control.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"388 - 398"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attentive Residual Stacked Neural Network with DRHFA for Early Tumor Detection 基于DRHFA的细心残差堆叠神经网络早期肿瘤检测

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600946

Priti V. Kale, Narendra M. Kandoi

{"title":"Attentive Residual Stacked Neural Network with DRHFA for Early Tumor Detection","authors":"Priti V. Kale, Narendra M. Kandoi","doi":"10.1134/S0005117925600946","DOIUrl":"10.1134/S0005117925600946","url":null,"abstract":"Improving patient outcomes requires liver cancer to be detected early, but challenges like data scarcity, imbalance, and model complexity hinder consistent high accuracy. This research proposes a novel data augmentation strategy using generative adversarial networks (GANs) to create synthetic tumor cases and enhance model robustness. The dynamic reduction with hierarchical feature aggregation (DRHFA) method is introduced to overcome high-dimensional data challenges. DRHFA employs adaptive recursive feature elimination (RFE) with harmony search optimization to dynamically reduce dimensions, preserving critical information efficiently. Hierarchical feature aggregation, implemented with attentive residual stacked neural network (AR-SNN), captures complex tumor characteristics at various abstraction levels for efficient feature extraction. AR-SNN integrates attention mechanisms, residual layers, and stacked autoencoders to address early liver tumor prediction challenges. It achieves remarkable accuracy with low complexity, presenting a promising solution for early liver tumor prediction in healthcare and oncology. The result reveals that the analysis carried out using Liver tumor detection dataset, the proposed method attained high accuracy of 0.99, low error as 0.0001, obtained high IoU as 99.8 and Dice score as 99.4 when compared to existing methods.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"362 - 374"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable AI and Evolutionary Green Power Optimization for Secure Signal Processing in SGs with Privacy-Preserving Traffic Signal Control 可解释人工智能和进化绿色功率优化在保隐私交通信号控制下的安全信号处理

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-07 DOI: 10.1134/S0005117925600740

T. Ragunthar, P. Ashok, Weiwei Jiang, A. Srithar, P. Suganthi, G. Manimala

{"title":"Explainable AI and Evolutionary Green Power Optimization for Secure Signal Processing in SGs with Privacy-Preserving Traffic Signal Control","authors":"T. Ragunthar, P. Ashok, Weiwei Jiang, A. Srithar, P. Suganthi, G. Manimala","doi":"10.1134/S0005117925600740","DOIUrl":"10.1134/S0005117925600740","url":null,"abstract":"The enormous amount of information generated by intelligent sensors and the need for rapid decision-making present significant challenges in signal processing, communication security, and system transparency. Existing AI-powered energy management platforms often function as “black boxes,” limiting interpretability and raising concerns related to data confidentiality, trust, and cyber-physical security. To address these issues, this study proposes an explainable artificial intelligence (XAI) and evolutionary green power optimization (EGPO) enabled secure signal processing framework for smart renewable energy grids with a particular emphasis on preserving data privacy in traffic signal regulation. The proposed approach integrates real-time signal analysis for fault detection, power estimation, and traffic-aware energy distribution with interpretable ML algorithms, including SHAP-enhanced deep neural networks and explainable reinforcement learning. Confidentiality is ensured through the use of distributed neural networks and homomorphic encryption (HE) while secure communication protocols and blockchain-based identity management enhance data integrity and security. Evolutionary optimization techniques are employed to improve energy allocation based on traffic conditions and to dynamically balance renewable energy loads. Experimental results demonstrate a significant reduction in communication delays and security threats, a 23% improvement in energy efficiency, and a 96.4% accuracy in anomaly detection, based on benchmark smart grid (SG) datasets and simulated traffic scenarios. This framework offers a highly intelligent, secure, and transparent foundation for next-generation power grids integrated with transportation networks.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 5-8","pages":"226 - 253"},"PeriodicalIF":0.6,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Power Quality Enhancement in Grid Connected Wind-PV Hybrid Systems with UPQC Control Using Whale Optimization Algorithm 应用Whale优化算法提高UPQC控制的并网风-光伏混合系统电能质量

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-07 DOI: 10.1134/S0005117925600818

R. Anand, S. Daison Stallon, D. Saravanakumar, G. Prakash

{"title":"Power Quality Enhancement in Grid Connected Wind-PV Hybrid Systems with UPQC Control Using Whale Optimization Algorithm","authors":"R. Anand, S. Daison Stallon, D. Saravanakumar, G. Prakash","doi":"10.1134/S0005117925600818","DOIUrl":"10.1134/S0005117925600818","url":null,"abstract":"This paper outlines a method for improving power quality in grid-connected wind-photovoltaic hybrid systems that employs a unified power quality conditioner (UPQC) optimized by the whale optimization algorithm (WOA). The system includes a doubly-fed induction generator (DFIG) for conversion of wind energy, which connects the rotor side converter (RSC) and grid side converter (GSC) via a DC-link capacitor to ensure efficient energy transfer and voltage stability. The UPQC is made up of a series active filter to reduce voltage disturbances like sags and swells, and a shunt active filter to deal with the existing concerns like harmonics and imbalance. The suggested WOA-based control dynamically adapts UPQC parameters in real time, making it more adaptable to changing wind and load circumstances. A comparison with existing techniques indicates the superiority of the suggested method, which achieves a total harmonic distortion (THD) of 1%, voltage sag of 0.75%, and voltage swell of 0.66%. These results show much superior performance than traditional approaches like ANFIS-fuzzy logic, PID, and ANN-based controllers. The proposed approach not only provides consistent and clean power injection into the grid, but it also improves system dependability and efficiency.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 5-8","pages":"117 - 132"},"PeriodicalIF":0.6,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Methods for Autonomous Movement of Mobile Robots over Rough Terrain 移动机器人在崎岖地形上的自主运动方法综述

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-07 DOI: 10.1134/S0005117925601113

A. D. Moscowsky, M. A. Rovbo, P. S. Sorokoumov, E. D. Moskovskaya

{"title":"A Review of Methods for Autonomous Movement of Mobile Robots over Rough Terrain","authors":"A. D. Moscowsky, M. A. Rovbo, P. S. Sorokoumov, E. D. Moskovskaya","doi":"10.1134/S0005117925601113","DOIUrl":"10.1134/S0005117925601113","url":null,"abstract":"Robots are now successfully used for a wide range of tasks: conveyor assembly, warehouse logistics, movement along city streets, and indoor servicing. However, problems arise when using them in less structured and less prepared environments. Many of them appear in unmanned ground vehicles operations: during cargo delivery, rescue operations, animal observation, forest monitoring, field cultivation, scientific measurements, and military applications. Reliable and safe movement in open spaces, including rugged terrain, requires specialized approaches that take into account the limitations of onboard sensors, unsuitable ground, unstructured terrain, and the lack of special markings and terrain data, such as 3D or marked maps typical for urban or indoor operations. The purpose of this review was to examine algorithms and methods for localization and navigation of mobile robots used in modern systems for controlling autonomous movement over rough terrain in order to determine the current technical level, relevant scientific and technical problems, and the main areas of research aimed at solving them. This paper presents the results of a systematic review, including the characteristics of the problems under consideration, the sensory systems used in the methods, and the limitations of the methods. The main trends are outlined and a number of problems in the field of development of control systems for off-road robots are highlighted. The results of the review can be used to select a suitable control method for the available robot resources and the specifics of the task, as well as to identify promising research directions in this area.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 5-8","pages":"189 - 214"},"PeriodicalIF":0.6,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of the Accuracy of Converting a Traditional Neural Network into a Pulsed One with Different Methods for Selecting the Neuron Threshold 利用不同的神经元阈值选择方法对传统神经网络转换为脉冲神经网络的准确性进行评价

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-07 DOI: 10.1134/S0005117925601137

R. B. Rybka, A. V. Serenko, A. V. Naumov, A. G. Sboev

引用次数: 0