Automation and Remote Control最新文献

A Semi-Markov Random Walk Process and Its Connection with Fractional Order Differential Equation 半马尔可夫随机游走过程及其与分数阶微分方程的关系

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S000511792560065X

Rovshan A. Bandaliyev, Elshan A. Ibayev, Konul K. Omarova

引用次数: 0

An Image Segmentation Method for Steel Slag Based on an Improved U-Net 基于改进U-Net的钢渣图像分割方法

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S000511792560096X

Hongkui Hu, Biao Chen, Wei Fang

{"title":"An Image Segmentation Method for Steel Slag Based on an Improved U-Net","authors":"Hongkui Hu, Biao Chen, Wei Fang","doi":"10.1134/S000511792560096X","DOIUrl":"10.1134/S000511792560096X","url":null,"abstract":"Micro-CT technology enables the acquisition of three-dimensional images of the internal microstructure of steel slag, providing essential information for the comprehensive utilization of slag tailings. However, the complex issue of multiphase segmentation in micro-CT images significantly hinders the effective advancement of subsequent research. Traditional segmentation methods require manual labor, which is not only time-consuming and labor-intensive but also inherently prone to errors, failing to meet contemporary industrial demands for high precision and efficiency. Therefore, achieving efficient and accurate segmentation of these complex micro-tomography images, particularly multiphase segmentation, is an urgent priority. To rapidly and accurately analyze the microstructure and mineral composition of steel slag, this paper proposes an improved U-Net-based steel slag image segmentation method utilizing deep learning algorithms. Using steel slag micrographs as the dataset, we innovatively employ a VCU-Net model based on high-level semantic feature extraction and a dual attention mechanism for segmentation training. Through rigorous experimental validation and analysis, the improved U-Net model achieves 3.16 and 3.61% improvements in mean intersection over union (MIoU) and mean pixel accuracy (MPA), respectively. Compared to PSPNet, Deeplabv3+, DDR-U-Net, and Swin-U-Net, the enhanced U-Net model achieves finer texture feature capture, demonstrates superior adaptability to complex mineral textures, and enables more precise identification of mineral boundary features, thereby improving the accuracy and efficiency of steel slag mineral recognition.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"349 - 361"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738921","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

The Impact of Obstacles on Data Exchange in a Multiagent Decentralized Robotic System 多智能体分散机器人系统中障碍对数据交换的影响

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S000511792560137X

P. S. Sorokoumov

引用次数: 0

Learning-Augmented IDA–PBC for Underactuated Mechanical Systems with Unmeasured Actuator Dynamics and Unmatched Disturbances 具有未测量驱动器动力学和不匹配扰动的欠驱动机械系统的学习增强IDA-PBC

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600922

Erol Can

{"title":"Learning-Augmented IDA–PBC for Underactuated Mechanical Systems with Unmeasured Actuator Dynamics and Unmatched Disturbances","authors":"Erol Can","doi":"10.1134/S0005117925600922","DOIUrl":"10.1134/S0005117925600922","url":null,"abstract":"Underactuated mechanical systems (UMSs) present significant challenges in control design due to limited actuation, nonlinear coupling, and susceptibility to unmatched and time-varying disturbances. Traditional passivity-based methods often assume full state availability and matched disturbances, limiting their applicability in uncertain, sensor-constrained environments. This study proposes a learning-augmented interconnection and damping assignment passivity-based control (iIDA–PBC) framework that enhances robustness and adaptability for UMSs. The approach integrates real-time disturbance estimation using Gaussian Math. Comput. Appl. (GPR) and Math. Comput. Appl. (LSTM) networks, alongside nonlinear observer designs for reconstructing unmeasured actuator states. The overall control architecture preserves the port-Hamiltonian structure while enabling adaptive compensation for unknown external perturbations. Theoretical analysis ensures Math. Comput. Appl. (ISS) under bounded estimation errors. Simulation results on a benchmark underactuated system demonstrate improved disturbance rejection, tracking accuracy, and robustness compared to conventional adaptive and passivity-based controllers. The proposed method is suitable for complex, partially observable systems such as aerial vehicles, autonomous robots, and marine platforms.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"305 - 321"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738732","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

Fast Tuning of Quadcopter Attitude Control Using a Low-Cost Test Bench 基于低成本试验台的四轴飞行器姿态控制快速调谐

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925601393

Nhu Man Nguyen, Huu Toan Le, Ngoc Diep Nguyen

引用次数: 0

Advanced Modeling and Adaptive Control of a Nonholonomic Differential-Drive Mobile Robot Using Deep Physics-Informed Neural Networks 基于深度物理信息神经网络的非完整微分驱动移动机器人高级建模与自适应控制

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600715

Youssef Haddout, Soufiane Haddout, Mourad El Ouali, Abdelbasset Boukdir

{"title":"Advanced Modeling and Adaptive Control of a Nonholonomic Differential-Drive Mobile Robot Using Deep Physics-Informed Neural Networks","authors":"Youssef Haddout, Soufiane Haddout, Mourad El Ouali, Abdelbasset Boukdir","doi":"10.1134/S0005117925600715","DOIUrl":"10.1134/S0005117925600715","url":null,"abstract":"This research integrates deep physics-informed neural networks (PINNs) with Krupková’s geometrical theory to propose a novel framework for modeling and directing nonholonomic robotic systems. We derive reduced equations of motion for a differential-drive mobile robot under dynamic constraints, such as time-varying payloads and terrain-induced friction, using geometrical mechanics. Deep PINNs are employed to solve these nonlinear equations and develop an adaptive control framework for robust trajectory tracking and motion planning. The originality lies in the seamless fusion of classical geometrical mechanics with modern deep learning, while the novelty is demonstrated through the application of deep PINNs to address nonholonomic constraints and nonconservative effects. Under nominal, high payload, and high friction situations, extensive numerical comparisons with Runge–Kutta (RK4) confirm the method’s higher accuracy (36–60% lower RMSE) and efficiency (25–30% faster computation). This work develops nonholonomic mechanics and robotics, with applications in autonomous navigation and planetary exploration.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"333 - 348"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738753","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

Advancing Traffic Prediction with GGTFN in Intelligent Transportation Systems 基于GGTFN的智能交通系统交通预测

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600958

V. Pallavi, B. Ramesh Naik

{"title":"Advancing Traffic Prediction with GGTFN in Intelligent Transportation Systems","authors":"V. Pallavi, B. Ramesh Naik","doi":"10.1134/S0005117925600958","DOIUrl":"10.1134/S0005117925600958","url":null,"abstract":"Intelligent transportation systems (ITSs) are revolutionizing transportation by integrating advanced technologies to optimize efficiency, safety, and sustainability. Traffic prediction, a core component of ITS is quite important in improving traffic management, enhancing public safety, and promoting sustainable urban mobility. However, deep learning models’ efficacy in traffic prediction is heavily reliant on the quality of input data. Data noise, biases, and missing values can significantly hinder model accuracy by introducing spurious correlations that do not reflect true causal relationships. Additionally, imbalanced training data can skew the model’s learning process, leading to suboptimal performance and reduced generalization. Traditional feature selection methods often find it difficult to depict intricate, nonlinear interactions in high-dimensional datasets, further complicating the task. To overcome these obstacles, this research proposes a novel graph-GRU temporal fusion network (GGTFN) that combines graph neural networks (GNNs) and gated recurrent units (GRUs) to capture both spatial and temporal dependencies in traffic data. The model also incorporates regression-based imputation during preprocessing to handle missing data and uses SMOTE-NC to address class imbalance. Experimental findings indicate that the GGTFN performs better than other previous techniques, achieving the lowest RMSE values for short- to medium-term traffic predictions (15–30 min). Although performance decreases for longer prediction horizons, the proposed model demonstrates robustness and effective generalization across diverse traffic conditions. This research sets the stage for more precise and effective traffic management, helping to the creation of more intelligent and environmentally friendly transportation networks.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"412 - 424"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738756","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

Unmanned Aerial Vehicle Image Intelligent Recognition System Based on Machine Learning Algorithm 基于机器学习算法的无人机图像智能识别系统

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-04-24 DOI: 10.1134/S0005117925600636

Songjian Dan

{"title":"Unmanned Aerial Vehicle Image Intelligent Recognition System Based on Machine Learning Algorithm","authors":"Songjian Dan","doi":"10.1134/S0005117925600636","DOIUrl":"10.1134/S0005117925600636","url":null,"abstract":"The development of science and technology has promoted the unmanned aerial vehicle (UAV) industry. Due to its small size, lightweight, low cost, and other characteristics, UAV can integrate with multiple industries, and promote social development, which broadens the use of UAV itself. UAVs have been widely used in aerial photography, agriculture, and disaster rescue. This paper analyzed the application of UAV in geological disaster rescue. Using UAV remote sensing to photograph the roads to the geological disaster area, the road conditions of different roads could be analyzed, providing the best rescue route in the disaster area. The current point-feature-based methods fail to accurately identify and analyze the target in the UAV image. This paper proposed a convolution neural network (CNN) based model to analyze the UAV image by automatically identifying the image targets. We investigated the accuracy of vehicle recognition using traditional UAV image recognition and our CNN-based model. The experimental results showed that the proposed method improved the average recognition accuracy by 9.35 and 9.08% in the road congestion environment and smooth roads, respectively, demonstrating the effectiveness of our proposed CNN-based method for intelligent recognition of UAV images.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"86 9-12","pages":"322 - 332"},"PeriodicalIF":0.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738375","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

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