Automation and Remote Control最新文献_第3页

Artificial Intelligence Driven Solar Tracking System for Enhanced Energy Efficiency and Environmental Sustainability 提高能源效率和环境可持续性的人工智能驱动太阳能跟踪系统

IF 0.5 4区计算机科学

Automation and Remote Control Pub Date : 2026-07-27 DOI: 10.1134/S000511792560106X

Divya Prabha Varadharajan, Sathiyasekar Kumarasamy

引用次数: 0

Parameterized Polynomial Approach for Joint Space Trajectory Optimization of Redundant Space Robots Using Modified PSO 基于改进粒子群算法的冗余空间机器人关节空间轨迹优化参数化多项式方法

IF 0.5 4区计算机科学

Automation and Remote Control Pub Date : 2026-07-27 DOI: 10.1134/S0005117925601216

Kumar Abhishek, Vijay Kumar Dalla

{"title":"Parameterized Polynomial Approach for Joint Space Trajectory Optimization of Redundant Space Robots Using Modified PSO","authors":"Kumar Abhishek, Vijay Kumar Dalla","doi":"10.1134/S0005117925601216","DOIUrl":"10.1134/S0005117925601216","url":null,"abstract":"The joint trajectory planning of a space robot in the Joint space is critical for accomplishing space missions. Furthermore, the presence of dynamic singularities restricts the reachable workspace of redundant space robots and results in excessively high joint velocities whenever such conditions are encountered. Therefore, this work presents an approach for trajectory planning and optimization of redundant space robots parameterized with joint trajectories to overcome these drawbacks. First, the kinematic formulation is developed using the Euler angles representation for optimal trajectory tracking of the space manipulator to eliminate dynamic singularities. Thereafter, a Bézier curve is employed to describe the joint trajectory using a fifth-order polynomial function. Next, a modified particle swarm optimization (MPSO) algorithm with a chaotic global-local best inertia weight is applied to obtain the optimal joint trajectories. Finally, various parameters—such as joint angles, joint angular rates, joint accelerations, end-effector (EE) attitude quaternion, EE position, and base attitude—are optimized to achieve the desired EE pose in minimal time. The simulation results demonstrate the efficacy of the proposed approach, confirming that the EE moves smoothly and achieves the desired pose with minimal deviation from the reference trajectory.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"87 2","pages":"111 - 136"},"PeriodicalIF":0.5,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614602","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

Energy Management in DC Micro Grid through Renewable Source Integration and Electric Vehicle Utilization Using a Hybrid Approach 基于混合方法的可再生能源集成和电动汽车利用的直流微电网能量管理

IF 0.5 4区计算机科学

Automation and Remote Control Pub Date : 2026-07-27 DOI: 10.1134/S000511792560123X

Dipti N. Kashyap, Sachin S. Pund, Sri Lakshmi, D. Ganeshkumar

引用次数: 0

Enhancing Security in Industrial Automation Using Zero Trust Architecture and Streaming Data Analytics 使用零信任架构和流数据分析增强工业自动化的安全性

IF 0.5 4区计算机科学

Automation and Remote Control Pub Date : 2026-07-27 DOI: 10.1134/S0005117925601071

Sri Ramya Siraparapu, M. Sucharitha, S. M. A. K. Azad

{"title":"Enhancing Security in Industrial Automation Using Zero Trust Architecture and Streaming Data Analytics","authors":"Sri Ramya Siraparapu, M. Sucharitha, S. M. A. K. Azad","doi":"10.1134/S0005117925601071","DOIUrl":"10.1134/S0005117925601071","url":null,"abstract":"In modern manufacturing systems industrial automation plays a pivotal role in enhancing efficiency, productivity, and overall system performance by increasing throughput and minimizing downtime. However, implementing real-time continuous data streaming and integrating diverse data types present significant challenges in event tracking. This paper explores the role of streaming frameworks such as Apache Kafka and Apache Flink in enabling seamless data transmission. Furthermore various machine learning (ML) methodologies are applied to optimize streaming data analytics by integrating diverse data types in industrial domains such automotive manufacturing, refineries, textiles, and environmental. The security of streaming data while maintaining efficiency in process control system (PCS) applications still remains a critical concern. This paper utilises zero trust architecture (ZTA) to enhance data security in industrial applications with principles continuous verification and least privilege access, the study safeguards real-time data streams against unauthorized access. Implementing ZTA enhances system resilience while maintaining efficiency in industrial automation environments. The proposed approach strengthens security and enables intelligent decision-making for process optimization. These findings confirm that ZTA enhances security in streaming-based automation systems. This research demonstrates the effectiveness of real-time data processing, ML-driven analytics, and security measures in optimizing industrial automation workflows. Addressing both efficiency and security concerns, the proposed framework establishes a strong foundation for advancing smart manufacturing and sustainable industrial operations.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"87 2","pages":"216 - 234"},"PeriodicalIF":0.5,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614600","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

MR-ProGradeNet: A Multiresolution Deep Learning Framework for Automated Prostate Cancer Grading from MRI MRI - progradenet：一个用于前列腺癌自动分级的多分辨率深度学习框架

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-06-02 DOI: 10.1134/S0005117925600983

Kranti Vithal Ghag, Jayashree Jagdale, Madhura Vyawahare

{"title":"MR-ProGradeNet: A Multiresolution Deep Learning Framework for Automated Prostate Cancer Grading from MRI","authors":"Kranti Vithal Ghag, Jayashree Jagdale, Madhura Vyawahare","doi":"10.1134/S0005117925600983","DOIUrl":"10.1134/S0005117925600983","url":null,"abstract":"Prostate cancer remains a leading cause of cancer-related morbidity in men, and its accurate aggressiveness grading is critical for optimizing treatment strategies. While multiparametric MRI (mpMRI) has revolutionized prostate cancer diagnostics through noninvasive visualization of anatomical and functional tissue contrasts, current automated grading frameworks face significant limitations due to interscanner heterogeneity, inadequate modeling of tumor microarchitecture, and insufficient clinical reliability. This study introduces MR-ProGradeNet (Multiresolution Prostate Grading Network), a novel deep learning architecture that systematically addresses these challenges via three synergistic modules. First, the A-HASH preprocessing module unifies modality-specific intensity profiles and spatial representations using learned histogram normalization and prostate-specialized UNet segmentation, ensuring scanner-invariant input harmonization. Second, the FRDF (fractal radiomic deep fusion) module captures fine-grained angiogenic textures and high-level semantic abstractions through dual-path processing of fractal dimension-enhanced DCE MRI and EfficientNet-encoded T2/ADC modalities, fused via gated attention. Finally, the ProMMGrader classification module incorporates label distribution learning and confidence-weighted ensembling across modalities, reflecting Gleason-grade uncertainty and reducing misclassification risk. A comprehensive evaluation on the public PROSTATEx dataset demonstrates that MR-ProGradeNet achieves 97.5% accuracy, a 0.974 F1-score, and an AUC of 0.999, substantially outperforming traditional CNNs and state-of-the-art prostate grading methods. This work represents a clinically aligned and interpretable advancement in AI-based mpMRI analysis, capable of enhancing diagnostic confidence and reproducibility in real-world multicenter settings.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"87 1","pages":"41 - 61"},"PeriodicalIF":0.6,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141683","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 Cyberbullying Text and Image Mining Using Improved Techniques of Artificial Intelligence and Deep Learning 基于人工智能和深度学习改进技术的网络欺凌文本和图像挖掘

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-06-02 DOI: 10.1134/S0005117925600995

R. Sangeethapriya, J. Akilandeswari

{"title":"A Cyberbullying Text and Image Mining Using Improved Techniques of Artificial Intelligence and Deep Learning","authors":"R. Sangeethapriya, J. Akilandeswari","doi":"10.1134/S0005117925600995","DOIUrl":"10.1134/S0005117925600995","url":null,"abstract":"Social media, in the era of digital information, is a useful tool for communication. Text categorization, image mining, and evaluation of sentiment have accumulated a lot of attention, partially because of the amount of unstructured data on social networking. Many of these standards are derived from online sharing networks, which provide a vibrant social environment and gather images with cyberbullying posts. These clusters provide rich data that is logically helpful for text retrieval and image classification. It is shown that two important deep learning (DL) and artificial intelligence (AI) methodologies are beneficial for evaluating toxicity from social media interactions (SMI). This paper describes the many procedures involved in toxic word analysis, such as preprocessing, evaluating, and displaying social media content. Using DL approaches, such as bidirectional encoder representations from transformers (BERT) to assess toxic terms in social interactions, the text is first categorized. A BERT-based legislative text classification technique is suggested to identify the policy field defined in the text more precisely. The approach first vectorizes the sentence-level attributes of the legislative text using the BERT-trained language model. The resulting feature vector is then fed into the classifier to do the classification. BERT categorizes text into predetermined categories by first extracting characteristics from the text using filters. The second approach properly identifies photos from social networks using AI techniques. A weighted stack on the social network image association network was created using a neural network. The harmful photographs were then filtered using AI by combining three distinct types of social network photos: RGB (red, green, and blue), grayscale, and depth. Three-dimensional conventional neural networks (3D CNNs) were built to gather and filter social media online bullying posts in real-time during a crisis event to categorize cyberbullying images.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"87 1","pages":"74 - 86"},"PeriodicalIF":0.6,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141684","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

Liquid Level Self-Tuning Control of Natural Gas Cyclone Gas-Liquid Separator Based on ANFTSMC Algorithm 基于ANFTSMC算法的天然气旋风气液分离器液位自整定控制

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-06-02 DOI: 10.1134/S0005117925601046

Rubin Chen, Baoping Hui, Ning Wang, Zhichao Li

引用次数: 0

Research on the Application of CNN in Autonomous Driving Behavior Prediction CNN在自动驾驶行为预测中的应用研究

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-06-02 DOI: 10.1134/S0005117925600612

Bingjie Zhao, Changjie Wu, Liang Zhao, Maoxuan Peng

{"title":"Research on the Application of CNN in Autonomous Driving Behavior Prediction","authors":"Bingjie Zhao, Changjie Wu, Liang Zhao, Maoxuan Peng","doi":"10.1134/S0005117925600612","DOIUrl":"10.1134/S0005117925600612","url":null,"abstract":"As autonomous driving advances towards real-world implementation, accurately predicting the behavior of traffic participants remains a critical challenge for ensuring both safety and efficiency in driving. This study emphasizes the use of convolutional neural networks (CNN), employing long short-term memory (LSTM) and multilayer perceptron (MLP) as comparative models to investigate deep learning applications in behavior prediction. Utilizing Kaggle’s “prediction_for_validation_data.csv” dataset, the research develops a predictive system following data cleaning, feature extraction, and oversampling procedures. Experimental results indicate that CNN effectively extracts visual features through its convolution-pooling architecture, achieving an accuracy of 94.12% along with a perfect ROC-AUC score. In low-dimensional scenarios, MLP matches CNN’s accuracy by leveraging multilayer nonlinear transformations; however, it lacks depth in feature representation. Conversely, LSTM demonstrates limited performance due to its reliance on minimal temporal features, attaining only 52.94% accuracy—underscoring its dependence on rich temporal data. This study underscores the suitability of CNN for spatial contexts, highlights MLP’s efficiency in low-dimensional settings, and points out LSTM’s requirement for datasets abundant in temporal information. Future research may focus on optimizing CNN architectures, exploring cross-model fusion techniques, and developing multisource datasets to enhance predictive capabilities.","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":"87 1","pages":"87 - 96"},"PeriodicalIF":0.6,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141722","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

Cluster Load Control of Photovoltaic Direct-Drive Inverter Air-Conditioners for Near-Zero Energy Buildings Based on Improved DEPSO Algorithm 基于改进DEPSO算法的近零能耗建筑光伏直驱逆变空调集群负荷控制

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-06-02 DOI: 10.1134/S0005117925601009

Ying Chen, Haofei Pei

引用次数: 0

A Method of Precise Positioning for Multiaxis Manipulator Motion Track Based on PLC Technology 基于PLC技术的多轴机械手运动轨迹精确定位方法

IF 0.6 4区计算机科学

Automation and Remote Control Pub Date : 2026-06-02 DOI: 10.1134/S0005117925601034

Ziyun Li

引用次数: 0