IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-05-30 DOI: 10.1016/j.conengprac.2025.106392

Qiang Liu , Kexuan Xu , Yating Fu , Jiang Liu , Ling Liu

{"title":"Time-delay assisted mechanism and adaptive LSTM hybrid train braking model of heavy haul trains","authors":"Qiang Liu , Kexuan Xu , Yating Fu , Jiang Liu , Ling Liu","doi":"10.1016/j.conengprac.2025.106392","DOIUrl":"10.1016/j.conengprac.2025.106392","url":null,"abstract":"<div><div>The train braking model (TBM) that describes the dynamic relations of operation speed, mileage, and control force is essential for achieving stable operation and precise stopping of heavy haul trains (HHTs). However, it is difficult to establish the TBM of HHTs due to complex characteristics: (i) the long body and air braking process of the HHTs may lead to unexpected time-delays of control force; and (ii) there are significant unmodeled dynamics caused by rough tracks and external poor environment. Traditional TBM does not take into account the unmodeled dynamics and time-delays caused by air transmission during braking. To address these issues, this study proposes a data mechanism hybrid modeling strategy, which incorporates a braking time-delay assisted mechanism model and an adaptive long and short-term memory (LSTM) model. A new Bayesian optimization based time-delay estimation method is first proposed to determine unknown time-delays of each carriage and the estimated time-delays are incorporated to generate the multi-point-mass kinetic mechanism model. Moreover, the error of the mechanism-driven model is adaptively compensated by a sliding window LSTM model to conduct the unmodeled dynamics. The effectiveness of the proposed method is demonstrated using the field data.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"163 ","pages":"Article 106392"},"PeriodicalIF":5.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Connecting the Unconnectable Through Feedback 通过反馈连接不可连接的部分

IF 6.3 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2025-05-30 DOI: 10.1109/lwc.2025.3575296

Yimeng Li, Yulin Shao

引用次数: 0

Spatial Mode Multiplexing for Fiber-Coupled IM/DD Optical Wireless Links with Misalignment 不对准光纤耦合IM/DD光无线链路的空间模复用

IF 8.3 2区计算机科学

IEEE Transactions on Communications Pub Date : 2025-05-30 DOI: 10.1109/tcomm.2025.3571947

Jinzhe Che, Shenjie Huang Majid Safari, Majid Safari

引用次数: 0

Multidimensional chaotic signals generation using deep learning and its application in image encryption 基于深度学习的多维混沌信号生成及其在图像加密中的应用

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-30 DOI: 10.1016/j.engappai.2025.111017

Shuang Zhou , Zhiji Tao , Uğur Erkan , Abdurrahim Toktas , Herbert Ho-Ching Iu , Yingqian Zhang , Hao Zhang

{"title":"Multidimensional chaotic signals generation using deep learning and its application in image encryption","authors":"Shuang Zhou , Zhiji Tao , Uğur Erkan , Abdurrahim Toktas , Herbert Ho-Ching Iu , Yingqian Zhang , Hao Zhang","doi":"10.1016/j.engappai.2025.111017","DOIUrl":"10.1016/j.engappai.2025.111017","url":null,"abstract":"<div><div>In this paper, we propose a novel artificial intelligence implemented approach to generate multi-dimensional chaotic signals using the Long- and Short-Term Time-Series Network (LSTNet) for a newly contrived Two-Stage pixel/bit level Scrambling and Dynamic Diffusion (TSSDD) color image encryption. Initially, we employ the hyperchaotic Lorenz and Chen chaotic systems to produce chaotic signals. Subsequently, the LSTNet model is trained to predict these produced multi-dimensional chaotic sequences and then it generates new multi-dimensional chaotic signals. Through analysis involving phase diagrams, largest Lyapunov exponent (LE), 0–1 test, Permutation Entropy (PE), Sample Entropy (SE), Correlation Dimension (CD) and National Institute of Standards and Technology (NIST), we observe that these applied artificial intelligence signals exhibit high chaotic states and randomness. Finally, we apply these signals to demonstrate the proposed TSSDD color image encryption wherein simulation experiments indicate competitive performance against common attacks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111017"},"PeriodicalIF":7.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Point-line feature-based vSLAM systems: A survey 基于点线特征的vSLAM系统：综述

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-05-30 DOI: 10.1016/j.eswa.2025.127574

Hangzhou Qu , Zhuhua Hu , Yaochi Zhao , Junlin Lu , Kunkun Ding , Guangfeng Liu , Yongqing Chen , Chunyan Shao

{"title":"Point-line feature-based vSLAM systems: A survey","authors":"Hangzhou Qu , Zhuhua Hu , Yaochi Zhao , Junlin Lu , Kunkun Ding , Guangfeng Liu , Yongqing Chen , Chunyan Shao","doi":"10.1016/j.eswa.2025.127574","DOIUrl":"10.1016/j.eswa.2025.127574","url":null,"abstract":"<div><div>The point-line feature-based vSLAM technology significantly enhances the accuracy and robustness of localization and mapping in complex environments by comprehensively utilizing both point and line geometric information. This paper provides a comprehensive survey of methods and applications for point-line feature-based Simultaneous Localization and Mapping (SLAM) systems. Firstly, it focuses on the core components of the visual frontend in SLAM systems, with a detailed analysis of line feature detection methods and their descriptors, covering both traditional algorithms and learning-based approaches, as well as further improvements to these methods. The paper also discusses several common line feature parameterization methods and different line feature matching strategies. In addition, the paper delves into the backend optimization and loop closure detection mechanisms of SLAM systems, which are critical factors in enhancing the system’s accuracy and robustness. By reviewing these methods and applications, this paper aims to provide a comprehensive understanding of integrated point-line SLAM systems, analyzing the strengths and weaknesses of different technologies, and exploring potential directions for future research. This work offers theoretical foundations and practical guidance from a global perspective for the subsequent design and optimization of SLAM systems.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"289 ","pages":"Article 127574"},"PeriodicalIF":7.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Joint Optimization of STARS-Assisted Air-Ground ISAC System Using Deep Reinforcement Learning 基于深度强化学习的stars辅助地空ISAC系统联合优化

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2025-05-30 DOI: 10.1109/tvt.2025.3575290

Liejing Qing, Wei Xiang, Xiang Ling, Weiyang Xu, Xinyang Li, Jin Liu

引用次数: 0

Multilabel Transfer Learning Method With Dynamic Multimetric for Coupling Fault Diagnosis 基于动态多度量的多标签迁移学习耦合故障诊断方法

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-30 DOI: 10.1109/tnnls.2025.3573090

Yaqi Xiao, Haiyin Zhou, Xuanying Zhou, Jiongqi Wang

引用次数: 0

DebriSense: THz-based Integrated Sensing and Communications (ISAC) for Debris Detection and Classification in the Internet of Space (IoS) 基于太赫兹的空间互联网碎片检测与分类综合传感与通信（ISAC）

IF 10.4 1区计算机科学

IEEE Transactions on Wireless Communications Pub Date : 2025-05-30 DOI: 10.1109/twc.2025.3572276

Haofan Dong, Ozgur B. Akan

引用次数: 0

HARBOR: Harnessing Bandwidth, Computation, and Batch for Fair QoE Having Collaborative Edge-AI Services in Industrial CPS HARBOR：利用带宽、计算和批处理实现公平的QoE，在工业CPS中提供协作边缘人工智能服务

IF 16.4 1区计算机科学

IEEE Journal on Selected Areas in Communications Pub Date : 2025-05-30 DOI: 10.1109/jsac.2025.3574583

Long Chen, Shaojie Zheng, Jigang Wu, Hong-Ning Dai, Dusit Niyato, Jiafu Wan, Jiale Huang

引用次数: 0

Small-Signal Modeling and Decoupling Control of a Doubly-Fed Linear Motor for Maglev Application 磁悬浮双馈直线电机的小信号建模与解耦控制

IF 7.7 1区工程技术

IEEE Transactions on Industrial Electronics Pub Date : 2025-05-30 DOI: 10.1109/tie.2025.3566762

Zhongshu Shao, Yeqin Wang, Zaimin Zhong, Xiusen Wang, Zhixun Ma

引用次数: 0

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