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Hybrid signaling-droop dc-link voltage control for trains with multiple onboard power sources

IEEE Access 2025年 13卷 75434-75447页

作者： Ahmad, Saad Saeed, Mariam Guerrero, Juan Manuel Muniategui, Iker Nuñez, Guillermo Larrazabal, Igor Briz, Fernando University of Oviedo Department of Electrical Computer & System Engineering Gijón33204 Spain Ingeteam Power Technology S.A. Traction R&D Zamudio48170 Spain

The increasing trend in efficiency and performance requirements of electric trains is leading to the deployment of more onboard energy storage elements. Hydrogen fuel cells along with battery systems are being considered by several train manufacturers. Control of the dc-link connecting all energy sources and loads can be challenging due to the need to guarantee reliable operation in all possible scenarios. This paper presents a dc-link control strategy for trains with multiple energy sources. The proposed method combines the advantages of dc bus signalling and droop methods, and allows smooth enabling and disabling of power sources by voltage command modification without any change in the overall control structure. Further, it is fault tolerant to communication loss between the battery and the central control. The proposed concepts are validated in multiple scenarios by means of simulation, as well as experimentally using a down-scaled test bench. © 2013 IEEE.

关键词： Electric traction

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Examining the Effectiveness of Academic Title Verification: A Case Study 24th

Examining the Effectiveness of Academic Title Verification: ...

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24th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication, RelStat 2024

作者： Panges, Ioannis Liagkou, Vasiliki Stamatiou, Yannis Stylios, Chrysostomos Department of Informatics and Telecommunications of the University of Ioannina Kostakoi Arta Greece Computer Technology Institute and Press "Diophantus" Rio Greece Business Administration Department of the University of Patras Patras Greece ATHENA Research Center Institute of Industrial System Patras Patras Greece

ISBN: (纸本)9783031875311

In the ever-changing realm of identity management, the incorporation of privacy-preserving technologies has become increasingly vital. Despite significant advancements in identity management systems and access control mechanisms designed to protect user data in accordance with recent GDPR guidelines, their implementation faces challenges due to complexity and non-compliance with existing identity management standards. Furthermore, the absence of user-friendly interfaces in these systems hinders widespread adoption, highlighting a critical flaw in their design and usability. In this paper, we present a use-case scenario for postgraduate and doctoral applications that utilize a highly efficient, scalable, and user-friendly identity management system that gives formal security, and privacy guarantees to all parties. The goal is to enhance the user experience and promote the adoption of best practices in the field. This system empowers users by allowing them to control their digital identities through secure, decentralized storage of verifiable credentials on their mobile devices. We hope this work will be a significant step forward in the evolution of identity management, offering a more secure and user-friendly alternative to existing systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Brain Tumour Identification Using MRI Images: Classification and Optimization Based Random-Coupled Neural Network with Cat Hunting Optimization

Brain Tumour Identification Using MRI Images: Classification...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Nandini N P. Vanajakshi Vidyarani H J Vani B A Vinod Kumar K P Shwethasree R Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology Bangalore Karnataka Department of Information Science and Engineering Vivekananda Institute of Technology Bangalore Karnataka Department of Computer Science and Business System Dr. Ambedkar Institute of Technology Bangalore Karnataka Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore Karnataka Department of Computer Science and Engineering Dr Ambedkar Institute of Technology Bangalore Karnataka

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Tumors in the brains may be Benign or malignant but they are growths of cells in the brain. Aches, seizures, vomiting, dementia, and impaired vision are generic symptoms of the ailment. In early diagnosis, detection of the disease and determining the right treatment method for the patients MRI scans are important. There are a number of Deep Learning-based detection techniques available, however their accuracy and error rates may be low. This study uses a Random-Coupled Neural Network with Cat Hunting Optimization (RCNNet-CHOpt) to identify and classify brain tumor in order to get around these issues. Only one kind of BraTS dataset is used by the suggested approach: The filters to enhance image quality and reduce noise, the input images are pre-processed using the Adaptive and Propagated Mesh Filtering (APMF). This is followed by feature extraction and classification using Mellin Transform (M-Trans) with Random-Coupled Neural Network (R-CNN). When combined, these two networks can accurately classify the images and identify important elements. Finally, to increase accuracy, the weight parameter of R-CNN is modified using the Cat Hunting Optimization (CHO) technique. The proposed method has a 99.8% recall and a 99.9% accuracy rate, outperforming existing methods. Among the primary advantages of the RCNNet-CHOpt are decreased false-positive rates and enhanced precision in identifying intricate brain tumor formations. Hence, it is an especially useful tool for clinical use in the identification of brain tumor because most of these applications manage large volumes of medical data.

关键词： Accuracy Magnetic resonance imaging Noise Brain tumors Medical treatment Transforms Real-time systems Optimization Biological neural networks Monitoring

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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IntelliCross: Adaptive Pedestrian Crossing system

IntelliCross: Adaptive Pedestrian Crossing System

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Advanced Research in Computing (ICARC), International Conference on

作者： Udayanga Dissanayake Dinusha Weerasekara Hansika Sumanasekara Dodamgoda Ishara Pipuni Wijesiri Menaka Moonamaldeniya Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computing Sri Lanka Institute of Information Technology Kandy Uni Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Urban traffic management at pedestrian crossings presents considerable issues, such as pedestrian safety, congestion, and effective prioritizing of emergency vehicles. Traditional traffic signal systems are frequently static, unable to respond to real-time changes in pedestrian flow, vehicle density, and environmental variables. To overcome these issues, an IoT-based adaptive pedestrian crossing system, "IntelliCross," is presented. The system detects emergency vehicle sirens using sound sensors and automatically adjusts pedestrian signals to green to prioritize emergency vehicle passage, resulting in faster response times and shorter delays. Furthermore, machine learning algorithms alter signal timings based on real-time pedestrian counts and vehicle density, assuring smooth traffic flow and pedestrian safety. Vulnerable pedestrians, such as the elderly and disabled, are accommodated by dynamically extending green light durations to ensure safe crossing. The technology also includes real-time meteorological data, such as rain, to extend green light durations and improve pedestrian safety. IntelliCross, by combining IoT sensors with machine learning, offers a scalable and cost-effective solution for improving urban traffic management, closing crucial gaps in present systems, and contributing to the development of smart cities. Public surveys demonstrate considerable support for systems that prioritize emergency vehicles while also assuring pedestrian safety, proving the system's ability to revolutionize urban traffic infrastructure.

关键词： Pedestrians Adaptive systems Rain Smart cities Green products Real-time systems Sensor systems Safety Vehicle dynamics Meteorology

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Online Flow Scheduling in Virtualized Time-Sensitive Networks: A Joint Admission Control and VNF Embedding Approach

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IEEE Transactions on Mobile Computing 2025年第06期24卷 4651-4667页

作者： Zhang, Yajing Chen, Cailian Li, Mingyan You, Chaoqun Guan, Xinping Quek, Tony Q. S. Shanghai Satellite Network Research Institute Co. Ltd. Shanghai200120 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Chongqing University College of Computer Science Chongqing400044 China Fudan University School of Computer Science Shanghai200433 China Singapore University of Technology and Design 487372 Singapore

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission Control (SmartAC), to cater to dynamic applications. The first stage, referred to as the static stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as the dynamic stage, fine-tunes VNE solutions obtained from the static stage to adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies. © 2002-2012 IEEE.

关键词： Budget control

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Conversational Voice User Interfaces Supporting Individuals with Down Syndrome: A Literature Review

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ACM Transactions on Accessible Computing 2025年第1期18卷 1-24页

作者： Franceli L. Cibrian Concepción Valdez Lauren Min Vivian Genaro Motti Fowler School of Engineering Chapman University System Orange California USA Department of Computer Science CICESE Ensenada Baja California Mexico Department of Information Sciences and Technology George Mason University Fairfax Virginia USA

Conversational Voice User Interfaces (CVUIs) are widely used in commercial applications such as personal assistants. CVUIs are beneficial for most users as they enable interaction through speech and natural language. However, recent studies indicate that underrepresented user groups, such as individuals with speech impairments and specifically those with Down syndrome, face challenges in using voice commands to control CVUIs. The anatomical and physiological differences affecting the voice, speech, fluency, and prosody of users with Down syndrome hinder their experience with CVUIs. This article presents the results of 43 papers related to the use of voice user interfaces supporting individuals with Down syndrome, showing that (1) the most used form factor for voice assistants are displays with or without speakers; (2) although most of the papers related to speech-to-text technologies are about datasets aimed at supporting speech recognition, most of them are created involving a small sample size; and (3) there is high interest in including individuals with Down syndrome in evaluation phases mainly to address their communication skills, therapy, and general assistance. We discuss the design and evaluation stages, as well as how to make CVUIs more accessible and inclusive for individuals with Down syndrome in particular and neurodiverse users in general.

关键词： Conversational Voice User Interfaces Down syndrome Accessibility Literature review

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Domain Connection based Unsupervised Domain Adaptation for Semantic Segmentation

Domain Connection based Unsupervised Domain Adaptation for S...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chunze Yang Xiaodong Zhang Peiyuan Tang Haoran Yuan Haojie Xin Zijiang James Yang School of Computer Science and Technology Xi ’an Jiaotong University Xi’an China School of Computer Science and Technology Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China Technology R&D Department Synkrotron Inc. Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Collecting and annotating data for semantic segmentation can end up costing a lot of time and energy. Unsupervised Domain Adaptation (UDA) for semantic segmentation allows models trained on certain source domain data (such as the GTA synthetic dataset) to be applied to certain target data (like the Cityscapes real dataset), significantly decreasing the requirement for target-domain manual pixel-level *** this paper, we suggest a cross-domain unified semantic segmentation network training framework, the Attention Distribution Adaptation Network (ADAN). It (1) proposes the Content Consistency Transformation (CCT), which maps source domain data to an intermediate domain that has a data distribution that is comparable to that of the target domain, and (2) introduces the Attention Adaptation Module (AAM), which bolsters the synchronization of attention between the intermediate domain and the target domain. ADAN achieved unprecedented mIoUs of 77.2 and 68.8 on GTA→Cityscapes and Synthia→Cityscapes, respectively, corresponding to improvements of +1.3 and +0.6 over the state of the art.

关键词： Training Adaptation models Semantic segmentation Manuals Signal processing Data models Active appearance model Synchronization Speech processing Synthetic data

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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Destroy and Repair Using Hyper-Graphs for Routing

arXiv

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arXiv 2025年

作者： Li, Ke Liu, Fei Wang, Zhengkun Zhang, Qingfu School of System Design and Intelligent Manufacturing Southern University of Science and Technology China Department of Computer Science City University of Hong Kong Hong Kong

Recent advancements in Neural Combinatorial Optimization (NCO) have shown promise in solving routing problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) without handcrafted designs. Research in this domain has explored two primary categories of methods: iterative and non-iterative. While non-iterative methods struggle to generate near-optimal solutions directly, iterative methods simplify the task by learning local search steps. However, existing iterative methods are often limited by restricted neighborhood searches, leading to suboptimal results. To address this limitation, we propose a novel approach that extends the search to larger neighborhoods by learning a destroy-and-repair strategy. Specifically, we introduce a Destroy-and-Repair framework based on Hyper-Graphs (DRHG). This framework reduces consecutive intact edges to hyper-edges, allowing the model to pay more attention to the destroyed part and decrease the complexity of encoding all nodes. Experiments demonstrate that DRHG achieves state-of-the-art performance on TSP with up to 10,000 nodes and shows strong generalization to real-world TSPLib and CVRPLib problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Combinatorial optimization

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