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检索条件"任意字段=17th International Conference on Distributed Computing and Intelligent Technology"

共 4247 条记录，以下是1-10 订阅

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A distributed Training Framework for Cross-Domain Heterogeneous Clusters 24

A Distributed Training Framework for Cross-Domain Heterogene...

引用

4th international conference on Signal Processing and Communication technology, SPCT 2024

作者： He, Ming Cao, Xiaodong Guo, Xi Cheng, Xinzhou Qin, Shouhao Li, Chenjun Dong, Runsha Xu, Lexi China Unicom Research Institute Beijing China Unicom Broadband Online Co. Ltd. Beijing China GuoChuang Software Co. Ltd. Anhui Hefei China

ISBN: (纸本)9798400710636

through the management and scheduling of cross-domain heterogeneous intelligent computing clusters, the sharing and coordination of computing resources can be achieved. However, it contains computing resources with different technological ecosystems and is difficult to manage, orchestrate and schedule efficiently. this paper focuses on task-level distributed training within cross-domain heterogeneous intelligent computing clusters, proposing a cross-domain heterogeneous distributed training framework that achieves unified resource management across domains and effectively allocates and schedules heterogeneous intelligent computing resources located in different geographic regions. Built upon the Parameter Server(PS) architecture, the framework addresses the ecological discrepancies caused by resource heterogeneity and coordinates heterogeneous intelligent computing hardware to accomplish a single computational task. A multi-layered scheduling architecture is adopted to tackle the issue of geographic isolation, enabling cross-domain distributed computing. Experimental validation demonstrates the effectiveness of the proposed framework, providing a reliable solution for collaborative scheduling in large-scale clusters across different geographical areas. © 2024 Copyright held by the owner/author(s).

关键词： computing power

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Model-Based Deep Learning for distributed Maneuvering Target Tracking 17th

Model-Based Deep Learning for Distributed Maneuvering Target...

引用

17th international conference on intelligent Robotics and Applications

作者： Yang, Feng Gao, Tongyang Zheng, Litao Liao, Pan Northwestern Polytech Univ Sch Automat Xian Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9789819607853;9789819607860

Traditional model-based distributed maneuvering target tracking methods often require prior knowledge of both sensor and target models, which can lead to significant degradation in tracking accuracy when faced with model mismatches. To address this problem, this paper presents a novel model-based deep learning distributed maneuvering target tracking method over a homogeneous sensor network. Specifically, two deep neural networks are designed to obtain noise-eliminated measurements and timely estimate the motion model of the target. Further, hybrid consensus on measurements and consensus on information method is utilized to fuse local filtering results. Finally, the simulation experiment demonstrates the effectiveness of the proposed method.

关键词： distributed Sensor Network Model-Based Deep Learning Maneuvering Target Tracking

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Enabling intelligent Observability Volume Management 17

Enabling Intelligent Observability Volume Management

引用

17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Naik, Priyanka Goel, Seep Raichstein, Eran Meth, Kalman Govindarajan, Kavya Chakraborty, Aishwariya Govindarajan, Chander IBM Research Bangalore India IBM Research Haifa Israel

ISBN: (纸本)9798331531195

In the era of centralized clouds, multi-cloud, and edge computing, adaptable observability is essential to efficiently manage the growing volume of metrics data. Observability Volume Manager (OVM) addresses the complexities of dynamic observability data management by offering tailored processing capabilities that ensures efficient and effective observability across diverse infrastructures. this demo highlights OVM's ability to perform dynamic transformations for fine-grained control over observability data collection, while significantly reducing the volume of data collected, processed, and stored. these optimizations drive substantial cost savings and position OVM as an essential solution for cost-efficient observability. © 2025 IEEE.

关键词： distributed cloud

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18th international conference on distributed computing and intelligent technology, ICDCIT 2022

18th International Conference on Distributed Computing and I...

引用

18th international conference on distributed computing and intelligent technology, ICDCIT 2022

ISBN: (纸本)9783030948757

the proceedings contain 19 papers. the special focus in this conference is on distributed computing and intelligent technology. the topics include: An Introduction to Graph Neural Networks from a distributed computing Perspective;Towards Temporally Uncertain Explainable AI Planning;the Impact of Synchronization in Parallel Stochastic Gradient Descent;a distributed Algorithm for Constructing an Independent Dominating Set;An Approach to Cost Minimization with EC2 Spot Instances Using VM Based Migration Policy;transforming Medical Resource Utilization Process to Verifiable Timed Automata Models in Cyber-Physical Systems;An SDN Implemented Adaptive Load Balancing Scheme for Mobile Networks;rewriting Logic and Petri Nets: A Natural Model for Reconfigurable distributed Systems;preface;Modern AI/ML Methods for Healthcare: Opportunities and Challenges;MCDPS: An Improved Global Scheduling Algorithm for Multiprocessor Mixed-Criticality Systems;replication Based Fault Tolerance Approach for Cloud;A Comparative Study on MFCC and Fundamental Frequency Based Speech Emotion Classification;efficient Traffic Routing in Smart Cities to Minimize Evacuation Time During Disasters;early Detection of Parkinson’s Disease as a Pre-diagnosis Tool Using Various Classification Techniques on Vocal Features;extracting Emotion Quotient of Viral Information Over Twitter;a Novel Modified Harmonic Mean Combined with Cohesion Score for Multi-document Summarization.

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distributed Adaptive Control of Multiple Robots for Cooperative Load Transportation 17th

Distributed Adaptive Control of Multiple Robots for Cooperat...

引用

17th international conference on intelligent Robotics and Applications

作者： Tan, Sicheng Zhang, Bo Xi, Runhao Pan, Jian Xiao, Bowen Shenzhen Univ Coll Mech & Control Engn Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Shenzhen City Joint Lab Autonomous Unmanned Syst Shenzhen 518060 Guangdong Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen 518107 Guangdong Peoples R China

ISBN: (纸本)9789819607730;9789819607747

this paper presents a model-reference-based adaptive control (MRAC) strategy for coordinating the actions of multiple omnidirectional mobile robots to transport a shared payload to a predetermined destination along a defined reference trajectory. A cohesive state-space model for the payload is developed, facilitating the streamlined development of control algorithms tailored for cooperative tasks. the distributed adaptive controller dynamically adjusts its parameters to ensure that the system behavior closely emulates that of an ideal reference model. Furthermore, the development of a Lyapunov function serves as a proof of the asymptotic stability of the closed-loop system. the effectiveness of this control methodology is highlighted by a simulation that depicts a scenario involving four omnidirectional mobile robots working in concert to transport a common payload. the simulation results validate the robustness and adaptability of the proposed MRAC method in a practical cooperative transportation scenario.

关键词： Cooperative transport mission Multi-robot cluster distributed adaptive controller

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Informative Path Planning for Multi-UUV Cooperative Search with distributed Q-Learning 1

引用

17th international conference on intelligent Robotics and Applications

作者： Han, Zhengqing Song, Guanglei Sun, Qi Jiao, Huifeng Wang, Yintao Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China China Ship Sci Res Ctr Wuxi 100076 Jiangsu Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

this paper presents a distributed Q-Learning based approach to search mine-like objects by multiple UUVs. We consider the complex undersea environments with regional variability in sensor characteristics. By applying a receiver operator characteristic curve analysis, the detection features of the sensor are exploited to achieve informative path planning. the study utilises a network aware communications model to construct the swarm formations in the presence of low bandwidth communications. A distributed Q-Learning based planner is employed to find an informative, communication-aware and safe path for each UUV. Simulation results show that simultaneous tasks can be cooperatively performed by a team of vehicles. Compared to the boustrophedon and greedy approaches, the Q-Learning based planner is shown to be more efficient in a time-constrained search mission under a nonhomogeneous environment.

关键词： Multi-UUV Information Gain Cooperative Search Communication Aware Q-Learning

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Federated Learning for Privacy Preserving intelligent Healthcare Application to Breast Cancer Detection 25

Federated Learning for Privacy Preserving Intelligent Health...

引用

26th international conference on distributed computing and Networking, ICDCN 2025

作者： Amritanjali Gupta, Ragav Birla Institute of Technology Mesra Ranchi Ranchi India Sri Mata Vaishno Devi University Jammu Katra India

ISBN: (纸本)9798400710629

Health data used in building intelligent medical care systems contain sensitive personal information of the patients. distributed storage, processing and sharing of data poses threats to the data privacy and security. Federated learning (FL) is an emerging approach that allows multiple entities to collaboratively train a model without sharing their data, thus preserving data privacy. this paper explores the implementation of federated learning techniques for deep learning-based breast cancer detection using the publicly available BRCA dataset. the efficacy of both centralized and federated approaches has been examined through various performance metrics. this work is an effort to reduce the gap between privacy-preserving techniques and practical healthcare applications, offering insights into the trade-offs between data privacy and model performance. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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O-RIDE: Open-RAN Innovation and Deployments Exploration 25

O-RIDE: Open-RAN Innovation and Deployments Exploration

引用

26th international conference on distributed computing and Networking, ICDCN 2025

作者： Kondepu, Koteswararao Tamma, Bheemarjuna Reddy Gudepu, Venkateswarlu Indian Institute of Technology Dharwad Dharwad India Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798400710629

the O-RIDE tutorial aims to provide participants with a comprehensive understanding of building the Open Radio Access Network (Open-RAN) platforms, followed by a demonstration of basic use cases - Key Performance Indicator Monitoring for performance monitoring and Traffic Steering for resource management. Open-RAN disaggregates traditional RAN components, allowing for greater flexibility, vendor neutrality, and innovation in modern telecom networks. the O-RIDE tutorial will showcase the setup and integration of Open-RAN components and their application in real-world scenarios through practical demonstrations, emphasizing hands-on learning and direct application of the technology. Participants will gain an understanding of key concepts in Open-RAN architecture, the role of the RAN intelligent Controller, and practical steps to monitor network performance and dynamically manage traffic. this O-RIDE tutorial suits researchers, students, and professionals interested in next-generation wireless networks. © 2025 Copyright held by the owner/author(s).

关键词： Radio access networks

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20th international conference on distributed computing and intelligent technology, ICDCIT 2024

20th International Conference on Distributed Computing and I...

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20th international conference on distributed computing and intelligent technology, ICDCIT 2024

ISBN: (纸本)9783031505829

the proceedings contain 24 papers. the special focus in this conference is on distributed computing and intelligent technology. the topics include: A Multi-class Classification for Detection of IoT Network Attacks Using Machine Learning Models;optimizing Sentiment Analysis on Twitter: Leveraging Hybrid Deep Learning Models for Enhanced Efficiency;identification of Onset and Progression of Alzheimer’s Disease Using Topological Data Analysis;enhancing Mario Gaming Using Optimized Reinforcement Learning;deep Learning in Industry 4.0: Transforming Manufacturing through Data-Driven Innovation;SASE: Sentiment Analysis with Aspect Specific Evaluation Using Deep Learning with Hybrid Contextual Embedding;a Genetic Algorithm-Based Heuristic for Rumour Minimization in Social Networks;a Machine Learning Driven Approach for Forecasting Parkinson’s Disease Progression Using Temporal Data;prediction of High-Resolution Soil Moisture Using Multi-source Data and Machine Learning;an Improved and Efficient distributed computing Framework with intelligent Task Scheduling;Evaluating Deep CNNs and Vision Transformers for Plant Leaf Disease Classification;intelligent Ensemble-Based Road Crack Detection: A Holistic View;online Payment Fraud Detection for Big Data;bursty Event Detection Model for Twitter;a General Variable Neighborhood Search Approach for the Clustered Traveling Salesman Problem with d-Relaxed Priority Rule;scheduling of Containerized Resources for Microservices in Cloud;ELITE: Energy and Latency-Optimized Task Offloading for DVFS-Enabled Resource-Constrained Devices in MEC;parking Problem by Oblivious Mobile Robots in Infinite Grids;the Computational Landscape of Autonomous Mobile Robots: the Visibility Perspective;coverage Criteria Based Testing of IoT Applications;An Online Algorithm for Cost Minimization of Amazon EC2 Burstable Resources;a Practical and Efficient Key-Aggregate Cryptosystem for Dynamic Access Control in Cloud Storage.

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. this system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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