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检索条件"任意字段=5th International Conference on Real-Time Computing Systems and Applications"

共 14949 条记录，以下是51-60 订阅

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5th international conference on computing and Network Communications, CoCoNet 2023

5th International Conference on Computing and Network Commun...

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5th international conference on computing and Network Communications, CoCoNet 2023

ISBN: (纸本)9789819745395

the proceedings contain 44 papers. the special focus in this conference is on computing and Network Communications. the topics include: Maximizing Efficiency: Unveiling the Potential of Kubernetes Metrics;resource-Aware Learning Automata for Low-Latency Task Scheduling in Fog computing;Design of High Gain Wideband Petal-Shaped Magneto-Electric Dipole Metal Antenna for 5G Sub-6 GHz and Wi-Fi applications;A Novel Billhook-Shaped 2-Element MIMO Array for Sub-6 GHz Millimeter Wave Application;energy-Efficient Transmission in Wireless Sensor Network Using Compressive Sensing;smart City Solutions for Traffic Management;an IoT-Assisted real-time Urban Air Pollution Data Collection System;real-time Stock Price Prediction Using Apache Kafka;RSS-Based Localization Using GRU and BiGRU Deep Learning Models in LoRaWAN-IoT Networks;locating and Routing for IoT-Based Fire Evacuation System;an Energy-Efficient Data Management Approach Using Sparse Compression in Future-Ready IoT Networks;a Framework for Developing IoT and Edge computing-Based Smart Transportation applications;a Mathematical Model for Data Traffic Management in Next-Generation IoT Networks;crowdsourced Mobile applications for Disaster Relief: A Case Study of Landslide Tracker and Amrita Kripa App Deployments in Sikkim Schools;design and Modeling of Modular Wheelchair for therapeutic Exercises;analyzing Data Traffic in IoT Networks: A Novel Graph-Based Architecture;A Lightweight Privacy-Preserving and Authentication Model (LiPAM-FSG) for Fog-Based Smart Grids;time Series Analysis and Rule Mining for Detecting Industrial Control System Data Injection Attacks;Radius-Based Authentication: UFrames’ Novel Approach to Prevent Account Sharing;deep Neural Network-Based Secure Energy-Efficient Power Allocation in an Interference Network;An Adaptive time Quantum-Based Efficient Round-Robin Algorithm for CPU Scheduling;low-Cost Advanced Undetectable Hardware-Based Malicious Keyboard;constructing a Blind Digital Signature Scheme

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Dynamic real-time Talent Team Discovery Based on Complex Network Analysis 24

Dynamic Real-Time Talent Team Discovery Based on Complex Net...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Pan, Yu Yao, Feng Li, Wei Wang, Pei School of Systems Engineering National University of Defense Technology Hunan Changsha China Department of Information Communication Army Academy of Armored Forces Beijing China

ISBN: (纸本)9798400709999

the real-time and precise identification of talent teams can offer essential support for talent development and augmentation. It plays a key role in efficiently and precisely assembling professional talent and constitutes a significant research focus within human resources. this paper utilizes complex network concepts and artificial intelligence methods to analyze dynamically and mine talent teams in real-time within evolving environments. It proposes a method of dynamic real-time talent team discovery that considers the academic relationships and research direction information of talent simultaneously. Firstly, a unified graph modeling method is proposed to fuse academic relationships and research direction information. then, we apply non-negative matrix factorization to obtain talent team partition at each time snapshot. Additionally, we employ the evolutionary clustering framework to smooth talent evolution, leveraging historical evolution information to achieve accurate talent team discovery at a continuous time. Extensive experiments demonstrate that the proposed method achieves excellent results on real datasets. the method has proven effective in practical applications, enabling accurate and timely talent team discovery. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Non-negative matrix factorization

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2025 IEEE 5th international conference on Power, Electronics and Computer applications, ICPECA 2025

2025 IEEE 5th International Conference on Power, Electronics...

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5th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2025

ISBN: (纸本)9798331533694

the proceedings contain 154 papers. the topics discussed include: transient stability analysis of power system considering multiple inverter aggregation model;development of the safeguard companion for secondary safety measures of relay protection;an open interconnection system for computing power based on service mesh;securing distributed power dispatching systems: a framework for cybersecurity and 5G integration;utilizing deep belief networks for power system state estimation and anomaly detection;fire detection approach for 10kV distribution room based on spatial perception and hybrid attention mechanism;research on the application of information sharing technology in distributed photovoltaic aggregation optimization control strategy;and construction and optimization of key quality problem graph of power equipment based on association rule mining.

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Research on Optimization Algorithm of Embedded AI real-time Deep Learning Model in IoT Devices 5

Research on Optimization Algorithm of Embedded AI Real-Time ...

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5th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Zheng, Yifan School of Big Data Fuzhou University of International Studies and Trade Fuzhou Fujian China

ISBN: (纸本)9798331533694

this paper focuses on the optimization algorithm of embedded AI real-time deep learning models in IoT devices, aiming to solve the problem of efficient and low-latency inference in resource-constrained environments. In order to solve the problems of high computational complexity and large memory occupation of existing models when running on embedded devices, this paper proposes a multi-stage optimization framework, which combines model compression, quantization and acceleration techniques to significantly improve the real-time performance of the model. We have introduced a structured pruning approach that reduces the amount of model parameters and computational overhead by removing redundant neurons and channels. Secondly, converting the floating-point number into a low-bit integer representation, which further reduces the memory occupation and computational complexity. In addition, an adaptive inference scheduling algorithm was designed to dynamically adjust the inference path according to the characteristics of the input data, which improved the response speed and energy efficiency ratio of the model. the optimized model in this paper achieves more than 50% improvement in inference speed in multiple IoT application scenarios, while maintaining a high accuracy rate. © 2025 IEEE.

关键词： adaptive inference scheduling embedded AI Internet of things model compression real-time optimization

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

5th International Conference on Computing, Communications, a...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

ISBN: (纸本)9789819773701

the proceedings contain 68 papers. the special focus in this conference is on computing, Communications, and Cyber Security. the topics include: Compact and High-Gain RFID Reader Antennas for Future Internet of things applications;Enabling Modern IoT Solutions with RFID Devices via an Integrated Feeding Network-Fed Ultra-Wide HPBW Patch Antenna Array;A Compact and Extremely Stiffness CPW-Fed Metal-Only Patch Antenna Array for Unlimited Lifetime 2U CubeSat Mission Capabilities;Jeans-Based UWB UHF Textile Patch Antenna for IoT applications;Design and Fabrication of PCB-Based Proximity Sensors for Application in Automatic Railway Crossing System;feaTrim-ViT: Vision Transformer Trimming with One Shot Neural Architecture Search in Continuous Optimisation Space and Efficient Feature Selection;detecting Depressive Symptoms on Social Media: A Comprehensive Review of Methodologies and Strategies for Suicide Prevention;enhancing Video Content Accessibility through YouTube Transcript Summarization;Enhanced Rear-End Collision Detection and Localization Scheme Using LSTM, DRNN, and DNN in Fog-Based Internet of Vehicles;security threats and Data Protection Methods Used in Cloud computing: A Review;load Balancing Algorithms for Cloud computing Performance: A Review;seamless Mobility: Innovating the Digital Service Landscape in Telecom Industry;Pandemic Pulse: Unveiling Insights with the Global Health Tracker through AI and ML;CNN-Based Model for real-time Identification of Face Masks;a Systematic Review of Deepfake Detection Using Learning Techniques and Vision Transformer;probabilistic Forecasting Prediction Model for Anticipating Days with Elevated Air Pollution;optimizing Graph Colorings: Unleashing the Power of Heuristic Algorithms for Enhanced Problem-Solving;a Load Balancing Approach Using Binary Search;enhancing Software Reliability through Hybrid Metaheuristic Optimization.

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Research on Optimization Algorithm and Simulation Model of On-Board Battery Energy Management 5

Research on Optimization Algorithm and Simulation Model of O...

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5th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Hu, Yinquan Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9798331533694

this paper proposes an on-board battery energy management optimization algorithm based on deep reinforcement learning (DRL). By adjusting the battery charging and discharging strategy in real time, it optimizes energy usage and prolongs battery life. the algorithm models the battery charging and discharging process as a Markov decision process (MDP) and designs an adaptive reward function, which can dynamically optimize battery performance under different driving environments. Experimental simulation results show that the system using this optimization algorithm has significant advantages in energy efficiency, charging cycle and battery life compared with the traditional PID control algorithm. In various driving scenarios such as simulated urban roads and highways, the optimization algorithm improves energy efficiency by 12.6%, reduces charging cycles by 15%, and prolongs battery life by 18%. these experimental results verify the application potential of deep reinforcement learning in on-board battery energy management and demonstrate its advantages and feasibility in practical applications. © 2025 IEEE.

关键词： Battery management systems

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Ground Plane Synchronization in VR applications Using Indoor Robots for Enhancing Immersion 5th

Ground Plane Synchronization in VR Applications Using Indoor...

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5th international conference on Data Science, Machine Learning and applications

作者： Divya, Udayan J. Hrishikesh, P. Sylesh, Nithin Menath, Madhav M. Yadukrishnan Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amritapuri India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Many techniques have been developed to improve locomotion while using VR applications. Although VR platforms or large open spaces allow natural locomotion, they are not feasible for all users. As a result, many users are confined to small workspaces which ensure their safety but severely affect immersion. In most cases, the VR application need not be aware of the spatial layout of the user's environment. this results in a workspace which does not take full advantage of the user's environment where, every time the user steps out of bounds in either the real world or the virtual world, their immersion with the virtual world detaches. Our work explores the possibility of synchronizing the ground plane of the virtual world to the ground plane of the real world using 2D mapping in an effort to create custom workspaces which ultimately enhances immersion by providing the user with freedom of natural movement without compromising their safety. We propose a pipeline that involves the use of indoor robots which allows VR developers to customize their applications according to the end user's environment thereby solving the restricted workspace problem.

关键词： Ground plane synchronization Telepresence robots VR SLAM Sub-optimal workspace

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

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5th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of things)-based system for early detection and management of forest fires. the system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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Mobile applications and Cloud computing in Healthcare 16

Mobile Applications and Cloud Computing in Healthcare

引用

16th international conference on ENTERprise Information systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Dan, Mihnea-Vlad Pop, Madalin-Dorin Politehnica University of Timioara Piaa Victoriei 2 Timioara300006 Romania

With the new advancements in cloud computing and internet of things (IoT), mobile applications are transforming the healthcare sector offering solutions for both users and researchers. the massive amounts of data generated by these kinds of applications can be used by researchers to reduce medical errors, for disease prevention, real-time alerts and patient monitoring. the aim of this paper is to present a solution for a mobile application for fitness and dieting, which can help users achieve a healthier lifestyle, through an interactive approach. the technologies used to achieve the goal are Flutter SDK for the frontend, allowing for an interactive, cross-platform application and Firebase for the backend and database services, a cloud solution allowing for data gathering that could be used in analysis and diagnosis. the article's scientific contribution includes not only helping in the process of gathering pertinent data for healthcare analysis through the mobile applications, but also reviewing previous research done in the field. © 2025 the Author(s).

关键词： Electronic health record

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Research on Sensor Data Spatiotemporal Synchronization Technology for Onboard Visual Navigation systems 5

Research on Sensor Data Spatiotemporal Synchronization Techn...

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5th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Yuan, Yanna Liu, Huimin Zhao, Chenqiang Aviation Industry Computing Technology Research Institute Xi'an China

ISBN: (纸本)9798331533694

Against the backdrop of the rapid development of unmanned aerial vehicle (UAV) technology, onboard visual navigation systems have become the core support technology for achieving precise positioning and efficient map construction of UAVs. this paper deeply explores the key technologies of sensor data spatiotemporal synchronization in onboard visual navigation systems, aiming to solve the synchronization issues in multi-sensor data fusion in variable environments, and enhance the navigation accuracy and reliability of UAVs. the research covers both spatial and temporal synchronization dimensions, with a focus on the technical implementation of Visual-Inertial Odometry (VIO). For spatial synchronization, a target-based method is proposed to construct an error equation for solving the extrinsic parameters between the camera and the Inertial Measurement Unit (IMU), achieving precise alignment of sensor coordinate systems through the identification and matching of visual target feature points. In terms of temporal synchronization, considering the data frequency differences between the camera and IMU, this paper designs a time alignment strategy that realizes efficient temporal alignment of data through data buffering and timestamp matching. Additionally, the initialization and local sliding window optimization process of the Visual-Inertial Odometry are detailed, achieving high-precision fusion of visual and inertial data through feature extraction, optical flow tracking, and motion structure recovery. Experimental results show that the methods proposed in this paper can technically provide strong support for the autonomous flight of UAVs, effectively improving the navigation performance of UAVs in complex environments, and enhancing the robustness of the system. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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