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共 34963 条记录，以下是141-150 订阅

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Neural Network-Driven Base Station Selection in Next-Generation Cellular Networks 17

Neural Network-Driven Base Station Selection in Next-Generat...

引用

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

作者： Harrithha, B. Kotagi, Vijeth J. Thakur, Rahul Indian Institute of Technology Dharwad Department of Computer Science and Engineering Karnataka Dharwad India Indian Institute of Technology Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798331531195

With the exponential increase in data consumption from activities like streaming, gaming, and IoT applications, existing network infrastructure is under significant strain. To address the growing demand for higher data rates and better coverage, increasing base station density and exploring data offloading in unlicensed spectrum have become essential. While higher base station density reduces the distance between devices and network nodes, enhancing signal strength and data throughput, it also complicates the process of base station selection for users, particularly for those at the cell edge. Thus, ever-increasing demand for high-speed, low-latency communication services in 5G and 5G advanced New Radio (NR) unlicensed (NR-U) networks necessitates intelligent and dynamic approaches for user admission control and resource *** paper presents a novel solution employing neural network technology to address these critical challenges. We first propose an efficient algorithm for admission control and resource allocation. Upon generation of training data using the above algorithm, we propose a neural network-based framework for cellular networks in unlicensed spectrum such as LTE/LTE-U and 5G/NR-U networks. © 2025 ieee.

关键词： Base stations

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Based on Bidirectional Adaptive Event-Triggered Load Frequency control of Interconnected Power systems Under DoS Attacks 8th

Based on Bidirectional Adaptive Event-Triggered Load Frequen...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Jiafang Peng, Chen Zhang, Yajian Chen, Ci School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9789819602247

As the power system gradually transform into a highly integrated cyber-physical system, The limited communication resources and DoS attacks in the network layer have challenged the stable frequency operation of power system. Therefore, this paper proposes a bidirectional adaptive event-triggered load frequency control (LFC) strategy combined with average dwell time technique. Firstly, the interconnected power system LFC system is modeled as a 0–1 switched model depending on DoS attacks. Secondly, a bidirectional adaptive event-triggered threshold update mechanism is proposed to increase the utilization of communication resources. Next, based on the average dwell time technology and Lyapunov function construction, the event-triggered parameter design and stabilization criteria are strictly derived. Finally, the proposed method are verified through the simulation of a two-area power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric power system interconnection

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial automation (ROBOTHIA), ieee international conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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Revolutionizing Global Logistics: Integrating Advanced Supply Chain Management Techniques in the Digital Era

Revolutionizing Global Logistics: Integrating Advanced Suppl...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramkumar, M. Siva Arun, M. Gopan, Gokul Giri, Jayant Al-Qawasmi, Khaled Sns College of Technology Dept of Ece India Saveetha University Department of Mechanical Engineering Chennai602105 India YeshwantraoChavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Saveetha University Department of Vlsi Microelectronics TN Chennai602105 India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509828

In the digital era, global logistics and supply chain management are undergoing a revolutionary transformation, driven by advancements in technology and interconnected systems. This evolution aims to enhance operational efficiency, reduce costs, and increase overall supply chain visibility. However, existing supply chain systems often suffer from challenges such as lack of real-time data, limited visibility, inefficiencies, and difficulties in tracking inventory across complex networks. These issues lead to delays, increased operational costs, and reduced customer satisfaction. The proposed framework, Revolutionizing Supply Chains using IoT (RSC-IoT), integrates cutting-edge Internet of Things (IoT) technologies to address these challenges. By embedding sensors and connected devices throughout the supply chain, RSC-IoT facilitates real-time data collection, enabling smarter decision-making, accurate demand forecasting, and enhanced inventory management. This system also improves visibility, helping stakeholders track goods, anticipate disruptions, and optimize resource allocation. The use of RSC-IoT will lead to improved supply chain resilience, faster response times, and increased operational efficiency. The system's ability to monitor and control key metrics, such as transportation times, inventory levels, and production schedules, offers unprecedented control and insight. The findings from the implementation of RSC-IoT reveal significant improvements in logistics performance. Enhanced real-time tracking, reduced lead times, and optimized resource utilization contribute to a more agile and cost-efficient global supply chain, demonstrating the transformative potential of IoT in logistics management. © 2025 ieee.

关键词： Customer satisfaction

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IoT-Based Wireless Electric Vehicle Charging Station 6th

IoT-Based Wireless Electric Vehicle Charging Station

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6th international conference on intelligent Communication, control and Devices, ICICCD 2024

作者： Mandapati, Honnesh Shaganti, Shashmith Balaji Bura, Nagasri Sudhakar Babu, Thanikanti Murali Krishna, T. Nwulu, Nnamdi Chaithanya Bharathi Institute of Technology Hyderabad India Centre for Cyber Physical Food Energy and Water Systems University of Johannesburg Johannesburg2006 South Africa

ISBN: (纸本)9789819783281

This article describes the development and implementation of a wireless charging station integrated with IoT technologies for electric vehicles (EVs). The proposed charging station features an automated charging mode activated by an IR sensor and a mobile app for monitoring and control. The system is intended to develop the EV charging infrastructure by giving real-time data on voltage percentage and locating nearby available charging stations based on the EV battery status. The prototype of the proposed system has been developed and integrated the system via IoT with mobile application. Further, the proposed prototype has been tested under various conditions. The obtained results confirm the effectiveness of the developed system, and further, it can be extended for high-rated system as well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Charging stations

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Simulation and Performance Analysis of Passive Cell Balancing Technique for Lithium batteries using Modified Generic model 3

Simulation and Performance Analysis of Passive Cell Balancin...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vasudha, Karanam Shubha Rao, K. Bnm Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798331515911

Modern society is increasingly adopting Electric Vehicles (EVs) due to fuel scarcity and the need to control pollution. Batteries are the most suitable energy storage devices for EVs;however, they have certain limitations, such as maintaining a safe operating temperature, risks of overcharging and deep discharging, and cell imbalance. Protecting batteries is crucial, and to ensure their safety, Battery Management systems (BMS) are employed. BMS performs tasks such as parameter estimation and cell balancing based on the battery model. In this paper, a Modified Generic Battery Model is developed to provide a more accurate representation of battery characteristics. Passive cell balancing is implemented for both the MATLAB inbuilt battery model and the developed battery model. The variation of the State of Charge (SoC) is analyzed during charging and discharging, with and without cell balancing, in both cases. It is observed that the balancing time is approximately 110 seconds in both cases. However, minor variations in the SoC are noted in the developed generic model, which could be eliminated by improving the control algorithm or using active cell balancing. This validates the accuracy of the developed generic battery model. Additionally, an analysis is performed during simulation to identify the optimum balancing resistor, which is found to range between 0.6-0.65 Ω. © 2025 ieee.

关键词： MATLAB

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Design of University Innovation and Entrepreneurship System Based on Cloud Model Data Mining Algorithm

Design of University Innovation and Entrepreneurship System ...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Ma, Yingying Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

This article delves into the design and implementation of innovation and entrepreneurship systems based on cloud model data mining algorithms in university environments. Firstly, through in-depth communication with stakeholders such as university management, teachers, and students, a comprehensive analysis and planning of the system's requirements were conducted. Next, the article provides a detailed description of the system architecture design, including key components such as data collection and management, data preprocessing, cloud model data mining, user interface, reporting and visualization, security and privacy protection, as well as user feedback and support. During the system development process, emphasis was placed on the design principles and construction of cloud model data mining algorithms, as well as the optimization measures taken to improve system efficiency and accuracy. The system implementation process follows a series of strategies, including preliminary planning, system development, data preparation and migration, system integration, user training, as well as system deployment and continuous maintenance. Each stage is carefully designed and executed to ensure the effectiveness and reliability of the system. After implementation, a comprehensive performance evaluation of the system was conducted, including accuracy, efficiency, and user satisfaction. The results showed that the system performed well in multiple aspects, but also pointed out some areas that needed improvement. Finally, the article summarizes the main advantages and limitations of the system, and puts forward suggestions for future work directions, including further optimizing user experience, algorithm performance, and expanding application scope. This study provides an efficient and reliable data support and decision-making assistance tool for innovation and entrepreneurship activities in universities, which is of great significance for promoting the development of the innovati

关键词： Decision making

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IoT in Smart Homes: Enhancing Lifestyle Through intelligent automation and control

IoT in Smart Homes: Enhancing Lifestyle Through Intelligent ...

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3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Sarada, W. Prashanth, K.V. Mary, A.Beneta Premkumar, K. Vijayan, Vinodh P. Radha, G. Rbvrr Women's College Department of Computer Science Narayanaguda Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Electronics & Communication Engineering Telangana Hyderabad India St.Joseph College of Engineering Department of Computer Science and Engineering Kancheepuram India St. Joseph College of Engineering Department of Cse Chennai India Mangalam College of Engineering Kerala Kottayam India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331542375

Internet of Things (IoT) functionality integrated into smart homes has transformed residents' ways of engaging with homes, from comfort, security, and energy saving aspects. This study aims at understanding the role played by IoT based smart automation and control in enhancing people's living standards within homes. Smart homes are designed utilizing applications that centralize the control of other smart devices like smart manage thermostat, smart lighting, home security cameras and smart voice assistant. These systems allow people to automate jobs, conserve and manage energy, and design environments to people's specific needs. Moreover, the study investigates threat mechanisms of IoT in smart home with respect to data privacy issues as well as the weaknesses of devices and the possibility of cyber threats. One of the major considerations of this work is focused on ways through which IoT systems can be designed to learn about users and their requirements as well as make decisions on their own using machine learning techniques. Thus, the study shows that through IoT technology not only by providing convenience and energy-saving opportunities, but also giving homeowners control to create and establish safer and more comfortable home environment. In this research, recommendations are made for the need to bring improvement on the reliability, security, and accessibility of IoT in home automation market it has made. © 2025 ieee.

关键词： Energy efficiency

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Research on Link Selection and Allocation for IoT Localization systems Based on an Improved Ant Colony Algorithm

Research on Link Selection and Allocation for IoT Localizati...

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6th international conference on cyber Security Intelligence and Analytics, CSIA 2024

作者： Zhang, Jiong Xu, Meng Wang, Liying Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (纸本)9783031882869

The swift evolution of Internet of Things (IoT) technology has promoted the innovation in many fields such as smart city, smart transportation, etc. Accurate positioning technology is one of the key links to achieve the IoT intelligent services, in the actual deployment, due to the complexity and diversity of the environment, the selection of communication links and data allocation between IoT devices face many challenges, to enhance the accuracy of IoT localization systems, this study introduces an optimized ant colony optimization approach for optimising link selection and resource allocation in IoT, this improves the traditional ant colony algorithm’s overall search capacity by introducing new heuristic pheromone updating rules and dynamic adjustment strategies, thus improving the positioning accuracy and reducing the network latency, and further reduces the energy consumption among nodes by optimising the algorithm for energy management. The experimental data indicates that the new algorithm surpasses the traditional one across various scenarios, especially in the large-scale IoT environment, its advantages in positioning accuracy, communication efficiency and energy consumption control are more significant, and this research result provides new ideas and technical support for the design of future IoT positioning systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ant colony optimization

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Achieving Balanced Allocation in Human Resources through LSTM Framework Method 1

Achieving Balanced Allocation in Human Resources through LST...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Giriprakash, A. Arunadevi, V. Rajalakshmi, K. Khan, Shamim Ahmad Jeyalakshmi, R. Ponni Valavan, M. Sakthi Institute of Information and Management Studies Department of Mba Pollachi India Dr. Mahalingam College of Engineering and Technology Department of Science and Humanities Pollachi India Glocal University Glocal School of Science & Technology Department of Ece Saharanpur India Affiliated to JNTU-K Mylavaram India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798331529246

This paper proposes a novel human resource management system leveraging Long Short-Term Memory (LSTM) networks for optimal HR allocation. The process begins with data acquisition, including performance metrics, attendance records, and employee details, followed by Min-Max normalization for uniform formatting. Principal Component Analysis (PCA) is employed for feature selection, reducing variables while retaining critical information. LSTM networks, with their ability to analyze sequential data and capture temporal dependencies, enable accurate predictions of workforce demands and optimal resource distribution. Experimental results demonstrate the model's superiority over conventional methods, achieving balanced HR allocation, improved workforce control, enhanced employee satisfaction, and higher organizational performance. This scalable approach aligns staffing with organizational objectives, preventing overstaffing or understaffing. By integrating advanced machine learning into HR management, the proposed system offers a significant step toward smarter workforce planning and improved HR practices, paving the way for efficient and adaptable organizational management. © 2025 ieee.

关键词： Personnel selection

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