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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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Vision-assisted applications based on deep learning 4

Vision-assisted applications based on deep learning

引用

4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Wang, Xingzheng Xi’an University of Electronic Science and Technology Xi’an710126 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

With the rapid development of technology and the widespread application of deep learning, computer vision technology has become an important tool to help visually impaired people. Traditional visual assistance systems are often limited by recognition accuracy and real-time performance, while deep learning technology provides new ideas for the development of visual assistance applications with its powerful feature extraction and learning capabilities. this application is based on existing deep learning models and deploys object detection algorithms to mobile devices for user convenience by testing the system in actual environments. the intelligent voice interaction function provided by this application enhances the user experience and interaction sense. the application features are relatively complete, not only allowing users to select images from the album but also providing camera photography as input, as well as real-time content detection and output. At the same time, the application provides voice input and output, greatly simplifying the difficulty of user operation, and also facilitating users to obtain output and make corresponding judgments faster. this application provides a new visual aid tool for visually impaired individuals, making their lives more convenient. © 2025 SPIE.

关键词： Vision aids

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Capacity optimization of WPTP hybrid system in desert areas 4

Capacity optimization of WPTP hybrid system in desert areas

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Ma, Shiyi Li, Outing Wang, Mengjun Li, Yang Wu, Feng Jiao, Ye New Energy Research Institute China Renewable Energy Engineering Institute Beijing China Shanghai Energy Interconnection Research Institute China Electric Power Research Institute Shanghai China College of Energy and Electrical Engineering Hohai University Nanjing China

ISBN: (纸本)9798350359589

A reasonable system capacity allocation scheme is basis for the utilization of renewable energy. Firstly, based on environment of desert areas, a capacity optimization model of WPTP hybrid system is established. then, to study the substitution of pumped storage units to certain thermal power units in the system, a comparative example of WPT power generation hybrid system is set. Finally, the capacity configuration results and indexes are analyzed to verify the advantages of pumped storage as an alternative to thermal power. © 2024 IEEE.

关键词： capacity optimization carbon emission reduction desert areas mixed-integer linear programming pumped storage

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An Innovative Q-learning and ACO Approaches for the Traveling Salesman Problem 7th

An Innovative Q-Learning and ACO Approaches for the Travelin...

引用

7th international conference on Logistics Operations Management (GOL)

作者： El Jaghaoui, Sanaa Benaini, Abdelhamid Elmiad, Aissa Kerkour Mohammed First Univ FSO LARI Oujda Morocco Normandie Univ Le Havre Fac Sci & Tech Le Havre France

ISBN: (纸本)9783031686337;9783031686344

In this study, we introduce an innovative Q-learning and Ant Colony optimization algorithms for solving the Traveling Salesman Problem (TSP). Given that an optimal tour for the TSP does not contain cross-edges, our approaches focus on minimizing the number of cross-edges in the tour. Experiments on TSPLIB instances, of sizes between 51 and 159, show the effectiveness of these approaches and highlight the rapid convergence to better solutions compared to classical methods based direct minimization the tour length.

关键词： Traveling Salesman Problem Intersection Minimization Q-learning Ant Colony optimization

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An Intelligent Collaborative Decision-Making Method for Multi-Target Tracking in Heterogeneous Unmanned Clusters 4

An Intelligent Collaborative Decision-Making Method for Mult...

引用

4th international conference on Artificial Intelligence, Virtual Reality and Visualization, AIVRV 2024

作者： Zhou, Wen-Hong Wang, Long Zhang, Qing-Jie Xu, Liang Aviation University of Air Force Jilin Changchun China

ISBN: (纸本)9798331528744

With the rapid development of artificial intelligence and robotics, unmanned aerial vehicle (UAV) swarm systems have received widespread attention from domestic and foreign experts, scholars, and research institutions for their applications in military and civilian fields. this research primarily focuses on intelligent collaborative decision-making methods for heterogeneous unmanned swarms in multi-target tracking tasks. In response to the challenges associated with the difficulty of obtaining global states and the complexity of collaborative decision-making in existing methods, a distributed collaborative decision-making strategy based on Multi-Agent Deep Reinforcement learning (MADRL) has been proposed. this strategy employs a data-driven approach that eliminates the need for precise modeling, utilizing a hierarchical attention mechanism to process heterogeneous information. Additionally, we have designed an experience-sharing training mechanism for heterogeneous swarms to enhance their intelligent collaborative decision-making capabilities and multi-target tracking performance. By establishing a simulation environment, various MADRL algorithms were implemented and experimentally validated. the experimental results show that the proposed method can effectively improve the collaborative tracking performance of UAV swarms. Future work will focus on hyperparameter tuning of algorithms, testing across different swarm scales, and analyzing algorithm scalability in diverse environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Empowering Autonomous Decision-Making through Quantum Reinforcement learning and Cognitive Neuromorphic Frameworks 4

Empowering Autonomous Decision-Making Through Quantum Reinfo...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balasubramani, R. Biradar, Vidyadevi G. Nmam Institute of Technology Department of Information Science and Engineering Udupi India

ISBN: (纸本)9798350352931

the concept of Agentic AI, which focuses on empowering autonomous systems with human-like decision-making capabilities, has gained substantial attention in recent years. By leveraging advanced reinforcement learning (RL) techniques and cognitive frameworks, Agentic AI aims to enable machines to make complex decisions in dynamic environments with minimal human intervention. this paper introduces Agentic AI, a novel approach that combines Quantum Reinforcement learning (QRL) and Cognitive Neuromorphic Frameworks to empower autonomous decision-making in complex environments. the proposed model leverages quantum computing principles to accelerate learning in reinforcement-based systems, while cognitive neuromorphic frameworks enhance adaptability and real-time decision-making by mimicking human brain functionality. the integration of QRL allows for faster convergence in learning algorithms, improving decision accuracy by 25% and reducing training time by 40%. Cognitive neuromorphic architectures offer enhanced scalability and resilience in dynamic environments, resulting in a 35% improvement in system robustness during testing. Simulations in autonomous navigation and robotic control show that Agentic AI achieves 20% more efficient task execution compared to traditional AI models. these values underscore the potential of the proposed system to revolutionize autonomous systems, providing a robust foundation for decision-making across a range of applications. © 2024 IEEE.

关键词： Quantum computers

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Data-Driven Battery Health optimization in Electric Vehicles: A Machine learning Approach 4

Data-Driven Battery Health Optimization in Electric Vehicles...

引用

4th IEEE international conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Pujari, Pradyumna Gnanasambanthan, Lakshya Rubi Sharma, Prabhav Sriramalakshmi, P. School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350383997

the burgeoning electric vehicle (EV) market hinges on maximizing battery health and lifespan. this research investigates the efficiency of machine learning (ML) in optimizing battery health through a data-driven approach. this article deals with a novel framework that harnesses battery and heating data in real driving cycles gleaned from the battery management system (BMS) and other vehicle components. this rich data undergoes rigorous processing and analysis via advanced ML algorithms such as Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and Support Vector Regression (SVR), aiming to achieve precise prediction of battery degradation, unveiling key health determinants and personalized battery management. Implementing this data-driven paradigm aims to yield substantial advancements in EV battery health, resulting in extended range and lifespan, enhanced safety, reliability and optimized charging strategies. this research stands as a significant contribution to the evolution of intelligent BMS systems, paving way for smarter, more efficient and sustainable EV battery management system. © 2024 IEEE.

关键词： Support vector regression

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Performance Evaluation of Coverage Prediction in 4G Networks Using Machine learning Classification Algorithm 10

Performance Evaluation of Coverage Prediction in 4G Networks...

引用

10th international conference on Wireless and Telematics, ICWT 2024

作者： Yuliana, Hajiar Iskandar Hendrawan Ridwan, Azwar Mudzakkir Charisma, Atik Somantri, Nivika Tiffany Universitas Jenderal Achmad Yani Faculty of Engineering Department of Electrical Engineering Indonesia Institut Teknologi Bandung Doctoral Program of School of Elecrtical Engineering and Informatics Indonesia Bandung Indonesia UIN Sunan Gunung Djati Bandung Department of Electrical Engineering Indonesia

ISBN: (纸本)9798350377057

Network coverage prediction is an important aspect of 4G network planning and optimization. In this study, we conducted a comprehensive analysis of the performance of various machine learning algorithms in predicting 4G network coverage. the data collected includes information on location, signal intensity, topography, and other important variables. Data pre-processing is performed to clean, normalize, and select relevant features. Next, we compared the performance of several machine learning algorithms, including Logistic Regression, KNN, Naïve Bayes, Decision Tree, SVM, XGBoost, AdaBoost, and Bayesian Network Classifier. the model training process is performed using subsets of training, validation, and testing data, with evaluation performed using various evaluation metrics such as accuracy, precision, recall, F1-score, RMSE, MAE, and R-squared. the experimental results show that Random Forest stands out with excellent performance, while Naïve Bayes tends to produce lower performance. the conclusion of this study provides valuable insights into the potential applications of machine learning algorithms in predicting 4G network coverage with accuracy and reliability. © 2024 IEEE.

关键词： Adversarial machine learning

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Distributionally Robust Beamforming for Multicell ISAC: A Chance Constrained optimization Approach 4

Distributionally Robust Beamforming for Multicell ISAC: A Ch...

引用

4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Xiong, Yu Yang, Kai Tongji University Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798331531225

Chance-constrained optimization provides a powerful framework for decision-making under uncertainty and finds wide applications in areas. Conventional chance-constrained optimization assumes the transmission of the entire dataset to a single server, which may result in privacy breaches. In this paper, we propose a federated chance-constrained optimization approach for robust beamforming for multi-cell integrated sensing and communication systems. the proposed robust beamforming technique aims to minimize the worst-case base station transmission power while satisfying the chance constraint of communication rate with channel state information uncertainty as well as beampattern gain constraint of sensing target. Comprehensive simulation studies have been conducted to evaluate the efficacy of the proposed method and to corroborate the theoretical claims. © 2024 IEEE.

关键词： Constrained optimization

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Enhancing UAV Network Efficiency through 6G⁺ Enabled Federated learning algorithms and Energy optimization Techniques 20

Enhancing UAV Network Efficiency through 6G<SUP>+</SUP> Enab...

引用

20th IEEE international Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Batalla, Jordi Mongay Markakis, Evangelos Bourdena, Athina Mastorakis, George Song, Houbing Univ Nicosia Dept Comp Sci Nicosia Cyprus Warsaw Univ Technol Warsaw Poland Hellen Mediterranean Univ Dept Elect & Comp Engn Iraklion Crete Greece Hellen Mediterranean Univ Dept Management Sci & Technol Agios Nikolaos Crete Greece Univ Maryland Dept Informat Syst Baltimore MD 21250 USA

ISBN: (纸本)9798350361261;9798350361278

this study presents an innovative approach to enhancing the efficiency of Unmanned Aerial Vehicles (UAV) in IoT networks. Employing UAVs as flying relays focuses on their role in data collection and support for terrestrial cellular networks. the central innovation lies in the application of Federated learning (FL), which processes data while ensuring user privacy and reducing communication overhead. Addressing the challenge of UAVs' limited battery life, which restricts sustained FL operations, we introduce the Enhanced UAV Network optimization Algorithm with Adaptive Spatial Play (ENUO-ASP). ENUO-ASP incorporates a modified Particle Swarm optimization (PSO) technique to determine optimal UAV placements, enhancing data collection by focusing on the Signal-to-Interference Ratio (SINR). Additionally, the paper utilizes the Deep Deterministic Policy Gradient (DDPG) method for dynamic resource allocation, optimizing energy consumption and reducing link latency between the UAV network and users. the findings indicate that the ENUO algorithm outperforms existing methods by achieving higher data rates and balanced SINR. Furthermore, the ASP resource allocation strategy improves FL execution, significantly lowering latency and energy use. this research contributes to the UAV-enabled communication field, offering a more efficient and performance-driven solution for advanced IoT applications.

关键词： UAV deep RL Resource Allocation IoT Network Coverage optimization Federated learning

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An Adaptive Blockchain Framework for Comprehensive Attack Protection for Securing the Internet of Medical things 4

An Adaptive Blockchain Framework for Comprehensive Attack Pr...

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4th international conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Goel, Punit Bhimanapati, Viharika Musunuri, Aravind Sundeep Avancha, Srikanthudu Shekhar, Sumit Alzubaidi, Laith H. Department of Management India Garhwal University Maharaja Agrasen Himalayan Uttarakhand India Southern University and A&m College Department of Computer Science LA United States Manipal University Department of E.C.E. India Bharathidasan University Department of Cse Tamilnadu Tiruchirappalli India Columbia University Department of Cse New York United States The Islamic University Najaf Iraq

ISBN: (纸本)9798331528324

IoMT (Internet of Medical things) encompasses smart medical devices, apps, and health care services. Internet-connected medical devices and applications provide healthcare systems with information. there are many complex challenges associated with IoT, especially in IoMT. Privacy, security, scalability, and data accessibility are the most important to consider. through the integration of Federated learning and Blockchain technology, data privacy, security, and efficiency are significantly enhanced in the IoMT framework. During model training, federated learning ensures that data remains on local devices to lower privacy risks and to improve collaboration between devices in the Internet of things. With this decentralized approach, localized model updates are aggregated and initialized by a central server, maintaining a consistent global model. Also, various performance parameters are compared with other existing algorithms. © 2024 IEEE.

关键词： Federated learning

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