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

共 13584 条记录，以下是761-770 订阅

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Reinforcement learning Quantum Local Search 4

Reinforcement Learning Quantum Local Search

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Liu, Chen-Yu Goan, Hsi-Sheng Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan National Taiwan University Department of Physics Taipei Taiwan

ISBN: (纸本)9798350343236

Quantum Local Search (QLS) is a promising approach that employs small-scale quantum computers to tackle large combinatorial optimization problems through local search on quantum hardware. However, the random selection of the sub-problem to solve in QLS may not be efficient. In this study, we propose a reinforcement learning (RL) based approach for training an agent to improve sub-problem selection in QLS beyond random selection. Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems, indicating the potential of combining RL techniques with Noisy Intermediate-scale Quantum (NISQ) algorithms. this research opens a promising direction for integrating RL into quantum computing to enhance the performance of optimization tasks. © 2023 IEEE.

关键词： Reinforcement learning

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Whale optimization Algorithm for Optimizing Extreme learning Machine in IGBT Aging Fault Prediction 4

Whale Optimization Algorithm for Optimizing Extreme Learning...

引用

4th international conference on Digital Data Processing, DDP 2024

作者： An, Xiuxiu Chen, Jinghao Dou, Yantao School of Mechanical Engineering Beijing Institute of Petrochemical Technology Beijing102617 China

ISBN: (纸本)9798331515706

the Insulated Gate Bipolar Transistor (IGBT) aging faults seriously affect the stable operation of power systems;therefore, it is necessary to predict these aging failures to enhance system safety. this paper proposes a prediction model for IGBT aging faults based on a Whale optimization Algorithm (WOA) optimized Extreme learning Machine (ELM). Using the accelerated aging dataset provided by the NASA Prognostics Center, the peak value of the collector-emitter turn-off voltage spike is selected as the failure prediction feature parameter. Exponential smoothing is employed for data preprocessing to reduce data volatility while preserving the original developmental trend, thus improving the network model's learning of the data's intrinsic characteristics. the WOA is utilized to optimize the ELM's hidden layer weights and biases, enhancing its performance. the fault diagnosis performance of the WOA-ELM model is validated against a fault test set and compared with the predictive results of the ELM and Long Short-Term Memory (LSTM) models. the tests show that the suggested framework achieves a high level of fault prediction precision, rendering it appropriate for forecasting aging faults in IGBTs and various electronic parts. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Integrating Cloud-based Data Mining algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

引用

4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

this study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. the analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. the model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

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Minor Surface Cracks Detection using SCD11 Convolutional Neural Network 4

Minor Surface Cracks Detection using SCD11 Convolutional Neu...

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Hussain, Adil Qureshi, Kashif Naseer Zaman, Faizan Aslam, Ayesha Tariq Changan Univ Sch Elect & Control Engn Xian Peoples R China Univ Limerick Dept Elect & Comp Engn Limerick Ireland

ISBN: (纸本)9798350354140;9798350354133

the manual detection of road cracks is a timeconsuming process. On the other hand, solutions that are based on deep learning are both speedy and accurate. Recently, several different Convolutional Neural Networks (CNN) based on deep learning have been proposed. However, the performance of the CNN models has varied. the major challenge is the computational resources required to train a pretrained CNN model;however, a lightweight CNN is more suitable for better training efficiency. In this study work, the SCD11 CNN model is implemented and compared with the pretrained CNN models, including Inception V2, VGG19, and Xception CNN. the models are trained and tested using the public dataset i.e., the Surface Cracks Dataset. the dataset is divided into training, validation and test sets. the SCD11 CNN along with the pre-trained CNN models are trained and validated and then tested using the splitting of the public dataset. Furthermore, the model's performance evaluation is performed by using a private dataset. the results show that the SCD11 CNN performs better than the pre-trained CNN models for both the public and private datasets.

关键词： Road Cracks SCD11 CNN Deep learning Cracks Detection Minor Cracks.

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Reinforcement learning-based path planning for unmanned surface vehicles 24

Reinforcement learning-based path planning for unmanned surf...

引用

4th international conference on Artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： Wang, Yizhou College of Underwater Acoustic Engineering Harbin Engineering University China

ISBN: (纸本)9798400710049

the study applies reinforcement learning methods to solve the unmanned surface vehicle (USV) path planning problem to find the shortest closed path among a limited number of mission nodes. It combines an attention mechanism with a critical evaluation mechanism to effectively solve the path planning problem in a complex marine environment by integrating traditional combinatorial optimization methods and reinforcement learning techniques. the method exploits the adaptive ability of reinforcement learning to gradually optimize and adjust the path-planning strategy through real-time interaction with the environment. Experiments demonstrate that the model can converge quickly and find shorter paths than traditional methods. In this paper, the method is used for the first time in the field of USV path planning and makes progress in path planning. © 2024 Copyright held by the owner/author(s).

关键词： Combinatorial optimization

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Multi-Label learning to Rank through Multi-Objective optimization 23

Multi-Label Learning to Rank through Multi-Objective Optimiz...

引用

29th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD)

作者： Mahapatra, Debabrata Dong, Chaosheng Chen, Yetian Momma, Michinari Natl Univ Singapore Singapore Singapore Amazon Seattle WA 98109 USA

ISBN: (纸本)9798400701030

learning to Rank (LTR) technique is ubiquitous in Information Retrieval systems, especially in search ranking applications. the relevance labels used to train ranking models are often noisy measurements of human behavior, such as product ratings in product searches. this results in non-unique ground truth rankings and ambiguity. To address this, Multi-Label LTR (MLLTR) is used to train models using multiple relevance criteria, capturing conflicting but important goals, such as product quality and purchase likelihood for improved revenue in product searches. this research leverages Multi-Objective optimization (MOO) in MLLTR and employs modern MOO algorithms to solve the problem. A general framework is proposed to combine label information to characterize trade-offs among goals, and allows for the use of gradient-based MOO algorithms. We test the proposed framework on four publicly available LTR datasets and one E-commerce dataset to show its efficacy.

关键词： learning to Rank Multi-Objective optimization

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Deep Reinforcement learning for Video Caching and Updating in Mobile Edge Computing Systems 4

Deep Reinforcement Learning for Video Caching and Updating i...

引用

4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Liang, Minqun Huang, Ying Zhang, Jiahao Ye, Wanque Guangxi Eco-engineering Vocational and Technical College College of Vehicle and Information Engineering Liuzhou China Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798331528973

Using mobile edge computing (MEC) servers to cache and update video resources can not only save network bandwidth and computing resources, but also adjust the bit rate of video playback according to real-time network conditions, providing users with ideal video quality of experience (QoE). In this work, we propose a novel video caching and updating optimization scheme, which enables the MEC server to provide video caching and updating services for users. To achieve this, we design a video caching and update optimization algorithm based on deep reinforcement learning adaptive bitrate selection, which can perceive environmental state information and determine its own caching strategy based on environmental feedback. We perform simulations and show that our algorithm can significantly reduce bandwidth and computing resources in the edge network and better improve video quality of service when compared with several existing algorithm. © 2024 IEEE.

关键词： Mobile edge computing

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4th international conference on Computing and Data Science

4th International Conference on Computing and Data Science

引用

4th international conference on Computing and Data Science, CONF-CDS 2022

the proceedings contain 32 papers. the topics discussed include: intelligent path planning of mobile robot based on genetic algorithm;accuracy improvement based on classic neural network: voting, restarting and quantization;two applications of manifold regularization in deep learning architectures;an automatic wound detection system empowered by deep learning;data generation using simulation technology to improve perception mechanism of autonomous vehicles;detection of ischemic brain stroke using deep learning;self-supervised approach to addressing zero-shot learning problem;fault diagnosis of high-speed railway turnout system based on improved group decision-making;machine learning and deep learning methods on breast cancer metastases detection;turnout failure diagnosis system based on group decision making strategy;and improved causal Bayesian optimization algorithm with counter-noise acquisition function and supervised prior estimation.

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Enhancing load balancing & Fault Tolerance in Real-Time applications Using SD-WAN 4

Enhancing load balancing & Fault Tolerance in Real-Time Appl...

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Makarem, Mohammed A. Algaolahi, Akram Q. Alhajri, Abdullrhman A. Razaz, Muneef A. King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran Saudi Arabia Taiz Univ Dept Comp Networks & Distributed Syst Taizi Yemen King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran Saudi Arabia

ISBN: (纸本)9798350354140;9798350354133

Wide Area Networks (WANs) are critical components of modern network architecture, connecting multiple locations within an organization, enabling Internet access, and supporting real-time data sharing and high availability for applications and enterprises. Traditional WAN architectures often struggle with traffic management, fault tolerance, and real-time application delivery, leading to inefficiencies. To tackle these challenges, Software-Defined Wide Area Network (SD-WAN) technologies have emerged, offering greater agility, scalability, and network performance. this study introduces a methodological approach that utilizes Fortinet SD-WAN solution to evaluate the effectiveness of SDWAN in enhancing load balancing and fault tolerance for realtime applications in small-scale organizations. the simulation results demonstrate that SD-WAN significantly improves network reliability and efficiency through dynamic traffic management and automatic failover mechanisms. this study underscores the potential of SD-WAN to provide superior performance compared to conventional networking solutions, making it a promising option for modern real-time applications.

关键词： SD-WAN (Software-Defined Wide Area Network) Load Balancing Fault Tolerance Real-time applications Network Performance optimization Traffic Engineering Traffic Management Latency Reduction.

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An Optimal Scheduling Algorithm for Intelligent Embedded Heterogeneous Multicore System 4th

An Optimal Scheduling Algorithm for Intelligent Embedded Het...

引用

4th international conference on Artificial Intelligence Logic and applications

作者： Chen, Xinyu Huang, Guopeng Chen, Yixiang East China Normal Univ Natl Engn Res Ctr Trustworthy Embedded Software Shanghai 200062 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

the intelligent embedded system is a computing system combining artificial intelligence science and embedded technology, the system combines general-purpose processor and FPGA to achieve stronger processing capability, but it also brings system hardware and software design challenges. this paper proposes an intelligent embedded system task scheduling algorithm based on heterogeneous multi-core, which reasonably allocates hardware resources on the FPGA to optimize energy consumption under the premise of meeting system reliability requirements. the algorithm adopts a critical path-based energy consumption pre-allocation strategy, and the task scheduling makes the system schedule length shortest based on system reliability. Experiments show that the method of this paper outperforms other algorithms by an average of 5.34% in terms of energy consumption, and by an average of 6.83% in terms of scheduling length, which reflects the reasonableness and advancement of the method of this paper.

关键词： Heterogeneous Multicore System Task Scheduling FPGA Energy optimization

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