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检索条件"任意字段=3rd International Conference on High Performance Computing in Science and Engineering, HPCSE 2017"

共 1581 条记录，以下是81-90 订阅

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Advanced Machine Learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

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Weighted Median Autoregressive Graph Filters 3

Weighted Median Autoregressive Graph Filters

引用

3rd international conference on high performance computing and Communication engineering, HPCCE 2023

作者： Liu, Shaodian Yan, Wenxu Shuai, Wei Wang, Wenyuan School of Internet of Things Engineering Jiangnan University Wuxi214000 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The graph filter can extract the desired features from the graph signal and filter out the noise signal. Most of the graph filters proposed in the literature are linear. Autoregressive moving average (ARMA) filter is a polynomial filter. Compared to finite-impulse response (FIR) graph filters, ARMA graph filters are robust to changes in the signal and/or graph, but are still linear. In this work, we propose a weighted median autoregressive graph filter (WMAF) based on a first-order ARMA graph filter. The proposed filter is a combination of weighted median filter in the traditional signal processing field and median autoregressive filters (MAF), and can be implemented in a distributed way. Compared with linear filter and MAF, the proposed WMAF filter has better filtering effect on pulse noise. In the denoising application of real sensor network data set, the filtered signal has a better signal-to-noise ratio. © 2024 SPIE.

关键词： Signal to noise ratio

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Optimization of Edge computing Task Offloading Based on Multi-Agent Game 3

Optimization of Edge Computing Task Offloading Based on Mult...

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3rd international conference on high performance computing and Communication engineering, HPCCE 2023

作者： Yang, Bo Liu, Yusheng Li, Hao Chen, Yimin Deng, Xuegang School of Information Science Yunnan University Kunming650500 China School of Agricultural Economics and Rural Development Renmin University Beijing100000 China Yunnan University School of Information Science & Engineering Yunnan Engineering Rese Arch Center of Smart Tourism Kunming Kunming650500 China Yunnan Development and Reform Commission Kunming China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

In a multi-access edge computing environment, optimizing task offloading under various constraints is a complex challenge. Traditional methods often neglect constraint relationships, leading to uneven resource allocation and suboptimal system performance. Additionally, these methods struggle to adapt to dynamic demands in edge computing. This paper introduces a novel distributed task offloading algorithm based on multi-agent deep reinforcement learning. This approach coordinates multiple agents in making task offloading decisions, aiming to achieve optimized task allocation while respecting constraints, thereby enhancing system performance and reducing latency and energy consumption. Simulation experiments in edge computing scenarios validate the effectiveness and stability of the proposed method. Results demonstrate that the multi-agent deep reinforcement learning approach outperforms traditional methods significantly. It excels in reducing task completion time and lowering terminal device energy consumption, affirming its effectiveness. This research offers a fresh perspective on task offloading strategies in edge computing, addressing limitations in traditional methods and providing a foundation for further exploration in this domain. © 2024 SPIE.

关键词： Reinforcement learning

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Deep Learning-Based Detection of Foliar Diseases in Apple Plants Using an Assembled CNN Model 3

Deep Learning-Based Detection of Foliar Diseases in Apple Pl...

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3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Sahu, Yatendra Bhargava, Arpita Thakur, Ghanshyam Singh Jain, Rishi IIIT Department of Computer Science and Engineering Bhopal India MANIT Department of Computer Application Bhopal India

ISBN: (纸本)9798350391565

The apple industry faces significant economic losses due to diseases and pests, contributing to low productivity levels. Detecting apple diseases promptly is essential for controlling their spread and improving overall productivity. However, early identification and diagnosis of these diseases are challenging due to various factors. In this work, we evaluated the performance of three distinct models: a custom CNN, Xception, and Resnet152V2. These models were tested on an expert-annotated dataset of apple diseases, comprising approximately 1821 high-quality RGB images across different categories of following types: C1: scab affected apple, C2: cedar apple affected by rust, C3: diseases of complex nature, and C4 samples of healthy apples. The custom CNN based proposed model trained for 50 epochs could yield an approximate accuracy of around 95%. © 2024 IEEE.

关键词： Plant diseases

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A Bayesian and SVM-based model for compositional analysis and identification of glass artifacts 3

A Bayesian and SVM-based model for compositional analysis an...

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3rd international conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Lin, Zaixin He, Huiyan Huang, Zihui Wang, Shijie Chen, Guangshun Zhu, Tianyuan Guangdong Province Zhuhai Institute of Science and Technology Jinwan District Zhuhai519041 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

In order to better target the protection of glass artifacts, this paper proposes a composition analysis and identification model of glass artifacts based on Bayesian and SVM, adopts a variety of analysis methods to extract and pre-process the data, establishes a composition analysis and identification model of artifacts and predicts the results for the next composition of glass artifacts, and finally validates the model, and the experimental results show that the SVM model obtains a high performance with high generalizability. © 2023 SPIE.

关键词： Glass

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Classification of Guitar Tab and Numbered Musical Notation using ResNet50 Network 3

Classification of Guitar Tab and Numbered Musical Notation u...

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3rd international conference on Artificial Intelligence, Automation, and high-performance computing, AIAHPC 2023

作者： Ma, Hangfei Zhang, Liumei Han, Yu School of Computer Science Xi’an Shiyou University Xi’an China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

With the advancement of deep learning and the growth of AI, a new concept for classifying music score images has been introduced. This paper presents a classification method for guitar tabs and numbered musical notation based on the ResNet50 network. The ResNet50 residual network model is used to extract features from music score images, and then the classification is performed based on the recognition probability. Results of the experiments indicate that the recognition accuracy of this model can reach 99.154%, suggesting that the ResNet50 network-based classification method holds good research potential in music score image classification. © 2023 SPIE.

关键词： Image classification

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The technique of parsing vehicle wiring harness tooling diagram based on pattern recognition 3

The technique of parsing vehicle wiring harness tooling diag...

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3rd international conference on high performance computing and Communication engineering, HPCCE 2023

作者： Guo, Zhibo Jiang, Wei Song, Yanhai Sun, Chenglin Wang, Nan Qiao, Xiaoli Changchun University of Science and Technology Changchun China Changchun CRRC Railway Vehicles Co. Ltd. Changchun China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

This paper analyzes the main elements of vehicle wire harness diagrams and summarizes the rules that a correct wire harness diagram should adhere to. Building upon this foundation, with the aid of Teigha, successful extraction of entities within vehicle wire harness diagrams has been achieved. Node Recognition Algorithm and Wire harness recognition algorithm have been designed, which can correctly analyze elements in the harness diagram and extract data from them. A comparative experiment was conducted with the traditional electronic wiring board, and the experimental results showed that the intelligent electronic wiring board using the algorithm shortened the average path resolution time from 4.3 hours to 7.6 seconds, and no errors occurred during the processes. © 2024 SPIE.

关键词： Vehicles

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IoT-Based Intelligent Transportation System for Passenger-Shuttle Taxi Matchmaking 3

IoT-Based Intelligent Transportation System for Passenger-Sh...

引用

3rd international conference on Distributed computing and high performance computing, DCHPC 2024

作者： Zamani, Mohammadreza Kalbasi, Amir Amirkabir University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350381580

As cities continue to grow in population, the demand for public transportation, particularly shuttle taxis, has increased. Shuttle taxis typically follow specific routes within or between cities. However, this transportation mode is not currently optimized, as taxi drivers often have to stop frequently to find passengers and inquire about their destination to see if it aligns with their route. With more stops, the average speed of taxis and other vehicles on the road decreases, leading to longer travel times for both shuttle taxis as well as other vehicles. Furthermore, frequent stop-start cycles increase fuel consumption, increase CO2 emissions, increase vehicles' wear and tear and, therefore, reduce vehicles' life. To address these issues, a matchmaking system between passengers and shuttle taxis called Shuttle Taxi with Targeted Stops (STTS) is proposed. Passengers can choose their taxi lines and get matched with a shuttle taxi with free capacity based on their source and destination locations. The STTS system aims to reduce travel times and enhance the overall quality of shuttle taxi services. Implementation of this system is expected to reduce urban traffic congestion, decrease the number of car stops, particularly taxis, and minimize fuel consumption and environmental impact caused by shuttle taxis and other vehicles on the road. Based on simulation results, the STTS method increases average speed of non-taxi cars and taxis up to 9.3% and 51.7%, respectively, while decreasing the average travel time of passengers by up to 20%. © 2024 IEEE.

关键词： Travel time

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Efficient Detection of Runways, Taxiways, and Aprons in Commercial Airports Using YOLOv8 and high-Resolution Satellite Imagery 3

Efficient Detection of Runways, Taxiways, and Aprons in Comm...

引用

3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Panneerselvam, Ramesh Kumar Yasen Shaik, Mohammed Sundarapalli, Hanish Venkat Prakash Gandham, Kumar N S L Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331523893

This study presents an advanced satellite-based detection system designed for the comprehensive identification of multiple facilities within both commercial and civilian airports. The system utilizes cutting-edge remote sensing technologies and machine learning algorithms to accurately detect and categorize various essential elements crucial for airport operations. These elements include runways, taxiways, and aprons. Using high-resolution satellite imagery, the system aims to improve airport infrastructure management, optimize safety protocols, and improve overall operational efficiency on a global scale. Leveraging deep learning techniques, specifically object detection models, we explore the suitability of different architectures such as Faster R-CNN, YoLO V8, detectors for accurately identifying these facilities. The project involves extensive data collection, preprocessing, model training, and evaluation to ensure robust performance. Considering the potential deployment on edge devices, the chosen models are optimized for efficiency without compromising accuracy. © 2025 IEEE.

关键词： Airport runways

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Wind power output error probability density distribution fitting based on Gaussian mixture function 3

Wind power output error probability density distribution fit...

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3rd international conference on Artificial Intelligence, Automation, and high-performance computing, AIAHPC 2023

作者： Ren, Yaming College of Mechanical and Control Engineering Guilin University of Technology Guilin China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

With the increasing scale of wind power, the impact of wind fluctuations on the power network must be considered. Considering the randomness of wind have a serious impact on power grid, we use Gaussian mixture function method to approximate the wind power output error probability density distribution. Specifically, we use Davidson-Boding index (DBI) to determine the optimal number of cluster center. Then, the fastest gradient descent method is employed to solve the weights of Gaussian function. Finally, the simulation results show that the Gaussian mixture function can effectively approximate the wind power output error probability density distribution. © 2023 SPIE.

关键词： Wind power

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