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Computational intelligence methods for green technology and sustainable development = proceedings of the International Conference GTSD2024.. Volume 1 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational intelligence methods for green technology and sustainable development/ edited by Yo-Ping Huang ... [et al.].
其他題名:	proceedings of the International Conference GTSD2024.
其他題名:	GTSD 2024
其他作者:	Huang, Yo-Ping.
團體作者:	International Conference on Green Technology and Sustainable Development
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	ix, 366 p. :ill. (some col.), digital ;24 cm.
內容註:	Deep Learning For Commercial Building Load Forecasting: Hyperparameter Fine-tuning Convolution Neural Network-Multivariate Multilayered Long Short-term Memory Time-series Model -- Guided Multi-task Lane Line Detection with Road-object Semantic Segmentation -- Neuronal Networks for Visual Inspection of Assembly Completeness and Correctness in Manufacturing -- Person Detection for Monitoring Individuals Accessing the Robot Working Zones Using YOLOv8 -- Improvement of Small Object Detection Effectiveness based on Swin Transformer -- Dynamic Traffic Optimization System: Leveraging IoT and Fog Computing for Enhanced Urban Mobility with the RAO Algorithm -- Skeleton-based Posture Estimation for Human Action Recognition using Deep Learning -- Enhancing Cost-Efficient Image Captioning through ExpansionNet v2 Optimization -- Lite-GrSeg: Lightweight Architecture for 3D Point Cloud Road-Scene Semantic Segmentation -- Identifying Traffic Congestion through Vehicle Counting and Motion Estimation -- Improved Demand Forecasting Using Artificial Neural Networks: Incorporating Economy Indicators through Feature Construction -- A Novel Method for Ultrasonic Sensor Modeling Using Support Vector Regression -- Bearing Fault Diagnosis Framework based on Few-Shot Learning with Distribution Consistency and Structural Reparameterization -- Embedded Machine Learning for EMG-Based Elbow Motion Recognition -- Empirical Evaluation of Hybrid Time Series Forecasting Method between ARIMA and RBFNN under Parallel Model -- Research on Geometry-based Algorithm to Avoid Collisions With Pedestrians for Autonomous Vehicles -- Artificial Intelligence Application in Urban Space in The Light of The EU Data Protection -- Inverse Characterization of Multilayered Composite Plates using Ultrasonic Guided Waves and Machine Learning Algorithms -- Leveraging Big Data for Competitive Advantage in Entrepreneurship: A Decade Of Insights -- A New Adaptive Optimal Control using Non-conventional Prescribed Performance for Severe Disturbance System -- Novel Adaptive Model-Free Speed Control for Uncertain Permanent Magnet Synchronous Motor -- Optimizing Parameters of Direct Adaptive Neural Sliding Mode Controller for Coupled Tank System using MDE Optimization Algorithm -- Adaptive Fuzzy Controller for Ballbot With Complex Uncertainties -- A Computer Vision-based Eye-tracking System Toward An Eye-controlled Powered Wheelchair -- Adaptive Model Predictive Control (Adp_MPC) Utilized in Autonomous Vehicle (AV) Assistance Systems -- Observer-Based Path Following Control Method for Mobile Robots with An Event-Triggering Mechanism -- Advanced Event-Triggered Tracking Control Applied for Three-DOF Hover Unmanned Aerial Vehicle System -- LiDAR-Based Smart Navigation and Mapping for Mobile Robot on ROS -- Adaptive Formation Control of Underactuated Autonomous Underwater Vehicles with Multiple Constraints.
Contained By:	Springer Nature eBook
標題:	Green technology - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-76197-3
ISBN:	9783031761973

Computational intelligence methods for green technology and sustainable development = proceedings of the International Conference GTSD2024.. Volume 1 /
Computational intelligence methods for green technology and sustainable developmentproceedings of the International Conference GTSD2024.Volume 1 /[electronic resource] :GTSD 2024edited by Yo-Ping Huang ... [et al.]. - Cham :Springer Nature Switzerland :2024. - ix, 366 p. :ill. (some col.), digital ;24 cm. - Lecture notes in networks and systems,11952367-3389 ;. - Lecture notes in networks and systems ;1195..

Deep Learning For Commercial Building Load Forecasting: Hyperparameter Fine-tuning Convolution Neural Network-Multivariate Multilayered Long Short-term Memory Time-series Model -- Guided Multi-task Lane Line Detection with Road-object Semantic Segmentation -- Neuronal Networks for Visual Inspection of Assembly Completeness and Correctness in Manufacturing -- Person Detection for Monitoring Individuals Accessing the Robot Working Zones Using YOLOv8 -- Improvement of Small Object Detection Effectiveness based on Swin Transformer -- Dynamic Traffic Optimization System: Leveraging IoT and Fog Computing for Enhanced Urban Mobility with the RAO Algorithm -- Skeleton-based Posture Estimation for Human Action Recognition using Deep Learning -- Enhancing Cost-Efficient Image Captioning through ExpansionNet v2 Optimization -- Lite-GrSeg: Lightweight Architecture for 3D Point Cloud Road-Scene Semantic Segmentation -- Identifying Traffic Congestion through Vehicle Counting and Motion Estimation -- Improved Demand Forecasting Using Artificial Neural Networks: Incorporating Economy Indicators through Feature Construction -- A Novel Method for Ultrasonic Sensor Modeling Using Support Vector Regression -- Bearing Fault Diagnosis Framework based on Few-Shot Learning with Distribution Consistency and Structural Reparameterization -- Embedded Machine Learning for EMG-Based Elbow Motion Recognition -- Empirical Evaluation of Hybrid Time Series Forecasting Method between ARIMA and RBFNN under Parallel Model -- Research on Geometry-based Algorithm to Avoid Collisions With Pedestrians for Autonomous Vehicles -- Artificial Intelligence Application in Urban Space in The Light of The EU Data Protection -- Inverse Characterization of Multilayered Composite Plates using Ultrasonic Guided Waves and Machine Learning Algorithms -- Leveraging Big Data for Competitive Advantage in Entrepreneurship: A Decade Of Insights -- A New Adaptive Optimal Control using Non-conventional Prescribed Performance for Severe Disturbance System -- Novel Adaptive Model-Free Speed Control for Uncertain Permanent Magnet Synchronous Motor -- Optimizing Parameters of Direct Adaptive Neural Sliding Mode Controller for Coupled Tank System using MDE Optimization Algorithm -- Adaptive Fuzzy Controller for Ballbot With Complex Uncertainties -- A Computer Vision-based Eye-tracking System Toward An Eye-controlled Powered Wheelchair -- Adaptive Model Predictive Control (Adp_MPC) Utilized in Autonomous Vehicle (AV) Assistance Systems -- Observer-Based Path Following Control Method for Mobile Robots with An Event-Triggering Mechanism -- Advanced Event-Triggered Tracking Control Applied for Three-DOF Hover Unmanned Aerial Vehicle System -- LiDAR-Based Smart Navigation and Mapping for Mobile Robot on ROS -- Adaptive Formation Control of Underactuated Autonomous Underwater Vehicles with Multiple Constraints.

This book is presented in two volumes, featuring peer-reviewed research papers from the 7th International Conference on Green Technology and Sustainable Development (GTSD), held in Ho Chi Minh City, Vietnam, from July 25 to 26, 2024. It highlights original research by experts from both academia and industry, centered on the theme of "Green Technology and Sustainable Development in the Industrial Revolution 4.0." The book underscores the critical importance of sustainability in education, technology, and economic development, while also showcasing the vital role of technological innovation in creating a greener future. The papers documented in this book cover a broad range of topics, including renewable energy systems, smart grids, artificial intelligence, robotics and intelligent systems, and computational intelligence, all with a focus on sustainable development, climate change mitigation, and environmental policy. These studies showcase cutting-edge technologies and innovative ideas related to green technology, offering actionable insights for advancing sustainable development across various sectors. The authors present research based on both experimental and numerical methods, offering solutions to current problems and optimizing existing methods. The insights and findings provided are valuable for industry experts, research institutions, universities, and anyone interested in advancing global sustainable development.

ISBN: 9783031761973

Standard No.: 10.1007/978-3-031-76197-3doiSubjects--Topical Terms:

3226773
Green technology
--Congresses.

LC Class. No.: TA170

Dewey Class. No.: 338.927

Computational intelligence methods for green technology and sustainable development = proceedings of the International Conference GTSD2024.. Volume 1 /
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