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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

共 11254 条记录，以下是4471-4480 订阅

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Research on Clean and Low-Carbon Building Power Supply Scheme Based on PEDF System 3

Research on Clean and Low-Carbon Building Power Supply Schem...

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3rd international conference on New Energy and Power engineering, ICNEPE 2023

作者： Kong, Yuhui Zhao, Zhan Zhang, Zhen Li, Shurong Guo, Yujing Wang, Hao Xiong'an Pinggao New Energy Technology Co. Ltd. Pinggao Group Co. Ltd Pingdingshan China State Grid Xiong'an New Area Company State Grid Hebei Electric Power Company Xiong'an New Area China

ISBN: (纸本)9798350393224

To effectively address the key issue of clean and low carbon building distribution network, which is how to increase the amount of distributed renewable energy generation and effectively consume renewable energy generation. Through the analysis of the development trend of building electrification, this paper takes a public building in Xiong'an New Area as an example to study the method of increasing the building new energy power generation and balancing the new energy volatility, and designs the PEDF (Photovoltaics, Energy storage, Direct current and Flexibility) low-voltage DC lighting power supply system that integrates clean energy, distributed energy storage and DC power supply technologies. The system uses photovoltaic power generation and energy storage devices to provide clean power, and realizes efficient power supply for lighting equipment through flexible low-voltage DC technology to reduce energy conversion losses, which realizes clean electrification of lighting in this building and promotes coordinated control of multiple energy sources and comprehensive energy efficiency management. It achieves the purpose of coordinating and complementing the source, network, load and storage, and reducing electrical energy consumption, making the building a near-zero carbon clean building on the electricity consumption side. © 2023 IEEE.

关键词： energy storage low-carbon building PEDF (Photovoltaics, Energy storage, Direct current and Flexibility) photovoltaic power consumption strategy

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Joint Task Offloading and Power Allocation for Delay-Sensitive Tasks in IIoT 3

Joint Task Offloading and Power Allocation for Delay-Sensiti...

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3rd international conference on Computing, Communication, Perception and Quantum technology, CCPQT 2024

作者： Wang, Yihang Jing, Tao Li, Xuehan Zhang, Boyang Liu, Qiuyue Gao, Bo Zhu, Minghao School of Electronics and Information Engineering Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Economics and Management Beijing Jiaotong University Beijing China

ISBN: (纸本)9798331528386

Fueled by technological advancements and the rising demand for intelligent manufacturing, the Industrial Internet of Things (IIoT) has rapidly evolved into a key driver of modern industrial transformation. In smart factories, heterogeneous devices generate a wide range of tasks, each with differing sensitivity to delay. This variability presents substantial challenges in task offloading and resource allocation. Many existing studies fail to consider these variations, resulting in suboptimal resource allocation. To tackle this issue, this paper introduces satisfaction of task (SOT) functions, which are designed to quantify the specific offloading requirements based on delay sensitivity of each task. Furthermore, a joint optimization framework is proposed, integrating task offloading and power allocation to maximize the SOT. A soft actor-critic (SAC) based algorithm is developed to solve the optimization problem and improve offloading efficiency. Simulation results show that the proposed approach better handles delay-sensitive tasks and demonstrates enhanced adaptability to diverse task types with varying delay sensitivities, outperforming current methods. © 2024 IEEE.

关键词： Smart manufacturing

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An Uncertainty-Aware Deep Learning-Based Model for COVID-19 Diagnosis 3

An Uncertainty-Aware Deep Learning-Based Model for COVID-19 ...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Reddy, L Chandra Sekhar Jayakarthik, R. Kiran, Ajmeera Sharma, Nipun Sharma, Swati Reddy, Pundru Chandra Shaker Hyderabad India Saveetha College of Liberal Arts and Sciences Department of Computer Science Tamil Nadu Chennai India MLR Institute of Technology Dundigal Computer Science and Engineering Telangana Hyderabad India Presidency University Electronics and Communication Engineering Karnataka Bangalore India School of Computer Science Engineering and Information Science Presidency University Karnataka Bangalore India

ISBN: (纸本)9798350317022

The application of deep-learning(DL) to the processing of medical images has been highly successful. A few groundbreaking DL-based works have achieved substantial progress in computerized screening of COVID-19 using chest X-ray( CXR) pictures in the wake of the current epidemic scenario caused by SARS-CoV-2. While DL models have shown promise, they currently lack a crucial component for use in medical image analysis: the ability to communicate uncertainty in the strategies prediction. Therefore, in this paper, we create a design for automatically detecting COVID19 from CXR images using an uncertainty-aware convolutional-neural-network(UACNN) and estimate the uncertainty in its predictions. In the suggested method, an EfficientNetB3 model is tuned using CXR pictures, and then the model is used in conjunction with Monte Carlo (MC) dropout. The posterior predictive distribution has been obtained after M forward passes of inference using MC dropout. The anticipation and design uncertainty are then determined by calculating the mean and entropy of the generated anticipative distribution. The COVID19CXr, XR-pictures, and Kaggle CXR datasets are used to test the suggested approach. Multi-class classification on the COVID19CXr dataset was completed with an accuracy of 99.81% using the proposed UACNN approach. For the purpose of diagnosing COVID-19 cases from CXR pictures, our suggested methodology demonstrates its superiority over the existing methodologies. © 2023 IEEE.

关键词： CNN COVID19 CXR deep-learning transfer-learning uncertainty-quantification

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Method of Wind Estimation for UAV Based on Hybrid Derivative-Free Extended Kalman Filter 3rd

Method of Wind Estimation for UAV Based on Hybrid Derivative...

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Xue, Peng Gao, Yanhui Li, Yue 92941 Unit Huludao China Key Laboratory of Unmanned Aerial Vehicle Technology Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789819711062

Providing accurate path following is a key challenge in achieving full autonomy for UAVs. These issues are particularly important for small unmanned aerial vehicle (UAV) applications due to their smaller size and lighter weight, which make them more susceptible to the effects of wind. To mitigate these challenges, a new method of wind field estimation and airspeed calibration is proposed. The method is based on the UAV's own GPS receiver, magnetic compass and atmospheric data computer sensor information through fusion algorithm. A Hybrid derivative-free extended Kalman filter algorithm (HDEKF) is used to estimate the proportional calibration coefficients of wind field information and true airspeed. Using a digital simulation platform for the flight control system of a UAV, the entire process of autonomous flight simulation is conducted under the condition of 2D constant wind. The simulation results demonstrate that the proposed method can accurately estimate the wind field information in both the straight flight and the turning section of the UAV route. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Unmanned aerial vehicles (UAV)

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A Task Allocation Algorithm of Loitering Munition Group for Regional Blockade Based on Event-Driven 1

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Zhu, Yunchong Li, Kebo Liu, Yuanhe Liang, Yan’gang College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Mission Changsha410073 China

ISBN: (数字)9789819710874

ISBN: (纸本)9789819710867

Propose an event-driven online task allocation algorithm to solve the problem of online task allocation for the Loitering Munition Group, which has no information about any targets in operational area. Firstly, analyzing the properties of enemy and friendly equipment in typical regional blockade operations and designing the process of unknown regional blockade operations. Secondly, propose the concept of "benefit-cost ratio" to drive the dynamic scrolling of the task allocation window, and achieve a fast response to sudden events such as new target appearance or projectile damage execution, and timely adjust the task allocation plan. Then, construct a simulation system for the Loitering Munition Group's regional blockade operation, conduct Monte Carlo simulation to verify the effectiveness and reliability of the online task allocation method. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Monte Carlo methods

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Reentry Trajectory Planning Based on Proximal Policy Optimization 1

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Shi, Xinyu Deng, Honbin Beijing Institute of Technology School of Mechatronical Engineering Beijing China

ISBN: (数字)9789819711079

ISBN: (纸本)9789819711062

In order to deal with the changing flight environment in the process of hypersonic vehicle reentry, a real-time trajectory planning algorithm based on Proximal Policy Optimization (PPO) was proposed. The proximal policy optimization is used to train the hypersonic vehicle reentry process, and the intelligent vehicle that can obtain the best trajectory control output according to the state input is obtained. Continuous roll Angle, discrete roll Angle, and continuous roll Angle change rate are selected as actions to study their training effects on the reentry process. The results show that the action space based on the change rate of the roll Angle converges faster and the reentry flight time is shorter. The trajectory planning method based on proximal strategy optimization can quickly generate the optimal trajectory of high speed aircraft. Compared with the trajectory planning algorithm based on pseudospectral method, the proposed method has the generalization ability to meet the accuracy requirements and can meet the needs of online real-time trajectory planning. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Reinforcement learning

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Multi-Strategy Improved Tuna Swarm Optimization Algorithm for Feature Selection of Network Intrusion Detection 3

Multi-Strategy Improved Tuna Swarm Optimization Algorithm fo...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Qin, Zhengbin Xu, Hui Jin, Yang Huang, Lang School of Computer Science Hubei University of Technology Wuhan China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

As for current network intrusion detection technology, it has become critical to improve the problems of low detection efficiency and low accuracy caused by feature *** improve this problem, we propose a multi-strategy improved Tuna Swarm Optimization (ITSO) algorithm and apply it to feature selection for network intrusion ***, an elite backward learning strategy is used to initialize the population and improve the quality of the solution in the initial stage to lay the foundation for the global optimum. Then, an improved sine cosine strategy is added to further optimize and balance the ability of local exploitation and global search before the tuna swarm starts feeding. Finally, the Levy flight-Cauchy variation strategy was added to the spiral foraging and parabolic foraging behavior of tuna schools to increase the diversity of group locations and improve the global search capability of the algorithm in network intrusion detection feature selection. The ITSO algorithm was tested against the original TSO algorithm and other classical algorithms in six benchmark functions. In terms of accuracy, it improved by 2.95% on the UNSW-NB15 dataset and 4.1% on the NSL-KDD dataset compared to the original TSO algorithm. Intrusion detection time is also greatly reduced. This effectively improves the performance of network intrusion detection. © 2023 SPIE.

关键词： Intrusion detection

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Evaluation of Focused Web Crawlers in Real-World Scenarios 3

Evaluation of Focused Web Crawlers in Real-World Scenarios

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Dhabliya, Dharmesh Alawadi, Ahmed Hussein Rai, Vijay Kumar Velusudha, N.T. Sarathy, K. Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study evaluates the practical performance of focused web crawlers, tools essential for extracting targeted data from the ever-expanding web. Moving beyond theoretical analysis, we configure crawlers for specific real-world scenarios such as academic research and social media monitoring, optimizing them for precise data retrieval across a dataset of 1,000 domains and 10 million URLs. Performance is measured through meticulous logging of resource use and data freshness, while quality is ensured via cross-verification with a benchmark dataset. Ethical compliance is rigorously maintained throughout the process. The comparative analysis of different crawlers provides a framework for their selection and deployment, culminating in actionable recommendations for their use in various industries, reflective of the complex demands faced by users in a dynamic digital landscape. © 2023 IEEE.

关键词： ASD prediction communication comparative analysis coverage data extraction diagnosis early detection efficiency ensemble techniques evaluation feature selection Focused web crawlers freshness hyperparameter tuning information retrieval machine learning model training multi-modal neuroimaging data neurodevelopmental disorder performance assessment predictive accuracy real-world scenarios repetitive behaviors social interaction validation

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

3rd International Conference on Artificial Intelligence, Rob...

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

ISBN: (纸本)9789819721993

The proceedings contain 33 papers. The special focus in this conference is on Artificial Intelligence, Robotics, and Communication. The topics include: Design and Research of UAV Fire Emergency and Intelligent Inspection System in the Valve Hall of Converter Station;Development Overview and Application Prospects of Advanced UUVs;bisporus Mushroom 3D Reconstruction for Digital Twin of Smart Factory;Research on Automatic Music Generation Based on LSTM;specific Emitter Identification Method Based on Aggregation Model with Metric Learning in Complex Field;fake Comment Detection Based on Generative Adversarial Networks;Shipping Risk Assessment Ship Inspection Method Based on Improved YOLOv5;spatiotemporal Prediction of Harmful Algal Blooms Based on Spatiotemporal Attention and Transformer;large-Scale Information Dissemination Algorithm Based on Random Mapping;Smoke Detection in Complex Scenery Using Improved Faster-RCNN;CoT-Enhanced CNN-ViT Fusion with Adaptive Token Learning for Precise Facial Expression Recognition;evaluation Method of Cognitive Level Based on Machine Learning and Wearable Device;ship Image Segmentation Based on Multi-dimensional Information and Feature Fusion;real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence;traffic Light Detection Based on Deep Learning;robot Navigation Based on Reinforcement Learning: An Overview;active Control technology of Supply Chain Intelligent Work Robot Based on Speech Enhancement technology;research on Security technology of Power Dispatching Data Network Based on Deep Learning;An Improved RRT Path Planning Method Incorporating Deep Reinforcement Learning for Space Multi-arm Robot;a Classification Denoising Algorithm Based on 3D Point Cloud Curvature;Comparative Analysis of the Effect of KPCA in PSO-GRU Combination Model.

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Deep Learning Techniques in Mobile Edge Computing for Internet of Medical Things 3

Deep Learning Techniques in Mobile Edge Computing for Intern...

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3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alawadhi, Abdulwadood Ahmad, R. Badlishah Almogahed, Abdullah Abrar, Ahmad Department of Computers and Information Technology Yemen Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Parit Raja Malaysia School of Computing Universiti Utara Malaysia InterNetWorks Research Laboratory Sintok-Kedah Malaysia

ISBN: (纸本)9798350305333

The fast expansion of the Internet of Medical Things (IoMT) has resulted in a ubiquitous home health diagnostic network. High patient demand results in high costs, short latency, and communication overload. As a result, 6G is the next generation of IoT, IoMT, and cellular networks, intending to considerably improve the quality of smart healthcare services through high throughput and decreased latency. So far, adopting cloud computing for time-critical applications and decreasing access delays to resources is difficult. Deep learning has been extensively employed to extract characteristics from complicated networks as artificial intelligence technology has advanced. On the other hand, deep learning models are often run in cloud computing data centres with tremendous processing resources. Conventional cloud computing systems primarily rely on the network, which has significant latency and security and privacy issues. One of the successful solutions is the Mobile Edge Computing (MEC) paradigm, which brings cloud computing services closer to the edge network and uses available resources. Mobile edge computing places computer and storage nodes near mobile devices at the Internet's edge, leading to considerable savings in system operating time, memory cost, and power usage. Deep learning is used in mobile edge computing to forecast changes in demand based on daily patient behaviours. It also prepares the network by scaling up network resources as required. This study aims to discuss IoMT and identfy the problems in deep learning for mobile edge computing technology and its applications. © 2023 IEEE.

关键词： Deep learning

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