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Efficient Double-layer Coverage Scheme for Multiple UAVs Assist Intelligent Transportation Systems 6

Efficient Double-layer Coverage Scheme for Multiple UAVs Ass...

引用

6th international conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Zhang, Hongwei Chen, Yikai Huang, Nuoxi Chen, Peiyi He, Xuewen Yang, Chao Guangdong University of Technology Guangdong Key Laboratory of IoT Information Technology School of Automation Guangzhou China

ISBN: (纸本)9798350386974

Sudden traffic congestion on road leads to a huge increasing in the computing tasks of vehicle users in Internet of vehicles, it is a challenge to meet the emerging real-time requirements of users. Due to fast movement and line-of-sight communication links, unmanned aerial vehicles (UAVs) are integrated into intelligent transportation systems (ITS) to assist the roadside units (RSUs). However, for the limited capacity of a single UAV, multi-UAV cooperative networking can optimizes task completed delay in ITS. In this paper, we construct vehicular edge computing networks with double-layer UAVs. According to the on-predicted historical data, the first-layer UAVs are pre-deployed at user aggregation points to receive and process tasks from covered user equipments (UEs). The second-layer UAVs hovers at service points. A joint optimization problem is proposed to minimize the task completed delay and the energy consumption of UAVs. In order to handle the continuous action space and discrete state in this problem, a deep deterministic policy gradient (DDPG)-based algorithm is proposed. It enables the system to determine the optima vehicle and UAV action strategy in dynamic traffic environments. Simulation results indicate that the proposed algorithm achieves rapid convergence compared to the normal single-layer UAVs coverage model. © 2024 IEEE.

关键词： Traffic congestion

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Research on the Application of Natural Language Processing Based on Deep Learning in Multi-task Learning 3

Research on the Application of Natural Language Processing B...

引用

3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Zhou, Zikai Yang, Diansheng Beijing Institute of Technology Beijing102488 China Guangzhou Saite Intelligent Technology Co. Ltd Guangdong 510705 China

ISBN: (纸本)9798350343922

This article comprehensively discusses the application of natural language processing (NLP) based on deep learning in multi-task learning (MTL). First, the article reviews the basic principles of deep learning, the development history of NLP, the concept of MTL and its application in various fields. Next, the article analyzes in detail the specific application cases of deep learning in NLP, such as language models, sentiment analysis and machine translation, and discusses the successful cases and method summaries of these technologies. In particular, this article focuses on the application of MTL in NLP, and experimentally verifies the advantages of MTL in performance improvement compared to the traditional single-task learning (STL) mode. The experimental part covers research methods and experimental design, data sets and experimental environment, experimental process and result analysis, as well as result discussion and theoretical significance. Finally, the article demonstrates the potential of MTL to improve model generalization capabilities in NLP tasks, and points out the limitations of the research and possible future research directions. The innovation of this study lies in the proposed new deep learning model structure, which is specially optimized for the application of MTL in NLP and its effectiveness is verified through experiments. © 2023 IEEE.

关键词： Adversarial machine learning

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Time Series Forecasting of Sea Level by Using Transformer Approach, with a Case Study in Pangandaran, Indonesia 8

Time Series Forecasting of Sea Level by Using Transformer Ap...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Sabililah, Ridho Nobelino Adytia, Didit Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350334012

Sea level prediction is essential information for citizens who live in the coastal area and plan to build structures, especially in the construction stage around the inshore and offshore locations. The statistical method and tidal harmonic analysis have been used to predict the sea level but require long terms historical sea level data to achieve reasonable accuracy. This paper uses Transformer deep learning approaches to predict sea data levels. This paper uses only four months of data in Pangandaran, Indonesia. We use the sea level dataset obtained from the Inexpensive Device for Sea Level measurement (IDSL). The model is trained to predict 1, 7, and 14 days. We also study the sensitivity of the model in terms of lookbacks. The performance of the Transformer was compared with two other popular deep-learning methods;RNN and LSTM. To forecast 14 days, the Transformer model results in a higher coefficient correlation (CC) of 0.993 and a lower root mean squared error (RMSE) value of 0.055 compared to the other two models. Moreover, the Transformer has a faster computing performance than the other two models. © 2023 IEEE.

关键词： Sea level

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An Edge computing for Monitoring Online Partial Discharge and Battery Management System in the Substation 6

An Edge Computing for Monitoring Online Partial Discharge an...

引用

6th international conference on Power, Energy and Innovations, ICPEI 2024

作者： Nguyen, Khai Phuc Vu, Nguyen Nam Thang, Nguyen Dac Quang, Pham Dang Vinh, Phan Thanh Ho Chi Minh City University of Technology Vietnam National University Ho Chi Minh City Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam EVN Southern Power Corporation SCADA Operating Center Ho Chi Minh City Viet Nam TechEdge Limited Company Ho Chi Minh City Viet Nam Soonchunhyang University Department of Future Convergence Technology Korea Republic of

ISBN: (纸本)9798350356779

This article proposes an application of edge computing for the online monitoring system for temperature, partial discharge in 24 kV switchgear, and battery health in a 110 kV substation. Nowadays, monitoring the electric system for faults is crucial for operator departments. Power cables naturally degrade over time, leading to partial discharge, which can eventually cause short circuits. Additionally, the health of batteries in substations, such as lead-acid and Ni-Cd batteries, is critical, as they supply DC voltage for vital equipment like relays and controls. Early detection of these abnormalities helps ensure the safety and reliability of the electric system by preventing equipment failures and outages. The system utilizes wireless primary sensors, enabling fast and flexible installation. Monitoring data is visualized through user-friendly web and mobile applications and stored on a server for in-depth analysis. This solution could be expanded to monitor other substations in the future. © 2024 IEEE.

关键词： Nickel cadmium batteries

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GEWO: An Efficient Prioritised Task Scheduling in Cloud Fog computing Environment 9

GEWO: An Efficient Prioritised Task Scheduling in Cloud Fog ...

引用

9th international conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Medishetti, Santhosh Kumar Swapna, G. Anusha, K. Karri, Ganesh Reddy Kuppusamy, P. VIT-AP University Amaravathi India Anurag University Hyderabad India Marri Laxman Reddy Institute of Technology India

ISBN: (纸本)9798350350845

The rapid expansion of Cloud-Fog computing (CFC) underscores the need for effective task scheduling (TS) strategies to optimize resource utilization and bolster system performance. This paper introduces a novel optimization algorithm, Golden Eagle Whale Optimization (GEWO), which combines the unique behaviors of Golden Eagles and Whales by integrating Golden Eagle Optimization (GEO) and Whale Optimization Algorithm (WOA). GEWO strives to achieve a balance between global exploration, emulating the soaring capabilities of Golden Eagles, and local exploitation, inspired by the strategic diving behaviors of Whales. Specifically designed to tackle the challenges posed by the dynamic and heterogeneous nature of cloud and fog computing environments, GEWO takes into account factors such as task characteristics, resource availability, and network conditions. By incorporating these elements into the optimization process, GEWO enhances convergence speed and solution quality, presenting a promising solution for efficient TS in CFC. To evaluate GEWO's efficacy, comprehensive experiments were conducted using diverse benchmark tasks and real-world CFC scenarios. Comparative analyses against state-of-the-art optimization algorithms reveal the superior performance of GEWO, showcasing a 26% reduction in task completion time, a 32 % improvement in resource utilization, and a 29% increase in energy efficiency. GEWO contributes to ongoing efforts aimed at enhancing the efficiency and scalability of cloud-fog systems, marking advancements in resource management and overall system performance. © 2024 IEEE.

关键词： Fog computing

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Adaptive Grasshopper Optimization Algorithm based Dynamic Cluster Head Selection and Energy Efficient Routing for Wireless Sensor Network (WSN) 3

Adaptive Grasshopper Optimization Algorithm based Dynamic Cl...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Kanimozhi, T. Belina, S. Sara, V.J. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350375190

Wireless Sensor Network (WSN) clustering is useful for developing routing algorithms that improves the scalability and longevity of the network. Cluster Head (CH) is essential for data transmission in the clustered WSN paradigm. Thus several cluster-based routing protocols have been introduced recently. As such, energy constraints affect both the duration of the network and energy-efficient routing. In this paper, Adaptive Grasshopper Optimization Algorithm (AGOA) is introduced for cluster-based routing and CH selection which increases routing efficiency, and maximizes network lifetime. Furthermore, high convergence rate and lower parameter complexity as compared to other optimization techniques. Here, Residual Energy (RE), distance to the Base Station (BS), distance to neighbors, node degree, and node centrality has been used for choosing the best CH. Additionally, the path between the BS and CH can be determined using AGOA. Lastly, the results routing protocols are evaluated using metrics including Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average energy consumption, and Network Lifetime. © 2024 IEEE.

关键词： Energy utilization

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Underwater Unmanned Vehicle Clusters Cooperative Positioning Method 14

Underwater Unmanned Vehicle Clusters Cooperative Positioning...

引用

14th IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2024

作者： Wu, Shijiao Zhang, Lingling Tang, Chengkai Gao, Yimeng School of Marine Science and Technology Northwestern Polytechnical University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China Beijing Institute of Astronautical System Engineering Beijing China

ISBN: (纸本)9798350366556

Nowadays, UUV clusters have become the main carriers for ocean environment monitoring. In this application, the positioning of UUV nodes during voyages is of great importance. A cooperative positioning method for UUV clusters is presented in this paper. It utilizes factor graphs to fuse ranging information from each UUV node, and predicts movement path based on multiple confocal positioning models. The comparison results demonstrate that the proposed method achieves higher positioning accuracy, faster convergence speed, and good suppression capability for mutation errors. © 2024 IEEE.

关键词： Unmanned underwater vehicles

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The Nanoscale technology and Photovoltaics convergence: A Detailed Analysis of the Quantum Leap Achieved by Silver Nanoparticle Incorporation into SiO2 Non- -reflective Coatings for Solar Cell Efficiency Enhancement 2

The Nanoscale Technology and Photovoltaics Convergence: A De...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Manikandan, S. Baskar, Radhika Sathish, G. Saveetha University Institute of Ece Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

The effectiveness of SiO2 solar cell coatings in reducing reflection, both with and without the presence of silver nanoparticles, was the primary focus of this study. Various parameters of single-link layer photovoltaic (PV) cells, including the thickness of the protective front layer and the SiO2 antireflective coatings with or without silver nanoparticles, were analysed through simulations conducted using the photovoltaic QCRF-FDTD simulator. The simulation outcomes revealed that the PV cell parameters exhibited a noticeable increase with varying thicknesses of the protective front layer composed of SiO2, reaching a maximum value of 150 nm in both scenarios. Furthermore, the addition of silver nanoparticles contributed to enhanced efficiency in the PV cell, reaching a peak of 19.75%, while without nanoparticles, the efficiency reached up to 19.48%. These findings underscore the potential of SiO2 antireflective coatings, along with the inclusion of silver nanoparticles, to enhance the efficiency of PV cells. This research holds promise for future advancements in solar cell technology. © 2024 IEEE.

关键词： anti-reflective coating comparative analysis efficiency FDTD simulation silver nanoparticles single junction layer solar electricity

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Adaptive Stabilization Based on Machine Learning for Column Generation 41

Adaptive Stabilization Based on Machine Learning for Column ...

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41st international conference on Machine Learning, ICML 2024

作者： Shen, Yunzhuang Sun, Yuan Li, Xiaodong Cao, Zhiguang Eberhard, Andrew Zhang, Guangquan Australian Artificial Intelligence Institute University of Technology Sydney Australia La Trobe Business School La Trobe University Australia School of Computing Technologies Royal Melbourne Institute of Technology Australia School of Computing and Information Systems Singapore Management University Singapore

Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem. A natural phenomenon in CG is the heavy oscillation of the dual values during iterations, which can lead to a substantial slowdown in the convergence rate. Stabilization techniques are devised to accelerate the convergence of dual values by using information beyond the state of the current subproblem. However, there remains a significant gap in obtaining more accurate dual values at an earlier stage. To further narrow this gap, this paper introduces a novel approach consisting of 1) a machine learning approach for accurate prediction of optimal dual solutions and 2) an adaptive stabilization technique that effectively capitalizes on accurate predictions. On the graph coloring problem, we show that our method achieves a significantly improved convergence rate compared to traditional methods. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Network Powered by computing 4

Network Powered by Computing

引用

4th international Science and technology conference "Modern Network Technologies", MoNeTec 2022

作者： Smeliansky, Ruslan Moscow State University Dept. of Computational Mathematics and Cybernetics Moscow Russia

ISBN: (数字)9781665472463

ISBN: (纸本)9781665472463

The paper presents the concept of Network Powered by computing (NPC)-computing infrastructure, which is the convergence of data networks with computing installations like DC, Edge, HPC. This concept is based on an analysis of the properties of modern applications and the requirements for the computational infrastructure for them. The functional architecture of NPC and the main problems on the way of its feasibility are described. © 2022 IEEE.

关键词： Computer architecture Planning convergence

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