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A Triple Learner Based energy efficient Scheduling for Multi-UAV Assisted Mobile edge computing

A Triple Learner Based Energy Efficient Scheduling for Multi...

引用

ieee international conference on Communications (ieee ICC)

作者： Chen, Jiayuan Yi, Changyan Li, Jialiuyuan Zhu, Kun Cai, Jun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781538674628

In this paper, an energy efficient scheduling problem for multiple unmanned aerial vehicle (UAV) assisted mobile edge computing is studied. In the considered model, UAVs act as mobile edge servers to provide computing services to end-users with task offloading requests. Unlike existing works, we allow UAVs to determine not only their trajectories but also decisions of whether returning to the depot for replenishing energies and updating application placements (due to limited batteries and storage capacities). Aiming to maximize the long-term energy efficiency of all UAVs, i.e., total amount of offloaded tasks computed by all UAVs over their total energy consumption, a joint optimization of UAVs' trajectory planning, energy renewal and application placement is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we reformulate such problem as three coupled multi-agent stochastic games, and then propose a novel triple learner based reinforcement learning approach, integrating a trajectory learner, an energy learner and an application learner, for reaching equilibriums. Simulations evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

关键词： Unmanned aerial vehicles (UAV)

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A Self-Supervised Representation Learning Method of Speech Recognition for Smart grid 19

A Self-Supervised Representation Learning Method of Speech R...

引用

2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Peng, Fei Zhang, Yunhou An, Tianyu Wang, Duoqin Chen, Zhikui Jin, Shan Northeast Branch of State Grid Corporation of China China Dalian University of Technology China

ISBN: (纸本)9798350346558

Recently, speech recognition has rapidly become a hot topic of machine learning and is widely applied in various domains. Despite impressive performance, speech recognition is still confronted with great challenges from different environmental factors in real world. To solve the challenges, a novel speech pattern recognition method for the pattern recognition task on speech data is proposed in the paper, which effectively combines machine learning techniques with acoustic model analysis. Specifically, the input speech data are first split into fragments, and then a clustering representation encoder network is utilized to extract the context representations of input speech data followed an aggregation module capturing clustering patterns. In the proposed method, the influence of environmental factors can be reduced as far as possible, and the robustness can be guaranteed effectively. experimental results demonstrate the superior performance of the proposed method. © 2022 ieee.

关键词： Speech recognition

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A Model Predictive Control-based Flexible Power Point Tracker for grid-tied Photovoltaic Inverters

A Model Predictive Control-based Flexible Power Point Tracke...

引用

2023 ieee international conference on energy Technologies for Future grids, eTFG 2023

作者： Kermadi, Mostefa Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Mokhlis, Hazlie Department of Electrical Engineering Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies HawthornVIC3122 Australia Universiti Malaya Department of Electrical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665471640

In distributed photovoltaic (PV) systems, the grid operator may impose the limit of power injected by the PV system at the point of common coupling. This power limit might be lower than the power generated by the PV system during the peak generation hour. In this work, we propose a flexible power point tracking (FPPT) method based on model predictive control for grid-connected photovoltaic inverters. A finite control set-model predictive controller (FCS-MPC) for the control of the inverter DC bus voltage and the grid current is employed to operate at the desired reference voltage set by the FPPT algorithm and inject the desired PV-generated power into the utility grid. The grid-side controller injects the power with a unity power factor using the voltage-oriented control principle in a rotational d-q reference frame. The rotation of the d-q reference frame is synchronized by the phase-locked loop algorithm. The validity of the proposed control system is confirmed through experimental tests conducted with the aid of solar array and grid simulators, a microlabbox controller, and a two-level inverter paired with an R-L filter. © 2023 ieee.

关键词： FPPT grid-integration MPC MPPT PV

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Automated Traffic Control for Sustainable Urban Mobility 3

Automated Traffic Control for Sustainable Urban Mobility

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Sudhakaran, Pradeep Koushik, C.R. George, Josh G. Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331532420

Nowadays, urban areas worldwide face several significant challenges in managing traffic, which leads to increases fuel consumption, pollution and travel time. Video-based object detection can collect useful data from video frames like vehicle count, speed, type of vehicle, license plates and more. This can be done efficiently and in a cost-effective way and will help improve safety and traffic management. Traditional systems rely on multiple older algorithms and different sensors to capture data that lacks accuracy. The proposed system integrates computer vision techniques and deep learning models like YOLOv8 using a single Convolutional Neural Network that divides the image into a grid. The count of the detected objects is further stored and using K-Means Clustering Algorithm, data is analyzed and further used for alerting the users about places with heavy congestion. Based on this congestion data, traffic lights will be changed based on the severity of the congestion instead of using regular timer-based traffic signals, thus, improving traffic control and safety. © 2024 ieee.

关键词： Convolutional neural networks

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ReALM: Region-empowered Antibody Language Model for Antibody Property Prediction

REALM: Region-Empowered Antibody Language Model for Antibody...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Nishino, Toru Kato, Noriji Tsutaoka, Takuya Li, Yuanzhong Ohue, Masahito Fujifilm Corporation Bio Science & Engineering Laboratories Tokyo Japan Fujifilm Corporation Bio Science & Engineering Laboratories Kaisei Japan School of Computing Institute of Science Tokyo Yokohama Japan

ISBN: (纸本)9798350386226

Protein language models (pLM) are beneficial to build antibody property prediction models. However, current pLMs lacks the ability to understand antibody properties because region and structure information is not effectively embedded. We propose the Region-empowered Antibody Language Model (ReALM), a pLM built by multi-task pretraining strategy of residue prediction and region prediction tasks in antibodies, to incorporate not only co-evolution but also region information of antibodies. We demonstrate that our ReALM improves the understanding of antibody properties, including hydrophobicity and thermo-stability. © 2024 ieee.

关键词： Prediction models

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Investigation of Tank Combat Simulation in Video Games 1

Investigation of Tank Combat Simulation in Video Games

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1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumawat, Bhawesh Sridevi, P.R. Sukanya Mehra, Arjun Khatoon, Naghma Varshney, Shivani Kaushik, Manas Manglik Madhav University Dept. of CSE Pindwara India Meenakshi A.H.E.R Dept. of Computer Science Chennai India Amity University Dept of CSE Jaipur India Usha Martin University Dept. of Computing and IT Jharkhand India Mangalayatn University Dept. of CSE Aligarh India Manipal University Dept. of Mechanical and Industrial Engineering Jaipur India

ISBN: (纸本)9798350356816

This research explores the development of a multiplayer, real-time strategy game centered around tank combat. Set in a futuristic, post-apocalyptic world, the game challenges players to maneuver tanks strategically, engage in combat with both human and AI-controlled adversaries, and capture enemy bases. Advanced AI algorithms are integrated to ensure challenging gameplay and maintain balance, while social features such as chat rooms and friend lists are incorporated to enhance player interaction and engagement. © 2024 ieee.

关键词： Tanks (containers)

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Detecting Anomalous Behaviours Based on System Logs: A Dynamic Graph Perspective 8

Detecting Anomalous Behaviours Based on System Logs: A Dynam...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Wang, Ming Wang, Yuman Wu, Tianqi Chen, Cen Qian, Kexiang State Grid Corporation of China Co. Ltd Beijing China State Grid Smart Grid Research Institute Co. Ltd China Sgri Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China State Grid Henan Electric Power Company China State Grid Henan Electric Power Research Institute China State Grid Laboratory of Power Monitoring System Cyber Security Technical Standard Verification China School of Information and Communication Engineering Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350302455

System logs provide rich information about the current system's state. To make use of system logs, various existing work extracts a causal graph from the log events, and then applies graph-based machine learning algorithms to detect the anomalous behaviors. One limitation of these work is that they treat the graph in a static way, neglecting the dynamic nature of anomalous behaviors. In this paper, we propose to detect anomalous behaviors based on system logs using dynamic graph modeling. Our method consists of several key steps, including negative sampling which helps to balance the dataset, edge encoding which encodes the spatial information for each behavior, and sequence modeling which further models the temporal information. experimental evaluations on real datasets demonstrate the effectiveness of the proposed approach. © 2023 ieee.

关键词： Graphic methods

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VeCA: Reliable and Confidential Resource Clustering for Volunteer edge-Cloud computing 12

VECA: Reliable and Confidential Resource Clustering for Volu...

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12th international conference on Cloud engineering

作者： Yeddulapalli, Hemanth Sai Alarcon, Mauro Lemus Roy, Upasana Neupane, Roshan Lal Gafurov, Durbek Mounesan, Motahare Debroy, Saptarshi Calyam, Prasad Univ Missouri Columbia Columbia MO 65211 USA CUNY New York NY USA

ISBN: (纸本)9798331528690;9798331528706

Volunteer edge-Cloud (VeC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogeneous and intermittent resources to support machine/deep learning (ML/DL) based workflows poses challenges in resource governance for reliability, and confidentiality for model/data privacy protection. There is a need for approaches to handle the volatility of volunteer edge node availability, and also to scale the confidential data-intensive workflow execution across a large number of VeC nodes. In this paper, we present VeCA, a reliable and confidential VeC resource clustering solution featuring three-fold methods tailored for executing ML/DL-based scientific workflows on VeC resources. Firstly, a capacity-based clustering approach enhances system reliability and minimizes VeC node search latency. Secondly, a novel two-phase, globally distributed scheduling scheme optimizes job allocation based on node attributes and using time-series-based Recurrent Neural Networks. Lastly, the integration of confidential computing ensures privacy preservation of the scientific workflows, where model and data information are not shared with VeC resources providers. We evaluate VeCA in a Function-as-a-Service (FaaS) cloud testbed that features OpenFaaS and MicroK8S to support two ML/DL-based scientific workflows viz., G2P-Deep (bioinformatics) and PAS-ML (health informatics). Results from tested experiments demonstrate that our proposed VeCA approach outperforms state-of-the-art methods;especially VeCA exhibits a two-fold reduction in VeC node search latency and over 20% improvement in productivity rates following execution failures compared to the next best method.

关键词： Volunteer edge-Cloud computing k-means Clustering Recurrent Neural Networks Confidential computing Function-as-a-Service Volatility

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A Parallel Simulation Framework Incorporating Machine Learning-Based Hotspot Detection for Accelerated Power grid Analysis 24

A Parallel Simulation Framework Incorporating Machine Learni...

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6th international Symposium on Machine Learning for CAD (MLCAD)

作者： Jiang, Yangfan Song, Jianfei Yang, Xiaoyu Dong, Xiao Sun, Songyu Lin, Yibo Jin, Zhou Yin, Xunzhao Zhuo, Cheng Zhejiang Univ Hangzhou Peoples R China China Univ Petr Beijing Peoples R China Peking Univ Beijing Peoples R China

ISBN: (数字)9798350363562

ISBN: (纸本)9798400706998

pecially as the scale extends to billions of nodes, making precise calculations across the entire spatial domain prohibitively expensive. Domain decomposition methods (DDM) facilitate block-wise parallel analysis of power distribution networks (PDN) through segmentation, yet they still face bottlenecks in computational load balance during parallel processing. Considering the vastness of PDN structures and the emphasis on verifying areas prone to significant IR drop (i.e., hotspot) rather than those less affected, our research introduces a machine learning (ML)-based method for detecting PDN hotspots. This method focuses on accurately identifying areas within sub-blocks where the hotspot intensity is high. We then propose a novel parallel approximation computing approach that integrates hotspot detection, enabling approximations in regions not marked as hotspots. This strategy optimizes computational resources and effectively accelerates analysis. Our experimental results demonstrate that the hotspot detection model achieves an average accuracy of 94.39%. The approximation strategy for non-hotspot sub-blocks results in 0.56-1.4% mean relative error, while achieving an average computational speedup of 3.21x over the CK-TSO parallel solver and 2.21x over the conventional DDM-based solver.

关键词： Power grid parallel hotspot machine learning

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Intelligent energy Management Strategies for Renewable energy Sources 1

Intelligent Energy Management Strategies for Renewable Energ...

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1st international conference on AIML-Applications for engineering and Technology, ICAeT 2025

作者： Patil, Sangita Deotale, Disha Mali, Ramesh MIT School of Computing MIT ADT University Department of Electrical & Electronics Engineering Pune412201 India MIT School of Computing MIT ADT University Department of Computer Science & Engineering Pune412201 India

ISBN: (纸本)9798350355611

There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine them with traditional energy to serve mini-grids or the isolated loads that are not linked to the main grid. In hybrid power system, many different energy sources are blended. every time, effective and efficient energy Management Strategies (eMS) are necessary when more than a source of energy is employed to supply a given load. This strategy directs the energy flow via the supply chain. This is necessary not just for standalone hybrid systems, but for hybrid power system that are linked to the main network. The intelligent control of hybrid MG has been the subject of a large number of serious research projects. A comprehensive assessment of the approaches used by a number of writers in their research work on applying energy management approaches is conducted. Standalone hybrid energy systems and the hybrid systems those are linked to the electrical grids are examples of these solutions. The aim of this paper is to give summary of various energy management schemes utilized in sustainable hybrid systems. Brief study is conducted for a number of freestanding and grid-linked hybrid system designs in order to make sure and find that which energy management technique that works well and will be applied for one renewable source is not always the best for another. © 2025 ieee.

关键词： Hybrid power

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