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Few-shot decision-making method for high-speed driving based on large model architecture 24

Few-shot decision-making method for high-speed driving based...

引用

3rd Asia conference on Algorithms, computing and Machine Learning, CACML 2024

作者： Yu, Yongshun Guan, Jifu Cheng, Lin Li, Yilin Xu, Yuxin School of Mechanical Engineering Beijing Institute of Technology No.5 Yard Zhongguancun Street Haidian District Beijing China Research Institute of Inner Mongolia First Machinery Group Co. Ltd China Jiamusi Explosion Proof Motor Research Institute Co. Ltd China

ISBN: (纸本)9798400716416

Compared with low-speed automatic driving, high-speed automatic driving often leads to safety accidents. This paper focuses on the decision-making of high-speed safe driving control, and combines the characteristics of expressway automatic driving, and puts forward a two-level bidirectional iterative algorithm. The real-time continuous control decision is modelled as an action sequence, and the action sequence is fitted through the transformer architecture. On this basis, this paper designs a novel two-layer learning algorithm to mine the context information between periodic sequence nodes and the association information between action sequence nodes. The system model and novel algorithm show fast convergence speed, strong Few-shot training ability and adaptability to different environments in the automatic driving environment of expressway. The results show that a large number of calculation problems caused by continuous control can be alleviated by establishing action sequence and periodic sequence. At the same time, through the combination of meta-learning and Transformer architecture, as well as the two-way iteration of the inner and outer layers, a better large model paradigm can be formed, and the problem of environmental adaptability can be solved by using Few-shots. © 2024 ACM.

关键词： Zero-shot learning

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Apple Leaf Disease Recognition: A Robust Federated Learning CNN Methodology

Apple Leaf Disease Recognition: A Robust Federated Learning ...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Mehta, Shiva Kukreja, Vinay Gupta, Richa Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350333244

Apple leaf diseases threaten apple orchard sustain ability and production worldwide. Accurate and early identification is essential for the successful care and control of many disorders. This article suggests a convolutional neural network (CNN) model for classifying apple leaf diseases based on federated learning. Using a dataset of 7,832 photos with a resolution of 224×224 pixels and five distinct classes of apple leaf disease, the model is intended to identify these diseases. Assuring data confidentiality and privacy, the federated learning architecture enables decentralized model training across several clients. The suggested model was tested on four customers, and the results showed that the model's precision, recall, and accuracy ranged from 93.51% to 94.92%, 93.22% to 94.72%, 93.33% to 94.81%, and 0.97 to 0.98, respectively. These findings show how the federated learning CNN model outperforms alternative deep learning architectures and conventional machine learning techniques in identifying apple leaf diseases. The model generalizes well to different data distributions based on consistent client performance, which qualifies it for use in practical applications. Future research should address federated learning's drawbacks and restrictions, such as heterogeneous data distribution, model convergence, and resource limits. Additionally, investigating future uses in agriculture and plant disease control may result in a better judgment and more environmentally friendly agricultural methods. © 2023 IEEE.

关键词： Neural network models

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Joint Optimization of Service Caching, Task Migration and Resource Allocation for Vehicular Edge computing with Energy Harvesting

Joint Optimization of Service Caching, Task Migration and Re...

引用

Computer Science, Electronic Information Engineering and Intelligent Control technology (CEI), IEEE international conference on

作者： Yi Zhang Qi Jiang Zhuo Ma Kofi Kwarteng Abrokwa School of Electrical Engineering and Automation Hefei University of Technology Heifei China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

Effective integration of available resources within edge nodes is essential to improve the performance of vehicular edge computing (VEC) to support various randomly offloaded tasks with limited computing capacity and constrained energy. This paper presents an intelligent adaptive resource integration strategy for VEC with harvesting energy. Service caching, task migration and resource allocation are jointly employed to accommodate the temporally and spatially varying computing demands. The optimization to minimize the long-term average task execution time under energy constraint is formulated as Markov decision processes and solved with a parameterized deep Q-network based learning algorithm. This algorithm employs a centralized training and distributed execution framework, where a parameter network and an action network respectively handle continuous and discrete decisions, effectively tackling the hybrid action space challenges in problem solving. Simulation results demonstrate that the proposed algorithm not only achieves faster convergence but also significantly improves system performance compared to benchmarks.

关键词： Training System performance Simulation Scalability Resource management Energy harvesting Time factors Optimization Edge computing convergence

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Vision-Inertial-based Adaptive State Estimation of Hexacopter with a Cable-Suspended Load

Vision-Inertial-based Adaptive State Estimation of Hexacopte...

引用

2022 IEEE international conference on Real-Time computing and Robotics, RCAR 2022

作者： Wang, Siqiang Liu, Jianheng Jiang, Xin Chen, Haoyao Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Tracking a given trajectory by aerial vehicles without an external positioning system is challenging in the natural environment since the computation power and sensory of the online positioning process are limited. We design a novel and theoretically-proved state estimator for an unmanned aerial vehicle with a cable-suspended load based on stability theory. The asymptotical convergence of our system is proved by the Lyapunov theory. The visual-inertial estimation algorithm, which we propose, consumes lower computation power compared with the optimization-based methods and provides high-frequency state estimation for the system. Several Gazebo-based simulations are performed to verify that the proposed algorithm is better than one state-of-the-art visual-inertial-based algorithm. The simulation results demonstrate the convergence and efficiency of the algorithm. © 2022 IEEE.

关键词： Cables

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Resource Allocation Algorithm Based on Modeling of Ubiquitous Network Node Capability 2

Resource Allocation Algorithm Based on Modeling of Ubiquitou...

引用

2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

作者： Song, Dai Chen, Xushan Rui, Lanlan Li, Wenjing Du, Zongpeng Li, Zhiqiang Lu, Lu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Infrastructure Network Technology Research Department China Mobile Research Institute Beijing China

With the continuous development and progress of the Internet, computing power is gradually moving from the center to the edge. In order to perceive, measure, schedule and manage the distributed computing resources, it is necessary to model and evaluate the node capability. Based on the development trend of network computing convergence and the demand of network computing service scheduling, this paper studies the unified measurement model and resource allocation algorithm for the integration of network computing and heterogeneous resources. The node resources are divided into four dimensions: computing, communication, memory and storage, and on this basis, the comprehensive ability evaluation index is proposed. Through simulation analysis, the effectiveness of the resource allocation algorithm based on node capability modeling is verified. © Published under licence by IOP Publishing Ltd.

关键词： computing power

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Indoor Location Based Tracking using Euclidean Distance Estimation (LTS-ED) 13

Indoor Location Based Tracking using Euclidean Distance Esti...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Ariffin, Sharifah H.S. Hian, Jee Chun Wahab, Nur Haliza Abdul School of Electrical Engineering Universiti Teknologi Malaysia Faculty of Engineering Johor Bahru Malaysia School of Computing Universiti Teknologi Malaysia Faculty of Engineering Johor Bahru Malaysia

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

To provide location-based services like indoor navigation systems or indoor crowd monitoring systems in an indoor environment, indoor location tracking systems are required. The implementation of an indoor location tracking system employing Wi-Fi signal strength will be the main topic of this study. However, there are several methods for tracking an indoor location, including fingerprinting, triangulation, and trilateration. The proposed system was created using a fingerprinting technique. The server, which is based on Java, and the client, which is based on Android, make up the system's two primary parts. Regarding Wi-Fi RSSI scanning, the client's application oversees removing weak received signal strength (RSSI), more specifically RSSI lower than -85dBm, while the server provides the location ID with the highest likelihood of matching based on offline training data kept in a database. The software algorithm powered by MySQL will be the only foundation for the indoor location monitoring system. The findings demonstrate that smaller-area location tracking provides more accuracy than larger-area tracking. © 2022 IEEE.

关键词： Location based services

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Energy-efficient UAV-based Edge computing Systems for XR Applications

Energy-efficient UAV-based Edge Computing Systems for XR App...

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Jaewoo Jung Seongah Jeong School of Electronic and Electrical Engineering Kyungpook National University Daegu South Korea

In this paper, we propose an energy-efficient unmanned aerial vehicle (UAV)-based mobile edge computing system to minimize the total energy consumption of mobile users (MUs) using X-Reality (XR) applications. Specifically, based on the characteristics of XR that have elements in common with neighboring users, we jointly optimize the UAV’s trajectory and resources for communication and computation, whose performances are verified via simulations.

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Coordinated Control Strategy and Economic Analysis of Flywheel Energy Storage Array for Wind Power Suppression 5

Coordinated Control Strategy and Economic Analysis of Flywhe...

引用

5th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Luo, Hairong Zhang, Qingping Gao, Bo Zhang, Rui Wu, Xin Ma, Zhiyong Electric Power Research Institute State Grid Ningxia Electric Power Co. Ltd. Yinchuan750000 China Advanced Flywheel Energy Storage Technology Research Center North China Electric Power University Beijing102206 China

ISBN: (纸本)9798350333442

With the increasing proportion of wind power connected to the grid, the randomness and fluctuation of its output power have an increasing impact on the stability of the grid, and there is an urgent need to smooth out its output power fluctuation. First, an unit model of IMW /0.25MWh flywheel energy storage (FES) is established and its charging and discharging characteristics are analyzed. Then, based on the improved wavelet packet decomposition and T Location-Scale (TLS) distribution method, a FES array capacity configuration method is proposed to determine the required capacity and power of FES array. Then, based on the proportional power distribution of FES units and the charge state control method, a coordinated control strategy for the FES array is proposed to mitigate wind farm power fluctuations, optimize power convergence in each FES unit, and maintain a reasonable charge state. Then, the economic model of wind farm and FES array is established. Finally, based on the operational data of a 15 MW wind farm, the proposed control strategy is simulated and validated, and the economics of the wind farm and FES array are analyzed. The results show that with this control strategy, the wind farm grid-connected power fluctuations can be achieved as specified. Relative to the system without energy storage, this method reduces the maximum output power fluctuations of the wind farm by 50.17% and 6.32% for 1 min and 10 min, respectively, and increases the annual revenue by 15%. © 2023 IEEE.

关键词： Flywheels

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Traffic Classification using Deep Learning Approach for End-to-End Slice Management in 5G/B5G 13

Traffic Classification using Deep Learning Approach for End-...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Mohammedali, Noor Abdalkarem Kanakis, Triantafyllos Al-Sherbaz, Ali Agyeman, Michael Opoku Northampton United Kingdom School of Computing and Engineering University of Gloucestershire Gloucestershire United Kingdom

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Network slicing is a key role in future networks. 5G networks are intended to meet different service demands of an application offered to users. 5G architecture is used to match the requirement of the Quality of Service (QoS) by addressing different scenarios in terms of latency, scalability and throughput with different service types. Using machine learning with network slicing allows network operators to create multiple virtual networks or slices on the same physical infrastructure. These slices are independent and customized. Precisely, These slices will be managed dynamically according to the requirements defined between the network operators and the users. For this research, multi-machine learning algorithms are used to train our model, classify network traffic and predict accurate slice type for each user. After the traffic classification, we compared and analysed the performance of various machine learning algorithms in terms of learning percentage, accuracy, precision and F1 score. © 2022 IEEE.

关键词： Quality of service

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Game-Based Service Requests and Channel Selection in Mobile Edge computing 15

Game-Based Service Requests and Channel Selection in Mobile ...

引用

15th international conference on Communication Software and Networks, ICCSN 2023

作者： Yin, Tong Chen, Xin Jiao, Libo Xing, Hua Min, Geyong School of Computer Science Beijing Information Science & Technology University Beijing China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350329360

Mobile edge computing (MEC) can provide service requests and resource allocation for user equipments (UEs), effectively reducing service delay for massive Internet-of-Things (IoT). Application providers cache services in the edge servers to reduce service requests delay. However, limited channel resources will affect transmission delay and communication quality when UEs request services from edge servers, thus reducing the user's Quality-of-Experience. In this paper, we investigate the service offloading problem between UEs and base stations (BSs), as well as between BSs and remote data centers (RDCs) in MEC systems with heterogeneous networks, considering channel selection between UEs and BSs. To minimize the service delay of each UE and thus improve user satisfaction, we propose game theory to model the problem as a non-cooperative game of channel selection and then demonstrate the existence of Nash equilibrium (NE). In order to find the ideal channel selection scheme, this paper proposes a best response based on a multi-user service request channel selection (SRCS) algorithm. Simulation results reveal that the proposed channel selection mechanism can improve user satisfaction and verify the effectiveness and convergence of the proposed algorithm. © 2023 IEEE.

关键词： Game theory

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