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Neural Network Algorithm in Automatic Allocation System of Water Resources 3

Neural Network Algorithm in Automatic Allocation System of W...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot systems. AIARS 2024

作者： Du, Lan Chen, Yao Wang, Jian Wanjiang University of Technology Office of Evaluation and Construction Ma'anshan China China Machinery International Engineering Design and Research Institute Co. Ltd Branch of East China Nanjing China

ISBN: (纸本)9798350376173

Since the beginning of the new century, the rational and efficient utilization of water resources has been one of the hot issues in the development of society. The emergence of the automatic allocation system for water resources makes the allocation of water resources more intelligent and scientific, and the neural network algorithm is one of the core technologies of the automatic allocation system of water resources. It uses big data and powerful computing power to provide intelligent, efficient and relatively safe solutions for the automatic allocation system of water resources. Fault detection and recovery, prediction and optimization, decision and scheduling, and security and risk management are the main application scenarios of the neural network algorithm. This paper will deeply study the automatic allocation system of water resources implemented by the neural network algorithm and the corresponding improvement measures. With the development of technology and the combination of other fields, the neural network algorithm applied to the automatic allocation of water resources system will be more perfect and provide a more intelligent, efficient and safe water resources management plan for the development of the industry. © 2024 IEEE.

关键词： neural network algorithm water resources allocation water resources automatic configuration system

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Evaluation of Equivalency Certificates for international Students using Quantum AI of Fairness and Efficiency QAA 3

Evaluation of Equivalency Certificates for International Stu...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Krishnan, Sumathy Surendran, R. Sathiamurthy, Maha Sweta Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Puducherry Technological University Department of Electronics and Communication Engineering Puducherry India

ISBN: (纸本)9798350369083

international student mobility is essential for developing worldwide cooperation and educational exchange. However, the existing method of evaluating international academic qualifications, which frequently relies on Equivalency Certificates, encounters notable obstacles such as subjectivity, lengthy processing periods, and possible biases. In order to tackle these problems, this research suggests an innovative method that use quantum amplitude amplification (QAA) to transform the process of evaluating equivalency certificates for overseas students. QAA utilises the computing capabilities of quantum algorithms to effectively and precisely assess the comparability of educational qualifications across various academic systems. Our project seeks to optimise the equivalency certificate evaluation process by combining quantum computing and artificial intelligence approaches. This will provide a more dependable and unbiased framework for assessing the qualifications of international students. This article provides a detailed analysis of current approaches and proposes new algorithms based on QAA. The main objective is to outline a plan for using QAA in the assessment of equivalency certificates. The prospective ramifications of this strategy encompass enhanced clarity, decreased processing durations, and heightened precision in assessing the qualifications of international students, so promoting seamless transitions into higher education or employment in foreign nations. This paper reviews current equivalence assessment methodologies, proposes QAA-based algorithms tailored for this purpose, and outlines a roadmap for applying QAI to equivalency certificate evaluation. Beyond enhancing accuracy and efficiency, this research has the potential to promote global creativity and collaboration by improving the evaluation process for international students. © 2024 IEEE.

关键词： Quantum computers

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Design and Implementation of a distributed Timing Task Scheduling System Based on Loosely Coupled Architecture 3

Design and Implementation of a Distributed Timing Task Sched...

引用

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

作者： Liu, Peng Deng, Chunyu Wang, Dazhong China Electric Power Research Institute Beijing China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

This paper implements Timing-Go, a distributed timed task scheduling system based on a loosely coupled architecture, which solves the problems of poor manageability, poor parallel processing capability, poor task scheduling capability and poor availability of existing timed task scheduling schemes. The system implements the automatic task registration mechanism through Java annotation technology, designs the task visualization management platform, and improves the efficiency of task management;proposes a timing task scheduling trigger scheme based on Quartz core elements, realizes the decoupling of scheduling process and execution process, and improves the performance of timing task scheduling;extends the functions of Quartz scheduling framework, introduces the workflow hierarchical processing mechanism of DAG model and the large task parallel processing mechanism based on consistency Hash algorithm, and improves the execution efficiency of timing tasks. Meanwhile, this paper also proposes a distributed synchronization control mechanism and a series of compensation mechanisms to guarantee the high availability of system operation and task execution. The Timing-Go system implemented in this paper has good performance and scalability, and its effectiveness has been verified by testing. © 2023 SPIE.

关键词： Quartz

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Holonic Learning: A Flexible Agent-based distributed Machine Learning Framework 23

Holonic Learning: A Flexible Agent-based Distributed Machine...

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23rd international conference on Autonomous Agents and Multiagent systems. AAMAS 2024

作者： Esmaeili, Ahmad Ghorrati, Zahra Matson, Eric T. Purdue University West Lafayette United States

Ever-increasing ubiquity of data and computational resources in the last decade have propelled a notable transition in the machine learning paradigm towards more distributed approaches. Such a transition seeks to not only tackle the scalability and resource distribution challenges but also to address pressing privacy and security concerns. To contribute to the ongoing discourse, this paper introduces Holonic Learning (HoL), a collaborative and privacy-focused learning framework designed for training deep learning models. By leveraging holonic concepts, the HoL framework establishes a structured self-similar hierarchy in the learning process, enabling more nuanced control over collaborations through the individual model aggregation approach of each holon, along with their intra-holon commitment and communication patterns. HoL, in its general form, provides extensive design and flexibility potentials. For empirical analysis and to demonstrate its effectiveness, this paper implements HoloAvg, a special variant of HoL that employs weighted averaging for model aggregation across all holons. The convergence of the proposed method is validated through experiments on both identically and independently distributed (IID) and Non-IID settings of the standard MNIST dataset. Furthermore, the performance behaviors of HoL are investigated under various holarchical designs and data distribution scenarios. The presented results affirm HoL's prowess in delivering competitive performance particularly, in the context of the Non-IID data distribution. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Edge computing

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Xphase3d: Memory-distributed Phase Retrieval for Reconstructing Large-Scale 3D Density Maps of Biological Macromolecules

Xphase3d: Memory-Distributed Phase Retrieval for Reconstruct...

引用

2024 IEEE international conference on Cluster computing, CLUSTER 2024

作者： Zhao, Wenyang Miyashita, Osamu Nakano, Miki Tama, Florence Institute of Transformative Bio-Molecules Nagoya University Nagoya Japan Center for Computational Science RIKEN Kobe Japan

ISBN: (纸本)9798350358711

Phase retrieval is a crucial step in processing data from advanced X-ray diffraction imaging experiments to analyze the 3D structure of biological macromolecules. However, when the 3D volume is large-scale and consists of numerous fine voxels, performing phase retrieval on it requires a significant amount of memory, often exceeding the capacity of individual workstations. To address this issue, we developed xphase3d, an open-source software package for performing multiprocessing parallel 3D phase retrieval to utilize distributed memory in clusters and supercomputers. Xphase3d has been successfully deployed on the supercomputer Fugaku and has contributed to research in the fields of structural biology and X-ray analysis. It is also compatible with other high-performance computing systems. © 2024 IEEE.

关键词： Supercomputers

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3rd international conference on Autonomous Unmanned systems. ICAUS 2023

3rd International Conference on Autonomous Unmanned Systems,...

引用

3rd international conference on Autonomous Unmanned systems. ICAUS 2023

ISBN: (纸本)9789819711024

The proceedings contain 93 papers. The special focus in this conference is on Autonomous Unmanned systems. The topics include: Multi-UAV Collaborative Search Strategy Based on Improved Genetic Algorithm;Joint Beamforming and Power Allocation in a MIMO Radar Network with Per-antenna Power Control;intelligent Trajectory Planning Algorithm for Unmanned Aerial Vehicles Based on Track Deception Against Radar Networks;Maneuvering Multi-target Tracking Based on DLSTM and Dynamic Association Gate;A DNN-Based GLMB Algorithm for Multi-sensor Multi-target Tracking with Dense Group Clutter;efficient Multiple Extended Target Tracking Based on Minimum Spanning Tree;improved Adaptive Sliding Mode Cooperative Guidance Law with Multi-constraints;a Cooperative Guidance Law with Multi-constraints for Intercepting Maneuvering Target;alleviating Local Optima and Enhancing Path Planning: A Deep Reinforcement Learning Approach for Autonomous Exploration;Research on Target Tracking Algorithm Between UAVs in Unknown Environment;a Maneuver Detection-Range Parameterized Cubature Kalman Filter for Bearings-Only Maneuvering Target Tracking;improvement of Ensemble Kalman Filter for Hypersonic Target Tracking;anti-bias Target Association Method Based on Laplacian Spectrum and Kuhn-Munkres Algorithm;Fully Automated UAV Cluster Pollution Source Detection Algorithm;Status and Application of Artificial Intelligence-Based Cooperative Positioning and Navigation Technology for UAV Clusters;a Fusion Method with Information Gains for distributed Multi-sensor Network;space Targets Tracking Algorithm Based on Improved State Equation with Universal Variable Updating;Multi-UAV Target Localization Based on 3D Object Detection and Visual Fusion;Real-World Experiments on Acoustic Detection of Small UAVs;relative Localization of Vehicle-to-Drone Coordination Based on Lidar;P-LVIO: A Plane-Based Lidar Visual Inertial Odometry in Urban Environments;a New Threat Assessment Method for Airborne Targets Based on Dynami

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3rd international conference on Autonomous Unmanned systems. ICAUS 2023

3rd International Conference on Autonomous Unmanned Systems,...

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3rd international conference on Autonomous Unmanned systems. ICAUS 2023

ISBN: (纸本)9789819710942

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Detection Method Of The distributed Photovoltaic Aggregation Attack 3

Detection Method Of The Distributed Photovoltaic Aggregation...

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3rd IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Guo, Jing Wang, Ziying Mao, Jiaming Guo, Yajuan Jiang, Haitao State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing China

ISBN: (纸本)9781665490788

With the gradual increase in the number of distributed photovoltaic systems.connected to the distribution network, various network attacks and abnormal attacks faced by the communication network will also increase. Therefore, it is very important to study the risk protection against distributed photovoltaic aggregation attacks and potential network anomaly attacks. Numerous distributed photovoltaic branches, circular operation, and flexible load fluctuation characteristics are important factors affecting the stable operation of traditional distribution networks and suffering from abnormal network attacks. This paper presents a mathematical model for distributed photovoltaic aggregation attacks, aiming at minimizing load fluctuations and transmission line power fluctuations in distributed photovoltaic network systems. to achieve detection of distributed photovoltaic aggregation attacks and related control and protection methods. © 2023 IEEE.

关键词： Electric power distribution

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3rd international conference on Autonomous Unmanned systems. ICAUS 2023

3rd International Conference on Autonomous Unmanned Systems,...

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3rd international conference on Autonomous Unmanned systems. ICAUS 2023

ISBN: (纸本)9789819710867

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Cloud-Enhanced Transfer Learning Framework for Medical Image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare 3

Cloud-Enhanced Transfer Learning Framework for Medical Image...

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3rd international conference on Integrated Circuits and Communication systems. ICICACS 2025

作者： Prabu, S. Chandra, N. Subhash Bhavani, M. Varma, V. K. Chaitanya Soni, Mukesh Mahendra Institute of Technology Department of Electronics and Communication Engineering Namakkal India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalle India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sagi Rama Krishnam Raju Engineering College Department of Mechanical Engineering Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331508456

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals. © 2025 IEEE.

关键词： distributed cloud

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