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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30781 条记录，以下是461-470 订阅

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Improving Human Activity Recognition Through Multisensor Data Fusion Techniques in Wireless sensor Networks 3

Improving Human Activity Recognition Through Multisensor Dat...

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3rd international conference on computing and Machine Intelligence (ICMI)

作者： Gargees, Rasha S. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Wireless sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity Recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector Machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Wireless sensor network(WSN) human activity recognition (HAR) multi-sensor datasets data fusion machine learning classification Covariance Intersection (CI) Kalman Filter (KF)

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Utilizing Distribution Optimization through Cloud computing in Wireless Networks 15

Utilizing Distribution Optimization through Cloud Computing ...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shaik, Intiaz Devineni, Sreenath Gorantla, Bhargavi Jiwani, Nasmin Kumar, D. Sathish United States Dept. of Information Technology University of The Cumberlands WilliamsburgKY United States Department of ECE Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350370249

Cloud computing is becoming part and parcel of the optimization of wireless networks for distributed mobile applications. Thanks to the scalable, distributed nature of Kubernetes which leverages the to-be had sources to construct disbursed dynamical structures that can be moreover optimized via cloud-enabled assets. To get the best performance there is a need to have distributed systems that can sense what resources are appropriate and efficiently consume the resources that are suitable for the given application being executed. These components are managed in cloud computing and thus can be used in an eco-friendly manner. distributed cloud computing can be used to optimize the performance of the network by using the resources currently available for distributed applications over wireless networks. Disbursed cloud computing for wi-fi networks is aimed at attaining the most efficient use of resources of the network by the least finding it suitable for distribution. Cloud computing may even offload the burden on mobile gadgets with the aid of centralizing certain factors of the network (consisting of parameters governing access to crucial components of the community), and ensuring that community resources are flippantly distributed to the ends of the network. It enhances the accuracy of network routing, paving the way for the best resources to be allocated & utilized optimally. In addition, cloud computing can enable applications to run on multiple devices concurrently, taking advantage of the distributed nature of the wireless network. ©2024 ieee.

关键词： distributed cloud

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Proceedings - 2024 ieee 31st international conference on High Performance computing, Data, and Analytics, HiPC 2024

Proceedings - 2024 IEEE 31st International Conference on Hig...

引用

31st Annual ieee international conference on High Performance computing, Data, and Analytics, HiPC 2024

ISBN: (纸本)9798331509095

The proceedings contain 26 papers. The topics discussed include: allocation strategies for disaggregated memory in HPC systems;retrospection on the performance analysis tools for large-scale HPC programs;scaling large language model training on frontier with low-bandwidth partitioning;exploring algorithmic design choices for low latency CNN deployment;HyperSack: distributed hyperparameter optimization for deep learning using resource-aware scheduling on heterogeneous GPU systems;GDBOD: density-based outlier detection exploiting efficient tree traversals on the GPU;multi-space tree with incremental construction for GPU-accelerated range queries;a more scalable sparse dynamic data exchange;and from bits to qubits: challenges in classical-quantum integration.

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Semantic Slicing across the distributed Intelligent 6G Wireless Networks 20

Semantic Slicing across the Distributed Intelligent 6G Wirel...

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20th Annual ieee international conference on Sensing, Communication, and Networking (SECON)

作者： Loven, Lauri Shahid, Hafiz Faheem Le Nguyen Harjula, Erkki Silven, Olli Pirttikangas, Susanna Lopez, Miguel Bordallo Univ Oulu Ctr Ubiquitous Comp Oulu Finland Univ Oulu Ctr Wireless Commun Oulu Finland Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland

ISBN: (纸本)9798350300529

In the age of the Internet of Things (IoT) and the expanding computing continuum, it's crucial to manage and share resources at the edges of networks. This position paper presents a new concept known as 'semantic slicing'. This approach harnesses the power of artificial intelligence (AI), wireless networks, edge computing, and sensing technologies to enable novel applications, optimize resource allocation, and streamline data processing and decision-making across complex systems spanning the computing continuum. Semantic slicing applies a deep understanding of the data and specific application requirements to intelligently allocate resources and distribute processing tasks in the computing continuum. This strategy allows for the creation of systems that are not only more efficient and responsive, but also better equipped to adapt to a variety of applications and services.

关键词： Semantics

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Approach to Acoustic Drone Detection System Development 12

Approach to Acoustic Drone Detection System Development

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12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Kochan, Roman Hots, Nataliya Fabirovskyy, Sergiy Storozh, Volodymyr Kosterov, Alexander Ozirkovskyy, Leonid Helesh, Anna Arkadii, Shulhai Kochan, Volodymyr Strykhalyuk, Bohdan Mykyichuk, Mykola Demydov, Ivan University of Bielsko-Biala Bielsko-Biala42-300 Poland Lviv Polytechnic National University Lviv79013 Ukraine State Scientific Research Institute for Metrology of Measuring and Control Systems Sistema Lviv79000 Ukraine I. Horbachevsky Ternopil State Medical University Ternopil46001 Ukraine West Ukrainian National University Ternopil46003 Ukraine

ISBN: (纸本)9798350358056

An analysis of drone detection methods was carried out in this paper. There is a reasoned choice of the optimal method for implementation based on the analysis of acoustic signals generated by drones. The set of methods of acoustic signal analysis is presented. These methods provide the measurement of drone coordinates and complement each other depending on the flight conditions. The structure of a distributed sensor network oriented on the online detection of drones and their tracking has been synthesized. The developed system provides the appropriate complexity as well as large flexibility and scalability. © 2023 ieee.

关键词： Acoustic signal Distributive sensor network Drone detection

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Optimal Location and Size of Inverter-Based distributed Generation Considering Power Loss Reduction, Voltage Profile Improvement and Power Quality Preservation 11

Optimal Location and Size of Inverter-Based Distributed Gene...

引用

11th international conference on Electrical, Electronic and computing Engineering, IcETRAN 2024

作者： Rakočević, Stevan Ćalasan, Martin Vujošević, Snežana University of Montenegro Faculty of Electrical Engineering Podgorica Montenegro

ISBN: (纸本)9798350386998

This paper addresses optimal allocation and sizing of inverter-based distributed Generation (DG) with the aim of reducing power loss, enhancing voltage profile and preserving power quality of radial distribution network. Problem is formulated as Mixed-Integer Non-Linear Programming (MINLP), and is solved using BONMIN solver in General Algebraic Modeling systems (GAMS) environment. The proposed approach is evaluated on ieee 69-bus test system, considering non-sinusoidal operating condition. Both single-objective and multi-objective optimization of loss and voltage deviation is considered, while analyzing impact of inverter-based DGs on Total Harmonic Distortion (THD) © 2024 ieee.

关键词： Mixed-integer linear programming

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Intelligent System for distributed Deep Learning Training Traffic Optimization 24

Intelligent System for Distributed Deep Learning Training Tr...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Lu, Weipeng Wu, Shuang Ou, Junhui Niu, Bing He, Jin Song, Kevin Zheng, Xinwei Ustc Hefei China Vmware Beijing China

ISBN: (纸本)9798350319934

distributed training crossing multiple computing nodes and accelerators has been the mainstream solution for large model training. Precedent work on distributed deep learning (DDL) training acceleration has focused on improving the efficiency of communication and GPU resource scheduling, with little attention given to network bandwidth usage. However, due to cluster GPU fragmentation, more than one training job is frequently assigned to the same node to reduce waiting time. The competition for network bandwidth among various distributed training jobs becomes a problem that cannot be overlooked, and this competition results in a considerable reduction in training speed. We propose a system deployed in cluster nodes that includes traffic monitoring, classification, and prediction of DDL training status and implement a priority-based traffic scheduling policy that considers DDL training features. We demonstrate that this policy is beneficial compared to Linux's default policy, which can increase training speed by approximately 18%. Additionally, we increase the training speed by approximately 20% in the case of continuous background traffic by increasing the bandwidth priority during DDL training. © 2022 ieee.

关键词： Deep learning

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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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Robust Fault Detection and Isolation of sensor in Neutral Time-Delay systems based on Space Geometric Approach

Robust Fault Detection and Isolation of Sensor in Neutral Ti...

引用

2023 ieee international conference on Signal Processing, Communications and computing, ICSPCC 2023

作者： Zhang, Zhiheng Hou, Yandong Chen, Zhengquan School of Artificial Intelligence Henu University Zhengzhou China School of Computer and Information Eegineering Henu University Kaifeng China

ISBN: (纸本)9798350316728

In this paper, the problem of sensor fault detection and isolation (FDI) for neutral time-delay systems with uncertain disturbances is studied. The sensor fault is transformed into actuator fault by state space equation. Then, a set of residual generators is designed by using space geometric approach, and the influence of disturbance and error signal on residual is weakened by using H-infinite optimization technology, so that the residual signal can satisfy the one-to-one correspondence with the sensor fault. Simulation results show the effectiveness and robustness of the proposed approach. © 2023 ieee.

关键词： Time delay

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distributed Training Optimization for DCU 5

Distributed Training Optimization for DCU

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5th international conference on Information Science, Parallel and distributed systems, ISPDS 2024

作者： Chen, Biaoyuan Li, Jianan Li, Pengfei Xie, Jingming Zhao, Rongcai Gao, Wei Han, Lin Zhengzhou University Zhengzhou China National Supercomputing Center Zhengzhou China National Supercomputing Center Zhengzhou Zhengzhou China

ISBN: (纸本)9798350352719

With the continuous advancement of large-scale models and expanding volumes of data, a single acceleration hardware is no longer sufficient to meet the training demands. Simply stacking multiple acceleration hardware together in parallel does not enhance training efficiency, but rather results in a waste of computing resources. Therefore, for existing distributed training parallel methods, we first establish the definition of parallel strategies and construct cost boundaries to enable the automatic selection of efficient parallel strategies. Additionally, we implement a time-balanced partition algorithm based on pipeline parallelism to achieve more equitable model partitioning at each stage and reduce the overall cost of model training time. Experiments conducted on a single machine with four DCU cards and a dual machine with eight DCU cards demonstrate that multiple models, supported by the automatic parallel strategy, exhibit outstanding distributed training performance. Simultaneously, the average acceleration ratio can reach 1.13× with pipeline optimization. In summary, automatic selection of strategies and pipeline parallelism optimization significantly enhances the efficiency of distributed training for large models. © 2024 ieee.

关键词： Digital storage

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