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共 6615 条记录，以下是81-90 订阅

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Analyzing Harmonic Effects on Synchronous Generator Supplying parallel PWM Rectifier in Hybrid Train systems 16

Analyzing Harmonic Effects on Synchronous Generator Supplyin...

引用

16th international conference on information Technology and Electrical Engineering, ICITEE 2024

作者： Wijaya, Fransisco Danang Alfat, Darma Adi Guna Technology Gadjah Mada University Department of Electrical Engineering and Information Yogyakarta Indonesia

ISBN: (纸本)9798350375817

Hybrid trains for transportation now days is increase in popularity due to many benefits than diesel trains. Hybrid trains discussed in this paper contain two generators and one battery as electrical power sources. Generator is connected in series with PWM rectifier and the output rectifier is connected in parallel with the battery. Unbalance PWM signal that controls an insulated gate bipolar transistor (IGBT) creates an unbalanced load to generator that generate harmonics distortion to AC power. This paper is written to analyze harmonic effects on synchronous generator supplying parallel PWM rectifier in hybrid train systems. The analysis uses two cases the first case is using two generator and one battery as power source, and the second case is using one generator and one battery as power source. Research result shows that if the load is higher, the THD will be lower. The results from first case simulation shows load 10% gets 4.08% harmonics distortion in first and second generator, load 20% get 3.66% and 3.397% for first and second generator, load 30% get 3.65% and 3.64% for first and second generator, load 40% get 3.62% and 3.61% for first and second generator. The results from second case simulation shows load 10% get 3.86%, load 20% get 3.68, load 30% get 3.67%, load 40% get 3.60% harmonics distortion for first generator. For load 60 until 70%, the result of total harmonic distortion in first and second case is the same which is load 60% get 3.55% and 3.57% for first and second generator, load 70% get 3.61% and 3.48% for first and second generator, load 80% get 3.60% and 3.46% for first and second generator, load 90% get 3.46% and 3.50% for first and second generator, load 100% get 3.48% and 3.44% for first and second generator. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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distributed Training Based on Horizontal Scaling for Food Image Classification 4

Distributed Training Based on Horizontal Scaling for Food Im...

引用

4th international conference on Embedded and distributed systems, EDiS 2024

作者： El Amin, Gasbaoui Mohammed Soumia, Benkrama Mostefa, Bendjima Sofiane, Abden Ikram, Kourtiche Tahri Mohammed University Laboratory of Tit Mathematics and Computer Science Department Bechar Algeria

ISBN: (纸本)9798331519872

Deep learning models for food image classification rely on vast amounts of data to effectively recognize and differentiate between various food items. However, training these models on such extensive datasets presents significant computational challenges. Big data and distributed training are inherently complementary in addressing these challenges. Big data involves large volumes of information that traditional single-machine processing cannot handle efficiently. distributed training offers a solution by enabling machine learning models to scale across multiple machines or nodes, thereby more effectively processing and learning from extensive datasets. By partitioning tasks into smaller chunks processed in parallel, distributed training optimizes computational resources, ensuring efficient analysis of big data while maintaining fault tolerance and performance scalability. This paper explores various strategies for distributed training and communication between GPUs. We compare the performance and utilization of a single P100 GPU versus two T4 GPUs on the Food-11 dataset, focusing on accuracy and training time. Our results indicate that using two GPUs yields slightly better performance than a single GPU, reducing the training time by 125 seconds, decreasing the validation dataset loss by 0.02, and increasing the accuracy by 1%. The overall accuracy achieved is 92%, with a corresponding loss of 0.28. © 2024 IEEE.

关键词： big data data distribution deep learning food images GPUs parallel processing PyTorch

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Anomaly Monitoring of Hydropower Units under Complex Conditions 5

Anomaly Monitoring of Hydropower Units under Complex Conditi...

引用

5th international conference on information Science, parallel and distributed systems, ISPDS 2024

作者： Chen, Siqi Wang, Hongteng Ma, Liyong School of Control Science and Engineering Qingdao China Huadian Electric Power Research Institute Co. Ltd Hangzhou China Harbin Institute of Technology School of Information Science and Engineering Weihai China

ISBN: (纸本)9798350352719

Hydropower has now become a fundamental industry and infrastructure for the social and economic development of our country. While hydropower brings convenience and benefits, it also comes with significant safety hazards. Therefore, real-time condition monitoring of hydropower units is necessary. This paper addressed the complex operational conditions of hydropower units by first dividing the data into mixed datasets containing both normal and abnormal data, and independent datasets where normal and abnormal data were separated. For the mixed datasets with both normal and abnormal data, the upper and lower bounds for anomaly detection were obtained using the standard deviation criterion based on normalized weighting and Monte Carlo simulation. For the independent datasets that follow a normal distribution, the dynamic upper and lower bounds for anomaly detection were determined based on the standard deviation criterion. For the independent datasets that do not follow a normal distribution, anomaly monitoring and calculation were performed based on multi-factor indicators. Experimental results show that when the proposed methods were applied to actual data from hydropower units under complex conditions, the accuracy of anomaly detection was relatively high, resulting in better monitoring of abnormal conditions. © 2024 IEEE.

关键词： complex conditions condition monitoring hydroelectric units generating set normal distribution standard deviation criterion

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Fully distributed Consensus of Multiagent systems under Uncertain Network Topology 24

Fully Distributed Consensus of Multiagent Systems under Unce...

引用

2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Han, Jin Gao, Xiaolong Xiong, Wei Du, Shengli Qiao, Junfei Beijing University of Technology Beijing China Baotou Reclaimed Water Resources and Sewage Treatment Co. Ltd Baotou China Baotou Water Group Baotou China

ISBN: (纸本)9798400710681

In this paper, the leader-following consensus problem of a class of first-order nonlinear multiagent systems is studied. Since the agent is usually driven by electromechanical devices in the working process and the system is in a complex communication network, this paper considers the unknown external disturbance of the agent’s actuator state and the topology diagram with unknown weights. By introducing adaptive parameters, a fully distributed leader-following consensus protocol is developed. It is worth noting that the design of the controller does not need to rely on any global information, which greatly broadens the practicality of the scheme. Finally, a simulation example is given to verify the effectiveness of the proposed strategy. © 2024 Copyright held by the owner/author(s).

关键词： Network topology

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EdgeAnchor: A Rapid and Balanced File Storage Strategy at the Network Edge 29

EdgeAnchor: A Rapid and Balanced File Storage Strategy at th...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Liu, Han Xie, Xingrui Zhang, Zhuopu Cheng, Geyao Luo, Lailong Guo, Deke National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9798350330717

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers' storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively. © 2023 IEEE.

关键词： dynamic network edge file storage load balance rapid indexing

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Efficient NTTRU Implementation on ARMv8 29

Efficient NTTRU Implementation on ARMv8

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Zhuo Zheng, Jieyu Zhao, Yunlei Fudan University School of Computer Science Shanghai China

ISBN: (纸本)9798350330717

To tackle the challenges introduced by quantum computers to traditional public key cryptography, the domain of post-quantum cryptography (PQC) has taken center stage. Within this domain, the evaluation of computational performance emerges as a pivotal yardstick. Notably, NTTRU stands for one of the most efficient PQC schemes for key encapsulation mechanisms (KEM). This paper introduces the first optimized implementation of NTTRU on ARMv8 architecture. By leveraging the capabilities of the NEON engine, we strategically optimize the core modules of NTTRU: NTT/INTT, polynomial base case multiplication, and polynomial inversion. These optimizations have resulted in remarkable performance gains of 7.37×, 6.10×, 5.91×, and 4.43×, respectively when compared to the reference implementation. For the whole implementation, we achieve performance improvement of 2.85×, 2.36×, and 3.27× in key generation, encapsulation, and decapsulation respectively. © 2023 IEEE.

关键词： Key encapsulation mechanism NEON parallel optimization NTTRU Number Theoretic Transform Post-quantum cryptography

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Performance Bound on Blind Estimation of Environmental Parameters and Radiation Source Locations with Misspecified Multipath TOA Measurements 16

Performance Bound on Blind Estimation of Environmental Param...

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Wen, Hao Jiao, Yang Zhang, Kehao Peng, Xiangyu Wan, Qun Nation Key Laboratory on Blind Signal Processing School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

Multipath propagation has attracted extensive attention due to its inclusion of environmental information. However, it is difficult to match multipath with the environment without a priori knowledge, making it challenging to exploit multipath. This paper focuses on the performance bound of the estimation problem in a rectangular room. Due to the lack of parameter information of the room in base station (BS), the estimation of environmental parameters and locations of user equipment (UE) distributed in the room are blind estimation problems. Measurements are the time of arrival (TOA) of first-order non-line-of-sight (NLOS) paths and line-of-sight (LOS) path between the BS and UEs, but the specific pairing between NLOS paths and the environment remains unknown. The paper first describes the pairing problem, which causes misspecified multipath TOA measurements, and subsequently derives the likelihood function and the performance bound known as the misspecified Cramer-Rao bound (MCRB) under various pairing conditions. Simulation results demonstrate that only under correct pairings or just a few wrong pairings, the MCRB will significantly less than the other case. © 2025 SPIE.

关键词： Cramer-Rao bounds

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A Comparative Evaluation of Zero Knowledge Proof Techniques 10

A Comparative Evaluation of Zero Knowledge Proof Techniques

引用

10th international conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Rostamkolaei Motlagh, Seyed Mohsen Pahl, Claus Barzegar, Hamid R. El Ioini, Nabil Free University of Bozen-Bolzano Bolzano39100 Italy University of Nottingham Semenyih43500 Malaysia

ISBN: (纸本)9789897587504

Common to many distributed systems such as the Internet-of-Things (IoT) is decentralisation, often with a growing number of devices with diverse computational capabilities and security requirements. If integrated into critical applications, ensuring secure communication and data integrity is critical. In particular, privacy and security concerns are growing with the rise of these architectures. Zero-Knowledge Proof (ZKP) techniques are solutions to improve security without compromising on privacy. While the advantages of ZKP techniques are well-documented, it is important to consider the inherent limitations of these distributed environments, such as restricted processing power, memory capacity, and energy constraints, when aiming to solve security concerns. Here, we select two prominent ZKP techniques, Bulletproof and zk-STARK, and experimentally compare a number of their variants for a set of architecture-specific assessment criteria. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： parallel processing systems

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Accelerating Look-Up Table based Matrix Multiplication on GPUs 29

Accelerating Look-Up Table based Matrix Multiplication on GP...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhao, Yuchen Gao, Lan Zhang, Weigong Wang, Jing Qiu, Dehui Capital Normal University Information Engineering College Beijing China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350330717

Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Approximated Matrix Multiplication (AMM) based on table look-ups can significantly reduce the pressure on computing units and memory bandwidth, and has great potential in large-scale machine learning applications. In this work, we speed up table look-ups on GPUs to improve the performance of matrix multiplication. To avoid random memory accesses in table look-ups, we propose a novel warp-wide data sharing execution model. With this execution model, we develop a GPU AMM library to speed up MADDNESS (the state-of-the-art AMM), named GPU-MADDNESS. The experimental results show that GPU-MADDNESS improves the performance by 103X on average, and outperforms the tiling implementation by up to 42%. © 2023 IEEE.

关键词： approximated matrix multiplication GPU performance optimization table look-ups

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Optimizing Data-Driven Federated Learning in UAV Networks 30

Optimizing Data-Driven Federated Learning in UAV Networks

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30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Li, Datian Xiao, Mingjun Xu, Yin Wu, Jie University of Science and Technology of China School of Computer Science and Technology & Suzhou Institute for Advanced Research China Temple University Department of Computer and Information Sciences United States

ISBN: (纸本)9798331515966

Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned Aerial Vehicle (UAV) networks, named DDFL, which features an innovative three-layer architecture. In this architecture, UAVs, inherently lacking data, serve as mobile clients and are tasked with periodically collecting desired data from a group of Points of Interest (PoIs) for FL training. Meanwhile, a Base Station (BS) coordinates these UAVs to efficiently train a high-quality global model. Our objective is to determine a data collection strategy for each UAV to minimize the time required to meet the global model's loss requirement within the constraint of energy consumption. Through theoretical analysis, we establish a bound for the convergence speed of DDFL and quantify the impact of collecting data from different PoIs on the global model's loss function. Leveraging these analyses, we formulate the PoI selection problem as a novel two-stage Combinatorial Multi-Armed Bandit (CMAB) problem with multiple constraints. We then propose an Adaptive Two-stage CMAB-based algorithm, named FedATC, to jointly optimize the data collection route and UAV velocity. Extensive simulations demonstrate that FedATC significantly reduces the time required to achieve desired model quality compared to state-of-the-art algorithms. © 2024 IEEE.

关键词： Federated learning

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