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Cyber-Physical distributed Intelligent Motor Fault Detection

SENSORS 2024年第15期24卷 5012页

作者： Al-Anbuky, Adnan Altaf, Saud Gheitasi, Alireza Auckland Univ Technol Sensor Network & Smart Environm Res Ctr SeNSe Auckland 1010 New Zealand

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber-physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber-physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates.

关键词： cyber-physical system fast Fourier transform motor fault detection artificial neural network distributed Internet of things

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splitDyn: Federated Split neural network for distributed Edge AI Applications

splitDyn: Federated Split Neural Network for Distributed Edg...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Khoa, Tran Anh Nguyen, Do-Van Dao, Minh-Son Zettsu, Koji National Institute of Information and Communications Technology Big Data Integration Research Center Tokyo Japan

ISBN: (纸本)9781665480451

Split learning (SL) is a popular distributed machine learning (ML) method used to enable ML. It divides a neural network based model into subnetworks. Then, it separately trains the subnetworks on distributed parties (e.g., client and server). In distributed ML, data are generated and collected on the client-side. In contrast, the collected data are processed using an application deployed on the server side. However, when applied in practice using Internet of things systems and clients, numerous obstacles occur because of limited configuration and resources. Dividing neural networks in the SL is the biggest problem and an open question in numerous studies. This study introduces splitDyn, which is a new dynamic SL solution to solve the aforementioned problems. This method provides a solution for eliminating their inherent drawbacks. The main idea is to apply a Round-Robin schedule to select the client for the training process. Then, the next idea is to use the Hungarian optimization algorithm to assign a layer to a client and enhance the accuracy. The proposed method reasonably achieved better accuracy and reduced processing time than the other learning models. Furthermore, it applies the incident datasets to predict the incident event and in edge computing for edge artificial intelligence (AI) applications. © 2022 IEEE.

关键词： Edge computing

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Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner

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INTEGRATED COMPUTER-AIDED ENGINEERING 2024年第1期31卷 19-41页

作者： Michailidis, Panagiotis Michailidis, Iakovos T. Gkelios, Sokratis Karatzinis, Georgios Kosmatopoulos, Elias B. Democritus Univ Thrace Dept Elect & Comp Engn Xanthi Greece Ctr Res & Technol Hellas ITI CERTH Informat Technol Inst Thessaloniki Greece

distributed Machine learning has delivered considerable advances in training neural networks by leveraging parallel processing, scalability, and fault tolerance to accelerate the process and improve model performance. However, training of large-size models has exhibited numerous challenges, due to the gradient dependence that conventional approaches integrate. To improve the training efficiency of such models, gradient-free distributed methodologies have emerged fostering the gradient-independent parallel processing and efficient utilization of resources across multiple devices or nodes. However, such approaches, are usually restricted to specific applications, due to their conceptual limitations: computational and communicational requirements between partitions, limited partitioning solely into layers, limited sequential learning between the different layers, as well as training a potential model in solely synchronous mode. In this paper, we propose and evaluate, the Neuro-distributed Cognitive Adaptive Optimization (ND-CAO) methodology, a novel gradient-free algorithm that enables the efficient distributed training of arbitrary types of neural networks, in both synchronous and asynchronous manner. Contrary to the majority of existing methodologies, ND-CAO is applicable to any possible splitting of a potential neural network, into blocks (partitions), with each of the blocks allowed to update its parameters fully asynchronously and independently of the rest of the blocks. Most importantly, no data exchange is required between the different blocks during training with the only information each block requires is the global performance of the model. Convergence of ND-CAO is mathematically established for generic neural network architectures, independently of the particular choices made, while four comprehensive experimental cases, considering different model architectures and image classification tasks, validate the algorithms' robustness and effectiveness in both syn

关键词： neural networks cognitive adaptive optimization gradient free training distributed learning model parallelism asynchronous training asynchronous training of neural networks

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A parallel approach to accelerate neural network hyperparameter selection for energy forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Criado-Ramon, D. Ruiz, L. G. B. Pegalajar, M. C. Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Granada Dept Software Engn Granada Spain

Finding the optimal hyperparameters of a neural network is a challenging task, usually done through a trial-and-error approach. Given the complexity of just training one neural network, particularly those with complex architectures and large input sizes, many implementations accelerated with GPU (Graphics processing Unit) and distributed and parallel technologies have come to light over the past decade. However, whenever the complexity of the neural network used is simple and the number of features per sample is small, these implementations become lackluster and provide almost no benefit from just using the CPU (Central processing Unit). As such, in this paper, we propose a novel parallelized approach that leverages GPU resources to simultaneously train multiple neural networks with different hyperparameters, maximizing resource utilization for smaller networks. The proposed method is evaluated on energy demand datasets from Spain and Uruguay, demonstrating consistent speedups of up to 1164x over TensorFlow and 410x over PyTorch.

关键词： neural networks Energy Time series forecasting Parallel computing Hyperparameter selection

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distributed source DOA estimation based on deep learning networks

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SIGNAL IMAGE AND VIDEO processing 2024年第10期18卷 7395-7403页

作者： Tian, Quan Cai, Ruiyan Qiu, Gongrun Luo, Yang Taizhou Univ Sch Elect & Informat Engn Taizhou 318000 Zhejiang Peoples R China Beijing 61618 Troops Beijing 100088 Peoples R China Peoples Liberat Army Gen Hosp Dept Orthoped Med Ctr 1 Beijing 100853 Peoples R China

With space electromagnetic environments becoming increasingly complex, the direction of arrival (DOA) estimation based on the point source model can no longer meet the requirements of spatial target location. Based on the characteristics of the distributed source, a new DOA estimation algorithm based on deep learning is proposed. The algorithm first maps the distributed source model into the point source model via a generative adversarial network (GAN) and further combines the subspace-based method to achieve central DOA estimation. Second, by constructing a deep neural network (DNN), the covariance matrix of the received signals is used as the input to estimate the angular spread of the distributed source. The experimental results show that the proposed algorithm can achieve better performance than the existing methods for a distributed source.

关键词： DOA estimation distributed source Generative adversarial network Deep neural network

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Research on Substation Monitoring and Fault Diagnosis Based on distributed Computing and Artificial neural network

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PARALLEL processing LETTERS 2023年第3期33卷 2340004-2340004页

作者： Yang, Fujun Li, Xiaohang Pingdingshan Power Supply Co Pingdingshan Peoples R China

In the Energy Conversion for Next-Generation Smart Cities, intelligent substation plays an important role in the power conversion. As an important guarantee for the stable operation of intelligent substation, the research on fault diagnosis technology is particularly important. In this paper, the acoustic characteristic diagnosis of substation equipment (take transformers for example) is researched and the application of "Voice Recognition + artificial neural network (ANN) " technology in substation fault diagnosis is analyzed. At the same time, the continuous online monitoring of the intelligent substation equipment will produce a large amount of monitoring data, which needs to be analyzed timely and effectively to understand the operating status of the equipment accurately. Because of this, this paper adopts distributed computing by establishing a real-time distributed computing platform, using open source technology to store the online monitoring of sound data into the computing platform for data processing to achieve the purpose of automatic fault detection and analysis. The results show that distributed computing can realize the intelligent analysis, storage, and visualization of equipment data in the substation, which provides data support for fault diagnosis. Besides, the fitting accuracy rates of ANN model are 95.123% for training process and the fitting accuracy rates of ANN model are 99.353% for training process and the overall fitting accuracy rates of ANN model are 95.478% and the error between the predicted value and the actual value of the 5 sound signals is within 5% in the fault diagnosis process. Consequently, the ANN model can accurately identify each fault sound of substation and achieve the purpose of fault diagnosis.

关键词： Substation monitoring fault diagnosis distributed computing artificial neural network

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A Survey on Parameter Server Architecture: Approaches for Optimizing distributed Centralized Learning

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IEEE ACCESS 2025年 13卷 30993-31015页

作者： Provatas, Nikodimos Konstantinou, Ioannis Koziris, Nectarios Natl Tech Univ Athens Sch Elect & Comp Engn Athens 11527 Greece Univ Thessaly Dept Informat & Telecommun Lamia 35100 Greece

Deep learning has emerged as a cornerstone technology across various domains, from image classification to natural language processing. However, the computational and data demands of training large-scale neural networks pose significant challenges. distributed learning approaches, particularly those leveraging data parallelism, have become critical to addressing these challenges. Among these, the parameter server architecture stands out as a widely adopted and scalable solution, enabling efficient training of large models across distributed systems. This survey provides a comprehensive exploration of the parameter server architecture, detailing its design principles and operation. It categorizes and critically analyzes research advancements across five key aspects: consistency control, network optimization, parameter management, straggler handling, and fault tolerance. By synthesizing insights from a wide range of studies, this work highlights the trade-offs and practical effectiveness of various approaches while identifying open challenges and future research directions. The survey aims to serve as a foundational resource for researchers and practitioners striving to enhance the performance and scalability of distributed deep learning systems.

关键词： Servers Training Data models Computational modeling Deep learning Surveys Parallel processing Training data Solid modeling neural networks distributed systems network parameter server synchronization centralized

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DistrEE: distributed Early Exit of Deep neural network Inference on Edge Devices

arXiv

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arXiv 2025年

作者： Peng, Xian Wu, Xin Xu, Lianming Wang, Li Fei, Aiguo Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

distributed DNN inference is becoming increasingly important as the demand for intelligent services at the network edge grows. By leveraging the power of distributed computing, edge devices can perform complicated and resource-hungry inference tasks previously only possible on powerful servers, enabling new applications in areas such as autonomous vehicles, industrial automation, and smart homes. However, it is challenging to achieve accurate and efficient distributed edge inference due to the fluctuating nature of the actual resources of the devices and the processing difficulty of the input data. In this work, we propose DistrEE, a distributed DNN inference framework that can exit model inference early to meet specific quality of service requirements. In particular, the framework firstly integrates model early exit and distributed inference for multi-node collaborative inferencing scenarios. Furthermore, it designs an early exit policy to control when the model inference terminates. Extensive simulation results demonstrate that DistrEE can efficiently realize efficient collaborative inference, achieving an effective trade-off between inference latency and accuracy. © 2025, CC BY.

关键词： Deep neural networks

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distributed neural network with TensorFlow on Human Activity Recognition over Multicore TPU 14

Distributed Neural Network with TensorFlow on Human Activity...

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14th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC)

作者： Kimm, Haklin Paik, Incheon East Stroudsburg Univ Dept Comp Sci East Stroudsburg PA 18301 USA Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781665438605

There have been increasing interests and success of applying deep learning neural networks to their big data platforms and workflows, say distributed Deep Learning. In this paper, we present distributed long short-term memory (dLSTM) neural network model using TensorFlow over multicore Tensor processing Unit (TPU) on Google Cloud. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. This model could extract human activity features automatically and classify them with a few model parameters. In the proposed model, the raw data collected by mobile sensors was fed into distributed multi-layer LSTM layers. Human activity recognition data from UCI machine-learning library have been applied to the proposed distributed LSTM (dLSTM) model to compare the efficiency of TensorFlow over CPU and TPU based on execution time, and evaluation metrics: accuracy, precision, recall and F1 score along with the use of Google Colab Notebook.

关键词： distributed LSTM Model TensorFlow Multicore TPU Human Activity Recognition

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Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on distributed Current Measurement and neural network Model 1st

Prediction and Evaluation Method of Modification Effect of L...

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1st Electrical Artificial Intelligence Conference, EAIC 2024

作者： Yizhuo, Wang Zhonlian, Li Long, Li Runhua, Li Xinglei, Cui Zhi, Fang School of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9789819648559

Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD excitation voltage amplitude, repetition frequency, discharge working gas flow rate and reaction medium flow rate were changed. The discharge current at different positions was measured by self-made current coil. The water contact angle and flashover voltage at the corresponding position were tested experimentally as the evaluation criteria of the modification effect. The feature extraction of distributed current is carried out by manual and image recognition methods, and the prediction and evaluation models are established by BP neural network and convolutional neural network (CNN) respectively. The accuracy and generalization ability of the two models are compared. The results show that the CNN model based on image recognition has higher accuracy and generalisation ability in predicting the water contact angle and flashover voltage on the material surface compared to the BP neural network model based on manual feature extraction. Compared with the BP neural network, the CNN model reduces the mean absolute error (MAE) by 41.3% and the root mean square error (RMSE) by 36.1% in predicting the water contact angle of the material surface, and the mean absolute error (MAE) reduces by 47.7% and the RMSE reduces by 40.2% in the flashover voltage. The experimental results with different processing distances are used to examine the generalisation ability of the two models, and the results show that

关键词： Electric insulating materials

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