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Machine Learning-based Fault Diagnosis for Distribution networks with distributed Renewable Energy Resources

Machine Learning-based Fault Diagnosis for Distribution Netw...

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IEEE Asia Power and Energy Engineering conference (APEEC)

作者： Bin Li Ruifeng Zhao Junqi Qiu Zhongshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Zhongshan China Power Dispatch and Control Center of Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

With the growing capacity of distributed renewable energy resources (RERs) integrated into distribution grids, the power flow distribution becomes more complex. Traditional fault diagnosis solutions are proving inadequate for the demands of the evolving distribution network, thereby diminishing the reliability and sensitivity of the protection system. To address this challenge, this paper introduces an innovative fault diagnosis approach for distribution networks incorporating RERs, leveraging signal processing techniques and machine learning algorithms. Initially, effective features are obtained from the measured current signals by the Hilbert-Huang transform (HHT). Subsequently, these fault features serve as inputs for training feed-forward neural networks to build fault diagnosis models (including detection, classification, and segment identification). Simulation tests are conducted on a 13-node distribution network with three different types of RERs. Simulation results show that the method can accurately diagnose distribution network faults, and is robust to fault inception angle variations, transition resistance, and noise interference.

关键词： Fault diagnosis Training Renewable energy sources Uncertainty Simulation Current measurement Noise

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Telemetry Data Compression Algorithm Based on Balanced Cyclic neural network and Deep Learning

Telemetry Data Compression Algorithm Based on Balanced Cycli...

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Image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Min Huang School of Computer Sichuan Technology and Business University Chengdu China

With the rapid development of telemetry and telecontrol technology, the amount of information for acquiring telemetry data is increasing, which requires a large amount of storage space when storing, a large bandwidth when transmitting, and data processing as fast as possible. Therefore, data compression technology is needed to solve this problem. The redundancy of telemetry data is also very large. Statistics show that the redundancy of data transmitted in the standard telemetry system reaches more than 90%. In order to reduce the signal space and relieve the pressure of data processing, it is necessary and important to use data compression technology in telemetry system. Considering the space performance and time performance of the lossless compression algorithm, several commonly used lossless compression algorithms are compared for telemetry noise data. The compression space performance of each algorithm still needs to be improved. Considering that the measured engineering data is distributed in a typical balanced way, we studied the anomaly detection problem of time series data with inter class balance, and proposed an anomaly detection problem based on recurrent neural network (RNN). Finally, the overall test of the developed data acquisition and compression system is carried out. The test results show that the system has completed the real-time acquisition and compression of six channels of rapidly varying parameters, and the compression ratio is more than 1.8:1. All indicators meet the design requirements.

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DDNN based data allocation method for IIoT 10

DDNN based data allocation method for IIoT

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10th Annual International conference of Information and Communication Technology (ICICT)

作者： Wang, Chuting Guo, Ruifeng Hu, Yi Yu, Haoyu Su, Wenju Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Shenyang Golding NC Tech Co Ltd Shenyang 110168 Peoples R China Shenyang Aircraft Ind Grp Co LTD Shenyang 110034 Peoples R China

With the complete application of artificial intelligence in the field of industrial production and manufacturing and the rapid development of edge computing, industrial processing sites often need to deploy machine learning tasks at edges and terminals. We propose a data allocation method based on distributed Deep neural networks (DDNN) framework, which allocates data to edge servers or stays locally for processing. DDNN divides deep learning tasks and deploys pre-trained shallow neural networks and deep neural networks at local or edges, respectively. However, all data is processed locally, and the failure is sent to the edge server or the cloud. It will lead to excessive pressure on local terminal equipment and long-term idle edge servers, which cannot meet industrial production's real-time requirements on user privacy and time-sensitive tasks. In this paper, the complexity and inference error rate of machine learning model, the data processing speed of local equipment and edge server, and the transmission time are comprehensively considered to establish the system model. A joint optimization problem is proposed to minimize the total data processing delay. The optimal solution is derived analytically, and the optimal data allocation methhod is given. Simulation experiments are designed to verify the method's effectiveness and study the influence of key parameters on the allocation method. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Machine Learning distributed Deep neural networks Data Allocation

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Improved Whale Optimization Algorithm and Optimized Long Short-Term Memory for DDoS Cyber Security Threat

Improved Whale Optimization Algorithm and Optimized Long Sho...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Sanjaikanth E Vadakkethil Kiran Polimetla Srikanth Velpula Piyush Kumar Pareek Dipti Sontakke Somanathan Pillai School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Dept. of Information Technology Adobe Inc Austin Texas USA Department of Electrical and Electronics Engineering SR University Warangal India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Consultant Capgemini Inc USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The distributed Denial of Service (DDoS) attacks is a critical cyber security threat that blocks the services for users and leads to important damage for reputation and effective customers. The existing methods provides low classification accuracy due to irrelevant features in classification performance. The Improved Whale Optimization Algorithm (IWOA) is proposed for the selecting of relevant features which improved the classification performance. The selected features are detected and classified by Optimized Long Short-Term Memory (OLSTM) that provided high detection rate of DDoS attacks. The one-hot encoding and min-max normalization techniques are used in the data pre-processing stage to improve the performance of classification. The CIC-DDoS 2019 dataset are used for evaluating the proposed method. The IWOA-OLSTM method attained highest accuracy 97.12%, precision 96.74%, recall 96.27%. f1-score 96.54% and DR 93.26% on CIC-DDoS 2019 dataset which is effective than Convolutional neural network (CNN) - Bidirectional Long Short-Term Memory (BiLSTM) and CNN-LSTM.

关键词： Deep learning Denial-of-service attack Feature extraction Whale optimization algorithms Encoding Classification algorithms Convolutional neural networks

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End-to-End Performance Optimization for Training Streaming Convolutional neural networks using Billion-Pixel Whole-Slide Images

End-to-End Performance Optimization for Training Streaming C...

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9th IEEE International conference on Big Data (IEEE BigData)

作者： Tao, Liang-Wei Hwu, An-Fong Huang, Yu-Jen Chen, Chi-Chung Yeh, Chao-Yuan Hung, Shih-Hao Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan aetherAI Co Ltd Taipei Taiwan

ISBN: (纸本)9781665439022

The combination of digital pathology and artificial intelligence attracts more and more attention. In order to take into account the global and detailed textural information, recent works proposed to use whole-slide images (WSI) with more than 10 million pixels to train artificial neural networks in pursuit of a high-precision model. However, such an approach faces new technical challenges. When researchers conduct neural network training on a graphics processing unit (GPU), the extremely high spatial resolution of WSI and the large amount of intermediate data generated during the training process exceed the memory capacity of the GPU. Therefore, streaming convolutional neural network (SCNN) was proposed to disassemble a large image into multiple smaller patches that can be forward and backward propagated under the limitation of GPU memory during the training process. However, in a multi-GPU high-performance computing (HPC) system, when the central processing unit (CPU) performs data preprocessing for the distributed training of multiple GPUs, decoding large images and data augmentation consume a lot of memory and CPU time, resulting in a performance bottleneck. This paper first presents a multithreaded image decoder optimized for multi-core CPUs to speedup WSI loading. We also propose a patch-level data augmentation algorithm and implement it on a dedicated GPU to distribute the augmented data to other GPUs for training the SCNN. We use the above method to train the ResNet-50 SCNN model by learning from the TCGA-LUAD and TCGA-LUSC datasets published by The Cancer Genome Atlas. In comparison to training the SCNN on multiple GPUs using the regular distributed data parallelism, our method can save up to 92.8% of memory and increase the training speed by up to 242.5% when training with 8 A100 GPUs on the NVIDIA DGX-A100 system.

关键词： Machine learning distributed computing Data preprocessing Performance analysis

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A mechanistic multi-area recurrent network model of decision-making 35

A mechanistic multi-area recurrent network model of decision...

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35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Kleinman, Michael Chandrasekaran, Chandramouli Kao, Jonathan C. Univ Calif Los Angeles Los Angeles CA 90095 USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781713845393

Recurrent neural networks (RNNs) trained on neuroscience-based tasks have been widely used as models for cortical areas performing analogous tasks. However, very few tasks involve a single cortical area, and instead require the coordination of multiple brain areas. Despite the importance of multi-area computation, there is a limited understanding of the principles underlying such computation. We propose to use multi-area RNNs with neuroscience-inspired architecture constraints to derive key features of multi-area computation. In particular, we show that incorporating multiple areas and Dale's Law is critical for biasing the networks to learn biologically plausible solutions. Additionally, we leverage the full observability of the RNNs to show that output-relevant information is preferentially propagated between areas. These results suggest that cortex uses modular computation to generate minimal sufficient representations of task information. More broadly, our results suggest that constrained multi-area RNNs can produce experimentally testable hypotheses for computations that occur within and across multiple brain areas, enabling new insights into distributed computation in neural systems.

关键词： Recurrent neural networks

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Innovative Approaches for Enhanced Security in Cloud network Traffic: An Adaptive Deep Learning Framework

Innovative Approaches for Enhanced Security in Cloud Network...

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Software, Systems and Information Technology (SSITCON), International conference on

作者： K Mala H S Annapurna Department of ISE CIT Gubbi Tumkur India Department of ISE SSIT SAAHE University Tumkur India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The rapid growth of cloud computing has introduced significant security challenges, particularly in protecting network traffic from advanced cyber threats. This research presents an adaptive deep learning framework to enhance cloud network security by detecting and mitigating threats in real-time. By leveraging multi-modal data sources, such as network traffic logs, system performance metrics, and user behavior analytics, the framework identifies threats like distributed Denial of Service (DDoS) attacks, malware, and unauthorized access. A comprehensive dataset, including network traffic data, system logs, and security alerts from various cloud environments, was collected. Pre-processing involved normalization, augmentation, and feature extraction tailored to multi-modal inputs. Convolutional neural networks (CNNs) analyzed network traffic features, while Random Forest and Gradient Boosting algorithms examined system and user behavior. The research also explores the impact of network conditions like bandwidth fluctuations and latency on model performance and proposes Internet of Things (IoT)-based monitoring for real-time data collection.

关键词： Deep learning Cloud computing Adaptation models Adaptive systems Accuracy Telecommunication traffic Real-time systems Data models Vectors Threat assessment

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TIPAngle: Traffic Tracking at City Scale by Pose Estimation of Pan and Tilt Intersection Cameras

TIPAngle: Traffic Tracking at City Scale by Pose Estimation ...

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International conference on VLSI Design

作者： Shreehari Jagadeesha Edward Andert Aviral Shrivastava SCAI Arizona State University

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Modern cities have hundreds to thousands of traffic cameras distributed across them, many of them with the capability to pan and tilt, but very often these pan and tilt cameras either do not have angle sensors or do not provide camera orientation feedback. This makes it difficult to robustly track traffic using these cameras. Several methods to automatically detect the camera pose have been proposed in literature, with the most popular and robust being deep learning-based approaches. However, they are compute intensive, require large amounts of training data, and generally cannot be run on embedded devices. In this paper, we propose TIPAngle - a Siamese neural network, lightweight training, and a highly optimized inference mechanism and toolset to estimate camera pose and thereby improve traffic tracking even when operators change the pose of the traffic cameras. TIPAngle is 18.45x times faster and 3x more accurate in determining the angle of a camera frame than a ResNet-18 based approach. We deploy TIPAngle to a Raspberry Pi CPU and show that processing an image takes an average of. 057s, equating to a frequency of about 17Hz on an embedded device.

关键词： Training Accuracy Inference mechanisms Urban areas neural networks Pose estimation Training data Very large scale integration Cameras Sensors

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Accelerating DCNNs via Cooperative Weight/Activation Compression 21st

Accelerating DCNNs via Cooperative Weight/Activation Compres...

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21st International conference on Algorithms and Architectures for Parallel processing (ICA3PP)

作者： Zhang, Yuhao Jiang, Xikun Wang, Xinyu Pan, Yudong Dong, Pusen Sun, Bin Shen, Zhaoyan Jia, Zhiping Shandong Univ Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9783030953911;9783030953904

Deep convolutional neural networks (DCNNs) have achieved great success in various applications. Nevertheless, training and deploying such DCNNs models require a huge amount of computation and storage resources. Weight pruning has emerged as an effective compression technique for DCNNs to reduce the consumption of hardware resources. However, existing weight pruning schemes neglect to simultaneously consider accuracy, compression ratio and hardware-efficiency in the design space, which inevitably leads to performance degradation. To overcome these limitations, in this work, we propose a cooperative weight/activation compression approach. Initially, we observe spatially insensitive consistency, namely the insensitive weight values that have no impact on accuracy distributed at the same spatial position in a mass of channels. Based on the key observation, we propose a cluster-based weight pattern pruning technique, which converges the channels with spatially insensitive consistency into a cluster and prunes them into the weight pattern with a uniform shape. In addition, there are many sparsity rows in the activation matrix due to the effect of the activation function-Rectifying Linear Unit (ReLU). We study the sparsity rows and find that rows with larger sparsity degree are insensitive to accuracy. Hence, we further propose a sparsity-row-based activation removal technique, which directly eliminates insensitive rows of activation. The two proposed techniques allow the hardware to execute at excellent parallel granularity and achieve better compression ratio with negligible accuracy loss. Experiment results on several popular DCNNs models show that our scheme reduces the computation by 63% on average.

关键词： Deep convolutional neural networks (DCNNs) Weight Activation Clustering Compression

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Edge-Detect: Edge-centric network Intrusion Detection using Deep neural network 18

Edge-Detect: Edge-centric Network Intrusion Detection using ...

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IEEE 18th Annual Consumer Communications and networking conference (CCNC)

作者： Singh, Praneet Jaykumar, Jishnu P. Pankaj, Akhil Mitra, Reshmi Indian Inst Sci Bengaluru India Southeast Missouri State Univ Cape Girardeau MO 63701 USA

ISBN: (纸本)9781728197944

Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints and is set to become part of a multi-billion industry. The resource constraints in this novel network infrastructure tier constricts the deployment of existing network Intrusion Detection System with Deep Learning models (DLM). We address this issue by developing a novel light, fast and accurate 'Edge-Detect' model, which detects distributed Denial of Service attack on edge nodes using DLM techniques. Our model can work within resource restrictions i.e. low power, memory and processing capabilities, to produce accurate results at a meaningful pace. It is built by creating layers of Long Short-Term Memory or Gated Recurrent Unit based cells, which are known for their excellent representation of sequential data. We designed a practical data science pipeline with Recurring neural network to learn from the network packet behavior in order to identify whether it is normal or attack-oriented. The model evaluation is from deployment on actual edge node represented by Raspberry Pi using current cybersecurity dataset (UNSW2015). Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of similar to 99% even with lower resource utilization in terms of cpu and memory. In addition, it is nearly 3 times smaller in size than the state-of-art model and yet requires a much lower testing time.

关键词： Edge computing Deep Learning Internet of Things DDoS Recurrent neural networks

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