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Interactive Image Segmentation Algorithm of Power Grid Equipment Based on Convolutional neural network

Interactive Image Segmentation Algorithm of Power Grid Equip...

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networking, Informatics and Computing (ICNETIC), International conference on

作者： Wei Yi Liang Chen Wei Xiong Linghao Zhang Shilun Zeng Jie Zhang Meishan Power Supply Company of State Grid Sichuan Electric Power Company Meishan China Sichuan Power Research Institute State Grid Chengdu China

Convolutional neural network combines low-level features to form more abstract high-level representation attribute categories or features, so as to discover the distributed feature representation of data. At the same time, convolutional neural network is also a research hotspot in the field of artificial intelligence and big data analysis in recent years. However, the convolutional neural network algorithm based on automatic encoder still has some limitations, such as random zeroing and noise injection during training, which leads to the loss of some key information and serious training time. Convolutional neural network has the characteristics of nonlinear nonconvexity, multi-layer hidden structure, feature vectorization, massive model parameters, etc., but its weak explanation is a huge obstacle to its theoretical development and practical application. With the rapid development of computer vision, the blind image processing technology is widely used in power grid, including fault identification of power grid equipment, monitoring of power grid equipment running status, etc. As the basic punishment of power grid equipment image processing, the segmentation algorithm of power grid equipment image has always been the focus of research. The previous image segmentation algorithms include edge-based segmentation algorithm, region-based segmentation algorithm, broad-based segmentation algorithm, interactive segmentation algorithm based on graph theory and so on.

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B-SMALL: A BAYESIAN neural network APPROACH TO SPARSE MODEL-AGNOSTIC META-LEARNING

B-SMALL: A BAYESIAN NEURAL NETWORK APPROACH TO SPARSE MODEL-...

引用

IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Madan, Anish Prasad, Ranjitha Indraprastha Inst Informat Technol Delhi New Delhi India

ISBN: (纸本)9781728176055

There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning based methods have been widely used in few-shot classification, regression, reinforcement learning, and domain adaptation. The model-agnostic meta-learning (MAML) algorithm is a well-known algorithm that obtains model parameter initialization at meta-training phase. In the meta-test phase, this initialization is rapidly adapted to new tasks by using gradient descent. However, meta-learning models are prone to overfitting since there are insufficient training tasks resulting in over-parameterized models with poor generalization performance for unseen tasks. In this paper, we propose a Bayesian neural network based MAML algorithm, which we refer to as the B-SMALL algorithm. The proposed framework incorporates a sparse variational loss term alongside the loss function of MAML, which uses a sparsifying approximated KL divergence as a regularizer. We demonstrate the performance of B-MAML using classification and regression tasks, and highlight that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model while performing at par or even outperforming the MAML approach. We also illustrate applicability of our approach in distributed sensor networks, where sparsity and meta-learning can be beneficial.

关键词： Meta-learning Bayesian neural networks overfitting variational dropout

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Model-distributed Inference in Multi-Source Edge networks

Model-Distributed Inference in Multi-Source Edge Networks

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Pengzhen Li Hulya Seferoglu Erdem Koyuncu Department of Electrical and Computer Engineering University of Illinois Chicago

distributed inference techniques can be broadly classified into data-distributed and model-distributed schemes. In data-distributed inference (DDI), each worker carries the entire deep neural network (DNN) model, but processes only a subset of the data. However, feeding the data to workers results in high communication costs especially when the data is large. An emerging paradigm is model-distributed inference (MDI), where each worker carries only a subset of DNN layers. In MDI, a source device that has data processes a few layers of DNN and sends the output to a neighboring device. This process ends when all layers are processed in a distributed manner. In this paper, we investigate MDI with multiple sources, i.e., when more than one device has data. We design a multisource MDI (MS-MDI), which optimizes task scheduling decisions across multiple source devices and workers. Experimental results on a real-life testbed of NVIDIA Jetson TX2 edge devices show that MS-MDI improves the inference time significantly as compared to baselines.

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Design and Application of Web-based Education Management System using Split and Kernel based Residual network

Design and Application of Web-based Education Management Sys...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International conference on

作者： Yan Wang School of Public Adminisration South West Minzu University Chengdu China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

A web based education management system is established to develop education system by enhancing quality of education and teaching model. However, the existing resource allocation model and teaching in web-based education system has limitations such as ineffective teaching due to inappropriate resource allocation affect the education system. To overcome this problem, a Split and Kernel based Residual network (SK-ResNet) is proposed to improve quality of education by allocating resources effectively and enhance teaching model in the web based education system. The split technique in ResNet model, divide the tasks and distributed across multiple servers or devices. By splitting and kernel optimizing the tasks, the load on any single system allows the model for faster content processing and delivery. At first, the data are acquired and fed into functional structure of education system, and finally analyzed and integrated into cloud computing based on SK-ResNet model. The experimental results of the proposed method achieved accuracy of 87.29% which is greater when compared to existing method such as Deep neural network (DNN) and Hybrid Deep Learning (HDL) model.

关键词： Deep learning Cloud computing Computational modeling Education Data models Servers Resource management Kernel Load modeling Residual neural networks

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Energy Consumption Prediction of Public Buildings Based on MEA-BP neural network 33

Energy Consumption Prediction of Public Buildings Based on M...

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第33届中国控制与决策会议

作者： Shuai Wang Hongyan Ma Jiaming Dou Yongxue Ye Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

ISBN: (纸本)9781665431293

Data mining technology is often adopted in construction engineering to provide relevant information and reference basis for public building energy consumption *** paper proposes an optimization algorithm applying back propagation(BP) neural network and mind evolutionary algorithm(MEA) to establish a model to predict building energy *** main weather data are used as inputs to the BP neural network and daily electricity consumption is used as an *** MEA is employed to optimise the structure of the network and to predict the energy consumption of *** results demonstrate that the optimized BP neural network has higher fitting accuracy,and the predicted load value is more consistent with the actual value.

关键词： Building energy consumption BP neural network Mind evolutionary algorithm

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OCO-GAT: An Accelerator for Graph Attention network with Optimized Calculation Order

OCO-GAT: An Accelerator for Graph Attention Network with Opt...

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IEEE conference on High Performance Extreme Computing (HPEC)

作者： Qi Liu Wenjin Huang WenLu Peng Yihua Huang School of Electriconics and Information Technology Sun Yat-sen University Guangzhou Guangdong China

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

The Graph Attention network (GAT) introduces an attention mechanism to focus on the most significant aspects of the data and utilizes weighted sums for aggregation. As a result, GAT exhibits superior performance in tasks involving graph data compared to previous Graph neural networks (GNNs). However, this also introduces a more complex computational process and stronger data dependencies, making previous GNN accelerators inadequate for GAT inference. Therefore, we propose an optimized calculation order for GAT along with the corresponding accelerator architecture, OCO-GAT, specifically tailored for GAT inference, which includes efficient pipeline design. Additionally, we introduce a distributed fine-grained on-chip storage scheme, ensuring computational parallelism while mitigating significant growth in storage resource consumption. We deployed OCO-GAT on Xilinx Alveo U250 FPGA and the experimental results demonstrate that optimized calculation order reduces the workload of division operations by an average of 17.2% across four datasets. OCO-GAT achieves high energy efficiency and improves inference performance from 1.2 × to 301.1× compared to CPU, GPU, and three peer works.

关键词： Attention mechanisms Pipelines Graphics processing units Parallel processing Graph neural networks Energy efficiency Hardware System-on-chip Peer-to-peer computing Field programmable gate arrays

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Convolutional Recurrent neural networks for the Classification of Cetacean Bioacoustic Patterns

Convolutional Recurrent Neural Networks for the Classificati...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dimitris N. Makropoulos Antigoni Tsiami Aristides Prospathopoulos Dimitris Kassis Alexandros Frantzis Emmanuel Skarsoulis George Piperakis Petros Maragos National Technical University of Athens Greece Hellenic Centre for Marine Research (HCMR) Institute of Oceanography Anavyssos Greece Pelagos Cetacean Research Institute Vouliagmeni Greece Foundation of Research and Technology - Hellas IACM Heraklion-Crete Greece

In this paper we focus on the development of a convolutional recurrent neural network (CRNN) to categorize biosignals collected in the Hellenic Trench, generated by two cetacean species, sperm whales (Physeter macrocephalus) and striped dolphins (Stenella coeruleoalba). We convert audio signals into mel-spectrograms and forward the input into a deep residual network (ResNet), designed to capture spectral patterns. Next, ResNet’s output is reshaped into a time-distributed layer and fed into recurrent network variants, Long Short-Term Memory (LSTMs) or Gated Recurrent Units (GRUs), able to recognize long-term time dependencies on extracted features. The hybrid network perfectly classifies audio signals into three categories (dolphins, sperm whales, ambient noise) while it also exhibits high learning ability on recognising intraclass representations of overlapping acoustic patterns (clicks vs whistles and clicks, both emitted by dolphins). The proposed scheme outperforms traditional Machine Learning (ML) techniques, baseline ResNet and LSTM architectures or their deep parallel combinations.

关键词： Time-frequency analysis Recurrent neural networks Convolution Supervised learning Whales Speech recognition Feature extraction

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B-ary tree push-pull method is provably efficient for distributed learning on heterogeneous data 24

B-ary tree push-pull method is provably efficient for distri...

引用

Proceedings of the 38th International conference on neural Information processing Systems

作者： Runze You Shi Pu The Chinese University of Hong Kong Shenzhen

ISBN: (纸本)9798331314385

This paper considers the distributed learning problem where a group of agents cooperatively minimizes the summation of their local cost functions based on peer-to-peer communication. Particularly, we propose a highly efficient algorithm, termed "B-ary Tree Push-Pull" (BTPP), that employs two B-ary spanning trees for distributing the information related to the parameters and stochastic gradients across the network. The simple method is efficient in communication since each agent interacts with at most (B + 1) neighbors per iteration. More importantly, BTPP achieves linear speedup for smooth nonconvex and strongly convex objective functions with only Õ(n) and Õ(1) transient iterations, respectively, significantly outperforming the state-of-the-art results to the best of our knowledge. Our code is available at https://***/ryou98/BTPP.

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Feature Selection and Classification of Email Spam Using Orthogonal Linear Jellyfish Swarm Optimizer

Feature Selection and Classification of Email Spam Using Ort...

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

作者： Laith Hussein Srikanth Velpula Vasanthakumar G U Alapati V Vasavi Sujatha R. Dineshkumar Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Dept of Information Technology Bhojreddy Engineering College for Women Hyderabad India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Spam emails are the cyber annoyances which causes the significant security attacks involves both financial as well as personal data. Detecting the new strains of the spam messages is challenging and it needs the efficient and reliable intelligent spam detection approach. This research proposes the Orthogonal Linear Design based Jellyfish Swarm Optimizer (OLJSO)-based feature selection approach for the email spam. Initially, UCI ML Repository dataset is collected and it is pre-processed by using tokenization, stop word removal, stemming and lemmatization. After pre-processing, the feature extraction is done by using Term Frequency-Inverse Document Frequency (TF-IDF) and the extracted features are selected by the utilization of OLJSO. After the feature selection process, the selected spam features are classified into two types such as spam and non-spam by the utilization of Artificial neural network (ANN). The effectiveness of the OLJSO is estimated by using various performance metrices and it achieves the accuracy of 99.09% and precision of 0.975 when compared to the existing methods such as Genetic Decision Tree processing with Natural Language processing (GDTPNLP).

关键词： Jellyfish Unsolicited e-mail Artificial neural networks Feature extraction Tokenization Space exploration Security

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Medical Remote Sensing and Internet of Things for Detection of Epileptic Seizure Using Deep neural networks

Medical Remote Sensing and Internet of Things for Detection ...

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distributed Computing and Optimization Techniques (ICDCOT), International conference on

作者： G. Vinuja N. Bharatha Devi Department of CSE Saveetha School of Engineering SIMATS Thandalam India

Medical Remote sensing and the Internet of Things (IoT) have emerged as powerful tools in the field of disease detection and monitoring. Early detection of infectious diseases is crucial in order to prevent outbreaks and minimize their impact on public health. This paper aims to explore suitable methods and algorithms that can effectively utilize medical remote sensing data and IoT devices to detect and identify infectious diseases at an early stage. In this research, the novel real-time personal health monitoring system WISE (Wearable IoT-cloud-based health monitoring system) is introduced. In the present research, the body area network (BAN) is used to collect the patient's EEG signal. These signals are sent from the patient's location to the cloud via wifi. To turn the EEG input signal into an image, the discrete wavelet transform (DWT) is initially employed. This EEG input image first undergoes data de-noising pre-processing. Wavelet decomposition of EEG data accurately captures and pinpoints transient patterns in both temporal and frequency domains. Classifiers based on multilayer perceptron neural networks (MLPNN) were created and their classification accuracy for EEG signals was tested. . As compared to the existing algorithm the accuracy rate in predicting an abnormal EEG was 98.1 %. The classifier is employed to assess whether a region is typical or anomalous, and it also gauges its effectiveness.

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