Anomaly Detection of Electro-data Based on Deep Convolutional Neural Network

As diversity of electro-data anomaly, the methods based on artificial feature are becoming more difficult to detect anomalies among a great deal of electro-data. Hence, this paper proposes a novel method which is based on deep convolutional neural network (DCNN) to detect anomaly electro-data. This method models the sample data with time information and electrical parameters, and labels them as normal or abnormal automatically. Further, the paper improves the designing DCNN to extract precise features from large scale of electro-data to get high accuracy. The results of the case analysis show that our method can detect anomaly electro-data more exact and stable than the traditional methods. The abnormal precision rate and abnormal recall rate of our approach reach 92.7% and 91.3% respectively.

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Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Evolutionary Intelligence ◽

10.1007/s12065-021-00598-7 ◽

2021 ◽

Author(s):

Sourav Das ◽

Anup Kumar Kolya

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Deep Convolutional Neural Network ◽

Predictive Analysis

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Large Scale Indexing and Searching Deep Convolutional Neural Network Features

Big Data Analytics and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/978-3-319-43946-4_14 ◽

2016 ◽

pp. 213-224 ◽

Cited By ~ 8

Author(s):

Giuseppe Amato ◽

Franca Debole ◽

Fabrizio Falchi ◽

Claudio Gennaro ◽

Fausto Rabitti

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Deep Convolutional Neural Network

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A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

Mathematical Problems in Engineering ◽

10.1155/2020/2616510 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Lei Si ◽

Xiangxiang Xiong ◽

Zhongbin Wang ◽

Chao Tan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Deep Convolutional Neural Network ◽

Accurate Identification ◽

Experiment System ◽

Speed Up ◽

Novel Method ◽

Coal Rock ◽

Mining Face

Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.

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Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018425 ◽

2019 ◽

Vol 33 ◽

pp. 8425-8432 ◽

Cited By ~ 7

Author(s):

Saihui Hou ◽

Zilei Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Random Number ◽

Random Number Generator ◽

Test Phase ◽

Deep Convolutional Neural Network ◽

Multiple Datasets ◽

Novel Method ◽

Fully Connected ◽

Activation Status

In this work, we propose a novel method named Weighted Channel Dropout (WCD) for the regularization of deep Convolutional Neural Network (CNN). Different from Dropout which randomly selects the neurons to set to zero in the fully-connected layers, WCD operates on the channels in the stack of convolutional layers. Specifically, WCD consists of two steps, i.e., Rating Channels and Selecting Channels, and three modules, i.e., Global Average Pooling, Weighted Random Selection and Random Number Generator. It filters the channels according to their activation status and can be plugged into any two consecutive layers, which unifies the original Dropout and Channel-Wise Dropout. WCD is totally parameter-free and deployed only in training phase with very slight computation cost. The network in test phase remains unchanged and thus the inference cost is not added at all. Besides, when combining with the existing networks, it requires no re-pretraining on ImageNet and thus is well-suited for the application on small datasets. Finally, WCD with VGGNet-16, ResNet-101, Inception-V3 are experimentally evaluated on multiple datasets. The extensive results demonstrate that WCD can bring consistent improvements over the baselines.

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