scholarly journals Improving Neural Network Detection Accuracy of Electric Power Bushings in Infrared Images by Hough Transform

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2931
Author(s):  
Hongshan Zhao ◽  
Zeyan Zhang
Keyword(s):  
Neural Network ◽  
Electric Power ◽  
Target Detection ◽  
Hough Transform ◽  
Image Method ◽  
Detection Accuracy ◽  
Infrared Images ◽  
Gap Statistic ◽  
The Neural Network ◽  
Bounding Boxes

To improve the neural network detection accuracy of the electric power bushings in infrared images, a modified algorithm based on the You Only Look Once version 2 (YOLOv2) network is proposed to achieve better recognition results. Specifically, YOLOv2 corresponds to a convolutional neural network (CNN), although its rotation invariance is poor, and some bounding boxes (BBs) exhibit certain deviations. To solve this problem, the standard Hough transform and image rotation are utilized to determine the optimal recognition angle for target detection, such that an optimal recognition effect of YOLOv2 on inclined objects (for example, bushing) is achieved. With respect to the problem that the BB is biased, the shape feature of the bushing is extracted by the Gap statistic algorithm, based on K-means clustering; thereafter, the sliding window (SW) is utilized to determine the optimal recognition area. Experimental verification indicates that the proposed rotating image method can improve the recognition effect, and the SW can further modify the BB. The accuracy of target detection increases to 97.33%, and the recall increases to 95%.

The Research of Intrusion Detection Based on Intelligentization

2014 ◽  
Vol 490-491 ◽  
pp. 1588-1591
Author(s):  
Liang Zhang ◽  
Hao Yue Sun ◽  
Guo Lv ◽  
Xiao Lu Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Convergence Speed ◽  
Global Property ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
The Neural Network

In this paper, the intelligentized way is applied to detecting anomaly intrusion. Based on the global property of genetic algorithm and the locality of neural network, this method effectively improves the convergence speed of the network and the detection accuracy rate. It not only avoids the defect of the neural network, but also improves the precision.

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 692-696
Author(s):  
Da Peng Chai ◽  
Qiang Qiang Xue ◽  
Ling Mei Wang ◽  
Xing Yong Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Electric Power ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Power Equipment ◽  
The Neural Network ◽  
Condition Monitoring System ◽  
Artificial Neural Network Ann

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).

The Analysis of Faults Detection Software Based on Improved Neural Network Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2044-2047
Author(s):  
Miao Yan ◽  
Zhi Bao Liu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Improved Method ◽  
Software Faults ◽  
Component Software ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Detection Software

The large-scale software is consisted of the components which are quite different. The detection accuracy of the traditional faults detection methods for the large-scale component software is not satisfactory. This paper proposes a large-scale software faults detection methods based on improved neural network combining the features of the large-scale software by computing the stable probability and building the neural network faults detection models. The proposed method can analyze the serial faults of the large-scale software to determine the positions of the faults. The experiment and simulation results show that the improved method for large-scale software fault detection can greatly improve the accuracy.

scholarly journals Plant Disease Detection and Monitoring Using Artificial Neural Network

2022 ◽  
Vol 10 (01) ◽  
pp. 715-722
Author(s):  
Stella I. Orakwue ◽  
Nkolika O. Nwazor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crop Production ◽  
Plant Disease ◽  
Disease Detection ◽  
Detection Accuracy ◽  
Major Threat ◽  
The Neural Network ◽  
Artificial Neural ◽  
Hyperspectral Sensor

Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.

scholarly journals E-Commerce Picture Text Recognition Information System Based on Deep Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Bin Zhao ◽  
WenYing Li ◽  
Qian Guo ◽  
RongRong Song
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Recognition Accuracy ◽  
Text Recognition ◽  
Detection Accuracy ◽  
Image Detection ◽  
Evaluation Indexes ◽  
Different Levels ◽  
Made In ◽  
Detection And Recognition

For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of channels in the application of traditional MWI-DenseNet network, a new GTNet network is proposed to improve the classification accuracy of commodities.The results show that at different levels of evaluation indexes, the dpFPN-Netv2 algorithm improved by DPFM + RFM fusion has higher target detection accuracy than RetinaNet-50 algorithm and other algorithms. And the detection time is 52 ms, which is significantly lower than 90 ms required for RetinaNet-50 detection. In terms of target recognition, compared with the traditional MWI-DenseNet neural network, the computation amount of the improved MWI DenseNet neural network is significantly reduced under different shunt ratios, and the recognition accuracy is significantly improved. The innovation of this study lies in improving the algorithm from the perspective of target detection and recognition, so as to change the previous improvement that only can be made in a single way.

scholarly journals Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions

2021 ◽  
Vol 12 ◽  
Author(s):  
Caroline Pinte ◽  
Mathis Fleury ◽  
Pierre Maurel
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Brain Activity ◽  
Relevant Information ◽  
Magnetic Resonance Images ◽  
Detection Accuracy ◽  
Simultaneous Acquisition ◽  
The Neural Network ◽  
Mri Sequences ◽  
Short Echo Time

The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling.

scholarly journals ForkDec: Accurate Detection for Selfish Mining Attacks

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhaojie Wang ◽  
Qingzhe Lv ◽  
Zhaobo Lu ◽  
Yilei Wang ◽  
Shengjie Yue
Keyword(s):  
Neural Network ◽  
Incentive Mechanism ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Data Set ◽  
Incentive Compatible ◽  
The Neural Network ◽  
Security Challenges ◽  
Excellent Research ◽  
Fully Connected

Incentive mechanism is the key to the success of the Bitcoin system as a permissionless blockchain. It encourages participants to contribute their computing resources to ensure the correctness and consistency of user transaction records. Selfish mining attacks, however, prove that Bitcoin’s incentive mechanism is not incentive-compatible, which is contrary to traditional views. Selfish mining attacks may cause the loss of mining power, especially those of honest participants, which brings great security challenges to the Bitcoin system. Although there are a series of studies against selfish mining behaviors, these works have certain limitations: either the existing protocol needs to be modified or the detection effect for attacks is not satisfactory. We propose the ForkDec, a high-accuracy system for selfish mining detection based on the fully connected neural network, for the purpose of effectively deterring selfish attackers. The neural network contains a total of 100 neurons (10 hidden layers and 10 neurons per layer), learned on a training set containing about 200,000 fork samples. The data set, used to train the model, is generated by a Bitcoin mining simulator that we preconstructed. We also applied ForkDec to the test set to evaluate the attack detection and achieved a detection accuracy of 99.03%. The evaluation experiment demonstrates that ForkDec has certain application value and excellent research prospects.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

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