A new reconstruction algorithm based on temporal neural network and its application in power quality disturbance data

2021 ◽  
pp. 002029402110197
Author(s):  
Yan Liu ◽  
Wei Tang ◽  
Yiduo Luan
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Large Scale ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Reconstruction Accuracy ◽  
Power Quality Disturbance ◽  
Fully Connected ◽  
Reconstruction Model ◽  
Reconstruction Time

The traditional reconstruction algorithms based on p-norm, limited by their reconstruction model and data processing mode, are prone to reconstruction failure or long reconstruction time. In order to break through the limitations, this paper proposes a reconstruction algorithm based on the temporal neural network (TCN). A new reconstruction model based on TCN is first established, which does not need sparse representation and has large-scale parallel processing. Next, a TCN with a fully connected layer and symmetrical zero-padding operation is designed to meet the reconstruction requirements, including non-causality and length-inconsistency. Moreover, the proposed algorithm is constructed and applied to power quality disturbance (PQD) data. Experimental results show that the proposed algorithm can implement the reconstruction task, demonstrating better reconstruction accuracy and less reconstruction time than OMP, ROMP, CoSaMP, and SP. Therefore, the proposed algorithm is more attractive when dictionary design is complicated, or real-time reconstruction is required.

A Reconstruction Model for 3D Human Motion Based on Images Processing

2014 ◽  
Vol 556-562 ◽  
pp. 5021-5023
Author(s):  
Zhi Yuan Yang
Keyword(s):  
Large Scale ◽  
Reconstruction Algorithm ◽  
Human Motion ◽  
Reconstruction Algorithms ◽  
Dynamic Features ◽  
3D Motion ◽  
Images Processing ◽  
Motion Images ◽  
Reconstruction Model ◽  
Complex Movement

Traditional 3D reconstruction algorithms use fixes shape base which hardly expresses the change parameters of complex movement and motion law of large-scale dynamic features, thereby leading to non-realistic reconstruction results. The paper proposes a new reconstruction algorithm for 3D motion images that corrects the neighborhood system of feature points by motion parameters and reasons number base K to ensure accuracy. The simulation results show that, the proposed algorithm avoids drawbacks of sports reconstruction results caused by the great randomness of motion state, thereby complete 3D motion images' reconstruction.

scholarly journals Power Quality Disturbance Identification and Optimization Based on Machine Learning

2021 ◽  
Author(s):  
Fei Long ◽  
Fen Liu ◽  
Xiangli Peng ◽  
Zheng Yu ◽  
Huan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Classification Method ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training ◽  
The Neural Network ◽  
Recognition Ability ◽  
Power Quality Disturbance ◽  
Depth Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

scholarly journals A Composite Control Method for Power Quality Enhancement: PI Controller with Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 673-680
Author(s):  
Pratibha Tiwari
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Large Scale ◽  
Control Method ◽  
Control Strategies ◽  
Research Work ◽  
Distribution Networks ◽  
Pi Controller ◽  
Quality Enhancement ◽  
Micro Grid

The number of installations of Micro-Grid will increase to quadruple by 2020. The purpose is to improve the power quality while reducing the cost and the consumption of electricity in transmission and distribution networks, using a hybrid system powered by solar and wind sources, as well as integrating storage devices. This paper reviews and discusses the Micro- Grid Model with PI controller and neural network for power quality enhancement. Then, a comparative study of different battery types used for large-scale electricity storage is carried out, followed by a review of control strategies. In this research work, designed a model in MATLAB 2015A, indicating all the power quality parameters.

scholarly journals Improved inference of chromosome conformation from images of labeled loci

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1521 ◽  
Author(s):  
Brian C. Ross ◽  
James C. Costello
Keyword(s):  
Large Scale ◽  
Reconstruction Algorithm ◽  
Reconstruction Accuracy ◽  
Chromosome Conformation ◽  
Imaging Technologies ◽  
Large Scale Problems

We previously published a method that infers chromosome conformation from images of fluorescently-tagged genomic loci, for the case when there are many loci labeled with each distinguishable color. Here we build on our previous work and improve the reconstruction algorithm to address previous limitations. We show that these improvements 1) increase the reconstruction accuracy and 2) allow the method to be used on large-scale problems involving several hundred labeled loci. Simulations indicate that full-chromosome reconstructions at 1/2 Mb resolution are possible using existing labeling and imaging technologies. The updated reconstruction code and the script files used for this paper are available at: https://github.com/heltilda/align3d.

scholarly journals Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Corentin Delacour ◽  
Aida Todri-Sanial
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Hebbian Learning ◽  
Hopfield Neural Network ◽  
Analytical Framework ◽  
Relaxation Oscillators ◽  
Oscillatory Neural Network ◽  
Network Functionality ◽  
Fully Connected ◽  
Neuromorphic Architecture

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

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