scholarly journals SUBSTRUCTURE ON-LINE TEST TECHNIQUE USING NON-LINEAR HYSTERESIS MODELLING WITH NEURAL NETWORK

2004 ◽  
Vol 69 (582) ◽  
pp. 73-80 ◽  
Author(s):  
Wonjik YANG ◽  
Yoshiaki NAKANO ◽  
Yasushi SANADA
Keyword(s):  
Neural Network ◽  
Test Technique ◽  
Non Linear ◽  
On Line ◽  
Line Test

scholarly journals SUBSTRUCTURE ON-LINE TEST TECHNIQUE USING REAL-TIME HYSTERESIS MODELING WITH NEURAL NETWORK

2005 ◽  
Vol 70 (589) ◽  
pp. 89-96
Author(s):  
Wonjik YANG ◽  
Yoshiaki NAKANO ◽  
Naruhito YAMAUCHI ◽  
Yasushi SANADA
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Test Technique ◽  
Hysteresis Modeling ◽  
On Line ◽  
Line Test

The Research on the Form Error Margin of the Auto Test System Design

2014 ◽  
Vol 912-914 ◽  
pp. 1022-1026
Author(s):  
Wei Li Yu
Keyword(s):  
Product Quality ◽  
System Design ◽  
Low Cost ◽  
Test System ◽  
Form Error ◽  
Test Technique ◽  
Error Margin ◽  
Manufacture Industry ◽  
On Line ◽  
Line Test

In the machine manufacture industry, the on-line test technique applies very extensively, the particularly automatic test technique is more outstanding. This article with the form error margin for example, designed a set of simple on-line test system to raise the accuracy of the machine product examination, assurance product quality, decline low cost depletion.

The Concept of Model-View-Controler (MVC) as Solution Software Development

CCIT Journal ◽  
2010 ◽  
Vol 3 (3) ◽  
pp. 377-402
Author(s):  
Ermatita Ermatita ◽  
Huda Ubaya ◽  
Dwirosa Indah
Keyword(s):  
Software Development ◽  
Model View Controller ◽  
On Line ◽  
Line Test ◽  
Model View

Pengembangan perangkat lunak adalah tugas kompleks dan membutuhkan adaptasi untuk mengakomodasi kebutuhan pengguna. Untuk membuat konsep dan perubahan perangkat lunak, dalam pemeliharaan, sekarang telah dikembangkan lebih mudah dalam pengembangan perangkat lunak, pola model-view-controller, yang merupakan arsitektur yang dapat membantu memfasilitasi dalam pengembangan dan pemeliharaan perangkat luna. Hal ini, karena dalam arsitektur model tiga-lapis, yaitu: tampilan dan pengontrolan dalam pembangunan dilakukan secara independen, sehingga dapat memberikan dahan dalam pengembangan dan pemeliharaan. Selain itu, arsitektur ini juga dapat melihat hal-hal yang sederhana dan menarik bagi pengguna. Software sistem on-line test adalah perangkat lunak yang memerlukan interaksi dengan pengguna, dan pemeliharaan perangkat adaptif. Karena sistem ujian on-line memerlukan pengembangan perangkat lunak untuk mengakomodasi kebutuhan ini berkembang dengan cepat. Makalah ini untuk menganalisis Model-View-Controller dan mencoba pembangunan, untuk menerapkannya dalam pengembangan perangkat lunak sistem pengujian on-line. 

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

scholarly journals Excitation Forces Estimation for Non-linear Wave Energy Converters: A Neural Network Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 12334-12339
Author(s):  
M. Bonfanti ◽  
F. Carapellese ◽  
S.A. Sirigu ◽  
G. Bracco ◽  
G. Mattiazzo
Keyword(s):  
Neural Network ◽  
Wave Energy ◽  
Linear Wave ◽  
Network Approach ◽  
Neural Network Approach ◽  
Wave Energy Converters ◽  
Non Linear ◽  
Energy Converters
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