UT System Composition and Welding Flaw Classification for SWP Stability Estimation

2004 ◽  
Vol 261-263 ◽  
pp. 1385-1390
Author(s):  
Jae Yeol Kim ◽  
Young Tae Yoo ◽  
Kyung Seok Song ◽  
Chang Hyun Kim ◽  
Dong Jo Yang
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bayesian Classifier ◽  
Ultrasonic Probe ◽  
Inspection Method ◽  
Stability Estimation ◽  
Inspection Robot ◽  
Flaw Classification

The purpose of this research is stability estimation of plant structure through classification and recognition about welding flaw in SWP(Spiral Welding Pipe). And, In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to signal processing code and pattern classifying code by user made programming code. Inspection robot is simply constructed as 2-axes because of welding bead with fixed pitch. So, inspection of welding part can be possible as composition of inspection part for tracking on welding line. For evaluation of flaw signal is reflected on welding flaw, user-made program codes are composed of signal processing and Bayesian classifier and perceptron neural network and back-propagation neural network. And then, we confirmed to superiority of neural network method compared with Bayesian classifier for classification and recognition rate. According to this result, we selected back-propagation neural network as classification and recognition method about the system of SWP stability Estimation[2]. Through this process, we proved efficiency on the system of SWP stability Estimation, and constructed on the base of the system of SWP stability Estimation for the application in industrial fields.

Digital Auscultation System of Traditional Chinese Medicine and Its Signals Acquisition

2011 ◽  
pp. 183-193
Author(s):  
Fanpeng Zhou ◽  
Jianjun Yan ◽  
Yiqin Wang ◽  
Fufeng Li ◽  
Chunming Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Signal Processing ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
New Technology ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pattern Recognition Methods ◽  
Linear And Nonlinear

Digital auscultation of Traditional Chinese Medicine (TCM) is a relatively new technology which has been developed for several years. This system makes diagnoses by analyzing sound signals of patients using signal processing and pattern recognition. The paper discusses TCM auscultation in both traditional and current digital auscultation methods. First, this article discusses demerits of traditional TCM auscultation methods. It is through these demerits that a conclusion is drawn that digital auscultation of TCM is indispensable. Then this article makes an introduction to voice analysis methods from linear and nonlinear analysis aspects to pattern recognition methods in common use. Finally this article establishes a new TCM digital auscultation system based on wavelet analysis and Back-propagation neural network (BPNN).

Application of Ultrasonic Test System for Test Performance Improvement of Welding Flaw

2006 ◽  
Vol 321-323 ◽  
pp. 1517-1521 ◽  
Author(s):  
Chang Hyun Kim ◽  
Jae Yeol Kim ◽  
Kyung Seok Song ◽  
Yong Hoon Cha
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Recognition Rate ◽  
Test System ◽  
Ultrasonic Test ◽  
Inspection System ◽  
Backpropagation Neural Network ◽  
Ultrasonic Probe ◽  
Inspection Method ◽  
Probability Neural Network

In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to signal processing code and pattern classifying code by user made programming code. For evaluation of flaw signal is reflected on welding flaw, user-made program codes are composed of signal processing and probability neural network (PNN) and backpropagation neural network (BPNN). And then, we actually confirmed to the theoretical advantage of each neural network method compared probability neural network with backpropagation neural network for classification and recognition rate. For the application of classifier to SWP inspection system, BPNN classifier is adequate in the first stage. And then, the application of PNN classifier is adequate as the next stage. Because of PNN application need enough sample data that is due to probabilistic density function.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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