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).

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

scholarly journals A Pattern Recognition Model of Python Programming using Artificial Neural Network via NeMo

2020 ◽  
Vol 8 (5) ◽  
pp. 109-113
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Programming Languages ◽  
Programming Language ◽  
High Performance ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Simulator ◽  
Source Codes ◽  
Python Programming

Background/Objectives: In the field of software development, the diversity of programming languages increases dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability to detect the pattern of a programming language characteristic by using NeMo(High-performance spiking neural network simulator) of neural network and testing the ability of this toolkit to provide detailed analyzable results. Methods/Statistical analysis: the method of achieving these objectives is by using a back propagation neural network via NeMo based on pattern recognition methodology. Findings: The results show that the NeMo neural network of pattern recognition can identify and recognize the pattern of python programming language with high accuracy. It also shows the ability of the NeMo toolkit to represent the analyzable results through a percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of NeMo simulator to provide beneficial platform for studying and analyzing the complexity of the backpropagation neural network model.

Comparision of Bupleurum scorzoneri folium Willd. Grouping from Different Habitats Based on Pattern Recognition with R Software

2011 ◽  
Vol 393-395 ◽  
pp. 1139-1142
Author(s):  
Wen Rui ◽  
Hong Yuan Chen ◽  
Yi Fan Feng ◽  
Zhong Feng Shi ◽  
Miao Miao Jiang
Keyword(s):  
Pattern Recognition ◽  
Chemical Composition ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Hierarchical Clustering ◽  
Chemical Data ◽  
R Software ◽  
Pattern Recognition Methods

Bupleurum scorzoneri folium Willd.(BSFW) is a traditional Chinese medicine which is widely distributed in China. To evaluate the quality of BSFW from different habitats, samples from 5 different areas in China were determined by UPLC/MS. The chemical data were dealed with hierarchical clustering, PCA, SPCA, PLSDA and SPLSDA using R software. The results show that these pattern recognition methods can fully reflect the chemical composition of different areas of BSFW, which make it possible to control the quality.

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.

A Back Propagation Artificial Neural Network Applied to Body Constitution in Traditional Chinese Medicine Modeling

2013 ◽  
Vol 659 ◽  
pp. 123-127
Author(s):  
Zhi Biao Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Architecture ◽  
Back Propagation ◽  
Good Prediction ◽  
Body Constitution ◽  
Yin Yang ◽  
Artificial Neural

In this paper, artificial neural network architecture is introduced to predict the Yin-Yang index of body constitution in traditional Chinese medicine (BCTCM). With pre-processing the inputting data by the median, the collected data is more consistent with the exact value of the characteristic parameters of BCTCM. Quasi-Newton algorithm is used to train the network model to accelerate the convergence speed of network training. Experiments show that, the result showed that they had good prediction accuracies for BCTTCM. The mean absolute error for 10 true measured points was 0.034. Therefore, the prediction model of BCTCM Yin-Yang index with BP neural network is doable.

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