http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Optimal feature selection using genetic algorithm for mechanicalfault detection of induction motor
Ngoc-Tu Nguyen,Hong-Hee Lee,Jeong-Min Kwon 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.3
Time-domain vibration signals are measured in all horizontal, axial, and vertical directions for induction motor mechanical fault diagnostics. Many features are extracted from these signals. The problem is how to find the good features among the feature set in order to receive reliable classifications. Based on specific distance criteria, a genetic algorithm (GA) is introduced to reduce the number of features by selecting optimized ones for fault classification purpose. A decision tree and multi-class support vector machine are used to illustrate the potentiality and efficiency of this selection method. Comparisons show that the diagnostic systems after selecting specific features perform better than the original system.
Decision Tree with Optimal Feature Selection for Bearing Fault Detection
Ngoc-Tu Nguyen,Hong-Hee Lee 전력전자학회 2008 JOURNAL OF POWER ELECTRONICS Vol.8 No.1
In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.
Fault Diagnosis of Induction Motor using Decision Tree with An Optimal Feature Selection
Ngoc-Tu Nguyen,Jeong-Min Kwon,Hong-Hee Lee 전력전자학회 2007 ICPE(ISPE)논문집 Vol.- No.-
Time vibration signals are measured to extract a feature set for fault diagnostics of induction motor. Feature selection by decision tree and genetic algorithm (GA) is presented in this paper to remove irrelevant information in the feature set. New data with the selected features is used to train a decision tree, which is an expert system for classification. Testing results show that systems with selected features can reliably diagnose different conditions of induction motor, which has better performance compared to original one without feature selection.
Lysosome based toxic detection in Saccharomyces cerevisiae using novel portable fluorometer
Ngoc-Tu Nguyen,안지영,방승혁,김동환,김덕민,김양훈,민지호 대한독성 유전단백체 학회 2016 Molecular & cellular toxicology Vol.12 No.2
Lysosome is an organelle in the cell, commonly used as biomonitoring tool in environmental pollution. In previous studies, the lysosomal proteome profiling in yeast was analyzed in response to different toxic chemicals, such as sodium meta-arsenite and tetracycline. A recombinant yeast contained the specific biomarker found in the previous studies was constructed for toxic detection. We evaluated green fluorescent intensity of the recombinant yeast exposed to toxic chemicals with dose dependent using by a portable kit designed by our laboratory. The results confirmed the potential of both the yeast and the portable kit to detect each of toxic chemical such as heavy metals, pesticides, or pharmaceuticals at an optimal dose that the intensity of fluorescent protein reached peak at. Therefore, the recombinant yeast and portable kit play an important role to detect various toxic chemicals.
A Study on Machine Fault Diagnosis using Decision Tree
Ngoc-Tu Nguyen,Jeong-Min Kwon,Hong-Hee Lee 대한전기학회 2007 Journal of Electrical Engineering & Technology Vol.2 No.4
The paper describes a way to diagnose machine condition based on the expert system. In this paper, an expert system - decision tree is built and experimented to diagnose and to detect machine defects. The main objective of this study is to provide a simple way to monitor machine status by synthesizing the knowledge and experiences on the diagnostic case histories of the rotating machinery. A traditional decision tree has been constructed using vibration-based inputs. Some case studies are provided to illustrate the application and advantages of the decision tree system for machine fault diagnosis.