Fast Feature Extraction Method for Faults Detection System

2018 ◽  
pp. 297-306
Author(s):  
Hongmin Wang ◽  
Xiaohui Zhu ◽  
Xiangyong Niu ◽  
Ping Xue
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Detection System ◽  
Feature Extraction Method

scholarly journals Recites fidelity detection system of al-Kautsar verse based on words using mel frequency cepstrum coefficients and cosine similarity

2019 ◽  
Vol 8 (1) ◽  
pp. 27-35
Author(s):  
Jans Hendry ◽  
Aditya Rachman ◽  
Dodi Zulherman
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Test Data ◽  
Extraction Method ◽  
Detection System ◽  
Test Word ◽  
Word Segmentation ◽  
Cosine Similarity ◽  
Feature Extraction Method ◽  
Similarity Method

In this study, a system has been developed to help detect the accuracy of the reading of the Koran in the Surah Al-Kautsar based on the accuracy of the number and pronunciation of words in one complete surah. This system is very dependent on the accuracy of word segmentation based on envelope signals. The feature extraction method used was Mel Frequency Cepstrum Coefficients (MFCC), while the Cosine Similarity method was used to detect the accuracy of the reading. From 60 data, 30 data were used for training, while the rest were for testing. From each of the 30 training and test data, 15 data were correct readings, and 15 other data were incorrect readings. System accuracy was measured by word-for-word recognition, which results in 100 % of recall and 98.96 % of precision for the training word data, and 100 % of recall and 99.65 % of precision for the test word data. For the overall reading of the surah, there were 15 correct readings and 14 incorrect readings that were recognized correctly.

A Freezing of Gait Detection System Based on Stepwise Feature Extraction Method

2020 ◽  
Author(s):  
Xihua Liu ◽  
Shengdi Chen ◽  
Kang Ren ◽  
Jin Zhao ◽  
Chao Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Detection System ◽  
Freezing Of Gait ◽  
Feature Extraction Method

Self-Organizing Feature Extraction Method for the High-Voltage Discharge Fault in Ultraviolet Imaging Detection System

2014 ◽  
Vol 494-495 ◽  
pp. 1731-1734
Author(s):  
Wei Wang ◽  
Li Xin Ma ◽  
Da Hai Huang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Extraction Method ◽  
Detection System ◽  
Feature Extraction Method ◽  
Operation Conditions ◽  
Electrical Faults ◽  
Fault State ◽  
High Voltage Discharge ◽  
Self Organizing

An ultraviolet detecting system for electrical faults is designed and built to locate the fault precisely and classify its pattern level as few studies have been done on. Self-organizing feature extraction method devotes itself to fault state recognition approach in high-voltage discharge. The established system including the filter system, image collection system, image preprocessing system, feature extraction, and pattern recognition could identify and classify device faults and operation conditions accurately tested by simulation.

Feature Extraction Method for Network Intrusion Detection Based on RS-KPCA

2014 ◽  
Vol 665 ◽  
pp. 706-711
Author(s):  
Fang Nian Wang ◽  
Shen Shen Wang ◽  
Yun Bai ◽  
Wan Fang Che
Keyword(s):  
Feature Extraction ◽  
Intrusion Detection ◽  
Extraction Method ◽  
Detection System ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Network Intrusion Detection ◽  
Contribution Rate ◽  
Feature Extraction Method ◽  
Network Intrusion

For the complexity and nonlinearity of the input characteristics in network intrusion detection system, a feature extraction method for network intrusion detection based on RS-KPCA is studied. Firstly, the Rough Set (RS) theory is used to select the valuable features, while the unnecessary features are removed. Then, the features of the intrusion detection sample data are extracted by the kernel principal component analysis (KPCA) algorithm. The number of new features is determined by the cumulative contribution rate. Simulation results show that this method can effectively remove the interference features, and has the advantages of obvious principal component feature and concentrated contribution rate, compared with PCA. Overall, the proposed method can effectively integrate the nonlinear features of the original data, reduce the dimension, and improve the intrusion detection performance.

Robust Texture Feature Extraction Method for Geometrical and Illumination Distortions

2009 ◽  
Vol 129 (5) ◽  
pp. 985-992 ◽  
Author(s):  
Norisuke Takao ◽  
Zhuo Liu ◽  
Shigeo Wada
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

A Feature Extraction Method of Computer Viruses Based on Artificial Immune and Code Relevance

2011 ◽  
Vol 34 (2) ◽  
pp. 204-215 ◽  
Author(s):  
Wei WANG ◽  
Peng-Tao ZHANG ◽  
Ying TAN ◽  
Xin-Gui HE
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Artificial Immune ◽  
Computer Viruses ◽  
Feature Extraction Method

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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