Parallel rough set based knowledge acquisition using MapReduce from big data

2012 ◽  
Author(s):  
Junbo Zhang ◽  
Tianrui Li ◽  
Yi Pan
Keyword(s):  
Big Data ◽  
Knowledge Acquisition ◽  
Rough Set

Study on Remote Control and Fault Diagnosis for Ultrahigh Speed Grinding

2007 ◽  
Vol 359-360 ◽  
pp. 518-522
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu ◽  
Xing Yu Jiang ◽  
Jian Yu Yang
Keyword(s):  
Remote Control ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Rough Set ◽  
Theoretical Research ◽  
Reduction Rule ◽  
Diagnosis System ◽  
Linear Speed ◽  
Fault Diagnosis System ◽  
Diagnosis Model

Remote control and fault diagnosis of ultrahigh speeding grinding is studied, which is based on the theory of rough set. Knowledge acquisition and reduction rule of fault diagnosis, realization method of remote control for ultrahigh speed grinding are studied, diagnosis model is established. Based on the theoretical research and ultrahigh speed grinder with a linear speed of 250 m/s, the remote control and fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running show that the environment is improved, the mental pressure of workers is relieved and the efficiency is improved. At the same time, it proves that the ability to diagnosis and the accuracy of diagnosis for the ultrahigh speed grinding are improved and the time for diagnosis is shortened by applying rough set.

Multi-source Knowledge Acquisition Model based on Rough Set

2014 ◽  
Vol 13 (7) ◽  
pp. 1386-1390
Author(s):  
Li Li ◽  
Lu Sun ◽  
Jiayang Wang
Keyword(s):  
Knowledge Acquisition ◽  
Rough Set ◽  
Model Based

The Incremental Knowledge Acquisition Based on Hash Algorithm

2016 ◽  
Vol 24 (03) ◽  
pp. 347-366 ◽  
Author(s):  
Qing-Hua Zhang ◽  
Long-Yang Yao ◽  
Guan-Sheng Zhang ◽  
Yu-Ke Xin
Keyword(s):  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Algorithm Analysis ◽  
Data Sets ◽  
Hash Algorithm ◽  
Acquisition Method

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

Research on Fault Diagnosis of Forklift’s Hydraulic System Based on Rough Set Theory

2012 ◽  
Vol 524-527 ◽  
pp. 819-823
Author(s):  
Xin Ping Su ◽  
Guang Kun Nie ◽  
Wei Xin Fan
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Hydraulic System ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Train Of Thought ◽  
Fault Information

An approach of forklift’s fault diagnostic knowledge acquisition and discrete date based on rough set theory was put forward, according to the rough set theory in fault diagnosis of fault tolerance, the use of rough set theory in fault knowledge attribute reduction and value reduction, as in incomplete fault information of forklift hydraulic system fault diagnosis provides a train of thought. The inferential strategy and process of fault diagnosis of hydraulic system for forklift were described. Examples show that the proposed approach is very effective.

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