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  • In Jour Smart Sensing And Intelligent Systems

 

Article

TARGET RECOGNITION BASED ON ROUGH SET WITH MULTI-SOURCE INFORMATION

As the attributes provided by multi-source information can be used to distinguish between the different species of targets, attributes recognition becomes the most important work in target recognition. In this paper, a new method for attributes recognition was proposed with rough set theory. It used a new way to described the target with a information system consisting of four elements, reduced the attribute value according to the mission requirements, valuated the attribute based on the degree

Cheng Zengping

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 1063–1084

Article

EMPIRICAL MODE DECOMPOSITION AND ROUGH SET ATTRIBUTE REDUCTION FOR ULTRASONIC FLAW SIGNAL CLASSIFICATION

Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is first used to obtain the intrinsic mode functions (IMFs) of original ultrasonic signals. Such IMFs and traditional time as well as frequency domain based statistical parameters are extracted as the initial features of flaw signal. After that, spectral clustering method is used for feature value discretization so that rough set

Yu Wang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1401–1420

Research paper

KNOWLEDGE-BASED MODELING FOR PREDICTING CANE SUGAR CRYSTALLIZATION STATE

This paper proposes a knowledge-based model applied to an experimental scale evaporative cane sugar crystallization process, which combines the methods of offline and online knowledge acquisition. Firstly, a data mining method based on rough set theory is utilized to extract information from the large quantity of relevant data obtained in experiment. This method products an offline predictive knowledge. Thereafter, a method for online knowledge learning and self-improvement is put forward

Yanmei Meng, Xian Yu, Haiping He, Zhihong Tang, Xiaochun Wang, Jian Chen

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 942–965

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