NDLI: Modeling high dimensional frequency spectral data based on virtual sample generation technique

Content Provider	IEEE Xplore Digital Library
Author	Jian Tang MeiYing Jia Zhuo Liu TianYou Chai Wen Yu
Copyright Year	2015
Description	Author affiliation: Dept. de Control Automatico, CINVESTAV-IPN, Mexico City, Mexico (Wen Yu) \|\| Res. Center of Autom., Northeastern Univ., Shenyang, China (Zhuo Liu; TianYou Chai) \|\| Res. Inst. of Comput. Technol., Beijing Jiaotong Univ., Beijing, China (Jian Tang; MeiYing Jia)
Abstract	In many situations, such as medical records of rare diseases, early stages of flexible manufacturing system and continuous industrial process, only small training samples can be obtained to construct prediction model. When modelling with high dimensional spectral data, it is very much difficulty to construct efficient and effective prediction model with such a small sample. This research proposes a new virtual sample generation (VSG) approach to model mechanical vibration and acoustic spectra. At first, prior knowledge about the actual training samples is used to produce input of virtual sample. Then, partial least squares (PLS) is used to extract spectral features for reducing features dimension. Thirdly, genetic algorithm (GA) and backup propagation neural networks (BPNN) based feasibility-based programming (FBP) model is used to generate virtual sample's output. At last, shell vibration and acoustic spectral data of a laboratory-scale ball mill are used to verify performance of the proposed method.
Starting Page	1090
Ending Page	1095
File Size	386438
Page Count	6
File Format	PDF
e-ISBN	9781467391047
DOI	10.1109/ICInfA.2015.7279449
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Vibrations Virtual sample generation Predictive models Feature extraction Data models Acoustics Selective ensemble learning Frequency spectral data Genetic algorithms Small sample modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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