NDLI: ANC system performance analysis using a multiple-LMS-based neural network for high-speed train's noise

Content Provider	IEEE Xplore Digital Library
Author	Hyeon Seok Jang Jong Il Bae Kyung Wan Koo Woo Young Ahn Kwon Soon Lee
Copyright Year	2012
Description	Author affiliation: Dept. of Defense Science & Technology, Hoseo University, Asan, Republic of Korea (Jong Il Bae) \|\| Dept. of Electrical Engineering, Pukyong University, Busan, Republic of Korea (Kyung Wan Koo) \|\| Dept. of Bio Information, Daejeon Health Sciences College, Republic of Korea (Woo Young Ahn) \|\| Dept. of Electrical Engineering, Dong-A University, Busan, Republic of Korea (Hyeon Seok Jang; Kwon Soon Lee)
Abstract	Since the eco-era is getting closer, the importance of noise reducing in the passenger cars of high-speed train is very important. The active noise is best choice to reduce low frequency noise because since the passive one too heavy for high speed trains where weight so critical. Also ANC able to reduce the ambient noise when the environmental-factor changes. In this paper, we presents the active noise control (ANC) system based on the least mean square (LMS) algorithm and the neural network algorithm for reducing the interior KTX noise. First, the pure noise of the KTX interior without passengers was measured. Then an LMS framework was constructed as a nominal ANC system, and an artificial single-layered perceptron model was designed as an auxiliary ANC to reduce the real-time residual noise due to the non-stationary and uncertain nature of noise. The parameter vector of the hybrid ANC was determined through online estimation to realize an adaptive ANC configuration by means of the steepest descent algorithm. A simulation experiment was performed to demonstrate the proposed ANC system using the previously mentioned realistic acoustic noise.
Starting Page	361
Ending Page	365
File Size	3927859
Page Count	5
File Format	PDF
ISBN	9781467301985
e-ISBN	9781467301992
DOI	10.1109/ICSAI.2012.6223634
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least Mean Square (LMS) Noise Neural networks Active Noise Control Noise Reduction High-Speed Rail Control systems Mathematical model Noise measurement Least squares approximation Equations Neural Network (NN)
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in