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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Wang Bing
Huaihai Institute of Technology
Computer Science & IT

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 1 
Strain Transfer and Test Research of Stick-up Fiber Bragg Grating Sensors Wang Bing; Wang Xiaoli
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 3: September 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i3.107

Abstract

Because of the flaws of fiber Bragg grating, needs to set up protective layer between the structure and fiber layer to protect the fiber grating. Firstly the strain transferring rules of the FBG sensors is analyzed, carefully analyze the main factors influencing the fiber Bragg grating strain sensor transfer, and analyze concretely effect of each factor, the fiber Bragg grating sensors embedded angle deviation is analyzed and influence on the measured results. Finally, by a series of repeated, coherent, dynamic and fatigue characteristic test, it is proved that the FBG sensor has applied value
Perception Neural Networks for Active Noise Control Systems Wang Bing; Wang Xiaoli
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 7: November 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In a response to a growing demand for environments of 70dB or less noise levels, many industrial sectors have focused with some form of noise control system. Active noise control (ANC) has proven to be the most effective technology. This paper mainly investigates application of neural network on self-adaptation system in active noise control (ANC). An active silencing control system is made which adopts a motional feedback loudspeaker as not a noise controlling source but a detecting sensor. The working fundamentals and the characteristics of the motional feedback loudspeaker are analyzed in detail. By analyzing each acoustical path, identification based adaptive linear neural network is built. This kind of identifying method can be achieved conveniently. The estimated result of each sound channel matches well with its real sound character, respectively. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1580
Co-Authors Wang Xiaoli