@article{Nguyen_Nguyen_Ngo_2016, title={Identifying undamaged-beam status based on singular spectrum analysis and wavelet neural networks}, volume={31}, url={https://vjs.ac.vn/index.php/jcc/article/view/6417}, DOI={10.15625/1813-9663/31/4/6417}, abstractNote={<p><span style="line-height: 115%; font-family: "Calibri","sans-serif"; font-size: 11pt; mso-ansi-language: EN-US; mso-bidi-font-family: ’Times New Roman’; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">In this paper, the identifying undamaged-beam status  based on singular spectrum analysis (SSA) and wavelet neural networks (WNN)  is presented. First, a database is built from measured sets and SSA which  works as a frequency-based filter. A WNN model is then designed which consists of the wavelet frame building, wavelet structure designing and  establishing a solution for training the WNN. Surveys via an experimental  apparatus for estimating the method are carried out. In this work, a  beam-typed iron frame, Micro-Electro-Mechanical (MEM) sensors and a  vibration-signal processing and measuring system named LAM_BRIDGE are all  used.</span></p>}, number={4}, journal={Journal of Computer Science and Cybernetics}, author={Nguyen, Dung Sy and Nguyen, Hung Quoc and Ngo, Nhi Kieu}, year={2016}, month={Jan.}, pages={341} }