TY - GEN
T1 - Timber Health Monitoring using piezoelectric sensor and machine learning
AU - Oiwa, Ryo
AU - Ito, Takumi
AU - Kawahara, Takayuki
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - The Timber Health Monitoring System, which enables constant monitoring of wooden buildings by artificial intelligence based analysis of the signals of a piezoelectric sensor attached to a piece of timber, is proposed. Basic verification was carried out by modeling timber damage and performing vibration tests. Analysis of the obtained waveform data using the k-nearest neighbor (k-NN) method and a support vector machine revealed that the proposed system has a strong classification performance. We also tried reducing the data dimensions by using principal component analysis and found that the classification rates barely decreased even if dimensional reduction was adopted. These results are promising for the realization of our proposed system.
AB - The Timber Health Monitoring System, which enables constant monitoring of wooden buildings by artificial intelligence based analysis of the signals of a piezoelectric sensor attached to a piece of timber, is proposed. Basic verification was carried out by modeling timber damage and performing vibration tests. Analysis of the obtained waveform data using the k-nearest neighbor (k-NN) method and a support vector machine revealed that the proposed system has a strong classification performance. We also tried reducing the data dimensions by using principal component analysis and found that the classification rates barely decreased even if dimensional reduction was adopted. These results are promising for the realization of our proposed system.
KW - Artificial Intelligence
KW - Piezoelectric Sensor
KW - Structural Health Monitoring
KW - Support Vector Machine
KW - k-Nearest Neighbor
UR - http://www.scopus.com/inward/record.url?scp=85028517360&partnerID=8YFLogxK
U2 - 10.1109/CIVEMSA.2017.7995313
DO - 10.1109/CIVEMSA.2017.7995313
M3 - Conference contribution
AN - SCOPUS:85028517360
T3 - 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017 - Proceedings
SP - 123
EP - 128
BT - 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017
Y2 - 26 June 2017 through 28 June 2017
ER -