Timber Health Monitoring using piezoelectric sensor and machine learning
Timber Health Monitoring using piezoelectric sensor and machine learning
复制标题
使用压电传感器和机器学习进行木材健康监测
DOI：
10.1109/civemsa.2017.7995313
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Takayuki Kawahara
中科院分区：
文献类型：
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作者：
Ryo Oiwa;Takumi Ito;Takayuki Kawahara
木材健康监测系统，它使木制建筑物的持续监测，通过人工智能为基础的分析的信号的压电传感器连接到一块木材，提出。通过建模木材损伤和进行振动测试进行了基本验证。使用k-近邻（k-NN）方法和支持向量机得到的波形数据的分析表明，该系统具有很强的分类性能。我们也尝试使用主成分分析来降低数据维度，发现即使采用降维，分类率也几乎没有下降。这些结果是有希望的实现我们提出的系统。
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.