TY - GEN
T1 - Machine Learning Algorithms to Detect Penetrations of PVDF
AU - Zhai, Yayu
AU - Song, Ping
AU - Chen, Xiaoxiao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Polyvinylidene fluoride (PVDF) has widely used in detecting the interplanetary dust. In the case of penetration and non-penetration, the output signals of the PVDF are quite different. Detecting whether particles penetrate PVDF is a crucial issue. We create a set of experimental equipment for collecting the signals from the PVDF. The equipment consists of particle emitter, shield, conditioning circuits and data acquisition equipment. 600 experiments are conducted. Among 200 experiments, the particles penetrate PVDF. We successfully distinguish penetration of PVDF using four machine learning algorithms: Anomaly Detection (AD), Artificial Neural Network (ANN), K-Nearest-Neighbors (KNN), and Support Vector Machines (SVM). We propose a unique evaluation criteria OP to evaluate the performance of four classifiers including their accuracy and computational time. The results show that ANN is the best machine learning algorithm for our problem, and AD is not suitable for our problem.
AB - Polyvinylidene fluoride (PVDF) has widely used in detecting the interplanetary dust. In the case of penetration and non-penetration, the output signals of the PVDF are quite different. Detecting whether particles penetrate PVDF is a crucial issue. We create a set of experimental equipment for collecting the signals from the PVDF. The equipment consists of particle emitter, shield, conditioning circuits and data acquisition equipment. 600 experiments are conducted. Among 200 experiments, the particles penetrate PVDF. We successfully distinguish penetration of PVDF using four machine learning algorithms: Anomaly Detection (AD), Artificial Neural Network (ANN), K-Nearest-Neighbors (KNN), and Support Vector Machines (SVM). We propose a unique evaluation criteria OP to evaluate the performance of four classifiers including their accuracy and computational time. The results show that ANN is the best machine learning algorithm for our problem, and AD is not suitable for our problem.
KW - PVDF
KW - classification
KW - feature extraction and reduction
KW - machine learning algorithms
UR - http://www.scopus.com/inward/record.url?scp=85063629453&partnerID=8YFLogxK
U2 - 10.1109/ICSESS.2018.8663727
DO - 10.1109/ICSESS.2018.8663727
M3 - Conference contribution
AN - SCOPUS:85063629453
T3 - Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
SP - 644
EP - 648
BT - ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science
A2 - Wenzheng, Li
A2 - Babu, M. Surendra Prasad
PB - IEEE Computer Society
T2 - 9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018
Y2 - 23 November 2018 through 25 November 2018
ER -
