A Method of Non-Contact Proximity Detection for Table Saws

• Main Authors: Chia-Wei Chang, 張家瑋
• Other Authors: Der-Chen Huang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/59094344124906671661

碩士 === 國立中興大學 === 資訊科學與工程學系 === 105 === 　　The rapid progress of modern society begin in Industrial Revolution. After into the twentieth century, the large number of electric applications gradual emerged. Electric power tools to help the operation more efficiently, but it also makes the operation accompanied by the risk of accidents. In the case of table saws, a large number of table saws are applied in the wood processing aspect, which is used for wood splitting such as cutting straight lines. However, it is often occur body, such as hands or fingers, is so close to the running saw blade that cause accidents. Therefore, if it can inform the occurrence as soon as possible, that can issue the warning in time. In doing so, operators can have more reaction time avoiding accidents occurred. 　　The backbone of this paper is establish the proximity detection for table saws. By setting the non-contact sensors, which collecting data when the object close to the saw. Simultaneously, establish an analysis system which is based on Probabilistic Neural Network (PNN) to determine the classification of collected data. Experimenting with a comprehensive data set from different people. Testing the situation of single hand and two hands sequentially. In each experiment, the accuracy can reach 95% and 89% individually. This paper further proposes a PNN learning-fixed model when the analysis system is used by single person. The accuracy can reach 97% in the case.