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Deep Neural Network Classification of Tactile Materials Explored by Tactile Sensor Array With Various Active-Cell Formations
- Deep Neural Network Classification of Tactile Materials Explored by Tactile Sensor Array With Various Active-Cell Formations
- Lim, Sung-Ho; Kim, Kyungsoo; Sim, Minkyung; Shin, Kwonsik; Lee, Doyoung; Park, Jiho; Jang, Jae Eun; Choi, Ji-Woong
- DGIST Authors
- Jang, Jae Eun; Choi, Ji-Woong
- Issue Date
- IEEE/ASME Transactions on Mechatronics, 25(4), 2134-2138
- Article Type
- Author Keywords
- Neural network applications; pattern classification; piezoelectric devices; tactile sensors; tactile system
- Reducing the input data of tactile sensory systems brings a large degree of freedom to real-world implementations from the perspectives of bandwidth and computational complexity. For this, in this letter, we suggest efficient active-cell formations with a high classification accuracy of tactile materials. By revealing that averaged Kullback-Leibler-divergence and common frequency component power to variance ratio are proportional to the classification accuracy, we showed that those methods can be useful in estimating valid active-cell formations.
- Institute of Electrical and Electronics Engineers
- Related Researcher
Jang, Jae Eun
Advanced Electronic Devices Research Group(AEDRG) - Jang Lab.
Nanoelectroinc device; 생체 신호 센싱 시스템 및 생체 모방 디바이스; 나노 통신 디바이스
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- Department of Information and Communication EngineeringAdvanced Electronic Devices Research Group(AEDRG) - Jang Lab.1. Journal Articles
Department of Information and Communication EngineeringCSP(Communication and Signal Processing) Lab1. Journal Articles
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