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Kikuo Asai,Norio Takase 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
We describe a method for estimating finger motion on the basis of the frequency conversion of electromyogram (EMG) signals and the image recognition by using a convolutional neural network (CNN). Since EMG signals are generated before finger motion, various EMG-based systems have been developed for smoothly controlling a robot hand. We used a simple CNN model for estimating finger motion by classifying images generated from a wavelet transform of EMG signals. The model has originally been used for document recognition, and it contains two pairs of convolution and pooling layers and two fully connected layers. A prototype system composed of inexpensive sensor devices was fabricated for acquiring EMG signals and capturing finger motion. The experimental results show that the test accuracy reached 83% in classifying EMG signals into four types; when a thumb opens or is closed, and fingers, except for the thumb, open or are closed.
Magnetic and charge orders in zigzag nanographene ribbons
Kikuo Harigaya,Atsushi Yamashiro,Yukihiro Shimoi,Katsunori Wakabayashi 한국물리학회 2004 Current Applied Physics Vol.4 No.6
We theoretically study the electronic states in graphene ribbons which are strongly aected by the edge states, the peculiar non-bonding molecular orbitals localized along the zigzag edges of the ribbons. New kinds of edge localized electronic states with spinand charge polarizations are found in the mean eld solutions of the extended Hubbard model with onsite and nearest-neighborCoulomb repulsions. These novel states appear due to the interplay between the edge states and the Fermi instabilities. We alsoexamine the competition between the charge polarized state and the spin polarized state to draw a phase diagram depending onCoulomb parameters. The results obtained by the mean eld calculations with the extended Hubbard model modied to include Coulomb integrals provide useful insights to understand and functionalize the nanoscale materials.