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Dynamics Learning Network with Structured Recurrent Modules
Miyanaga, Yoshikazu,Tochinai, Koji,Li, Yisheng 대한전자공학회 1994 ISPACS:Intelligent Signal Processing and Communica Vol.1 No.1
In this report, a local connected recurrent neural network, and a new learning algorithm are proposed. The network which has the ability to memorize and regenerate complex dynamics is constructed by adaptive oscillating modules. This module consists of two simply neuron nodes with recurrent connections. In the new learning algorithm, each module can be trained in independently with suitable sped for given input data. The network's size is also adaptively determined in learning process. Finally, some simulation results are demonstrated to verify the effectiveness of the proposed network structure and the learning algorithm.
Performance Evaluation on 3-D object recognition using a restricted neural network
Miyanaga, Yoshikazu,Motoyoshi, Katsuhito,Tochinai, Koji 대한전자공학회 1996 APCCAS:Asia Pacific Conference on Circuits And Sys Vol.1 No.1
This report introduces a new approach on a recognition system of three dimensional, i.e., 3D, objects. The proposed system is based on a restricted multi-layered neural network. For the performance evaluation of this network, the experiment in which some similar objects are used for recognition is demonstrated in this report.
ADAPTIVE RECOGNITION OF HAND-WRITTEN KANJI CHARACTERS USING SELF-ORGANIZED NEURAL NETWORK
Miyanaga, Yoshikazu,Tochinai, Koji,Kondo, Masanori,Hayashi, Masato 대한전자공학회 1994 ISPACS:Intelligent Signal Processing and Communica Vol.1 No.1
This port introduces an image recognition system for hand-written Kanji characters. The method is based on a self-organized neural network and a single layer perception network. The self-organized neural network is used for adaptive clustering. The single perception network is used for recognition. It is well known that a large amount of time is required in the training by a mullti-layered perception when some cluster distributions have complicated structures. However, since only the simplest perception is applied in this proposed system, a quite short time is enough to learn training data. The reason why multi-layered perception is not required to recognize data in this system is based on the use of a self-organized network. The self-organized network can change a complicated structure of cluster distribution to a simple structure without the loss of information. Thus, it can be shown that the simple perception is enough to recognize even nonlinear characteristic distribution.
Lee, J.,Miyanaga, Y.,Ueda, M.,Hohng, S. Biophysical Society ; Published for the Biophysica 2012 Biophysical journal Vol.103 No.8
There is no confocal microscope optimized for single-molecule imaging in live cells and superresolution fluorescence imaging. By combining the swiftness of the line-scanning method and the high sensitivity of wide-field detection, we have developed a, to our knowledge, novel confocal fluorescence microscope with a good optical-sectioning capability (1.0 μm), fast frame rates (<33 fps), and superior fluorescence detection efficiency. Full compatibility of the microscope with conventional cell-imaging techniques allowed us to do single-molecule imaging with a great ease at arbitrary depths of live cells. With the new microscope, we monitored diffusion motion of fluorescently labeled cAMP receptors of Dictyostelium discoideum at both the basal and apical surfaces and obtained superresolution fluorescence images of microtubules of COS-7 cells at depths in the range 0-85 μm from the surface of a coverglass.
High Power Lasers and Their New Applications
Yasukazu Izawa,Noriaki Miyanaga,Junji Kawanaka,Koichi Yamakawa 한국광학회 2008 Current Optics and Photonics Vol.12 No.3
Recent progress in high power lasers enables us to access a regime of high-energy-density and/or ultra-strong fields that was not accessible before, opening up a fundamentally new physical domain which includes laboratory astrophysics and laser nuclear physics. In this article, new applications of high-energy and ultra-intense laser will be reviewed.