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 Results 1 - 11 of 11  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
SCE 2023-01-20
Tokyo Kikai-Shinko-Kaikan Bldg.
(Primary: On-site, Secondary: Online)
Introduction of a fluctuation mechanism of the oscillation frequency of the oscillator-based random number generator using Josephson oscillation
Takeshi Onomi (Fukuoka Inst. Tech.) SCE2022-15
An oscillator-based true random number generator using superconducting single flux quantum circuits and Josephson oscill... [more] SCE2022-15
SCE 2015-08-05
Kanagawa Yokohama National Univ. Demonstration of a relaxation oscillator based on a superconducting Schmitt trigger inverter
Takeshi Onomi (Fukuoka Inst. Tech.) SCE2015-17
A new relaxation oscillator using a superconducting Schmitt trigger inverter is proposed and tested. The superconducting... [more] SCE2015-17
Miyagi Tohoku University A Comparison of Back Propagation Learning between the Inverse-function Delayless Model and a Conventional Model
Yuta Horiuchi (Tohoku Univ), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku Univ) NC2014-26
For the combinatorial optimization problem using the hopfield model, avoidance of the local minimum problem is important... [more] NC2014-26
Miyagi Tohoku University The Relation between Dispersion of Initial Values and Pre-training of Deep Neural Networks
Seitaro Shinagawa (Tohoku univ.), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku univ.) NC2014-27
 [more] NC2014-27
SCE 2014-07-23
Tokyo Kikai-Shinko-Kaikan Bldg. Superconducting Schmitt trigger inverter and its application
Takeshi Onomi (Tohoku Univ.) SCE2014-28
A new superconducting Schmitt trigger inverter and a relaxation oscillator are proposed. The proposed superconducting Sc... [more] SCE2014-28
NLP 2014-06-30
Miyagi Tohoku Univ. Backpropagation learning using inverse function delay-less model
Yuta Horiuchi (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-25
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. ID model has a oscillation capa... [more] NLP2014-25
NLP 2014-06-30
Miyagi Tohoku Univ. Study on the hardware of the Bidirectional Associative Memories by using the Inverse Function Delayless model
Chunyu Bao, Takeshi Onomi, Yoshihiro Hayakawa, Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2014-26
In conventional macro models such as the Hopfield model, the problems that are caused by the solution of the network not... [more] NLP2014-26
NLP 2014-07-01
Miyagi Tohoku Univ. Learning Restricted Boltzmann Machine with discrete learning parameter
Seitaro Shinagawa (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-27
Recently, the method of Deep Neural Network (DNN) with hierarchical learning has been remarkable for performance to solv... [more] NLP2014-27
SCE 2014-01-24
Tokyo Kikaishinkou-kaikan Bldg. Analysis of rf-SQUID ladder circuits with a single flux quantum signal for the transmission direction
Yuya Tsuji, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) SCE2013-51
Although SFQ circuit technique is very predominant in respect of power consumption, the circuit system of a semiconducto... [more] SCE2013-51
SCE 2014-01-24
Tokyo Kikaishinkou-kaikan Bldg. Comparison of the final addition circuit in SFQ parallel multiplier with a tree structure partial product adder circuit
Akifumi Yamada, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) SCE2013-52
A single flux quantum (SFQ) circuit is capable of high-speed operation in a few 10 GHz, and it has a big advantage compa... [more] SCE2013-52
NLP 2010-03-10
Tokyo   Neural Networks and the Application to the 4-Queen Problem
Yusuke Maenami, Takeshi Onomi (Tohoku Univ), Yoshihiro Hayakawa (Sendai Nat Coll. of Tech.), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2009-172
A combination optimization problem is generally NP difficulty or NP completeness. When problem size becomes large, it is... [more] NLP2009-172
 Results 1 - 11 of 11  /   
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