Masaru Teranishi

发表

Classification of bill fatigue levels by feature-selected acoustic energy pattern using competitive neural network

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Stable Learning for Neural Network Tomography by Using Back Projected Image

Masaru Teranishi, Keita Oka, Masahiro Aramoto, 2014, DCAI.

Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training

Masaru Teranishi, Hidetoshi Takeno, Shimpei Matsumoto, 2019, ICANN.

A new neuron model "cone" with fast convergence rate and its application to pattern recognition

Minoru Fukumi, Sigeru Omatu, Masaru Teranishi, 1991, Systems and Computers in Japan.

Personal peculiarity classification of flat finishing tool motion by using self-organizing maps for skill training

Shinpei Matsumoto, Masaru Teranishi, Hidetoshi Takeno, 2015, 2015 10th Asian Control Conference (ASCC).

Image restoration of confocal microscopy based on deconvolution algorithms for biological structure

Michifumi Yoshioka, Masaru Teranishi, Isao Yamashita, 2015, 2015 10th Asian Control Conference (ASCC).

Noise Reduction of Acoustic Data of Bill under Noisy Environment Using Adaptive Digital Filter and Neural Network

Michifumi Yoshioka, Sigeru Omatu, Masaru Teranishi, 2009 .

Peculiarity Classification of Flat Finishing Motion Based on Tool Trajectory by Using Self-organizing Maps

Shimpei Matsumoto, Masaru Teranishi, Hidetoshi Takeno, 2018, DCAI.

A brush coating skill training system for manufacturing education at Japanese elementary and junior high schools

Shimpei Matsumoto, Tatsushi Tokuyasu, Masaru Teranishi, 2015, Artificial Life and Robotics.

Fatigue level estimation of monetary bills based on frequency band acoustic signals with feature selection by supervised SOM

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2010 .

Measurement Method of Fatigue Level of Bill based on Bending Rigidity

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2007 .

Fatigue Level Estimation of Bill by Using Supervised SOM Based on Feature-Selected Acoustic Energy Pattern

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

Improved neural network tomography by initial learning with coarse reconstructed image

Masaru Teranishi, 2016, Neurocomputing.

Fatigue level estimation of bill based on feature-selected frequency band acoustic signal by using supervised SOM

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2009 .

New and Used Bill Money Classification Using Spectral Information Based on Acoustic Data of Banking Machine

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 1997 .

Personal Peculiarity Classification of Flat Finishing Motion for Skill Training by Using Expanding Self-Organizing Maps

Shinpei Matsumoto, Masaru Teranishi, Hidetoshi Takeno, 2016, DCAI.

Classification of New and Used Bills Using Acoustic Cepstrum of a Banking Machine by Neural Networks

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 1999 .

Continuous fatigue level estimation for the classification of fatigued bills based on an acoustic signal feature by a supervised SOM

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2009, Artificial Life and Robotics.

Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2008 .

New and Used Bills Classification Using Competitive Neural Network Based on Cepstrum Pattern

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 1998, ICONIP.

Personal Peculiarity Classification of Flat Finishing Skill Training by using Torus type Self-Organizing Maps

Shinpei Matsumoto, Masaru Teranishi, Hidetoshi Takeno, 2017, DCAI.

Flat Finishing Skill Training Software System Based on Personal Peculiarity Classification

Shinpei Matsumoto, Masaru Teranishi, Hidetoshi Takeno, 2017, SoMeT.

Peculiarity Classification of Flat Finishing Motion Based on Tool Trajectory by Using Self-organizing Maps Part 2: Improvement of Clustering Performance Based on Codebook Vector Density

Shimpei Matsumoto, Masaru Teranishi, Hidetoshi Takeno, 2019, DCAI.

Neuro-classification of Bill Fatigue Levels Based on Acoustic Wavelet Components

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2002, ICANN.

New and used bills classification for cepstrum patterns

Toshihisa Kosaka, Masaru Teranishi, Sigeru Omato, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Blur Restoration of Confocal Microscopy with Depth and Horizontal Dependent PSF

Michifumi Yoshioka, Masaru Teranishi, Yuichi Morioka, 2018, DCAI.

Neuro-Classification of Currency Fatigue Levels Based on Acoustic Cepstrum Patterns

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2000, J. Adv. Comput. Intell. Intell. Informatics.

Fatigue level estimation of bill based on feature-selected acoustic energy pattern by using supervised SOM

Sigeru Omatu, Toshihisa Kosaka, Masaru Teranishi, 2008, CSTST.

Development of Flat Finishing Skill Training System Based on Personal Peculiarity Classification of Tool Trajectory

Shimpei Matsumoto, Masaru Teranishi, Hidetoshi Takeno, 2018, 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI).

Examining efficient instructional methods for computer-aided brush coating skill training system in elementary and secondary education

Shimpei Matsumoto, Tatsushi Tokuyasu, Masaru Teranishi, 2017, Artificial Life and Robotics.