Comprehensive monitoring cycle classification of centralized monitoring substations based on knowledge map and competitive neural network

An advanced neuro-classification of new and used bills using the spectral patterns is proposed. An acoustic spectral pattern is obtained from the output of the two-stage adaptive digital filters (ADFs) for time-series acoustic data. The acoustic spectral patterns are fed to a competitive neural network, and classified into some categories which show worn-out degrees of the bill. The proposed method is based on extension of an ADF, an individual adaptation (IA) algorithm, and a learning vector quantization (LVQ) algorithm. The experimental results show that the proposed method is useful to classify new and used bills.

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

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium ◽

10.1109/ijcnn.2000.859404 ◽

2000 ◽

Cited By ~ 4

Author(s):

M. Teranishi ◽

S. Omatu ◽

T. Kosaka

Keyword(s):

Neural Network ◽

Acoustic Energy ◽

Competitive Neural Network

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A competitive neural network model for the classification of individual lumbar muscle response patterns

Journal of Biomechanics ◽

10.1016/0021-9290(94)90070-1 ◽

1994 ◽

Vol 27 (5) ◽

pp. 593

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Response Patterns ◽

Muscle Response ◽

Competitive Neural Network ◽

Lumbar Muscle

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Classification of polycystic ovary based on ultrasound images using competitive neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/971/1/012005 ◽

2018 ◽

Vol 971 ◽

pp. 012005 ◽

Cited By ~ 2

Author(s):

R M Dewi ◽

Adiwijaya ◽

U N Wisesty ◽

Jondri

Keyword(s):

Neural Network ◽

Polycystic Ovary ◽

Ultrasound Images ◽

Competitive Neural Network

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

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2000.p0018 ◽

2000 ◽

Vol 4 (1) ◽

pp. 18-23 ◽

Cited By ~ 6

Author(s):

Masaru Teranishi ◽

◽

sigeru Omatu ◽

Toshihisa Kosaka ◽

◽

...

Keyword(s):

Neural Network ◽

Vector Quantization ◽

Acoustic Signal ◽

Learning Vector Quantization ◽

Experimental Results ◽

New Method ◽

Competitive Neural Network ◽

Good Classification

This paper proposes a new method to classify currencies into different fatigue levels. Acoustic cepstrum patterns obtained from an acoustic signal generated by a currency passing through a banking machine are used for classification. The acoustic cepstrum patterns are fed to a competitive neural network with the Learning Vector Quantization (LVQ) algorithm, and classified the currency into three fatigue levels. The experimental results show that the proposed method is useful for classification of fatigue levels of currencies, and the LVQ algorithm performs a good classification.

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