A modified algorithm for the fast processing with the Growing Self-Organizing Map

2006 ◽  
Author(s):  
Yaohua Yu ◽  
Damminda Alahakoon
Keyword(s):  
Self Organizing Map ◽  
Fast Processing ◽  
Modified Algorithm ◽  
Self Organizing

scholarly journals Improvement of Algorithm in the Particle Tracking Velocimetry Using Self-Organizing Maps

1970 ◽  
Vol 7 (1) ◽  
pp. 6-23 ◽  
Author(s):  
Shashidhar Ram Joshi
Keyword(s):  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
A Priori ◽  
Maximum Flow ◽  
Tracking Algorithm ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Particle Tracking Algorithm ◽  
Modified Algorithm ◽  
Self Organizing

The neural network techniques are becoming a useful tool for the particle tracking algorithm of the PIV system software and among others, the self-organizing maps (SOM) model seems to have turned out particularly effective for this purpose. This is mainly because of the performance of the particle tracking itself, capacity of dealing with unpaired particles between two frames and no necessity for a priori knowledge on the flow field (e.g. maximum flow rate) to be measured. Initially, concept of SOM was applied to PIV by Labonte. It was modified by Ohmi and further modified algorithm is developed using the concept of Delta-Bar-Delta rule. It is a heuristic algorithm for modifying the learning rate as training progresses. Earlier, the treatment of unpaired particles, a specific problem to any type of PIV, is not fully considered and thereby, the tracking goes unsuccessfully for some particles. The present research is to bring about further improvement and practicability in this promising particle tracking algorithm. The computational complexity can be reduced employing modified algorithm compared to other algorithms. The modified algorithm is tested in the light of the synthetic PIV standard image as well as in particle images obtained from visualization experiments.Key words: Delta-Bar Delta, Dynamic Threshold Binarization, HVD Algorithm, Labonte's SOM, Modified Algorithm, Ohmi's SOM, Particle Image Velocimetry(PIV), Particle Tracking Velocimetry(PTV), Self-Organizing Map(SOM), Single Threshold Binarization.Journal of the Institute of Engineering, Vol. 7, No. 1,  2009, July, pp. 6-23doi: 10.3126/jie.v7i1.2057

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

2011 ◽  
Vol 131 (1) ◽  
pp. 160-166 ◽  
Author(s):  
Yutaka Suzuki ◽  
Mizuya Fukasawa ◽  
Osamu Sakata ◽  
Hatsuhiro Kato ◽  
Asobu Hattori ◽  
...  
Keyword(s):  
Support System ◽  
Self Organizing Map ◽  
Self Organizing

Classification of Heat Wave Events in Seoul Using Self-Organizing Map

2018 ◽  
Vol 9 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Seung-Yoon Back ◽  
Sang-Wook Kim ◽  
Myung-Il Jung ◽  
Joon-Woo Roh ◽  
Seok-Woo Son
Keyword(s):  
Heat Wave ◽  
Self Organizing Map ◽  
Self Organizing

Spherical Self-Organizing Map Detects MYBL 1 As Candidate Gene for Triple-Negative Breast Cancer

2015 ◽  
Vol 3 (2) ◽  
pp. 94-101 ◽  
Author(s):  
Masashi Ikeda ◽  
Kazuki Kumon ◽  
Kazuya Omoto ◽  
Yuh Sugii ◽  
Akifumi Mizutani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Candidate Gene ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Self Organizing Map ◽  
Self Organizing
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