A Convolutional Deep Self-Organizing Map Feature extraction for machine learning

2020 ◽  
Vol 79 (27-28) ◽  
pp. 19451-19470 ◽  
Author(s):  
Mohamed Sakkari ◽  
Mourad Zaied
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map

2014 ◽  
Vol 8 (1) ◽  
pp. 101
Author(s):  
Ambarwati Ambarwati ◽  
Edi Winarko
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
System Application ◽  
Input Data ◽  
Information Resources ◽  
The Self ◽  
News Article ◽  
Self Organizing Map ◽  
Map Algorithm ◽  
Self Organizing

AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing Map. Artikel berita digunakan sebagai input data. Kemudian sistem melakukan pemrosesan data untuk dikelompokkan. Proses yang dilakukan sistem meliputi preprocessing, feature extraction, clustering dan visualize.Sistem yang dikembangkan mampu menampilkan hasil clustering dengan algoritma Self Organizing Map dan memberikan visualisasi dengan smoothed data histograms berupa island map dari artikel berita. Selain itu sistem dapat menampilkan koleksi dokumen dari lima kategori berita yang ada pada tiap tahunnya dan banyaknya kata (histogram kata) yang sering muncul pada tiap arikel berita. Pengujian dari sistem ini dengan memasukan artikel berita, kemudian sistem memprosesnya dan mampu memberikan hasil cluster dari artikel berita yang dimasukan. Kata kunci—Pengelompokkan berita Indonesia, pengelompokkan berdasar histogram kata, pengelompokan berita menggunakan SOM  Abstract News is awaited information resources by humans every day. Human reading the news with the desired category. If the computer able to news clustering with automatically, humans of course will be easier to read the news according to the desired category. News clustering in the form of news articles with automatically very interesting because it organizes news articles manually takes time and costs not a little bit.The purpose of this research is to create a system application for grouping news articles by using the Self Organizing Map algorithm. News article be used as input into the system. News articles used as input data. Then the system performs data processing until to be clustered. Processes performed by the system covers: preprocessing, feature extraction, clustering and visualize.The system developed is able to display the results clustering of the Self Organizing Map algorithm and gives visualization of the Smoothed Data Histograms in the form of island map from news articles. Additionally the system can display a word histogram and news articles from five categories news in each year. Testing of this system by entering the news articles, then the system performs data processing and gives results of a cluster from news articles that input. Keywords—Indonesia news clustering, clustering based on words histograms, news clustering using SOM

scholarly journals Computational timbre and tonal system similarity analysis of the music of Northern Myanmar-based Kachin compared to Xinjiang-based Uyghur ethnic groups

2021 ◽  
Author(s):  
Rolf Bader ◽  
Michael Blaß ◽  
Jonas Franke
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Standard Deviation ◽  
Ethnic Groups ◽  
Western China ◽  
Similarity Analysis ◽  
Self Organizing Map ◽  
Musical Piece ◽  
Sound Recordings ◽  
Spectral Centroid

The music of Northern Myanmar Kachin ethnic group is compared to the music of western China, Xijiang based Uyghur music, using timbre and pitch feature extraction and machine learning. Although separated by Tibet, the muqam tradition of Xinjiang might be found in Kachin music due to myths of Kachin origin, as well as linguistic similarities, e.g., the Kachin term 'makan' for a musical piece. Extractions were performed using the apollon and COMSAR (Computational Music and Sound Archiving) frameworks, on which the Ethnographic Sound Recordings Archive (ESRA) is based, using ethnographic recordings from ESRA next to additional pieces. In terms of pitch, tonal systems were compared using Kohonen self-organizing map (SOM), which clearly clusters Kachin and Uyghur musical pieces. This is mainly caused by the Xinjiang muqam music showing just fifth and fourth, while Kachin pieces tend to have a higher fifth and fourth, next to other dissimilarities. Also, the timbre features of spectral centroid and spectral sharpness standard deviation clearly tells Uyghur from Kachin pieces, where Uyghur music shows much larger deviations. Although more features will be compared in the future, like rhythm or melody, these already strong findings might introduce an alternative comparison methodology of ethnic groups beyond traditional linguistic definitions.

Feature Extraction-Based Deep Self-Organizing Map

2022 ◽  
Author(s):  
Mohamed Sakkari ◽  
Monia Hamdi ◽  
Hela Elmannai ◽  
Abeer AlGarni ◽  
Mourad Zaied
Keyword(s):  
Feature Extraction ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Feature Extraction of Structures in Sea Water Using Self-Organizing Maps and Electromagnetic Waves

2016 ◽  
Vol 16 (3) ◽  
pp. 261
Author(s):  
Murilo Teixeira Silva ◽  
Lurimar Smera Batista ◽  
Frederico Medeiros Vasconcelos De Albuquerque
Keyword(s):  
Feature Extraction ◽  
Electromagnetic Waves ◽  
Sea Water ◽  
Low Frequency ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Virtual Sensors ◽  
Study Region ◽  
Direct Model ◽  
Self Organizing

<pre><!--StartFragment-->The use of Self-Organizing Map (<span>SOM</span>) algorithm for feature extraction and dimensionality reduction applied to underwater object detection with Low Frequency Electromagnetic Waves is presented. Computer simulation is used to generate a direct model for the study region, and a Self Organizing Map Algorithm is used to fit the data and return a similar model, with smaller dimensionality and same characteristics. Results show that virtual sensors are created by the <span>SOM</span> algorithm with consistent predictions, filling the resolution gap of the input data. These results are useful for fastening decision making algorithms by reducing the number of inputs to a group of significant data.<!--EndFragment--></pre>

Fault Diagnosis Method for Rectifier Circuit Based on Self-Organizing Map

2013 ◽  
Vol 321-324 ◽  
pp. 1930-1933 ◽  
Author(s):  
Run Xia Shen ◽  
Yi Min Lu ◽  
Qian Qian Liang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Self Organizing Map ◽  
Rectifier Circuit ◽  
Three Phase ◽  
Bridge Rectifier ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Relevant Experiment ◽  
Self Organizing

Fault feature extraction and recognition play crucial role in fault diagnosis. In this paper, a fault diagnosis method for three-phase fully-controlled bridge rectifier circuit based on Self-Organizing Map network is proposed. The method utilized the three phase AC input current as fault detection data. Then, perform spectrum analysis with the FFT, the fault data is trained through a Self-Organizing Map network for diagnosis. Simulation and relevant experiment verifying the proposed algorithm can classify various types of power electronics device faults accurately and rapidly.

scholarly journals Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Mojtaba Khanzadeh ◽  
Prahalada Rao ◽  
Ruholla Jafari-Marandi ◽  
Brian K. Smith ◽  
Mark A. Tschopp ◽  
...  
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Process Conditions ◽  
Complex Geometries ◽  
Self Organizing Map ◽  
Geometric Accuracy ◽  
Fused Filament Fabrication ◽  
Unsupervised Machine Learning ◽  
Geometric Deviations ◽  
Self Organizing

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

scholarly journals Performance evaluation of the self-organizing map for feature extraction

2006 ◽  
Vol 111 (C5) ◽  
Author(s):  
Yonggang Liu ◽  
Robert H. Weisberg ◽  
Christopher N. K. Mooers
Keyword(s):  
Feature Extraction ◽  
Performance Evaluation ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing

Feature extraction of time-series process images in an aerated agitation vessel using self organizing map

2009 ◽  
Vol 73 (1-3) ◽  
pp. 60-70 ◽  
Author(s):  
Hideyuki Matsumoto ◽  
Ryuichi Masumoto ◽  
Chiaki Kuroda
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Self Organizing Map ◽  
Self Organizing
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