scholarly journals Principal Component Analysis and Self-Organizing Map for Visualizing and Classifying Fire Risks in Forest Regions

2007 ◽  
Vol 16 (2) ◽  
pp. 44-51 ◽  
Author(s):  
Suwardi Annas ◽  
Takenori Kanai ◽  
Shuhei Koyama
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Self Organizing Map ◽  
Forest Regions ◽  
Self Organizing

scholarly journals Evaluation of Piezodiagnostics Approach for Leaks Detection in a Pipe Loop

2016 ◽  
Vol 713 ◽  
pp. 107-110 ◽  
Author(s):  
Jhonatan Camacho-Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Oscar Pérez
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Leak Detection ◽  
Component Analysis ◽  
Self Organizing Map ◽  
Pipe Surface ◽  
Robust Solution ◽  
Operational Conditions ◽  
Cross Correlation Analysis ◽  
Self Organizing

Pipe leaks detection has a great economic, environmental and safety impact. Although several methods have been developed to solve the leak detection problem, some drawbacks such as continuous monitoring and robustness should be addressed yet. Thus, this paper presents the main results of using a leaks detection and classification methodology, which takes advantage of piezodiagnostics principle. It consists of: i) transmitting/sensing guided waves along the pipe surface by means of piezoelectric device ii) representing statistically the cross-correlated piezoelectric measurements by using Principal Component Analysis iii) identifying leaks by using error indexes computed from a statistical baseline model and iv) verifying the performance of the methodology by using a Self-Organizing Map as visualization tool and considering different leak scenario. In this sense, the methodology was experimentally evaluated in a carbon-steel pipe loop under different leaks scenarios, with several sizes and locations. In addition, the sensitivity of the methodology to temperature, humidity and pressure variations was experimentally validated. Therefore, the effectiveness of the methodology to detect and classify pipe leaks, under varying environmental and operational conditions, was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, and Self-Organizing Maps, become as promising solution in the field of structural health monitoring and specifically to achieve robust solution for pipe leak detection.

scholarly journals Principal Component Analysis and Self-Organizing Map Clustering for Student Browsing Behaviour Analysis

2019 ◽  
Vol 163 ◽  
pp. 550-559
Author(s):  
Nor Bahiah Ahmad ◽  
Umi Farhana Alias ◽  
Nadirah Mohamad ◽  
Norazah Yusof
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Self Organizing Map ◽  
Behaviour Analysis ◽  
Self Organizing

An Affective Approach to Developing Marketing Strategies of Mineral Water

2012 ◽  
Vol 16 (4) ◽  
pp. 514-520 ◽  
Author(s):  
Junzo Watada ◽  
◽  
Le Yu ◽  
Munenori Shibata ◽  
Marzuki Khalid ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Mineral Water ◽  
Principal Component ◽  
Component Analysis ◽  
Marketing Strategies ◽  
Taste Preferences ◽  
Self Organizing Map ◽  
Water Based ◽  
Self Organizing

This study is concerned with the development of marketing strategies for mineral water based on consumers’ taste preferences, by analyzing the taste components of mineral water. In this study, we used a twodimensional analysis to classify taste data. We conducted a correlation analysis to identify the characteristics of taste data. We applied a combination of principal component analysis and self-organizing map to classify mineral water tastes. Based on this evaluation, we identified some marketing strategies in the conclusion. According to this study, the taste of mineral water is not determined by the origin and is not influenced by the hardness of the water.

scholarly journals Monitoring of Casting Quality Using Principal Component Analysis Based Self-Organizing Map

2021 ◽  
Author(s):  
Hocine Bendjama ◽  
Salah BOUHOUCHE ◽  
Salim AOUABDI ◽  
Jürgen BAST
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Casting Process ◽  
Component Analysis ◽  
Casting Defects ◽  
Self Organizing Map ◽  
Casting Quality ◽  
Metric Distances ◽  
Reduced Data ◽  
Self Organizing

Abstract The monitoring of casting quality is very important to ensure the safe operation of casting processes. In this paper, in order to improve the accurate detection of casting defects, a combined method based on Principal Component Analysis (PCA) and Self-Organizing Map (SOM) is presented. The proposed method reduces the dimensionality of the original data by the projection of the data onto a smaller subspace through PCA. It uses Hotelling’s T2 and Q statistics as essential features for characterizing the process functionality. The SOM is used to improve the separation between casting defects. It computes the metric distances based similarity, using the T2 and Q (T2Q) statistics as input. A comparative study between conventional SOM, SOM with reduced data and SOM with selected features is examined. The proposed method is used to identify the running conditions of the low pressure lost foam casting process. The monitoring results indicate that the SOM based on T2Q as feature vectors remains important comparatively to conventional SOM and SOM based on reduced data.

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