• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.264-271
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• 2017

Purpose: Field operation data contains missing values or outliers due to various causes of the data collection process, so caution is required when utilizing RAM analysis results by field operation data. The purpose of this study is to present a method to minimize the RAM analysis error of the field data to improve the accuracy. Methods: Statistical methods are presented for processing of the outliers and the missing values of the field operating data, and after analyzing the RAM, the differences between before and after applying the technique are discussed. Results: The availability is estimated to be lower by 6.8 to 23.5% than that before processing, and it is judged that the processing of the missing values and outliers greatly affect the RAM analysis result. Conclusion: RAM analysis of OO weapon system was performed and suggestions for improvement of RAM analysis were presented through comparison with the new and current method. Data analysis results without appropriate treatment of error values may result in incorrect conclusions leading to inappropriate decisions and actions.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.721-731
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• 2017

In this study, principal component analysis methods using Lasso penalty are introduced. There are two popular methods that apply Lasso penalty to principal component analysis. The first method is to find an optimal vector of linear combination as the regression coefficient vector of regressing for each principal component on the original data matrix with Lasso penalty (elastic net penalty in general). The second method is to find an optimal vector of linear combination by minimizing the residual matrix obtained from approximating the original matrix by the singular value decomposition with Lasso penalty. In this study, we have reviewed two methods of principal components using Lasso penalty in detail, and shown that these methods have an advantage especially in applying to data sets that have more variables than cases. Also, these methods are compared in an application to a real data set using R program. More specifically, these methods are applied to the crime data in Ahamad (1967), which has more variables than cases.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.39-49
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• 2001

When an appropriate model is fitted to explain a certain categorical data, outlying cell detection plays very important role to reduce the lack of fit. There exist many statistical methods to identify outlying cells in contingency table. In this paper, correspondence analysis is applied to identify one or two outlying cells. When corresponding relationships between categories of the row and columns are explored, we find that outlying cells could be identified via the correspondence analysis map.

• Proceedings of the KSRS Conference
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• v.2
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• pp.926-929
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• 2006

Some features of vertical structure of the frontal interaction zone of the warm Kuroshio Current and cold Oyashio Current are known from 1930 from analysis of ship data. Ship data however do not allow carrying out the area detailed survey opposite to satellite infrared (IR) observations which possess by high spatial and temporal resolution. Analysis of NOAA AVHRR IR images demonstrated that process of formation and development of the Kuroshio warm core rings is highly complex. They are formed as a result of development of anticyclonic meanders of the warm Kuroshio waters and spin off them from the current. Joint analysis of thermal infrared images and altimetry data has also indicated that interaction of eddies to the frontal zone plays a crucial role in formation of large eddies moving to the Southern Kuril region.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• The Research Journal of the Costume Culture
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• v.10 no.6
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• pp.601-616
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• 2002

Qualitative research is a method widely used in marketing research. However, the method has seldom been used in fashion marketing research in Korea. The purpose of this study was to prove that using both qualitative and quantitative research methods in main stage is much useful than using qualitative research method only in exploratory stage. Qualitative data were gathered by conducting Focus Group Interview(FGI) with 48 college students. Quantitative data were gathered by surveying college students, and 487 questionnaires were used in the statistical analysis. The data were analyzed using content analysis, mean, standard deviation, and t-test. As a result, FGI, one of the tools used in qualitative research methods, was proved to be useful in revealing consumers´deep emotional needs as well as purchase motives. FGI also revealed information which quantitative research method tools such as survey could have missed. Therefore, it is best to use multiple methods-simultaneous use of quantitative and qualitative methods-to understand fast changing consumers´needs and purchase motives.

• Korean Journal of Computational Design and Engineering
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• v.10 no.5
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• pp.314-327
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• 2005

This paper describes the volumetric data modeling and analysis methods that employ volumetric NURBS or VNURBS that represents heterogeneous objects or fields in multidimensional space. For volumetric data modeling, we formulate the construction algorithms involving the scattered data approximation and the curvilinear grid data interpolation. And then the computational algorithms are presented for the geometric and mathematical analysis of the volume data set with the VNURBS model. Finally, we apply the modeling and analysis methods to various field applications including grid generation, flow visualization, implicit surface modeling, and image morphing. Those application examples verify the usefulness and extensibility of our VNUBRS representation in the context of volume modeling and analysis.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.27-39
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• 2018

In this paper, we describe the algorithms, EEG classification methods, and position data analysis methods using EEG and ADS1299 sensors. In addition, it is necessary to manage the amount of real-time data of location data and EEG data and to extract data efficiently. To do this, we explain the process of extracting important information from a vast amount of data through a cloud server. The electrical signals extracted from the brain are measured to determine the psychological state and health status, and the measured positions can be collected using the position sensor and triangulation method.