• Journal of Ginseng Research
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• v.37 no.3
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• pp.341-348
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• 2013

Identification of the origins of Panax ginseng has been issued in Korea scientifically and economically. We describe a metabolomics approach used for discrimination and prediction of ginseng roots from different origins in Korea. The fresh ginseng roots from six ginseng cooperative associations (Gangwon, Gaeseong, Punggi, Chungbuk, Jeonbuk, and Anseong) were analyzed by UPLC-MS-based approach combined with orthogonal projections to latent structure-discriminant analysis multivariate analysis. The ginsengs from Gangwon and Gaeseong were easily differentiated. We further analyzed the metabolomics results in subgroups. Punggi, Chungbuk, Jeonbuk, and Anseong ginseng could be easily differentiated by the first two orthogonal components. As a validation of the discrimination model, we performed blind prediction tests of sample origins using an external test set. Our model predicted their geographical origins as 99.7% probability. The robust discriminatory power and statistical validity of our method suggest its general applicability for determining the origins of P. ginseng samples.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.67-77
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• 2007

We tried new interpreting about the work of art in this paper. The work of art respects the intention of the artist to make it and interprets intention until now. After critics distinguish by a period, an area that they set to philosophical thought which is the time and interpreted. We set to each one subjectivity and interpreted between artist to make the work of art and appreciator. But in this paper, we tied various criteria which appreciates the work of art. We tried so that we presented the intimacy each other newly. Otherwise we tied with the subjectivity of the individual and are the try to be an objectification low through statistical technique. We looked into the culture and art in the introduction and explain the discussion about the work of art interpreting which the main subject. We set the category 6 area, and explain an each criteria explanation and assessment method. We tried to propose new interpreting as the intimacy to be multi-variate data analysis result of the assessment analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.593-598
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• 2005

At-site analysis is not appropriate if the record length is shorter than target return period T. If the record length is longer than 27 years, then at-site analysis may be sufficient(Institute of Hydrology, 1999). However, in such a case, regional frequency analysis is recommended for purpose of comparison. Record lengths of annual maximum rainfall data in Korea are usually shorter than 50 years. It is therefore essential to apply regional frequency analysis for estimating rainfall quantiles of more than 100 years return period. In this research, regional rainfall frequency analysis is performed for hourly rainfall data of South Korea. Homogeneous regions are idntified by clusgter analysis which is a standard method of statistical multivariate analysis for dividing a data set into groups. An appropriate distribution is chosen by goodness-of-fit test. GLO is found to be an appropriate distribution as a result of goodness-of-fit measure (Hosking & Wallis, 1997). Simulation experiments are performed to check the performance of frequency analysis techniques. The effects of discordant sites on quantiles are considered.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.251-259
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• 2006

Evaluation of the work of art with intention of the artist different is not a possibility of free oneself from the limit which estimates an evaluation at value of appreciator. We tried new interpreting about the work of art in this paper. The work of art respects the intention of the artist to make it and interprets intention until now. After critics distinguish by a period, an area that they set to philosophical thought which is the time and interpreted. We set to each one subjectivity and interpreted between artist to make the work of art and appreciator. But in this paper, we tied various criteria which appreciates the work of art. We tried so that we presented the intimacy each other newly. Otherwise we tied with the subjectivity of the individual and are the try to be an objectification low through statistical technique. We looked into the culture and art in the introduction and explain the discussion about the work of art interpreting which the main subject. We set the category 6 area, and explain an each criteria explanation and assessment method. We tried to propose new interpreting as the intimacy to be multivariate data analysis result of the assessment analysis. Stopping from the thing which sees the work of art knows, it will be able to give meaning thing from this research prerequisite.

• Proceedings of the KSRS Conference
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• v.2
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• pp.706-709
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• 2006

Cancer is one of the major causes of death; however, the survival rate can be increased if discovered at an early stage for timely treatment. According to the statistics of the World Health Organization of 2002, breast cancer was the most prevalent cancer for all cancers occurring in women worldwide, and it account for 16.8% of entire cancers inflicting Korean women today. In order to classify the type of breast cancer whether it is benign or malignant, this study was conducted with the use of the discriminant analysis and the decision tree of data mining with the breast cancer data disclosed on the web. The discriminant analysis is a statistical method to seek certain discriminant criteria and discriminant function to separate the population groups on the basis of observation values obtained from two or more population groups, and use the values obtained to allow the existing observation value to the population group thereto. The decision tree analyzes the record of data collected in the part to show it with the pattern existing in between them, namely, the combination of attribute for the characteristics of each class and make the classification model tree. Through this type of analysis, it may obtain the systematic information on the factors that cause the breast cancer in advance and prevent the risk of recurrence after the surgery.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.555-566
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• 2017

For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.

• Journal of the Ergonomics Society of Korea
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• v.30 no.5
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• pp.683-688
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• 2011

Objective: The aim of this study is to develop a distributed representative human model(DRHM) generation and analysis system. Background: DRHMs are used for a product with multiple-size categories such as clothing and shoes. It is not easy for a product designer to explore an optimal sizing system by applying various distributed methods because of their complexity and time demand. Method: Studies related to DRHM generation were reviewed and the RHM generation interfaces of three digital human model simulation systems(Jack$^{(R)}$, RAMSIS$^{(R)}$, and CATIA Human$^{(R)}$) were reviewed. Results: DRHM generation steps are implemented by providing sophisticated interfaces which offer various statistical techniques and visualization methods with ease. Conclusion: The DRHM system can analyze the multivariate accommodation percentage of a sizing system, provide body sizes of generated DRHMs, and visualize generated grids and DRHMs. Application: The DRHM generation and analysis system can be of great use to determine an optimal sizing system for a multiple-size product by comparing various sizing system candidates.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.592-599
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• 2015

In big data environment, we need new approach for big data analysis, because the characteristics of big data, such as volume, variety, and velocity, can analyze entire data for inferring population. But traditional methods of statistics were focused on small data called random sample extracted from population. So, the classical analyses based on statistics are not suitable to big data analysis. To solve this problem, we propose an approach to efficient big data analysis. In this paper, we consider a big data analysis using principal component analysis, which is popular method in multivariate statistics. To verify the performance of our research, we carry out diverse simulation studies.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.294-303
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• 2022

The purpose of this study is to examine the effects of sensory integration training on children with developmental delays. To achieve this goal, an educational experiment is conducted in five main areas: gross motor ability, fine motor ability, adaptive ability, language and social ability in children with developmental delay. The study subjects were children with developmental delays aged 3-6 years diagnosed at Beijing Institute of Pediatrics and Beijing Medical University and received sensory integration intervention and homebased training at the Golden Rain Forest Beijing Tongzhou Center from 2018 to 2021. According to the purpose of the analysis, the data collected are subjected to descriptive statistics using SPSS 21.0 statistical program, Two-way MANOVA analysis, and data analysis method of multivariate analysis is used to process the collected data. In addition, a total of 39 subjects were selected, including 19 children who received sensory integration training and 20 children who only received family training. The results show that the sensory integration training group outperformed the home training group in all aspects and developmental quotient, but the home training group also showed higher levels of significance for improvements in gross motor, fine motor and developmental quotient.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.