• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.1-14
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• 2000

The methods of sensitivity analysis for linear programming can be classified in two types: sensitivity analysis using an optimal solution, and sensitivity analysis using an approximate optimal solution. As the methods of sensitivity analysis using an optimal solution, there are three sensitivity analysis methods: sensitivity analysis using an optimal basis, positive sensitivity analysis, and optimal partition sensitivity analysis. Since they may provide different characteristic regions under degeneracy, it is not easy to understand and apply the results of the three methods. In this paper, we propose a generalized sensitivity analysis that can integrate the three existing methods of sensitivity analysis. When a right-hand side or a cost coefficient is perturbed, the generalized sensitivity analysis gives different characteristic regions according to the controlling index set that denotes the set of variables allowed to have positive values in optimal solutions to the perturbed problem. We show that the three existing sensitivity analysis methods are special cases of the generalized sensitivity analysis, and present some properties of the generalized sensitivity analysis.

• Herbal Formula Science
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• v.17 no.1
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• pp.75-83
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• 2009

Objective : The purpose of this study is to indicate of problems in statistical analysis method of "The Korean Journal of oriental Medical Prescription" and we will be proposed the useful application of the statistical analysis method. Methods : In this paper, we were analysed statistical analysis methodology from published journal articles "The Korean Journal of Oriental Medical Prescription" December, year 2000 to December, year 2008. We were investigated of problems in application of structured analysis methods those journal articles that including statistical analysis techniques and analysis methods. Results : 1. A random allocation of the experimental group and control groups are important factors in the planning process of statistical analysis. However, there are less explanation those journal articles. 2. There are no consideration in specimen size that there will be considerate by the level of significance and statistical test. 3. Many article authors were confused between parametric methods and non-parametric methods that they were applied parametric statistical analysis methods although inapplicable sample size. 4. There were applied the parametric methods consists of t-test instead non-parametric methods in the comparison of average intergroup relations. 5. There were less understanding posterior analysis and were confused with t-test. Conclusion : Our goal was to outline the key methods with a brief discussion of problems(statistical analysis methods), avenues for solutions. we recommend authors to use an appropriate statistical analysis methods for obtaining a more cautions results.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Journal of Engineering Education Research
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• v.23 no.1
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• pp.59-64
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• 2020

The purpose of this study is to develop capstone design model of gender analysis methods suitable for engineering education field and examine improvements and effects by applying it to actual lessons for biomedical engineering students. Case study was performed to achieve the purpose of the study. Twelve gender analysis methods were applied to 'biomedical engineering capstone design' which was major course offered by department of biomedical engineering at J university. After the students understood how to analyze gender analysis methods and cases, they decided project topics and presented what gender analysis methods were applied for each project. Additionally, the results of analysis showed that the students were more able to understand the differences between men and women of all ages and try to narrow down the differences. They also found that they could contribute to development of new added value of knowledge and technology that reflected the needs of both men and women by applying gender analysis methods in system development.

• Journal of Environmental Health Sciences
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• v.38 no.5
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• pp.369-379
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• 2012

Objectives: Spatial analysis is useful for understanding complicated causal relationships. This paper focuses trends and appling methods for spatial analysis associated with environmental asbestos exposure. Methods: Literature review and reflection of experience of authors were conducted to know academic background of spatial analysis, appling methods on epidemiology and asbestos exposure. Results: Spatial analysis based on spatial autocorrelation provides a variety of methods through which to conduct mapping, cluster analysis, diffusion, interpolation, and identification. Cause of disease occurrence can be investigated through spatial analysis. Appropriate methods can be applied according to contagiousness and continuity. Spatial analysis for asbestos exposure source is needed to study asbestos related diseases. Although a great amount of research has used spatial analysis to study exposure assessment and distribution of disease occurrence, these studies tend to focus on the construction of a thematic map without different forms of analysis. Recently, spatial analysis has been advanced by merging with web tools, mobile computing, statistical packages, social network analysis, and big data. Conclusions: Because the trend in spatial analysis has evolved from simple marking into a variety of forms of analyses, environmental researchers including asbestos exposure study are required to be aware of recent trends.

• Journal of the Ergonomics Society of Korea
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• v.22 no.3
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• pp.75-91
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• 2003

Evaluation of repetitiveness for hand-intensive tasks is essential to determine the level of risk for upper-extremity musculoskeletal disorders at the workplace. Many measures and methods have been introduced for repetitiveness assessment: however, our understanding of the differences among these measures and methods is lacking. The present study compared the repetitiveness measures and measurement/analysis methods to help practitioners apply the proper repetitiveness assessment methodology in the workplace. By reviewing 51 studies of repetitiveness assessment, measures and corresponding measurement/analysis methods were surveyed. Of the repetitiveness measures, two types of dimensions (frequency and time) and corresponding types of analysis scopes were identified. According to the dimensional and analysis-scope types. the repetitiveness measures were categorized and then the surveyed studies were counted for each measure. It is identified that frequency measures have used 2.7 times higher than time measures and the frequency of wrist motions has been most frequently used in repetitiveness assessment. Furthermore, the measurement methods were categorized into objective and subjective methods, and the analysis methods into statistical and spectral methods. Lastly, eight factors (accuracy, reliability. sensitivity. efficiency. ease of use. applicability. interference. and robustness) were listed to be considered in selecting the appropriate assessment methodology.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.89-106
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• 2009

There have been many new advances in the development of improved clustering methods for microarray data analysis, but traditional clustering methods are still often used in genomic data analysis, which maY be more due to their conceptual simplicity and their broad usability in commercial software packages than to their intrinsic merits. Thus, it is crucial to assess the performance of each existing method through a comprehensive comparative analysis so as to provide informed guidelines on choosing clustering methods. In this study, we investigated existing clustering methods applied to microarray data in various real scenarios. To this end, we focused on how the various methods differ, and why a particular method does not perform well. We applied both internal and external validation methods to the following eight clustering methods using various simulated data sets and real microarray data sets.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.265-276
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• 2015

In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.193-200
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• 2000

Numerical analysis of slope stability is presented using slice method, static seismic analysis methods, and earthquake response analysis methods. Static seismic force is considered as 0.2g while vertical static seismic force is not considered in analysis. For earthquake response analysis, Hachinohe-wave is applied. Safety factor calculated using slice method for failure surface. Calculating methods are Bishop's method and Janhu's method. Static seismic analysis was applied using Mhor-Coulomb model and earthquake response analysis was applied using non-linear elastic model.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1103-1113
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• 2011

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.