• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3779-3784
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• 2012

Real-time process monitoring and diagnosis of industrial processes is one of important operational tasks for quality and safety reasons. The objective of fault diagnosis or identification is to find process variables responsible for causing a specific fault in the process. This helps process operators to investigate root causes more effectively. This work assesses the applicability of combining a nonlinear statistical technique of kernel Fisher discriminant analysis with a preprocessing method as a tool of on-line fault identification. To compare its performance to existing linear principal component analysis (PCA) identification scheme, a case study on a benchmark process was performed to show that the fault identification scheme produced more reliable diagnosis results than linear method.

• Journal of Nutrition and Health
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• v.44 no.1
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• pp.41-48
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• 2011

Constructs with seven latent evaluation indicators and 18 observable survey questions were developed by food and nutrition experts to calculate a food safety recognition and practice index for children. The purpose of this study was to suggest statistical approaches to test construction validity on the constructs, obtain weights of the evaluation indicators, and develop questionnaires to calculate a children's food recognition and practice index. Survey data of 2,400 elementary fifth grade students were used as empirical results. Test validity was evaluated by exploratory factor analysis and confirmed to be highly significant by confirmatory factor analysis [i.e., linear structural relations (LISREL) analysis]. Standardized path coefficients of the LISREL analysis were suggested based on weights, and the weights were compared using the AHP and Delphi methods.

• Journal of Soil and Groundwater Environment
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• v.17 no.1
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• pp.22-32
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• 2012

This study suggests a method to grade groundwater quality quantitatively using statistical approaches for evaluating the quality of groundwater in wells included in the Groundwater Quality Monitoring Network (GQMN). The proposed analysis method is applied to GQMN data from 2001 to 2008 for nitrate nitrogen, chloride, trichloroethylene, potential of hydrogen (pH), and electrical conductivity. The analysis results are obtained as groundwater quality grades of the groundwater representing each of the monitoring stations. The degree of groundwater contamination is analysed for water quality parameters, district, and usage. The results show that the degree of groundwater contamination is relatively high by nitrate nitrogen, bacteria and electrical conductivity and at Seoul, Incheon, Gwangju, Gyeonggido and Jeollado. The degree of contamination by nitrate nitrogen and trichloroethylene is especially high when the groundwater is used for agricultural and industrial water, respectively. It is evaluated that potable groudnwater in GQMN is significantly vulnerable to nitrate nitrogen and bacteria contamination.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.9-18
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• 2022

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.

• The Korean Journal of Emergency Medical Services
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• v.25 no.3
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• pp.163-178
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• 2021

Purpose: This study explored a plan for improving the overall preventive activity of Korean Citizen-Corps-Active-in-Disaster (CAIND). Methods: The study used the documentary survey and the technical approach methods. This way, detailed information regarding civil anti-disaster organizations was found in scholarly monographs, specialty publications, and master's/doctoral dissertations. It further utilized the statistical yearbooks between 2015 and 2018 from the Korean Ministry of Public Administration, Security, and Fire Service, and the National Statistical Office, to discover practical problems through a statistical analysis. Volunteer activities being inherent, related issues were reviewed at the same time to for purposes of clarifying the characteristics of disaster prevention activity by CAIND. Results: The study provided four major suggestions for improvement. First, the quota management system of Korean CAIND considering the characteristics of rural areas should be supplemented. Second, the education and training systems of Korean CAIND should be established to satisfy regional conditions. Third, management members' readership competency in operating organizations, including that of the Korean CAIND captain in charge, should be strengthened. Fourth, the reward system for Korean CAIND activities should be improved. Conclusion: In the future, the results of this study are expected to be utilized as a basic data to develop Korean CAIND.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.37-48
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• 2023

Shape analysis is a method for measuring, describing and comparing the shape of objects in geometric space. An important aspect is to obtain Procrustes distance based on least square method. We note that the shape is all the geometrical information that remains when location, scale and rotational effects are filtered out from an object. However, and unfortunately, when we cannot measure some landmarks which are some biologically or geometrically meaningful points of any object, it is not possible to measure the variation of all shapes of an object, including that of the incomplete object. Hence, we need to replace the missing landmarks. In particular, Albers and Gower (2010) studied the missing rows of configurations in Procrustes analysis. They noted that the convergence of their approach can be quite slow. In this study, alternatively, we derive an algorithm for estimating the missing landmarks based on the pre-shapes. The pre-shape is invariant under the location and scaling of the original configuration with the centroid size of the pre-shape being one. Therefore we expect that we can reduce the amount of total computing time for obtaining the estimate of the missing landmarks.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.349-359
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• 2023

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.89-108
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• 2023

This study provides a comprehensive analysis of recent studies conducted on the topic of nowcasting in order to enhance the accuracy and promptness of official statistical data. Furthermore, we propose an alternative approach involving the utilization of real-time data and its corresponding collection methods to effectively operate a real-time nowcasting model capable of accurately capturing the current economic condition. We explore high-frequency real-time data that can predict economic indicators in both the public and private sectors and propose a pipeline for data collection processing and modeling that is based on cloud platforms. Furthermore we validate the essential elements required for the implementation of real-time nowcasting, as well as their data management protocols to ensure the reliability and consistency needed for accurate forecasting of official statistical indicators.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.349-363
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• 2024

Compositional data refers to data where the sum of the values of the components is a constant, hence the sample space is defined as a simplex making it impossible to apply statistical methods developed in the usual Euclidean vector space. A natural approach to overcome this restriction is to consider an appropriate transformation which moves the sample space onto the Euclidean space, and log-ratio typed transformations, such as the additive log-ratio (ALR), the centered log-ratio (CLR) and the isometric log-ratio (ILR) transformations, have been mostly conducted. However, in scenarios with sparsity, where certain components take on exact zero values, these log-ratio type transformations may not be effective. In this work, we mainly suggest an alternative transformation, that is the square-root transformation which moves the original sample space onto the directional space. We compare the square-root transformation with the log-ratio typed transformation by the simulation study and the real data example. In the real data example, we applied both types of transformations to the USG% data obtained from NBA, and used a density based clustering method, DBSCAN (density-based spatial clustering of applications with noise), to show the result.

• Structural Engineering and Mechanics
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• v.91 no.3
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• pp.279-289
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• 2024

The purpose of this study is to represent a useful alternative for the preliminary seismic vulnerability assessment of existing reinforced concrete buildings by introducing a statistical approach employing the binary logistic regression technique. Two different predictive statistical models, namely full and reduced models, were generated utilizing building characteristics obtained from the damage database compiled after 1999 Düzce earthquake. Among the inspected building parameters, number of stories, overhang ratio, priority index, soft story index, normalized redundancy ratio and normalized lateral stiffness index were specifically selected as the predictor variables for vulnerability classification. As a result, normalized redundancy ratio and soft story index were identified as the most significant predictors affecting seismic vulnerability in terms of life safety performance level. In conclusion, it is revealed that both models are capable of classifying the set of buildings being severely damaged or collapsed with a balanced accuracy of 73%, hence, both are able to filter out high-priority buildings for life safety performance assessment. Thus, in this study, having the same high accuracy as the full model, the reduced model using fewer predictors is proposed as a simple and viable classifier for determining life safety levels of reinforced concrete buildings in the preliminary seismic risk assessment.