• The Korean Journal of Applied Statistics
• /
• v.6 no.2
• /
• pp.201-215
• /
• 1993

The health status of workers in a foundry was analyzed in a study which consisted of evaluations of respiratory health together with environmental measurements. The results from environmental measurements showed values exceeding permissible exposure limits. A t-test was done with log transformed and untransformed data to examine the statistical significance for the noncompliance with exposure standards. For the analysis of categorical health outcomes, $\chi$-square test with 2 $\times$ 2 tables and logistic regression analysis were employed. For continuous variables, multiple linear regression was done against assessed risk factors. Pros and cons of different parameters in the compliance (or noncompliance) testing were presented. Respiratory function did not show any relation with occupational exposures, which may be due to the healthy worker effects. Strategies for controlling time dependent covariates were discussed in relation to the healthy worker effect. The scope of statistical analysis in occupational health studies is still limited in Korea without a suitable external comparison group such as credible vital statistics for the whole nation. Internal comparisons between different exposure status often result in unstable estimates of effect, and proportional morbidity study is discussed as an alternative potential research tool.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.4
• /
• pp.337-347
• /
• 2018

Weather causes much of the risk of agricultural activity. For efficient farming, we need to use weather information. Modern agriculture has been developed to create high added value through convergence with state-of-the-art Information and Communication Technology (ICT). This study deals with the quality control algorithms of weather monitoring equipment through Ubiquitous Sensor Network (USN) observational equipment for efficient cultivation of cabbage. Accurate weather observations are important. To achieve this goal, the Korea Meteorological Administration, for example, developed various quality control algorithms to determine regularity of the observation. The research data of this study were obtained from five USN stations, which were installed in Anbandegi and Gwinemi from 2015 to 2017. Quality control algorithms were developed for flat line check, temporal outliers check, time series consistency check and spatial outliers check. Finally, the quality control algorithms proposed in this study can also identify potential abnormal observations taking into account the temporal and spatial characteristics of weather data. It is expected to be useful for efficient management of highland cabbage production by providing quality-controlled weather data.

• The Korean Journal of Applied Statistics
• /
• v.32 no.2
• /
• pp.173-185
• /
• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• The Korean Journal of Applied Statistics
• /
• v.32 no.4
• /
• pp.607-618
• /
• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.

• The Journal of the Korea Contents Association
• /
• v.22 no.4
• /
• pp.433-444
• /
• 2022

This study aimed to identify the change trajectories and clusters of Korean women's family values and depression levels, and the factors affecting depression, to use balanced panel data from the 1st to 7th rounds of the Korea Longitudinal Survey of Women and Families(KLSWF). The subjects of this study were 5,048 female panelists who participated in the KLSWF, and LSTM analysis was conducted using Python to divide the clusters of Korean women suffering from depression. In addition, descriptive statistical analysis, ANOVA, multinomial logistic regression analysis, and hierarchical regression analysis were analyzed using SPSS 23.0. Results, It was confirmed that women's depression increased with age, and family values had a significant impact on depression. It was found that the more open the marriage values of women in the married group, the higher the level of depression. The family values trajectory and depression level of the analyzed subjects were not a single pattern, but included four clusters. To prevent depression among Korean women and provide more concrete interventions, a humanities and sociological system that can identify depression groups should be prepared.

• Journal of Digital Convergence
• /
• v.20 no.2
• /
• pp.203-215
• /
• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• Journal of Internet Computing and Services
• /
• v.16 no.3
• /
• pp.79-93
• /
• 2015

With the rapid development of information technology, the ways of usage have changed drastically. The ways and efficiency of traditional service application to data processing already could not satisfy the requirements of modern users. Nowadays, users have already understood the importance of data. Therefore, the processing and saving of big data have become the main research of the Internet service company. In China, with the rise and explosion of 115 Cloud leads to other technology companies have began to join the battle of cloud services market. Although currently Chinese cloud services are still mainly dominated by cloud storage service, the series of service contents based on cloud storage service have been affirmed by users, and users willing to try these new ways of services. Thus, how to let users to keep using cloud services has become a topic that worth for exploring and researching. The academia often uses the TAM model with statistical analysis to analyze and check the attitude of users in using the system. However, the basic TAM model obviously already could not satisfy the increasing scale of system. Therefore, the appropriate expansion and adjustment to the TAM model (i. e. TAM2 or TAM3) are very necessary. This study has used the status of Chinese internet users and the related researches in other areas in order to expand and improve the TAM model by adding the brand influence, hardware environment and external environments to fulfill the purpose of this study. Based on the research model, the questionnaires were developed and online survey was conducted targeting the cloud services users of four Chinese main cities. Data were obtained from 210 respondents were used for analysis to validate the research model. The analysis results show that the external factors which are service contents, and brand influence have a positive influence to perceived usefulness and perceived ease of use. However, the external factor hardware environment only has a positive influence to the factor of perceived ease of use. Furthermore, the perceived security factor that is influenced by brand influence has a positive influence persistent intention to use. Persistent intention to use also was influenced by the perceived usefulness and persistent intention to use was influenced by the perceived ease of use. Finally, this research analyzed external variables' attributes using other perspective and tried to explain the attributes. It presents Chinese cloud service users are more interested in fundamental cloud services than extended services. In private cloud services, both of increased user size and cooperation among companies are important in the study. This study presents useful opinions for the purpose of strengthening attitude for private cloud service users can use this service persistently. Overall, it can be summarized by considering the all three external factors could make Chinese users keep using the personal could services. In addition, the results of this study can provide strong references to technology companies including cloud service provider, internet service provider, and smart phone service provider which are main clients are Chinese users.

• The Mathematical Education
• /
• v.63 no.2
• /
• pp.123-138
• /
• 2024

This study described the utilization of ChatGPT in teaching and students' learning processes for the course "Introductory Mathematics for Artificial Intelligence (Math4AI)" at 'S' University. We developed a customized ChatGPT and presented a learning model in which students supplement their knowledge of the topic at hand by utilizing this model. More specifically, first, students learn the concepts and questions of the course textbook by themselves. Then, for any question they are unsure of, students may submit any questions (keywords or open problem numbers from the textbook) to our own ChatGPT at https://math4ai.solgitmath.com/ to get help. Notably, we optimized ChatGPT and minimized inaccurate information by fully utilizing various types of data related to the subject, such as textbooks, labs, discussion records, and codes at http://matrix.skku.ac.kr/Math4AI-ChatGPT/. In this model, when students have questions while studying the textbook by themselves, they can ask mathematical concepts, keywords, theorems, examples, and problems in natural language through the ChatGPT interface. Our customized ChatGPT then provides the relevant terms, concepts, and sample answers based on previous students' discussions and/or samples of Python or R code that have been used in the discussion. Furthermore, by providing students with real-time, optimized advice based on their level, we can provide personalized education not only for the Math4AI course, but also for any other courses in college math education. The present study, which incorporates our ChatGPT model into the teaching and learning process in the course, shows promising applicability of AI technology to other college math courses (for instance, calculus, linear algebra, discrete mathematics, engineering mathematics, and basic statistics) and in K-12 math education as well as the Lifespan Learning and Continuing Education.