• Journal of Korean Home Economics Education Association
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• v.33 no.4
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• pp.31-48
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• 2021

The purpose of this study was to provide an understanding of the trajectories of multidimensional health among older adults, including depression, chronic diseases, and cognitive function. Data were drawn from the 1-6 waves of the Korean Longitudinal Study of Ageing(KLoSA), and a sample of 2,059 respondents aged 65 and older at baseline was used for the analyses. Latent growth curve models and growth mixture models were used to explore the changes in depression, chronic diseases, cognitive function, and heterogeneous trajectories among them. One-way ANOVAs with Scheffé post-hoc analysis and chi-square tests were used to find differences in sociodemographic characteristics, health behaviors, and life satisfaction across the latent trajectory classes. Latent growth curve models revealed that depressive symptoms and the number of chronic diseases increased over time, while cognitive function showed gradual decreases. Three heterogeneous patterns of multidimensional health trajectories were identified: normal aging, increase in chronic diseases, and chronic deterioration. Significant differences were observed in sociodemographic characteristics, health behaviors, and life satisfaction across the three latent classes. In particular, low educational attainment, household income, and life satisfaction were associated with the chronic deterioration class. Based on the findings, we discussed suggestions for health promotion education targeting older adults. This study also emphasizes the importance of home economics education in promoting health literacy across the life course.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.

• Journal of Science Education
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• v.47 no.1
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• pp.24-36
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• 2023

In this study, we investigated chemistry teachers' the Stage of Concern (SoC) and the Level of Use (LoU) regarding competency assessment, which was emphasized along with the introduction of the 2015 revised curriculum. A questionnaire was developed based on the CBAM, and responses from 123 chemistry teachers were analyzed. The frequency was investigated for both SoC and LoU, and then the chi-square test was performed according to demographic variables. As a result of the SoC analysis, most of the teachers stayed in stage 3 (management concern, 26.8%) and stage 2 (personal concern, 19.5%). Additionally, among the demographic variables, there was a statistically significant difference in whether or not related education experience was present during the pre-service teacher period. In LoU analysis, Level III (mechanical) was the most frequent (26.8%), followed by Level I (orientation, 22.8%), Level II (preparation, 13.8%). In LoU, there was also a statistically significant difference in whether or not related education experience was present during the pre-service teacher period. The Spearman correlation coefficient between SoC and LoU in the competency assessment was .298 and there was a positive correlation. Based on the above results, educational implications for improving the concern and use of chemistry teachers for competency assessment were discussed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.4
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• pp.259-267
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• 2022

The existing method of deriving the fail-safe design requirements for the domestic developed rotary-wing aircraft system may miss the factors that cause critical system function failures, when being applied to the latest integrated avionics system. It is because the existing method analyzes the severity effect of the failures caused by a single item. To solve the issue, we present a systematic analysis procedure for deriving fail-safe design requirements of system architecture by utilizing functional hazard assessment and development assurance level analysis of SAE ARP4754A, international standard for complex system development. To demonstrate that our proposed procedure can be a solution for the aforementioned issue, we set up experimental environments that include common factors that can cause critical function failures of a system, and we conducted a cross-validation with the existing method. As a result, we showed that the proposed procedure can identify the potential critical common factors that the existing method have missed, and that the proposed procedure can derive fail-safe design requirements to control the common factors.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.96-105
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• 2017

In this paper, we investigated the age classification from the speaker by analyzing the voice calls of the emergency center. We classified the adult and elderly from the call center calls using behavioral speech utterances and SVM(Support Vector Machine) which is a machine learning classifier. We selected two behavioral speech utterances through analysis of the call data from the emergency center: Silent Pause and Turn-taking latency. First, the criteria for age classification selected through analysis based on the behavioral speech utterances of the emergency call center and then it was significant(p <0.05) through statistical analysis. We analyzed 200 datasets (adult: 100, elderly: 100) by the 5 fold cross-validation using the SVM(Support Vector Machine) classifier. As a result, we achieved 70% accuracy using two behavioral speech utterances. It is higher accuracy than one behavioral speech utterance. These results can be suggested age classification as a new method which is used behavioral speech utterances and will be classified by combining acoustic information(MFCC) with new behavioral speech utterances of the real voice data in the further work. Furthermore, it will contribute to the development of the emergency situation judgment system related to the age classification.

• Analytical Science and Technology
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• v.22 no.1
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• pp.101-108
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• 2009

The bioequivalence of two pioglitazone tablets, Actos$^{(R)}$ tablet (Takeda Chemical Industries, reference drug) and Pioglitazone tablet (Boryung Company, test drug) was evaluated according to the guidelines of Korea Food and Drug Administration. Twenty-eight healthy male Korean volunteers received each medicine (pioglitazone dose of 30 mg) in a $2{\times}2$ crossover study with one week washout interval. After drug administration, blood samples were collected at specific time intervals from 0-36 hours. The plasma concentrations of pioglitazone were determined by high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The total chromatographic run time was 5 min and calibration curves were linear over the concentration range of 5-2000 ng/mL for pioglitazone. The method was validated for selectivity, sensitivity, linearity, accuracy and precision. The pharmacokinetic parameters were determined from the plasma concentration-time profiles of both formulations. The primary calculated pharmacokinetic parameters were compared statistically to evaluate bioequivalence between the two preparations. The 90% confidence intervals of the $AUC_t$ ratio and the $C_{max}$ ratio for Pioglitazone tablet and Actos$^{(R)}$ tablet were log0.9422~log1.1040 and log0.9200~log1.1556, respectively. Based on the statistical considerations, we can conclude that the test drug, Pioglitazone tablet was bioequivalent to the reference drug, Actos$^{(R)}$ tablet.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.407-414
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• 2023

In this study, an automated jack-up system, applicable to various fields, was employed for 3D concrete printing and developed as a simultaneous liftable 3D concrete printing system. This developed printing system enables safe and precise jack-up by monitoring the measured jack-up distance using Pearson correlation coefficient analysis and a hydraulic system with interquartile range analysis in real-time during 3D concrete printing operations. It is possible to secure the quality of 3D concrete printing structures, which is essential for expanding the application of 3D concrete printing to construct larger structures. Specimens were printed using both conventional 3D concrete printing and simultaneous liftable 3D concrete printing to evaluate the system performance. The printed specimens were investigated using a 3D scanner. The layer-wise diameter and angle of intersection of the scanned specimens were measured, and an analysis was performed to verify the advantages of the simultaneous liftable 3D concrete printing.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.391-398
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• 2023

In this paper, a penalized maximum likelihood estimation (PMLE) method that applies a penalty to increase the accuracy of a basis-screening-based Kriging model (BSKM) is introduced. The maximum order and set of basis functions used in the BSKM are determined according to their importance. In this regard, the cross-validation error (CVE) for the basis functions is employed as an indicator of importance. When constructing the Kriging model (KM), the maximum order of basis functions is determined, the importance of each basis function is evaluated according to the corresponding maximum order, and finally the optimal set of basis functions is determined. This optimal set is created by adding basis functions one by one in order of importance until the CVE of the KM is minimized. In this process, the KM must be generated repeatedly. Simultaneously, hyper-parameters representing correlations between datasets must be calculated through the maximum likelihood evaluation method. Given that the optimal set of basis functions depends on such hyper-parameters, it has a significant impact on the accuracy of the KM. The PMLE method is applied to accurately calculate hyper-parameters. It was confirmed that the accuracy of a BSKM can be improved by applying it to Branin-Hoo problem.

• Journal of Family Resource Management and Policy Review
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• v.27 no.3
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• pp.21-37
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• 2023

This study examines the living conditions and life satisfaction of single-person households according to their life cycle. The survey was conducted from August to October 2022, and respondents were single-person households from Kimpo, South Korea. The respondents were categorized to three groups namely, young, middle-aged, and older adults by life cycle, and items regarding their sociodemographic background, personal life, family relations, and life satisfaction were included in the analysis. Descriptive statistics, Chi-square analysis, analysis of variance, and multiple regression analysis were performed. The key findings were as follows. First, significant differences were found according to life cycle in the respondents' diet, clothing, housing, financial and home management, self-care, and leisure life. Second, the variables that significantly affected the level of life satisfaction of single-person households were the life cycle with young adults showing a higher level of satisfaction; having a family member to care; living alone voluntarily, discrimination experience; community awareness; and ties with the local community. Based on the results, it is clear that single-person households have different needs and problems in each stage of life cycle. Thus, to reflect the different experiences and needs of single-person households by life cycle, tailored policy and programs should be provided for young, middle-aged, and older adult single-person households.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.