• Journal of Ethics
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• no.82
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• pp.73-125
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• 2011

This article aims to search for moral educational implication of J. D. Greene's recent neuro-scientific approaches to deontological ethics. Recently new technique in neuroscience such as fMRI is applied to moral and social psychological concepts or terms, and 'affective primacy' and 'automaticity' principles are highlighted as basic concepts of the new paradigm. When these principles are introduced to ethical theories, it makes rooms of new and different interpretations of them. J. D. Greene et al. claim that deontological moral judgments or theories are just a kind of post hoc rationalization for intuitions or emotions by ways of neuroscientific findings and evolutionary interpretation. For example, Kant's categorical imperative in which a maxim should be universalizable to be as a principle, might be a product of moral intuition. Firstly this article tries to search for intellectual backgrounds of the social intuitionalism where Greens' thought originates. Secondly, this article tries to collect and summarize his arguments about moral dilemma responses, personal-impersonal dilemma catergorizing hypothesis, fMRI data interpretations by ways of evolutionary theory, cultural and social psychological theories, application to deontological and consequential theories, and his suggestion that deontological ethics shoud be rejected as a normative ethical thought and consequentialism be a promising theory etc. Thirdly, this tries to analyse and critically exam those aspects and argumentation, especially from viewpoints of the ethicists whose various strategies seek to defeat Greene's claims. Fourthly, this article criticizes that his arguments make a few critical mistakes in methodology and data interpretation. Last, this article seeks to find its implications for moral education in korea, in which in spite of incomplete argumentation of his neuroscientific approach to morality, neuroethics needs to be introduced as a new approach and educational content, and critical materials as well.

• 한국노년학
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• v.27 no.2
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• pp.323-334
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• 2007

This cross-sectional descriptive study was performed to investigate the relationship among attitude toward DNR orders, depression, and self-esteem in the elderly. Method: The participants of this study were 99 elderly individuals who were hospitalized in four university hospitals in Seoul and Kangwon-do from October 1, 2006 to October 21, 2006. The data were collected using self-administered questionnaires. Results: The mean scores were 3.99 for attitude toward DNR orders(range of 1-5), 6.64 for depression(range of 1-15), and 26.83 for self-esteem(range of 10-40). Self-esteem was significantly correlated with attitude toward DNR orders(r=.200, p=.047). About half of the participants(49.5%) responded that the proper time for obtaining DNR consent was when they were healthy and could express their own intentions and make the decision by themselves. Most of the participants showed a positive attitude toward DNR orders. The participants preferred to make the DNR decision when they were healthy. Therefore, health care providers working with the elderly should try to discuss the DNR decision with their patients when they are conscious and able to make the DNR decision by themselves rather than leaving the decisions up to the patient's family members

• 한국노년학
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• v.29 no.4
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• pp.1479-1500
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• 2009

This study examined BMI distributions among older adults in three different countries: the U.S., Japan, and Korea. The paper also explored differences in the factors predicting BMI in the three countries using three data sets: the U.S. Longitudinal Study of Aging (LSOA II, 8,589 persons), the Nihon University Japanese Longitudinal Study of Aging (NUJLSOA, 2,888 persons), and the Korean Longitudinal Study of Ageing (KLoSA, 2,397 persons). Descriptive analysis and multiple regression were performed. Japanese older adults were somewhat lighter than Koreans with fewer people at the upper end of the BMI distribution. Distributions of BMI among both Koreans and Japanese are shifted leftward relative to Americans. There is less dispersion in the distribution of BMI for Koreans and Japanese than among Americans. The association between socioeconomic variables and BMI is stronger in the U.S. and Japan than in Korea. Demographic variables are strong predictors of BMI in Korea. In Japan, all health behaviors have significant effects on BMI. It is concluded that the relationships between behavioral, demographical, and socioeconomic factors and BMI are not the same across countries. Results have policy implications for the involvement of health practitioners in helping older adults to control weight.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.15-17
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• 2022

In the aftermath of the avian influenza that occurred from the second half of 2020 to the beginning of 2021, 17.8 million laying hens were slaughtered. Although the government invested more than 100 billion won for egg imports as a measure to stabilize prices, the effort was not that easy. The sharp volatility of egg prices negatively affected both consumers and poultry farmers, so measures were needed to stabilize egg prices. To this end, the egg prices were successfully predicted in this study by using the analysis algorithm of a machine learning regression. For price prediction, a total of 8 independent variables, including monthly broiler chicken production statistics for 2012-2021 of the Korean Poultry Association and the slaughter performance of the national statistics portal (kosis), have been selected to be used. The Root Mean Square Error (RMSE), which indicates the difference between the predicted price and the actual price, is at the level of 103 (won), which can be interpreted as explaining the egg prices relatively well predicted. Accurate prediction of egg prices lead to flexible adjustment of egg production weeks for laying hens, which can help decision-making about stocking of laying hens. This result is expected to help secure egg price stability.

• 한국노년학
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• v.39 no.2
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• pp.231-240
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• 2019

The study aimed to identify risk factors for falls as well as hospitalization status according to disease and demographic characteristics of demented inpatients by investigating the in-depth Injury Patient Surveillance System data collected by Korea Centers for Disease Control and Prevention(KCDC). Older adults over 60 years old who were diagnosed with dementia were included(n=1,732). Their data were analyzed after being assigned to either a fall group or a non-fall group. STATA was used for statistical analyses, such as frequency analysis, chi-square (χ²) test, and logistics regression. It was found that 8.0% of the demented inpatients experienced falls. According to the analysis on category of fall and non-fall group were statistically significant difference in age and Charlson Comorbidity Index(CCI) and bone density deficiency. Based on the logistic regression analysis of factors affecting falls, older adults over 80 are 2.386 times more likely to fall and based on a target with a CCI of 0, the risk of falls is 0.421 times lower, finally based on those without bone density disorder, the fall risk for those with bone density disorder was 3.581 times higher. Therefore, we expect that the important about the factors relating to falls identified in this can not only be found valuable for educating inpatients with dementia and care-givers, but also be used as reference that supports clinical professionals to make decisions on falls management for patients with dementia.

• Economic Analysis
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• v.24 no.3
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• pp.1-36
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• 2018

When it comes to explaining the relationship between inventory investment and business fluctuations, the production smoothing theory and the stock-out avoidance theory take contradictory stances. Decision-making related to inventory investments of corporations is thought to be influenced by both motives, but the relative sizes or directions of their respective influences can differ depending upon the phase of the business cycle. Against this backdrop, this paper differs from existing studies in that it theoretically tests the relative significances of the production smoothing and stock-out avoidance motives in the inventory investment dynamics, while placing its analytical focus on determining the existence and patterns of the asymmetric dynamics of inventory investment over the business cycle phases. To this end this paper sets up a non-linear model that is expanded from the existing linear inventory investment model, and checks whether its predictive power is better than that of the existing model. The results of analysis confirm the nature of the asymmetric dynamics of inventory investment over the business cycle phases. A stock-out avoidance motive appears but there is no significant production smoothing motive in boom times. In downturns, in contrast, the stock-out avoidance motive is insignificant, but a quality of asymmetric dynamics in which changes in inventory cause the deepening of recessions, due to the non-convexity of production costs proposed by Ramey (1991), is detected. This paper confirms that a model considering the asymmetric dynamics of inventory investment can have better predictive power than one that does not consider it, through within-sample and out-of-sample predictions and various predictive power tests. These research results are expected to be useful for economic forecasting, through their enhancement of the understandings of the inventory investment dynamics and of the nature of its business cycle destabilization.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.383-384
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• 2022

Following the development of technologies such as digital twin, IoT, and AI after the 4th industrial revolution, decision-making problems are being solved based on high-dimensional data analysis. This has recently been applied to the port logistics sector, and a number of studies on big data analysis, deep learning predictions, and simulations have been conducted on container terminals to improve port productivity. These high-dimensional data analysis techniques generally require a large number of data. However, the global port environment has changed due to the COVID-19 pandemic in 2020. It is not appropriate to apply data before the COVID-19 outbreak to the current port environment, and the data after the outbreak was not sufficiently collected to apply it to data analysis such as deep learning. Therefore, this study intends to present a port data augmentation method for data analysis as one of these problem-solving methods. To this end, we generate the container stowage situation of the yard through a generative adversarial neural network model in terms of container terminal operation, and verify similarity through statistical distribution verification between real and augmented data.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.481-492
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• 2023

This study explores how to build a Korean dataset to extract information from text using generative large language models. In modern society, mixed information circulates rapidly, and effectively categorizing and extracting it is crucial to the decision-making process. However, there is still a lack of Korean datasets for training. To overcome this, this study attempts to extract information using text-based zero-shot learning using a generative large language model to build a purposeful Korean dataset. In this study, the language model is instructed to output the desired result through prompt engineering in the form of "system"-"instruction"-"source input"-"output format", and the dataset is built by utilizing the in-context learning characteristics of the language model through input sentences. We validate our approach by comparing the generated dataset with the existing benchmark dataset, and achieve 25.47% higher performance compared to the KLUE-RoBERTa-large model for the relation information extraction task. The results of this study are expected to contribute to AI research by showing the feasibility of extracting knowledge elements from Korean text. Furthermore, this methodology can be utilized for various fields and purposes, and has potential for building various Korean datasets.

• Knowledge Management Research
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• v.24 no.3
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• pp.95-125
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• 2023

As the aviation industry has rapidly been grown, there are more factors for customers to consider when choosing an airline. In response, airlines are trying to increase customer value by providing high-quality services and differentiated experiential value. While early customer value research centered on utilitarian value, which is the trade-off between cost and benefit in terms of utility for products and services, the importance of experiential value has recently been emphasized. However, experiential value needs to be studied in a specific context that fully represents customer preferences because what constitutes customer value changes depending on the product or service context. In addition, customer value has an important influence on customers' decision-making, so it is necessary for airlines to accurately understand what constitutes customer value. In this study, we collected customer reviews and ratings from Skytrax, a website specializing in airlines, and utilized the BERTopic technique to derive factors of customer value. The results revealed nine factors that constitute customer value in airlines, and six of them are related to customer satisfaction. This study proposes a new methodology that enables a granular understanding of customer value and provides airlines with specific directions for improving service quality.

• Knowledge Management Research
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• v.24 no.3
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• pp.195-215
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• 2023

In the telecommunications service industry, until now, it has been possible for Network Operators (NOs) to secure a competitive advantage to increase subscribers and profits through network investment. However, amid a big change to digital economy, network investment fails to lead to increase profits. These days platform companies without holing network infrastructure have a more competitive advantage and take more profits. This makes NOs gradually lose interest in network investment. The purpose of this paper is to find policy measures to promote network investment in digital economy. Specifically, we identify the factors influencing the network investment and promising policy measures energizing the investment, and then analyze their priorities and derive policy implications through Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). The results of this paper show that market competition is more preferred to public intervention in promoting network investment. However, in order to guarantee and expand the universal access to network, it is necessary to consider expanding the role of the public, focusing on non-economic areas.