• Journal of Korean Academy of Nursing Administration
• /
• v.3 no.1
• /
• pp.51-64
• /
• 1997

The purpose of the study was to introduce an integrated approach of new Casuistry and specified principlism in resolving ethical problems and studying nursing ethics. In studying clinical ethics and nursing ethics, there is no systematic research method. While nurses often experience ethical dilemmas in practice, much of previous research on nursing ethics has focused merely on describing the existing problems. In addition, ethists presented theoretical analysis and critics rather than providing the specific problems solving strategies. There is a need in clinical situations for an integrated method which can provide the objective description for existing problem situations as well as specific problem solving methods. We inherit two distinct ways of discussing ethical issues. One of these frames these issues in terms of principles, rules, and other general ideas; the other focuses on the specific features of particular kinds of moral cases. In the first way general ethical rules relate to specific moral cases in a theoretical manner, with universal rules serving as "axioms" from which particular moral judgments are deduced as theorems. In the seconds, this relation is frankly practical. with general moral rules serving as "maxims", which can be fully understood only in terms of the paradigmatic cases that define their meaning and force. Theoretical arguments are structured in ways that free them from any dependence on the circumstances of their presentation and ensure them a validity of a kind that is not affected by the practical context of use. In formal arguments particular conclusions are deduced from("entailed by") the initial axioms or universal principles that are the apex of the argument. So the truth or certainty that attaches to those axioms flows downward to the specific instances to be "proved". In the language of formal logic, the axioms are major premises, the facts that specify the present instance are minor premises, and the conclusion to be "proved" is deduced (follows necessarily) from the initial presises. Practical arguments, by contrast, involve a wider range of factors than formal deductions and are read with an eye to their occasion of use. Instead of aiming at strict entailments, they draw on the outcomes of previous experience, carrying over the procedures used to resolve earlier problems and reapply them in new problmatic situations. Practical arguments depend for their power on how closely the present circumstances resemble those of the earlier precedent cases for which this particular type of argument was originally devised. So. in practical arguments, the truths and certitudes established in the precedent cases pass sideways, so as to provide "resolutions" of later problems. In the language of rational analysis, the facts of the present case define the gounds on which any resolution must be based; the general considerations that carried wight in similar situations provide warrants that help settle future cases. So the resolution of any problem holds good presumptively; its strengh depends on the similarities between the present case and the prededents; and its soundness can be challenged (or rebutted) in situations that are recognized ans exceptional. Jonsen & Toulmin (1988), and Jonsen (1991) introduce New Casuistry as a practical method. The oxford English Dictionary defines casuistry quite accurately as "that part of ethics which resolves cases of conscience, applying the general rules of religion and morality to particular instances in which circumstances alter cases or in which there appears to be a conflict of duties." They modified the casuistry of the medieval ages to use in clinical situations which is characterized by "the typology of cases and the analogy as an inference method". A case is the unit of analysis. The structure of case was made with interaction of situation and moral rules. The situation is what surrounds or stands around. The moral rule is the essence of case. The analogy can be objective because "the grounds, the warrants, the theoretical backing, the modal qualifiers" are identified in the cases. The specified principlism was the method that Degrazia (1992) integrated the principlism and the specification introduced by Richardson (1990). In this method, the principle is specified by adding information about limitations of the scope and restricting the range of the principle. This should be substantive qualifications. The integrated method is an combination of the New Casuistry and the specified principlism. For example, the study was "Ethical problems experienced by nurses in the care of terminally ill patients"(Um, 1994). A semi-structured in-depth interview was conducted for fifteen nurses who mainly took care of terminally ill patients. The first stage, twenty one cases were identified as relevant to the topic, and then were classified to four types of problems. For instance, one of these types was the patient's refusal of care. The second stage, the ethical problems in the case were defined, and then the case was analyzed. This was to analyze the reasons, the ethical values, and the related ethical principles in the cases. Then the interpretation was synthetically done by integration of the result of analysis and the situation. The third stage was the ordering phase of the cases, which was done according to the result of the interpretation and the common principles in the cases. The first two stages describe the methodology of new casuistry, and the final stage was for the methodology of the specified principlism. The common principles were the principle of autonomy and the principle of caring. The principle of autonomy was specified; when competent patients refused care, nurse should discontinue the care to respect for the patients' decision. The principle of caring was also specified; when the competent patients refused care, nurses should continue to provide the care in spite of the patients' refusal to preserve their life. These specification may lead the opposite behavior, which emphasizes the importance of nurse's will and intentions to make their decision in the clinical situations.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.111-126
• /
• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.1-32
• /
• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.55-78
• /
• 2021

Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.

• Journal of the Daesoon Academy of Sciences
• /
• v.33
• /
• pp.31-62
• /
• 2019

The purpose of this article is to study on the Daesoon Cosmology of the Correlative Relation between Mugeuk and Taegeuk. Daesoon cosmology is a cosmology based on the juxtaposition between the Gucheon Sangje and the world. In this article, I would like to say that this theory in Daesoon Thought was developed in three stages: the phase of the Mugeuk Transcendence of Gucheon Sangje, the stage of the Taegeuk Immanence, and the phase of the Grand Opening of the Later World between Mugeuk and Taegeuk as a correlative gentle reign. First of all, the phase of the Mugeuk Transcendence of Gucheon Sangje has been revealed as a yin-yang relationship. The stage of the Taegeuk Immanence represents the togetherness of harmony and co-prosperity between yin and yang, and the phase of the Grand Opening of the Later World between Mukeuk and Taegeuk refers to the unshakable accomplishment of its character and energy. It will be said that this is due to the practical mechanism in the correct balance of yin-yang making a four stage cycle of birth, growth, harvest, and storage. In addition, the Daesoon stage of the settlement of yin and yang is revealed as a change in the growth of all things and the formation of the inner circle. The mental growth reveals the characteristics of everything in the world, each trying to shine at the height of their own respective life as they grow up energetically. The dominant culture of cerebral communion renders a soft and elegant mood and combines yin and yang to elevate the heavenly and earthly period through transcendental change into sympathetic understanding. The stage of the Grand Opening of the Later World between Mugeuk and Taegeuk is one of the earliest days of the lunar month and also the inner circle of Taegeuk. It is in line with Ken Wilbur's integrated model as a step to the true degrees to develop into a world with brightened degrees. It is a beautiful and peaceful scene where celestial maidens play music, the firewood burns, and the scholars command thunder and lightning playfully. Human beings achieve a state of happiness as a free beings who lives as gods upon the earth. This is the world of theGrand Opening of the Later World between Mugeuk and Taegeuk. Daesoon Thought was succeeded by Dojeon in 1958, when Dojeon emerged as the successor in the lineage of religious orthodoxy and was assigned the task of handling Dao in its entirety. In addition, Daesoon is a circle and represents freedom and commonly shared happiness among the populous. Cosmology in the Daesoon Thought will enable us to understand deep dimensions and the identity of members as individuals within an inner circle of correlation between transcendence and immanence. This present study tries to analyze the public effects philologically and also the mutual correlation by utilizing the truthfulness of literature and rational interpretation. The outlook for the future in Daesoon Thought also leads to the one-way communication of Daesoon as a circle.