• The Korean Journal of Financial Management
• /
• v.24 no.4
• /
• pp.75-106
• /
• 2007

In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.39-54
• /
• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.131-154
• /
• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.1-27
• /
• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• 대한공업교육학회지
• /
• v.44 no.2
• /
• pp.28-46
• /
• 2019

The purpose of this study is to use as a basic data of establishing operating Course-based National Technical Qualification(CNTQ) support program by examining the educational needs for operating CNTQ of vocational school teachers, and to contribute to the vocational school settlement of CNTQ course. To achieve those purposes, this study drew 27 tasks performed by teachers operating CNTQ. Also, it surveyed the perceived importance and the performance. The findings of this study are as follows. First, it is showed that 'selection of qualification fields and confirmation of organization criteria, organization of educational training time by competency unit, organization of subjects and establishment of competency unit operating plan by grade and semester, selection of teaching materials, implementation of education and training, establishment of evaluation plan, implementation of evaluation, re-education and re-evaluation students with grades under 40%, guidance of paper evaluation, guidance of practical evaluation, guidance of interview evaluation' are the first priority tasks in the result of the need analysis. Second, it is indicated that 'application of external evaluation, guidance to retake an exam for failure' are the secondary priority tasks. According to these results, the following conclusions were made. First, it will be more positive effects if the educational needs in the next CNTQ support program include contents of the first priority tasks. Second, it is indicated that the priority of the educational needs for tasks of operating plan stages is commonly high. In particular, the highest ranking in the result means that it is completely supported from the first step on operating course. It is expected that the program which teachers on operating the course of similar qualification fields share each operating experience is effective. Third, the priority of the educational needs for external evaluation step ranked high. External evaluation has a different level of difficulty and a form of practical evaluation output according to qualification fields, so the method of guidance has to be different. It needs the program constructed by similar fields.

• Korean Journal of Culture and Social Issue
• /
• v.29 no.1
• /
• pp.75-99
• /
• 2023

The current study aimed to explore the suicidal behaviors and risk factors of completed suicides using psychological autopsy and use them as index variables to classify suicidal types. In addition, this study looked into the influential factors that affect each suicidal type. related to suicidal behaviors and suicidal risk factors by psychological autopsy. In addiction, the distinctions among the classes were analyzed. For this, psychological autopsies were conducted on the families and the close ones of 128 completed suicides. Then, the index variables were finally chosen for classifying suicidal types. The selected index variables for suicidal risk factors were mental disorders, suicide/self-harm, significant changes in physical appearance, marital conflict, adjustment and relationship issues at work/school, unemployment/layoff, jobless status and serious financial problems. The selected index variables for suicidal behaviors were expressing their suicidal attempts, writing suicidal notes, asking for help, the time/place/method of suicidal behavior, past suicidal/self-harm experience and the first person who witnessed the suicide. The Latent Class Analysis(LCA) and the 3-step method were used for classifying suicidal types. Then external variables(financial changes, cohabitation, existence of stressors, changes in stress level or relationships and family members with mental disorder/alchohol problems/ physical disorders, and work/school stisfaction) were applied for distinguishing classes. As a result, 5 classes(financial problems, adjustment problems, complex problems, psychiatric problems, and response to event[s]) were revealed on suicidal behaviors and 3 classes(residence- suicidal attempt- found by family, nonresidence- nonsuicidal attempt- found by acquaintances, residence- nonsuicidal attempt- found by family) were presented on suicidal risk factors. External variables such as gender, marital status, cohabitation, changes in relationships significantly differentiated among the 3 classes. Especially, class 3(residence- nonsuicidal attempt- found by family) tended to cohabit with others, were married, and had a significantly high level of interpersonal conflicts. When comparing the 5 classes of suicidal risk factors, auxiliary variables such as economic changes, cohabitation, stress, relationship changes, and family-related problems, and school/work satisfaction significantly differentiated the 5 classes. Especially class 3 (complex problems) experienced comparatively less family-related problems, but showed an aggravating level of personal stress. Suicial prevention strategies should be provided considering the characteristics of each class and the influential factors.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.151-159
• /
• 2016

This paper proposes a ceramic heating element-based tankless instant electric water heater for hand/face washing that does not require a lot of hot water. The heating module, which heats the input water and outputs hot water, operates the ceramic heating element detecting input water using a flow sensor. Inside of the heating module is designed to form one flow path in order to get almost $15^{\circ}C$ increased heated water compared to the input water temperature within 2 second after 1.5 liter per minute water supply. The design validity is verified using a heat flow analysis of the water flow and temperature variations inside of the heating module also. Based on the design data, the heating module is constructed including a single rod-type ceramic heating element. After that, a prototype system having temperature setting function by three steps were constructed. The prototype system is connected to a 1.5 liter per minute water supply line, and the water output temperature and time measurement experiments confirmed that the proposed system output the heated water increased by $18.3^{\circ}C$ in case of third step setting within 2 second after water supply. And standby power is under 1 W and peak power does not exceed the permissible range for the general house usage. Several performance results verify that the proposed tankless instant electric water heater is applicable for the washstand of the house, highway rest area and factory so on as winter-time hand/face washing.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.2
• /
• pp.74-84
• /
• 2020

This study was conducted to discover the meaning and the essential structure of the experiences of nursing students' introductory clinical practice. The participants were eight nursing students who had carried out the introductory clinical practice at a hospital. The data was collected from July 15 2019 to July 31, 2019 through focus group discussions and in-depth individual interviews using non-structured questionnaires. The data was analyzed by Colaizzi's phenomenological analysis methodology. The introductory clinical practice experienced by nursing students was categorized as followed: 'Feel worried and concern about expectations at the same time', 'Hospital experience as reality', 'Becoming accustomed to complexed emotion's, 'The first step of becoming a nurse', and 'Preparation for the future'. The five categories were expanded in the same context according to time. The essential structure of the clinical introductory practice experiences of the nursing students revealed by the study is that they begin to practice feeling worried and concerned about expectations, and concern about experience and adaption to various situations, emotions and preparation for the future. Based on the results obtained from this study, it is necessary to develop a realistic and effective education program before starting clinical practice.

• The Journal of the Korea Contents Association
• /
• v.8 no.3
• /
• pp.156-172
• /
• 2008

The point of departure is the Popularity of the electronic games among the youth generation. This study attempts to make up a questionnaire containing the questions which are intended for the youth generation realistically and at the same time in a meaningful way pedagogically. Any researcher who wants to understand the youth culture at the present time is necessary to approach the youth generation in a positive attitude of learning, so asking the questions to the youth generation is as important as having the answers. That is to say, this paper is not a statistical analysis of the questionnaire, nor a empirical research of youth's reception of the electronic games. Now that the emphasis of the paper is located on the very way of approaching the youth generation concerning the electronic games, this study starts with the university students in the first place because they are in a more advantageous milieu for conversation in the classroom on the subject. To be sure, this study will be able to cover the whole area of primary, junior or senior high-school by way of some modifications. Conclusively, this paper aims at providing with practical ideas of teaching, which immediately can be appropriated into the classroom by the teachers in the actual field, and drawing attention to the potential educational contents of the cultural products. Furthermore, the questionnaire proposed in the paper is meant for the first step towards the aesthetics of the electronic games with a view to the game-imagination.

• Journal of KIISE:Computer Systems and Theory
• /
• v.35 no.9_10
• /
• pp.485-496
• /
• 2008

As the complexity of SoC (System-on-Chip) design increases dramatically. traditional system performance analysis and verification methods based on RTL (Register Transfer Level) are no more valid for increasing time-to-market pressure. Therefore a new design methodology is desperately required for system verification in early design stages. and hardware software (HW-SW) cosimulation at TLM (Transaction Level Modeling) level has been researched widely for solving this problem. However, most of HW-SW cosimulators support few restricted ion levels only, which makes it difficult to integrate HW-SW cosimulators with different ion levels. To overcome this difficulty, this paper proposes a multipurpose framework for HW SW cosimulation to provide systematic SoC design flow starting from software application design. It supports various design techniques flexibly for each design step, and various HW-SW cosimulators. Since a platform design is possible independently of ion levels and description languages, it allows us to generate simulation models with various ion levels. We verified the proposed framework to model a commercial SoC platform based on an ARM9 processor. It was also proved that this framework could be used for the performance optimization of an MJPEG example up to 44% successfully.