• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• Trans-
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• v.13
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• pp.117-140
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• 2022

Recently, artificial intelligence technology is being used throughout the industry. Currently, Currently, AI art generators are used in the NFT industry, and works using them have been exhibited and sold. AI art generators in the art field include Gated Photos, Google Deep Dream, Sketch-RNN, and Auto Draw. AI art generators in the music field are Beat Blender, Google Doodle Bach, AIVA, Duet, and Neural Synth. The characteristics of AI art generators are as follows. First, AI art generator in the art field are being used to create new works based on existing work data. Second, it is possible to quickly and quickly derive creative results to provide ideas to creators, or to implement various creative materials. In the future, AI art generators are expected to have a great influence on content planning and production such as visual art, music composition, literature, and movie.

• Design Convergence Study
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• v.15 no.2
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• pp.237-250
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• 2016

Along with rapid development, progress of the network technology and digital information technology, human are stepping into the intelligent society of internet. Thereby the quality of living environment and working environment are keep improving. Under the big background of internet era, the timeliness and convenience of smart home system has been improved greatly. While lots of smart products have gradually penetrated into people's daily life. The household appliances are among most popular ones. This paper is intended to compare smart features of household living products among most representative brands in China, Japan and South Korea. The smart features include self-learning, self-adapting, self-coordinating, self-diagnosing, self-inferring, self-organizing, and self adjusting. As result, most smart features of these products showed great similarity. While some features were dominated according to countries such as remote control feature in Korea, energy saving feature in Japan, and one button operation feature in China.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1663-1669
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• 2018

Pair programming is a software development technique in which two programmers work together at one computer. One writes code while the other reviews the code, and they switch roles frequently. Pair-programming practice in school programming lab is expected to improve the learning performance, provide collaboration experience, and promote interactions between students. This work finds out how students accept pair-programming, what make students reluctant to join pair-programming by repeated questionnaire surveys in a college programming lab class. Based on these findings aome guidelines for school pair-programming are provided. First, students should be allowed to choose to do pair-programming or not. Second, various obstacles that make students hesitate to switch roles should be removed. Third, the pair matching should be made with great care.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.487-492
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• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• The Journal of Korean Philosophical History
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• no.29
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• pp.153-178
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• 2010

The aim of this research was to examine the traditional Confucian view of self-realization in East Asia and the meaning of life implied therein. The researcher closely reviewed the phase of self-realization of both Confucius and Mencius who are central in Confucianism, especially in the primordial Confucianism, and after investigating maturity of personality as well as educational characteristics thereof, the researcher tried to elicit its modern significance. In Analects, Confucius who is the founder of Confucianism mentioned about 'the pleasure of studying and practicing what he has learned'(學而時習 "Hagisiseup" in Korean), since after, his past was then just the process of self-realization that lasted throughout life. That is, the six phases of self-realization, to wit, 'bending on learning(志學, "Jihak")-'standing firm'(而立, "Irip")-'having no doubts'(不惑, "Bulhok")-'knowing the decrees of Heaven'(知天命, "Jicheonmyeong")-'ear being obedient organ for the reception of truth' (耳順, "Isun")-'able to follow what my heart desires without transgressing what is right'(從心, "Jongsim"), are lying hidden and undeveloped during lifetime, and, at the same time, these phases illustrate the state of enlightenment of life in an in-depth manner. By showing the process of living which is being sublimated in respect of quality, and by going through important process of self-innovation up to six times during lifetime, Confucius edifies us the activity of complete self-realization as well as the importance of education and learning. Meanwhile, these are connected to Mencius in a similar pattern, and strong influence of the characteristics of the learning of the mind and heart( 心學, "Simhak") based on his philosophy permeates the self-actualization phase of Mencius. Mencius' self-actualization phase is expressed in terms of six stages, viz., Person of Goodness(善人, "Seonin")-Trustworthy Person(信人, "Sinin")-Person of Beauty(美人, "Miin")-Great Person(大人, "Daein")-Sage(聖人, "Seongin")-Divine Person(神人, "Sinin"), and these six phases of self-actualization process are educational and learning model for people who dream actualization of perfect personality during their lifetime. Confucian and Mencian view of self-realization congruent with self-discipline internally, and it also reveals a stereotype of human externally. These are a process of performing organic ideals in order for cultivating oneself and regulating others(修己治人, pronounced 'sugichiin' in Korean) which has been pursued by Confucianism. Briefly, these self-realization phases are the arts of living that will lay foundation for "Being Born Human, pronounced Saramim' in Korean" and for becoming "Fully Human, 'Sarmadoem'" and finally for "Human Feelingness, 'Saramdaum'

• Archives of design research
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• v.18 no.2 s.60
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• pp.243-254
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• 2005

Great Britain and the United States and Finland are having an interest in long policy subject about child design education through early design education. And they approaches and practices it systematically. The research about the design learning program instance of advanced nation of primary school's design education for various objective is necessary for use with the fundamental reference data for an elementary design education. And so, This research presented the program instance investigation and analysis result of British primary school's design education. U.K is teaching an primary design education from two subjects of Art & Design and Design and Technology which is a legal subject with national curriculum. The analysis result of design relation unit learning program of two subjects is: Design relation unit learning programs of 'Design and Technology' subject's 20 unit which except 4 food relation unit is largely scientific engineering contents that include utility function contents in part. The reason is as behavior styles based on Design process solve problems scientifically & rationally. Design relation 6 units in subject of Art & Design which except the units which relates with the pure fine arts and architecture in 19 units is aesthetic-symbolic and utility-functional contents largely. And so, the result was analyzed about relation of scientific-engineering content of 'Arts & Design' subject is insufficient comparing with 'Design and Technology' subject Specially, I think that the design relation's unit learning program instances of 'Design and Technology' subject of the British primary school which have been presented by this research paper is a possibility becoming one reference model for a program development. And so I expects that this research could be applied in the program development for the primary design education of primary teacher & education agency.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• Journal of The Korean Association of Information Education
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• v.10 no.1
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• pp.25-35
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• 2006

Computer education currently executed at elementary schools showed problems of system of education curriculum, repetition, and lack of consistent system as a result of analyzing index for information and communication technology, education curriculum, and related literatures, and most of the education contents have difficulty to nurture logic thinking and problem-solving ability since they are composed mainly of software function learning. Concerning this issue, this study suggests an innovated computer education curriculum with reinforced information ethics field with computer principle, algorithm, and programming, in other words, a corrected and supplemented version of former content system based on computer science guidance cases of ACM education curriculum model of USA, computer education curriculum of state Tennessee, and information technology education curriculum of Great Britain judging that introduction of computer science factors are desperate to improve computer education curriculum in elementary schools.