• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Korean Journal of Heritage: History & Science
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• v.50 no.2
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• pp.94-115
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• 2017

'Arirang folk song in the Democratic People's Republic of Korea' was inscribed to the Representative List of the Intangible Cultural Heritage of Humanity in 2014 and 'Tradition of kimchi-making in the Democratic People's Republic of Korea' followed in 2015. It is presumed that North Korea was influenced by the Republic of Korea inscribing 'Arirang, lyrical folk song in the Republic of Korea' to the list in 2012 as well as 'Kimjang, making and sharing kimchi in the Republic of Korea' in 2013. These cases show the necessity (or possibility) of cultural exchanges between the two Koreas through UNESCO ICH lists. The purpose of this article is to explore the possibility of inter-Korean cultural integration. Therefore, I would like to review UNESCO's ICH policy and examine the ways of cooperation and joint nominations to the Representative List of Intangible Cultural Heritage of Humanity between the two Koreas. First, I reviewed the amendments to the laws and regulations of the two Koreas and how the two countries applied the UNESCO Convention for the Safeguarding of the Intangible Cultural Heritage. Although the cultural exchange is a non-political field, given the situation between South and North Korea, it is influenced by politics. Therefore, we devised a stepwise development plan, divided into four phases: infrastructure development, cooperation and promotion, diversification, and policymaking and alternative development. First a target group will be needed. In this regard, joint nominations to the Representative List of the UNESCO Intangible Cultural Heritage of Humanity will be suitable for cooperation. Both countries have already started separate nominations on shared ICH elements to the UNESCO lists. Therefore, I have selected a few elements as examples that can be considered for joint nominations. The selected items are makgeolli (traditional liquor), jang (traditional soybean sauce), gayangju (homebrewed liquor), gudeul (Korean floor heating system), and jasu (traditional embroidery). Cooperation should start with sharing information on ICH elements. A pilot project for joint nomination can be implemented and then a mid-term plan can be established for future implementation. When shared ICH elements are inscribed on UNESCO ICH lists, various activities can be considered as follow-ups, such as institution visits, performances, exhibitions, and joint monitoring of the intangible cultural heritage. Mutual cooperation of the two Koreas' intangible cultural heritage will be a unique example between the divided countries, so its value will be recognized as a symbol of cultural cooperation. In addition, it will be a foundation for cultural integration of the two Koreas, and it will show the value of their unique ICH to the world. At the same time, it will become a good example for joint nominations to the Representative List recommended by UNESCO.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.167-174
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• 2019

A process-oriented crop growth model can simulate the biophysical process of rice under diverse environmental and management conditions, which would make it more versatile than an empirical crop model. In the present study, we examined chronology and background of the development of the rice growth models in Korea, which would provide insights on the needs for improvement of the models. The rice crop growth models were introduced in Korea in the late 80s. Until 2000s, these crop models have been used to simulate the yield in a specific area in Korea. Since then, improvement of crop growth models has been made to take into account biological characteristics of rice growth and development in more detail. Still, the use of the crop growth models has been limited to the assessment of climate change impact on crop production. Efforts have been made to apply the crop growth model, e.g., the CERES-Rice model, to develop decision support system for crop management at a farm level. However, the decision support system based on a crop growth model was attractive to a small number of stakeholders most likely due to scarcity of on-site weather data and reliable parameter sets for cultivars grown in Korea. The wide use of the crop growth models would be facilitated by approaches to extend spatial availability of reliable weather data, which could be either measured on-site or estimates using spatial interpolation. New approaches for calibration of cultivar parameters for new cultivars would also help lower hurdles to crop growth models.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.581-588
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• 2019

MRI is superior when contrasted to help the organization generate artifacts resolution, but also affect the diagnosis and create a image that can not be read. Metal is inserted into the tooth, it is necessary to often be inhibited in imaging by causing the geometric distortion due to the majority and if the difference between the magnetic susceptibility of a ferromagnetic material or paramagnetic reducing them. The purpose of this study is to conduct a metal artefact in accordance with the analysis using a diamagnetic material. The magnetic material include a wire for the orthodontic bracket and a stainless steel was used as a diamagnetic material was used copper, zinc, bismuth. Testing equipment is sequenced using 1.5T, 3T was used was measured using a SE, TSE, GE, EPI. A self-produced phantom material was used for agarose gel (10%) to a uniform signal artifacts causing materials are stainless steel were tested by placing in the center of the phantom and cover inspection of the positive cube diamagnetic material of 10mm each length.After a measurement artefact artifact zone settings area was calculated using the Wand tool After setting the Low Threshold value of 10 in the image obtained by subtracting images, including magnetic material from a pure tool phantom images using Image J. Metal artifacts occur in stainless steel metal artifact reduction was greatest in the image with the bismuth diamagnetic materials of copper and zinc is slightly reduced, but the difference in degree will not greater. The reason for this is thought to be due to hayeotgi offset most of the susceptibility in bismuth diamagnetic susceptibility of most small ferromagnetic. Most came with less artifacts in image of bismuth in both 1.5T and 3T. Sequence-specific artifact reduction was most reduced artifacts from the TSE 1.5T 3T was reduced in the most artifacts from SE. Signal-to-noise ratio was the lowest SNR is low, appears in the implant, the 1.5T was the Implant + Bi Cu and Zn showed similar results to each other. Therefore, the results of artifacts variation of diamagnetic material, magnetic susceptibility (${\chi}$) is the most this shows the reduced aspect lower than the implant artificial metal artifacts criteria in the video using low bismuth susceptibility to low material the more metal artifacts It was found that the decrease. Therefore, based on the study on the increase, the metal artifacts reduction for the whole, as well as dental prosthesis future orthodontic materials in a way that can even reduce the artifact does not appear which has been pointed out as a disadvantage of the solutions of conventional metal artifact It is considered to be material.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Management & Information Systems Review
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• v.38 no.2
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• pp.67-81
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• 2019

In accordance with K-IFRS 1109, financial instruments are classified to amortized cost (AC), fair value through other comprehensive income (FVOCI) and fair value through profit or loss (FVPL). And disposal gains are prohibited to be recirculated for net income when FVOCI financial instruments would be sold in the future, so-called recirculation prohibition. This research investigates whether accumulated other comprehensive income of available-for sale financial assets(AFS) under K-IFRS 1039, could affect reclassified amounts to the FVPL securities from the AFS securities. Also, this study investigates the effects of the reported income on the reclassified FVPL, because CEOs are likely to try earnings management when net income is predicted to be less than target or is low, comparing other firms. As a result of empirical analysis, first, I find that accumulated other comprehensive income of the AFS has a positive impact on the reclassified FVPL. Second, level of reporting income has no significant impact on the reclassified FVPL. Third, interaction effects are significantly positive on the firms which have more other comprehensive income and less level of reported income. Fourth, the effects of the bank and securities are more distinct than those of the manufactures. This study is the first research to investigate earnings management through AFS at the timing of the first adoption of K-IFRS 1109. Empirical results of this study provide evidence of earnings management on the reclassification of FVPL which gives meaningful implications to regulators, academic researchers and auditors.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.