• Korean journal of applied entomology
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• v.61 no.3
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• pp.399-408
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• 2022

There has been much debate on the morphometric divergence between the recently identified Apis cerana koreana and Apis cerana honey bees. The aim of this study was to obtain phenotypic information that can be used to compare A. c. koreana data with other A. cerana subspecies data from open resources and determine breeding results on the basis of morphometric traits. To differentiate A. c. koreana, we investigated 22 classic morphological characteristics; royal jelly secretion; and the weight of workers, queens, and drones of A. c. koreana bred in Korea. To define the selection results, we used the geometric morphometric method. The artificially selected A. c. koreana secreted significantly more royal jelly (1.18 times) than the naturally selected A. c. koreana, which positively influenced the health of the colonies. These honey bees were identified more clearly with the geometric morphometric method than with the classic morphometric method, which is traditionally used to determine the subspecies. Large trends were noted for A. c. koreana on the basis of our results and literature from the 1980s regarding A. cerana sizes in Korea (tarsal index, length of forewing, and cubital index were measured). The cluster analysis revealed the proximity of A. c. koreana, A. cerana in China, and A. c. indica on the basis of eight classic characters, which, perhaps, relay the origin of the honey bees. The results of this study defined the morphometric responses of A. c. koreana honey bees to geographic isolation, climate change, and selection, which are important to identify, protect, and preserve honey bee stock in Korea.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of Climate Change Research
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• v.3 no.4
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• pp.245-257
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• 2012

Vulnerability assessment has been required for establish climate adaptation plan to prevent damage from climate change. In this study we assessed vulnerability with 1 km resolution and determined which sectors have the highest priority in each municipality of Gangwon province based on the result of vulnerability assessment. All sectors of vulnerability assessment are composed of three criteria; sensitivity, exposure and adaptation capacity. And suitable indicators of each sector were selected and spatial data set was prepared using GIS. Priority of vulnerability was classified with the degree of vulnerability in present and variation in vulnerability between present and future. The results of vulnerability assessment were different among municipalities due to the contribution of indicators. Present and future trends in vulnerability showed similar results but high vulnerable area was predicted to expand in the future. In addition increase in temperature led whole area to be more vulnerable generally. The result of prioritizing sectors of vulnerability indicated the most considerable sectors within a municipality. Also, the municipalities which have similar geographic, climatic and social conditions tended to be classified as the same priority class. The method of vulnerability assessment and determining priorities suggested in this study could be used to support decision makers to establish adaptation plan of local area.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.737-760
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• 2022

For the geological disposal of high-level radioactive wastes (HLW), an understanding of deep subsurface environment is essential through geological, hydrogeological, geochemical, and geotechnical investigations. Although South Korea plans the geological disposal of HLW, only a few studies have been conducted for characterizing the geochemistry of deep subsurface environment. To guide the hydrogeochemical research for selecting suitable repository sites, this study overviewed the status and trends in hydrogeochemical characterization of deep groundwater for the deep geological disposal of HLW in developed countries. As a result of examining the selection process of geological disposal sites in 8 countries including USA, Canada, Finland, Sweden, France, Japan, Germany, and Switzerland, the following geochemical parameters were needed for the geochemical characterization of deep subsurface environment: major and minor elements and isotopes (e.g., ³⁴S and ¹⁸O of SO₄^2-, ¹³C and ¹⁴C of DIC, ²H and ¹⁸O of water) of both groundwater and pore water (in aquitard), fracture-filling minerals, organic materials, colloids, and oxidation-reduction indicators (e.g., Eh, Fe²⁺/Fe³⁺, H₂S/SO₄^2-, NH₄⁺/NO₃^-). A suitable repository was selected based on the integrated interpretation of these geochemical data from deep subsurface. In South Korea, hydrochemical types and evolutionary patterns of deep groundwater were identified using artificial neural networks (e.g., Self-Organizing Map), and the impact of shallow groundwater mixing was evaluated based on multivariate statistics (e.g., M3 modeling). The relationship between fracture-filling minerals and groundwater chemistry also has been investigated through a reaction-path modeling. However, these previous studies in South Korea had been conducted without some important geochemical data including isotopes, oxidationreduction indicators and DOC, mainly due to the lack of available data. Therefore, a detailed geochemical investigation is required over the country to collect these hydrochemical data to select a geological disposal site based on scientific evidence.

• Korean Journal of Agricultural Science
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• v.33 no.2
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• pp.149-157
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• 2006

The objective of this research was to know useful genetic characteristics for breeding program of corn. These materials were inbreds after selfing of six or eight generations and had useful genetic informations. The emergence of Danyang Waxy Corn was faster than those of other inbred lines at 2~3 days in field conditions. The plant height of Dangyang Waxy Corn was the highest at 8 days after emergence. However the plant height of New Dangjin was the shortest with 6.25cm at 8 days after emergence, the fresh weight of New Dangjin was 0.046g at 2 days after emergence but that of Dangyang was the heaviest with 0.180g. The fresh weight of 9 inbred lines had more increments in 2 days after emergence. The mean values of dry weight also showed similar trends in 9 inbred lines. The shoot dry weight of inbred lines, Dangyang and New Dangjin was 0.045g and 0.018g at 8 days after emergence, respectively. The root length of inbred line, Dangyang, was the longest with 64.4cm at 8 days after emergence. But the root length of New Dangjin was the shortest with 20.4cm at 8 days after emergence. The fresh weight of endosperm was 0.35g at 2 days after emergence and 0.26g at 8 days after emergence in Dangyang Waxy Corn, because of reduced nutrition of endosperm.

• Economic and Environmental Geology
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• v.47 no.6
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• pp.589-599
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• 2014

This study selected optimal sites in Myeongsasimni located in west coast of Korea for stratigraphic research containing extreme climate event during quaternary period by spatio-temporal analyses of changes in sedimentary environment and land use employing 1918 topographic map, 2000 digital terrain map, 1976 and 2012 air photographies. The study area shows no significant changes in topographic characteristics that hilly areas with relatively large variations in elevation are distributed over north and south part of the study area, and sand dues are developed along the coast line. Moreover, flat low lying areas are located at the back side of the sand dues. The movement of surface run off and sediment loads shows two major trends of inland direction flow from back sides of sand dunes and outland direction flow from high terrains inland, and the two flows merge into the stream located in the center of the study area. Two sink with individual area of $0.2km^2$ are observed in Yongjeong-ri and Jaryong-ri which are located in south central part and south part of the study area, respectively. In addition, sea level change simulation reveals that $3.4km^2$ and $3.64km^2$ are inundated with 3 m of sea level rise in 1918 and 2000, respectively, and it would contribute to chase sea level change records preserved in stratigraphy. The inundated areas overlaps well with sink areas where it indicates the low lying areas located in south cental and south part of the study area are identical for sediment accumulation. The areas with minimal human impact on sediment records over last 100 years are $3.51km^2$ distributed over central and south part of the study area with the land use changes of mud and rice field in 1918 to rice field in 2012. The candidate sites of $0.15km^2$ in central part and $0.09km^2$ in south part are identified for preferable locations of geologic record of extreme climate events during quaternary period based on the overlay analysis of optimal sedimentary environment and land use changes.

• Journal of The Korean Association For Science Education
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• v.18 no.4
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• pp.601-615
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• 1998

Establishing and evaluating science education policies and revising and monitoring the effectiveness of science curriculum should be based upon the results of systematic and scientific research studies. Advanced nations have already been administering and developing national level science assessments for these purposes. The science assessments administered in Korea have been reported having many limitations and problems, and not succeeded in providing data for science education policy making and curriculum reform. The major purpose of the study is developing national level science knowledge assessment system in order to identify longitudinal trends of elementary and secondary school students science knowledge achievements. The research team consisted of science education experts and teachers from various school levels, decided the directions and major elements of national level science knowledge assessment with the consultation of educational evaluation experts. Item developing ability of the researchers was improved by seminars? and workshops on national assessment in advanced nations and developing skills of writing science items. Nearly 500 items were developed and revised. Pilot test was administered with 958 students at various school levels. 380 items were selected and tested with 8766 students, and the characteristics were analyzed in terms of item response theory. The target populations for national level science knowledge assessment are 5th-grade of elementary school, 2nd-grade of middle school, 1st and 2nd-grade of high school students. The proper period for the assessment is February every year. Multi-stage clustered sampling method is desirable and rotated forms are recommendable for the test format. Bridge items should be introduced to compare the results of multiple tests, and various grades. Anchor items should also be used for longitudinal interpretations of the results. The items for elementary school require low to medium abilities, for middle school and first grade of high school require medium to high abilities and for 2nd-grade of high school high abilities. The discrimination ability of the items developed is high.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.13-19
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• 2014

In the past, Korea agro-climatic zone except Jeju-do was classified into nineteen based on rice culture by using air temperature, precipitation, and sunshine duration etc. during rice growing periods. It has been used for selecting safety zone of rice cultivation and countermeasures to meteorological disasters. In this study, the climatic variables such as air temperature, precipitation, and sunshine duration of twenty agro-climatic zones including Jeju-do were compared decennially (1970's, 1980's, 1990's, and 2000's). The meteorological data were obtained in Meteorological Information Portal Service System-Disaster Prevention, Korea Meteorological Administration. The temperature of 1970s, 1980s, 1990s, and 2000s were $12.0{\pm}0.14^{\circ}C$, $11.9{\pm}0.13^{\circ}C$, $12.2{\pm}0.14^{\circ}C$, and $12.6{\pm}0.13^{\circ}C$, respectively. The precipitation of 1970s, 1980s, 1990s, and 2000s were $1,270.3{\pm}20.05mm$, $1,343.0{\pm}26.01mm$, $1,350.6{\pm}27.13mm$, and $1,416.8{\pm}24.87mm$, respectively. And the sunshine duration of 1970s, 1980s, 1990s, and 2000s were $421.7{\pm}18.37hours$, $2,352.4{\pm}15.01hours$, $2,196.3{\pm}12.32hours$, and $2,146.8{\pm}15.37hours$, respectively. The temperature in Middle-Inland zone ($+1.2^{\circ}C$) and Eastern-Southern zone ($+1.1^{\circ}C$) remarkably increased. The temperature increased most in Taebak highly Cold zone ($+364mm$) and Taebak moderately Cold Zone ($+326mm$). The sunshine duration decreased most in Middle-Inland Zone (-995 hours). The temperature (F=2.708, df=3, p= 0.046) and precipitation (F=5.037, df=3, p=0.002) increased significantly among seasons while the sunshine duration decreased significantly(F=26.181, df=3, p<0.0001) among seasons. In further study, it will need to reclassify agro-climatic zone of rice and it will need to conduct studies on safe cropping season, growth and developing of rice, and cultivation management system etc. based on reclassified agro-climatic zone.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.