• Information Systems Review
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• v.21 no.4
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• pp.1-29
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• 2019

The knowledge resources take a role in promoting the sustainable growth of organization. Therefore, it is important for the members of organization to acquire knowledge consistently so that the company can continue to grow. Knowledge service is the field that provides information and infrastructure which enable the members of organization to acquire new knowledge. As we recognized the importance of knowledge services, we analyzed the level of knowledge service management and development through the impact of knowledge quality on user capabilities. First, the matrix of knowledge patterns was presented based on the type of information and the level of customer interaction. According to patterns, the knowledge service was classified into three types of information providing, information analysis, and infrastructure, and then the results of structural model analysis were presented for each type. It found that the impact of knowledge service quality on user competence was different according to the type of service. The results suggested new indicators for measuring the performance of knowledge services, and provided information for reconstructing services based on the user considering the integrated operation of knowledge service and organizational designing knowledge service.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Analytical Science and Technology
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• v.23 no.1
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• pp.1-14
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• 2010

It is of importance that all countries in worldwide, including EU and China, have adopted the Restrictions on the use of certain Hazardous Substances (RoHS) for all electronics. IEC62321 document, which was published by the International Electronics Committee (IEC) can have conflicts with the standards in the market. On the contrary Publicly Accessible Specification (PAS) for sampling published by IEC TC111 can be adopted for complementary application. In this work, we tried to find a route to disassemble and disjoint cellular phone sample, based on PAS and compare the screening methods available in the market. For this work, the cellular phone produced in 2001, before the regulation was born, was chosen for better detection. Although X-ray Fluorescence (XRF) showed excellent performance for screening, fast and easy handling, it can give information on the surface, not the bulk, and have some limitations due to significant matrix interference and lack of variety of standards for quantification. It means that screening with XRF sometimes requires supplementary tool. There are several techniques available in the market of analytical instruments. Laser ablation (LA) ICP-MS, energy dispersive (ED) XRF and scanning electron microscope (SEM)-energy dispersive X-ray (EDX) were demonstrated for screening a cellular phone. For quantitative determination, graphite furnace atomic absorption spectrometry (GF-AAS) was employed. Experimental results for Pb in a battery showed large difference in analytical results in between XRF and GF-AAS, i.e., 0.92% and 5.67%, respectively. In addition, the standard deviation of XRF was extremely large in the range of 23-168%, compared with that in the range of 1.9-92.3% for LA-ICP-MS. In conclusion, GF-AAS was required for quantitative analysis although EDX was used for screening. In this work, it was proved that LA-ICP-MS can be used as a screening method for fast analysis to determine hazardous elements in electrical products.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.14 no.1
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• pp.48-55
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• 2009

Seasonal variations of microalgal biomass and community composition in both the sediment and the seawater were investigated by HPLC pigment analysis in an estuarine muddy sandflat of Gwangyang Bay from January to November 2002. Based on the photosynthetic pigments, fucoxanthin, diadinoxanthin, and diatoxanthin were the most dominant pigments all the year round, indicating that diatoms were the predominant algal groups of both the sediment and the seawater in Gwangyang Bay. The other algal pigments except the diatom-marker pigments showed relatively low concentrations. Microphytobenthic chlorophyll ${\alpha}$ concentrations in the upper layer (0.5 cm) of sediments ranged from 3.44 (March at the middle site of the tidal flat) to 169 (July at the upper site) mg $m^{-2}$, with the annual mean concentrations of $68.4{\pm}45.5,\;21.3{\pm}14.3,\;22.9{\pm}15.6mg\;m^{-2}$ at the upper, middle, and lower tidal sites, respectively. Depth-integrated chlorophyll ${\alpha}$ concentrations in the overlying water column ranged from 1.66 (November) to 11.7 (July) mg $m^{-2}$, with an annual mean of $6.96{\pm}3.04mg\;m^{-2}$. Microphytobenthic biomasses were about 3${\sim}$10 times higher than depth-integrated phytoplankton biomass in the overlying water column. The physical characteristics of this shallow estuarine tidal flat, similarity in taxonomic composition of the phytoplankton and microphytobenthos, and similar seasonal patterns in their biomasses suggest that resuspended microphytobenthos are an important component of phytoplankton biomass in Gwangyang Bay. Therefore, considering the importance of microphytobenthos as possible food source for the estuarine benthic and pelagic consumers, a consistent monitoring work on the behavior of microphytobenthos is needed in the tidal flat ecosystems.

• The Korean Journal of Archival Studies
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• no.47
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• pp.95-130
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• 2016

The job satisfaction of records managers is of importance because it affects their work performance and retention. The purpose of this study is to investigate records managers' job satisfaction and to identify factors affecting records manager's job satisfaction to find the way to improve their job satisfaction. Specific questions of the study are as follows: 1) What is the job satisfaction of records managers? 2) Are factors affecting job satisfaction different depending on record managers' personal characteristics? 3) What are the most influential factors on job satisfaction? To do that, questionnaires were used to gather data from 60 domestic records managers working in different types of records centers. Data analyses included descriptive statistics, one-way ANOVA, independent t-test, and multiple-regression analysis. Additionally, interviews with 2 record managers were conducted to collect opinions on factors affecting job dissatisfaction and recommendations for improving their job satisfaction. Important findings of the study are as follows: First, the respondents are moderately satisfied with their jobs (3.2 out of 5 points). The level of job satisfaction is different depending on years of career, years of employment, number of personnel the respondent is working with in the records center, and etc. The number of personnel the respondent is working with was found to be the most influential factor. Second, multiple-regression analysis result shows that motivation factors(satisfaction factors) are more influential than hygiene factors (dissatisfaction factors) on the respondents' job satisfaction, which confirms Herzberg's two factor theory. More specifically, 'work ethic,' one of motivator factors, has the greatest influence, followed by 'procedural impartiality', 'communication', 'job characteristic', 'distributive justice', and 'working conditions.' Based on the results, this study suggests several ways to improve record managers' job satisfaction level. First, the awareness of records management should be increased. The respondents indicated that their job dissatisfaction is usually derived from a lack of the awareness of records management. Therefore, every chief of organizations, National Archives of Korea, and records managers themselves should try to raise the awareness of records management. Especially, records managers should make stronger efforts to attract the office's attention. Second, records managers ought to establish their identity as records management profession. Also, they should participate in various activities of the archival community to overcome the limitation of individuals.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.8-9
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• 2017

Soybean yield has been low and unstable in Japan and other areas in East Asia, despite long history of cultivation. This is contrasting with consistent increase of yield in North and South America. This presentation tries to describe perspective of breaking stagnation of soybean yield in East Asia, considering the factors of the different yields between regions. Large amount of rainfall with occasional dry-spell in the summer is a nature of monsoon climate and as frequently stated excess water is the factor of low and unstable soybean yield. For example, there exists a great deal of field-to-field variation in yield of 'Tanbaguro' soybean, which is reputed for high market value and thus cultivated intensively and this results in low average yield. According to our field survey, a major portion of yield variation occurs in early growth period. Soybean production on drained paddy fields is also vulnerable to drought stress after flowering. An analysis at the above study site demonstrated a substantial field-to-field variation of canopy transpiration activity in the mid-summer, but the variation of pod-set was not as large as that of early growth. As frequently mentioned by the contest winners of good practice farming, avoidance of excess water problem in the early growth period is of greatest importance. A series of technological development took place in Japan in crop management for stable crop establishment and growth, that includes seed-bed preparation with ridge and/or chisel ploughing, adjustment of seed moisture content, seed treatment with mancozeb+metalaxyl and the water table control system, FOEAS. A unique success is seen in the tidal swamp area in South Sumatra with the Saturated Soil Culture (SSC), which is for managing acidity problem of pyrite soils. In 2016, an average yield of $2.4tha^{-1}$ was recorded for a 450 ha area with SSC (Ghulamahdi 2017, personal communication). This is a sort of raised bed culture and thus the moisture condition is kept markedly stable during growth period. For genetic control, too, many attempts are on-going for better emergence and plant growth after emergence under excess water. There seems to exist two aspects of excess water resistance, one related to phytophthora resistance and the other with better growth under excess water. The improvement for the latter is particularly challenging and genomic approach is expected to be effectively utilized. The crop model simulation would estimate/evaluate the impact of environmental and genetic factors. But comprehensive crop models for soybean are mainly for cultivations on upland fields and crop response to excess water is not fully accounted for. A soybean model for production on drained paddy fields under monsoon climate is demanded to coordinate technological development under changing climate. We recently recognized that the yield potential of recent US cultivars is greater than that of Japanese cultivars and this also may be responsible for different yield trends. Cultivar comparisons proved that higher yields are associated with greater biomass production specifically during early seed filling, in which high and well sustained activity of leaf gas exchange is related. In fact, the leaf stomatal conductance is considered to have been improved during last a couple of decades in the USA through selections for high yield in several crop species. It is suspected that priority to product quality of soybean as food crop, especially large seed size in Japan, did not allow efficient improvement of productivity. We also recently found a substantial variation of yielding performance under an environment of Indonesia among divergent cultivars from tropical and temperate regions through in a part biomass productivity. Gas exchange activity again seems to be involved. Unlike in North America where transpiration adjustment is considered necessary to avoid terminal drought, under the monsoon climate with wet summer plants with higher activity of gas exchange than current level might be advantageous. In order to explore higher or better-adjusted canopy function, the methodological development is demanded for canopy-level evaluation of transpiration activity. The stagnation of soybean yield would be broken through controlling variable water environment and breeding efforts to improve the quality-oriented cultivars for stable and high yield.

• Analytical Science and Technology
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• v.28 no.6
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• pp.409-416
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• 2015

ICP-AES has been used in many laboratories due to the advantages of wide calibration range and multi-element analysis, but it may give erroneous results and suffer from spectral interference due to the large number of emission lines associated with each element. In this study, certified reference materials (CRMs) and field samples were analyzed by ICP-AES and HG-AAS according to the official Korean testing method for soil pollution to investigate analytical problems. The applicability of HG-ICP-AES was also tested as an alternative method. HG-AAS showed good accuracies (90.8~106.3%) in all CRMs, while ICP-AES deviated from the desired range in CRMs with low arsenic and high Fe/Al. The accuracy in CRM030 was estimated as below 39% at the wavelength of 193.696 nm by ICP-AES. Significant partial overlaps and sloping background interferences were observed near to 193.696 nm with the presence of 50 mg/L Fe and Al. Most CRMs were quantified with few or no interferences of Fe and Al at 188.980 nm. ICP-AES properly assessed low and high level arsenic for field samples, at 188.980 nm and 193.696 nm, respectively. The importance of the choice of measurement wavelengths corresponding to relative arsenic level should be noted. Because interferences were affected by the sample matrix, operation conditions and instrument figures, the analysts were required to consider spectral interferences and compare the analytical performance of the recommended wavelengths. HG-ICP-AES was evaluated as a suitable alternative method for ICP-AES due to improvement of the detection limit, wide calibration ranges, and reduced spectral interferences by HG.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.2
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• pp.29-39
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• 2011

Recent interests in the quality of urban space have raised awareness on the role and importance of landscape planning. Although laws and guidelines are officially ready to be imposed as for landscape planning, we do not have concrete materials that can be utilized in the course of practices. The aim of this paper in this regard is to disclose the possible difference in awareness on 'visual landscape control elements' among experts engaged with urban space planning. The expert groups are distinguished to three which are for a planning, design and engineering and the survey is made by questionnaires. The results are analyzed through basic technology statistics in SPSS and independent-sample t-test provided. The survey is done by tens of 'control elements' and the result is that specially, group 1 and group 2 in mixed landscape has the most discrepancy in awareness on those elements but relatively, they has less discrepancy in awareness on compare with other groups through all landscape area. In case of artificial landscape and mixed landscape in 'landscape controled area', the result for comparing between G1 and G2 is that there are the most discrepancy in awareness which are 7 control elements. In case of mixed landscape in 'landscape promoted area', there are 4 control elements for discrepancy in awareness between G2 and G3 which is quite different. The control elements which show the most discrepancy in awareness is height, floor space and building to land ratio in order. The shape elements has only discrepancy in awareness for comparing between G1 and G2 of artificial landscape in 'landscape controled area'. In terms of the average evaluation score of the appropriateness of these control elements, G1 seems to appreciate the role of these elements in systematic landscape planning more than the other group does. In other words, relatively low scores are given by G2 as for the overall functionality of visual landscape control elements. The texture, floor space and building of land ratio has low evaluation score for all area and types. It means that it should reverify for appropriateness of performance for landscape planning as visual landscape control elements.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.