• Korean Journal of Environmental Agriculture
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• v.11 no.1
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• pp.50-58
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• 1992

Studies were conducted to determine the combined effect of uniconazole [(E) -1-(4-chlorophenyl)-4, 4-demethyl 2-(1,2,4 triazol-1-yl)-1-penten-3-ol] and silver thiosulfate $[Ag {(S_2O_3)}^3\;_2-]$ (STS) on reduction of ozone injury in tomato plants(Lycopersicon esculentum Mill. 'Pink Glory'). Plants were given a 50ml soil drench of uniconazole at concentrations of 0, 0.001, 0.01 and 0.1 mg/pot at the stage of emerging 4th leaf. Two days prior to ozone fumigation, STS solution contained 0.05% Tween-20 was also sprayed at concentrations of 0, 0.3 and 0.6 mM. Uniconazole at 0.01 mg/pot and STS at 0.6 mM were effective in providing protection against ozone exposure(20h at 0.2ppm) without severe retardation of plant height and chemical phytotoxicity, respectively. Combined treatment with uniconazole, STS significantly reduced ozone injury at the lower concentration than a single treatment with uniconazole or STS. Uniconazole treatment reduced plant height, stem elongation and transpiration rate on a whole plant level and increased chlorophyll concentration. STS did not give any effect on plant growth and chlorophyll content but increased transpiration rate in non-ozone-fumigated plants. Ethylene production in the leaves of ozone-fumigated plants was decreased by uniconazole and STS pretreatment, but there was no protective effect on epinasty of leaves in uniconazole-treated plants. STS increased ethylene production in non-ozone-fumigated plants, but it significantly reduced the degree of epinasty and defoliation of cotyledons when plants were exposed to ozone. Uniconazole slightly increased superoxide dismutase and peroxidase activities. But STS showed little or no effects on such free radical scavengers. Day of flowering after seeding was shortened and percentages of fruit set were increased by uniconazole treatment. STS was highly effective on protecting reduction of fruit set resulting from ozone fumigation. These results suggest that combined use of uniconazole and STS should provide miximum protection against ozone injury without growth retardation resulting in yield loss.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.444-451
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• 2022

Recently, long-term cultivation is becoming more common with the increase in tomato hydroponics. In hydroponics, it is very important to supply an appropriate nutrient solution considering the nutrient and moisture requirements of crops, in terms of productivity, resource use, and environmental conservation. Since seasonal environmental changes appear severely in long-term cultivation, it is so critical to manage irrigation control considering these changes. Therefore, this study was carried out to investigate the effect of irrigation volume on growth and yield in tomato long-term cultivation using coir substrate. The irrigation volume was adjusted at 4 levels (high, medium high, medium low and low) by different irrigation frequency. Irrigation scheduling (frequency) was controlled based on solar radiation which measured by radiation sensor installed outside the greenhouse and performed whenever accumulated solar radiation energy reached set value. Set value of integrated solar radiation was changed by the growing season. The results revealed that the higher irrigation volume caused the higher drainage rate, which could prevent the EC of drainage from rising excessively. As the cultivation period elapsed, the EC of the drainage increased. And the lower irrigation volume supplied, the more the increase in EC of the drainage. Plant length was shorter in the low irrigation volume treatment compared to the other treatments. But irrigation volume did not affect the number of nodes and fruit clusters. The number of fruit settings was not significantly affected by the irrigation volume in general, but high irrigation volume significantly decreased fruit setting and yield of the 12-15th cluster developed during low temperature period. Blossom-end rot occurred early with a high incidence rate in the low irrigation volume treatment group. The highest weight fruits was obtained from the high irrigation treatment group, while the medium high treatment group had the highest total yield. As a result of the experiment, it could be confirmed the effect of irrigation amount on the nutrient and moisture stabilization in the root zone and yield, in addition to the importance of proper irrigation control when cultivating tomato plants hydroponically using coir substrate. Therefore, it is necessary to continue the research on this topic, as it is judged that the precise irrigation control algorithm based on root zone-information applied to the integrated environmental control system, will contribute to the improvement of crop productivity as well as the development of hydroponics control techniques.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.91-110
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• 2011

This paper analyzes the cases where the hotels have increased their services and enhanced their work process through IT solutions to cope with computerization globalization. Also the cases have been studies where national hotels use the CRM solution internally to respond effectively to customers requests, increase customer analysis, and build marketing strategies. In particular, this study discusses the introduction of the CRM solutions and CRM sales business and marketing services using a process for utilizing the presumed, CRM by introducing effective DEA(Data Envelopment Analysis). First, the comparison has done regarding the relative efficiency of L Company with the CCR model, then compared L Company's restaurants and facilities' effectiveness through BCC model. L Company reached a conclusion that it is important to precisely create and manage sales data which are the preliminary data for CRM, and for that reason it made it possible to save sales data generated by POS system on each sales performance database. In order to do that, it newly established Oracle POS system and LORIS POS system concerned with restaurants for food and beverage as well as rooms, and made it possible to stably generate and manage sales data and manage. Moreover, it set up a composite database to control comprehensively the results of work processes during a specific period by collecting customer registration information and made it possible to systematically control the information on sales performances. By establishing a system which unifies database and managing it comprehensively, impeccability of data has been greatly enhanced and a problem which generated asymmetric data could be thoroughly solved. Using data accumulated on the comprehensive database, sales data can be analyzed, categorized, classified through data mining engine imbedded in Polaris CRM and the results can be organized on data mart to provide them in the form of CRM application data. By transforming original sales data into forms which are easy to handle and saving them on data mart separately, it enabled acquiring well-organized data with ease when engaging in various marketing operations, holding a morning meeting and working on decision-making. By using summarized data at data mart, it was possible to process marketing operations such as telemarketing, direct mailing, internet marketing service and service product developments for perceived customers; moreover, information on customer perceptions which is one of CRM's end-products could feed back into the comprehensive database. This research was undertaken to find out how effectively CRM has been employed by comparing and analyzing the management performance of each enterprise site and store after introducing CRM to Hotel enterprises using DEA technique. According to the research results, efficiency evaluation for each site was calculated through input and output factors to find out comparative CRM system usage efficiency of L's Company four sites; moreover, with regard to stores, the sizes of workforce and budget application show a huge difference and so does the each store efficiency. Furthermore, by using the DEA technique, it could assess which sites have comparatively high efficiency and which don't by comparing and evaluating hotel enterprises IT project outcomes such as CRM introduction using the CCR model for each site of the related enterprises. By using the BCC model, it could comparatively evaluate the outcome of CRM usage at each store of A site, which is representative of L Company, and as a result, it could figure out which stores maintain high efficiency in using CRM and which don't. It analyzed the cases of CRM introduction at L Company, which is a hotel enterprise, and precisely evaluated them through DEA. L Company analyzed the customer analysis system by introducing CRM and achieved to provide customers identified through client analysis data with one to one tailored services. Moreover, it could come up with a plan to differentiate the service for customers who revisit by assessing customer discernment rate. As tasks to be solved in the future, it is required to do research on the process analysis which can lead to a specific outcome such as increased sales volumes by carrying on test marketing, target marketing using CRM. Furthermore, it is also necessary to do research on efficiency evaluation in accordance with linkages between other IT solutions such as ERP and CRM system.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.77-84
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• 2019

Objectives : The Illness Intrusiveness Rating Scale (IIRS) is a well-validated self-report instrument for assessing negative impact of chronic illness and/or adverse effects of its treatment on everyday life domains. Although extensive literature probed its psychometric properties in medical illness, little attention was paid for its validity for psychiatric population. This study aimed to test factorial structure of the Korean Version of the IIRS (IIRS-K) in a consecutive sample of psychiatric outpatients. Methods : Data set of 307 first-visit patients of psychiatric clinic at Guri Hanyang univ. Hospital were used. Exploratory and confirmatory factor analysis, internal consistency were tested in IIRS-K. We also checked Spearman's correlation analysis between IIRS-K, Zung's self-report anxiety scale and Zung's self-report depression scale. Results : 76.9% of the patients were with anxiety disorder and depressive disorder. The principal component factor analysis of the IIRS-K extracted three-factor structure accounted for 63.2% of total variance that was contextually similar to the original English version. This three-factor solution showed the best fit when tested confirmatory factor analysis compared to the original IIRS, two-factor model of IIRS-K suggested from medical outpatients, and one-factor solution. The IIRS-K also showed good internal consistency (Cronbach's α=0.90) and good convergent validity with anxiety and depression scales. Conclusions : The IIRS-K showed the three-factor structure that was similar but not identical to original version. Overall, this study proved factorial validity of the IIRS-K and it can be used for Korean clinical population.

• Restorative Dentistry and Endodontics
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• v.28 no.1
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• pp.89-99
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• 2003

The purpose of this study was to investigate the bonding of resin- based root canal sealer, AH26 when the sealer was applied as a thin layer between dentine and gutta-percha surface. In this study forty non-caries extracted human molars and resin-based root canal sealer(AH 26, DeTrey/Dentsply, Germany) were used. Disks of gutta-percha, 6mm in diameter.6mm thick (Diadent/Dentsply, Korea) for thermoplastic obturation were used and dentin surfaces were treated with 2% NaOCl(Group 1) or 2%NaOCl+17% EDTA(Group 3). Disks of gutta-Percha, 6mm in diameter.6mm thick (Diadent/Dentsply, Korea) for conventional obturation were used and dentin surface were treated with 2% NaOCl(Group 2) or 2%NaOCl+17% EDTA(Group 4). Enamel was removed by a horizontal section 1mm below the deepest portion of the central occlusal groove by using a watercooled low speed diamond saw. A second horizontal section was done around cementoenamel junction. Exposed dentin surface was cut to approximately $8{\times}8{\;}mm$ rectangular shape and was ground against 320, 400, 600 grade silicon carbide abrasive paper serially. After grinding, the dentine surface were soaked in a solution of 2% NaOCl for 30 minutes and twenty of specimens were treated with 17% EDTA solution for 1 minute. The treated specimens were washed and dried, Root canal sealer, AH26 was prepared according to the manufacture's instructions The Gutta-percha and dentin surface were coated with a thin layer of the freshly mixed seal or. The specimens were left overnight at room temperature. After their initial set, they were transferred to an incubator at $37$^{\circ}C$ for 72 h. After 72 hours, resin blocks were made. The resin block was serially sectioned vertically into stick of $1{\cdot}1mm$. Twenty sticks were prepared from each group. After that, tensile bond strength f3r each stick was measured with Microtensile Tester Failure patterns of the specimens at the interface between gutta-percha and dentin were observed under the SEM(x1000) and Stereomicroscope (LEICA M42O, Meyer Inst., TX U.S.A) at 1.25 x25 magnification. The results were statistically analysed by using a One-way ANOVA and Tukey's test. The results were as follows; 1. Tensile bond strengths($mean{\pm}SD$) were expressed with ascending order as follows: Group 1, $3.09{\pm}$ 1.05Mpa : Group 2, $6.23{\pm}1.16MPa$ : Group 3, $7.12{\pm}1.07MPa$ : Group 4, $10.32{\pm}2.06MPa$. 2. Tensile bond strengths of the group 2 and 4 used disks of gutta-percha for conventional obturation were significantly higher than that of the group 1 and 3 used fir thermoplastic obturation. (p < 0.05). 3. Tensile bond strengths of the group 3 and 4 treated with 2% NaOC1+17% EDTA were significantly higher than that of the group 1 and 2 treated with 2% NaOCl. (p < 0.05). 4. In analysis of failure patterns at the interface between sealer and gutta-percha, there were observed 49 (61%)cases of adhesive failure patterns and 31 (39%) cases of mixed failures patterns.

• Korean Journal of Food Science and Technology
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• v.33 no.1
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• pp.33-39
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• 2001

To develop a method for separation process using Sep-pak $C_18$, simultaneous and systematic analysis of 8 permitted and 11 non-permitted synthetic food colors in Korea, optimization of analysis conditions for reverse phase ion-pair high performance liquid chromatography was carried out. For the best result of Sep-pak $C_18$ separation the pH of color standard mixture solution was $5{\sim}6$ and 0.1% HCl-methanol solution were set as eluent. The colors eluated from Sep-pak $C_18$ cartridge were determined and confirmed by high performance liquid chromatography with a photodiode array detector at 420 nm for yellow colors type, at 520 nm for red colors type, at 600 nm for blue and green colors type and at 254 nm for mixed colors. Conditions for HPLC analysis were as follows: column, Symmetry $C_18$ (5 m, 3.9 mm $i.d.{\times}150\;mm$); mobile phase, 0.025 M ammonium acetate (containing 0.01 M tetrabutylammonium bromide) : acetonitrile : methanol (65 : 25 : 10) and 0.025 M ammonium acetate(containing 0.01 M tetrabutylammonium bromide) : acetonitrile : methanol (40 : 50 : 10); flow rate, 1 mL/min. It takes 35 minutes for simultaneaus analysis and 18 minutes for systematic analysis. The detection limits range of each colors were $0.01{\sim}0.05\;{\mu}g/g$.