• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.4
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• pp.40-48
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• 2023

This study is part of a foundational research effort aimed at developing a suitability index for breeding grounds related to avian activities along the domestic South and West coasts, including islands. Focus Group Interviews (FGI) and Analytic Hierarchy Process (AHP) analyses were conducted. The results are as follows. First, as a result of determining the value of the suitability of coastal bird breeding sites, the 'Natural Value(0.763)' was higher than the 'Artificial Value(0.237)'. Other artificial values were identified as sub-ranked except for 'Protected Areas' to ensure continuous integrity of breeding spaces. Second, as a result of re-establishing the 25 evaluation items classified in the two-time FGI as higher concepts, nine natural values and five artificial values were finally selected as a total of 14. Third, the results of the mid-classification evaluation of the importance of the suitability of coastal bird breeding sites were identified in the order of 'Ecological Value(0.392)', 'Topographic Value(0.251)', 'Passive Interference(0.124)', 'Geological Value(0.120)', and 'Active Interference(0.113)'. Fourth, the results of the priority of evaluation items of coastal bird breeding sites were in the order of 'Vegetation Distribution (0.187)', 'Area of Mudflats(0.118)', 'Presence or Absence of Mudflats(0.092)', 'Appearance of Natural Enemies(0.087)', 'Protected Areas(0.08)', 'Island Area (0.069)', 'Over-Breeding devastation(0.064)', 'Soil Composition Ratio(0.056)', 'Distance from Land(0.054)', 'Ocean farm area (0.045)', 'Cultivated land area(0.041)', 'Cultivation behavior(0.038)', 'Angle of the Surface(0.036)', and 'Land Use(0.033)'. It is judged that the weighting result value of the evaluation items derived in this study can be used for priority evaluation focusing on the coastal bird breeding area space. However, it seems that the correlation with the unique habitat suitability of bird individuals needs to be supplemented, and spatial analysis research incorporating species-specific characteristics will be left as a future task.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Life Science
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• v.18 no.8
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• pp.1053-1058
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• 2008

Glutathione is a well known chemotherapeutic agent for liver disease and is a popular nutritional supplement in the United States. Previous our studies reported the suppressive effects of glutathione-enriched Saccharomyces cerevisiae FF-8 strain (FF-8GY) on carbon tetrachloride- and alcohol-induced hepatotoxicity. The primary objective of this study was to investigate the comparative effects of FF-8GY and commercially available glutathione-enriched yeast extract (GYE) against the oxidative stress in alcohol-induced fatty liver of rats. The lipid peroxidative index (thiobarbituric acid-reactive substances, TBARS) and antioxidant status (reduced glutathione level) were used to monitor those protective roles of FF-8GY or GYE treatment. When the rat was treated alcohol, the TBARS levels in the whole liver and the subfractions of microsomal and mitochondria were significantly increased but these were significantly decreased by FF-8GY treatment and tended to be lowered by GYE treatment. The concentration of hepatic glutathione is known to be closely associated with antioxidant system and this was slightly deplete in the alcohol-induced rats, but this was recovered by treating with FF-8GY. However, the glutathione concentration was more significantly decreased in the GYE supplementation in alcohol feeding rats. Alcohol treatment also negatively affected the serum total protein and albumin, but these were significantly increased near normal levels in FF-8GY coadministered rats. These results suggest that glutathione-enriched Saccharomyces cerevisiae FF-8 strain may have positively mediate the alcohol-induced oxidative stress, and this effect was more pronounced in FF-8GY compared to GYE.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Korean Society of Forest Science
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• v.70 no.1
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• pp.91-102
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• 1985

Growth and biomass production of natural stands of Quercus variabilis in relation to tree density were studied to obtain basic guide lines for future tending operation. Two natural stands of Quercus variabilis located at 900m (A stand: 6,600trees/ha, $15.84m^2/ha$, $\frac{19}{17-20}$) and 800m (B stand: 4,300trees/ha, $16.65m^2/ha$, $\frac{20}{17-21}$) elevation in Sancheong, Kyongnam Province were selected for the comparative study and following results were obtained through a sample plot method. After diameter of individual trees in the sample plots was measured, twelve average trees from each diameter class were cut felled to measure dry weight of $W_S$, $W_B$, $W_L$, $W_{Ba}$, and standing biomass and biomass production rates by a allometrior regressions related to $D^2H$. Vertical distribution of leaves along the stems indicated that photosynthesis was carried out 2.2m above the ground in Stand A and 1.2m in Stand B. Maximum photosynthesis was located 4.2m and 6.2m above the ground in Stand A and B, respectively. Leaf area index was 4.25ha/ha for Stand A, and 3.89ha/ha for Stand B. Above-ground standing biomass was 49.51 ton/ha for Stand A and 59.20 ton/ha and net annual production was 6.75 ton/ha/yr. for Stand A and 8.99 ton/ha/yr. for Stand B. The ratio of net annual production to standing biomass was 17.5% for Stand A and 16.7% for Stand B. Net assimilation rate was 2.75kg/kg/yr. for Stand A and 3.58kg/kg/yr. for Stand B. Stem wood production rate was 1.46kg/kg/yr. for Stand A and 2.09kg/kg/yr. for Stand B. Bark production rate was 0.60 kg/kg/yr. for Stand A and 0.34kg/kg/yr. for Stand B. Above data indicated that Stand B utilized growing spaces and sites more efficiently than Stand A. It is concluded chat productivity of natural stands of Quercus variabilis can be enhanced through optimization of basal areas and number of tree per hectare and that sound management of natural oak stands should be based on systematic sampling of the area for periodic productivity estimation.

• Journal of Preventive Medicine and Public Health
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• v.35 no.3
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• pp.205-213
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• 2002

Objective : Whether exposure to chronic noise induces an increase in blood pressure, or the development of hypertension, has not been established. A cohort study was performed to identify the effects of chronic noise exposure on blood pressure. Methods : 530 males working at a metal manufacturing factory in Busan, Korea were enrolled for the study. They were monitored for 9 consecutive years, from 1991 to 1999, with an annual health check-up. The subjects were divided into 4 groups, which were determines by noise level categories(NLC) according to noise intensity ; NLC-I: office workers, exposed to noise a level below 60dB(A) ; NLC-II: field technical supporters or supervisors, frequently exposed to workplace noise, wearing no hearing protection device; NLC-III: workers, exposed to workplace noise below 85dB(A), wearing ear plugs or muffs; NLC-IV: workers, exposed to workplace noise over 85 dB(A), wearing both ear plugs and muffs. Results : After controlling possible confoundens, such as baseline age, smoking, alcohol intake, exercise, family history of hypertension, systolic(SBP) of diastolic blood pressure(DBP) and changes in BMI (body mass index), the pooled mean for the systolic blood pressures, over the duration of the study period, were 3.8mmHg, 2.0mmHg and 1.7mmHg higher in NLC-IV, NLC-III NLC-II groups, respectively, than in the NLC-I group. There were no significant differences in the diastolic blood pressures between the groups. Conclusion : This study suggests that chronic noise exposure increases systolic blood pressure independently, among male workers.

• Journal of Nutrition and Health
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• v.50 no.2
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• pp.133-141
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• 2017

Purpose: The objective of this study was to examine the association of temperature-fluctuation with freshness quality in various foods. Methods: We investigated the effects of storage conditions on antioxidant activities of cherries and romaine lettuce during storage at $0.7{\pm}0.6^{\circ}C$, $1.2{\pm}1.4^{\circ}C$, and $1.6{\pm}2.8^{\circ}C$. Cherries and romaine lettuce were stored for a period of 9 days and 7 days, respectively. We also analyzed the effects of storage conditions on fresh quality of beef and salmon during storage at $-0.3{\pm}0.8^{\circ}C$, $-0.6{\pm}2.3^{\circ}C$, and $-1.5{\pm}4.4^{\circ}C$. Both of them were stored for a period of 14 days. Results: The amount of water loss was highest in beef, and the microbial count was also the highest at $-1.5{\pm}4.4^{\circ}C$. In the case of salmon, there was no difference in water loss according to storage, and TBA value was significantly increased at $-1.5{\pm}4.4^{\circ}C$. Moisture retention was the highest at $0.7{\pm}0.6^{\circ}C$ in both romaine lettuce and cherry samples. The contents of polyphenol and flavonoid were significantly higher in cherries, and content of polyphenols in romaine lettuce was significantly higher at $0.7{\pm}0.6^{\circ}C$ (p < 0.05). DPPH activity decreased in the order of $0.7{\pm}0.6^{\circ}C$ > $1.2{\pm}1.4^{\circ}C$ > $1.6{\pm}2.8^{\circ}C$ over 7 days. Conclusion: The results indicate that temperature-fluctuation may affect qualities of foods stored in a refrigerator.

• Journal of the East Asian Society of Dietary Life
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• v.19 no.4
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• pp.579-584
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• 2009

In this experiment, three different diets were produced to investigate the effects of Jeju native chicken eggs fed with citrus peel on the profiles of rats. The first diet did not contain any eggs (TS), the second diet contained 10% eggs and no citrus peels (T0), and the third diet contained 10% chicken egg with citrus peels (T1). These diets were provided to 11-week-old male rats for four weeks. The weight gain, feed intake and feed efficiency were not significantly different between the three treatment groups, TS, T0, and T1. The weight of the liver was significantly higher in T0 and T1 than TS (p<0.05), but the weights of the kidney and epididymal fat pad were not significantly different between the TS, T0, and T1 groups. The total lipid, phospholipid, triglycerides, total cholesterol and LDL cholesterol did not significantly difference among the TS, T0, and T1 groups. The HDL cholesterol and HDL cholesterol/ total cholesterol of T1 were the highest among the groups, and the atherogenic index of T1 was the lowest among the groups (p<0.05). The total protein of TS was significantly lower in T0 and T1, and the albumin of T1 was the highest among the groups (p<0.05). The albumin/globulin ratio of TS was significantly higher in T0 and T1, but the creatinine of T0 was significantly higher in T0 and T1 (p<0.05). The blood sugar and blood pigment were not significantly different between the TS, T0, and T1 groups. The $\gamma$-GTP of T1 was the lowest among the groups (p<0.05), but the ALT, AST and ALP did not show significant difference among the TS, T0, and T1 groups.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.600-619
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• 2006

According to recent technical advances on sensors and mobile devices, processing of data streams generated by the devices is becoming an important research issue. The data stream of real values obtained at continuous time points is called streaming time-series. Due to the unique features of streaming time-series that are different from those of traditional time-series, similarity matching problem on the streaming time-series should be solved in a new way. In this paper, we propose an efficient algorithm for streaming time- series matching problem that supports normalization transform. While the existing algorithms compare streaming time-series without any transform, the algorithm proposed in the paper compares them after they are normalization-transformed. The normalization transform is useful for finding time-series that have similar fluctuation trends even though they consist of distant element values. The major contributions of this paper are as follows. (1) By using a theorem presented in the context of subsequence matching that supports normalization transform[4], we propose a simple algorithm for solving the problem. (2) For improving search performance, we extend the simple algorithm to use $k\;({\geq}\;1)$ indexes. (3) For a given k, for achieving optimal search performance of the extended algorithm, we present an approximation method for choosing k window sizes to construct k indexes. (4) Based on the notion of continuity[8] on streaming time-series, we further extend our algorithm so that it can simultaneously obtain the search results for $m\;({\geq}\;1)$ time points from present $t_0$ to a time point $(t_0+m-1)$ in the near future by retrieving the index only once. (5) Through a series of experiments, we compare search performances of the algorithms proposed in this paper, and show their performance trends according to k and m values. To the best of our knowledge, since there has been no algorithm that solves the same problem presented in this paper, we compare search performances of our algorithms with the sequential scan algorithm. The experiment result showed that our algorithms outperformed the sequential scan algorithm by up to 13.2 times. The performances of our algorithms should be more improved, as k is increased.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.177-198
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• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.