• Land and Housing Review
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• v.4 no.1
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• pp.77-87
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• 2013

About 1,800 villages have released from Greenbelt since Greenbelt-reform-policy for readjustment of the area was promoted after 1997. Even though the government intended to attract planned development & improvement of these lifted villages through District Unit Plan and designating the lifted area as low-rise and low-density zoning considering the characteristics of the Greenbelt region, there are still many problems to be solved: a lack of funds, insufficient capability for self-improvement and unexecuted SOCs in long-term etc. It seems that these problems are caused by focusing on the lifting areas itself instead of researching deeply the condition and characteristics of the villages and searching proper direction/plans of improvement before lifting Greenbelt In addition, the existing plan of village improvement and management was not considering physical and spacial characteristics of the areas, social and economic situation of residents and relationship between the villages and surrounding cities, though these conditions are different among each villages, and the related regulations are applied uniformly across all the villages and those have been causing many civil appeals and environmental problems. In these respects, this study aims to consider the problems of the lifted villages using the existing researches on them and to make typology by characteristics-data of the villages and to establish improvement strategies of each types. In this study, the villages were classified into 5 types as a result of cluster analysis on 424 villages among all 1,800 through variables of locational potentiality : location, accessibility, size and form of village, condition of regulations etc. According to function of the villages, they were divided into 4 types: urban-type, rural-type, industrial-type and neighborhood-centered-type. This study also drew 4 improvement-strategy-types by combination of locational potentiality and village-function : type of improving life-environment, type of improving production-infra, type of inducing-planned-improvement and type of constructing center-of living-circle. Finally, this study suggested the directions of the each 4 types to desirable improvement and management which could be used to make and complement plans for village improvement.

• Korean Journal of Environment and Ecology
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• v.27 no.1
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• pp.113-126
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• 2013

This study was performed to introduce ecological education program as for reporting the current vegetation state within the remaining trees as ecological golf course and to provide basic data. The survey site was S Golf course, which is located in Woonseodong, Incheon city. Its total area was about $3,298,428m^2$, but the relict forest was about $225,143m^2$. Existing landuse, topological structure, the flora, actual vegetation, and plants community structure survey were performed within the relict forest. As result of comparing and analyzing the existing land use, the relict forest was distributed in the forested areas (89.2%) and around the area (10.8%) which had been bare land and SAMMOK earthen ramparts. There were two courses (Ocean and Hanul) with the relict forests. The ocean course was compared of a natural forest, such as Quercus spp. mixed forest, Quercus acutissima forest, Pinus thunbergii forest within the rock fields, and an artificial forest (Ailanthus altissima-Robinia pseudoacacia forest, Robinia pseudoacacia forest) and Quercus acutissima - Elaeagnus umbellata forest. On the Hanul course, Pinus rigida forest and Robinia pseudoacacia forest were the main vegetation, which were artificial forest. It was the contrast aspect of vegetation species in a natural forest, a restoration forest and an artificial forest, which were Q. spp. mixed forest 26~28 species in a natural forest within the vegetation type per investigation area, 3 Pinus thunbergii forest species, and 5~7 artificial forest species on the Hanul course. Based on these vegetation status, the Ocean course was designed into ecological theme spaces named 'Quercus spp.' indigenous forest, 'Pinus thunbergii' restoration forest and ecological story of 'SAMMOK earthen ramparts'. The Hannul course was designed into an artificial forest observation area of 'Robinia pseudoacacia' and 'Pinus rigida' and a fragrance forest area of 'Robinia pseudoacacia'. At the time of the discussion about the introduction of eco-friendly approval system of golf course, it would be estimated that this survey would work as a major material not only raising awareness of the golf course on the ecological environment but also providing programs that can contribute to the community.

• Journal of Nutrition and Health
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• v.54 no.2
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• pp.165-178
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• 2021

Purpose: This study examined the eating-out patterns of Korean infants and school-aged children and compared diet quality. Methods: Data were obtained from the 2016-2018 Korea National Health and Nutrition Examination Survey. The subjects were 306 children aged 3 to 11 years old that ate dinner at restaurants. Percentage energy intakes of 24 food groups were calculated, and cluster analysis was used to identify eating-out patterns. Diet quality was assessed by calculating percentage energy and nutrient intakes using one-third of the 2015 Dietary Reference Intakes for Korean (KDRIs), nutrient adequacy ratio (NAR), mean adequacy ratio (MAR), and index of nutritional quality (INQ). Results: Cluster analysis identified 2 eating-out patterns, that is, a 'rice-centered' (53%) and a 'mixed diet' (47%) pattern. For those with the mixed diet pattern, ratios of carbohydrates, protein, and fat to total calories were 48:20:31, whereas for the rice-centered pattern, ratios were 62:15:21 (p < 0.001). Intakes of energy and most nutrients in the mixed diet pattern were excessive, but the intakes of the most nutrients in the rice-centered pattern were much lower than their KDRIs. MARs were higher for the mixed diet pattern than the rice-centered pattern (0.74 vs. 0.66) (p < 0.001), and INQs for vitamin C (p = 0.007) and calcium (p = 0.018) were lower for the rice-centered pattern, whereas INQ for iron (p = 0.003) was lower for the mixed diet pattern. Conclusion: The quality of meals for infants and school-aged children depended on eating-out patterns, but the rice-centered and mixed diet patterns both failed to provide an appropriately balanced meal pattern. The results of this study suggest that healthy menus need to be developed for children in restaurants.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.417-426
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• 2023

Rapid climate change has resulted in glacial retreat and increased meltwater inputs in the Antarctic Peninsula, including King George Island where Marian Cove is located. Consequently, these phenomena are expected to induce changes in the water column light properties, which in turn will affect phytoplankton communities. To comprehend the effects of glacial retreat on the marine ecosystem in Marian Cove, we investigated on phytoplankton biomass (chlorophyll-a, chl-a) and various environment parameters in this area in December 2021 and January 2022. The average temperature at the euphotic depth in January 2022 (1.41 ± 0.13 ℃) was higher than that in December 2021 (0.87 ± 0.17 ℃). Contrastingly, the average salinity was lower in January 2022 (33.9 ± 0.10 psu) than in December 2021 (34.1 ± 0.12 psu). Major nutrients, including dissolved inorganic nitrogen, phosphate, and silicate, were sufficiently high, and thus, did not act as limiting factors for phytoplankton biomass. In December 2021 and January 2022, the mean chl-a concentrations were 1.03 ± 0.64 and 0.66 ± 0.15㎍ L^-1, respectively. The mean concentration of suspended particulate matter (SPM) was 24.9 ± 3.54 mgL^-1 during the study period, with elevated values observed in the vicinity of the inner glacier. However, relative lower chl-a concentrations were observed near the inner glacier, possibly due to high SPM load from the glacier, resulting in reduced light attenuation by SPM shading. Furthermore, the proportion of nanophytoplankton exceeded 70% in the inner cove, contributing to elevated mean fractions of nanophytoplankton in the glacier retreat marine ecosystem. Overall, our study indicated that freshwater and SPM inputs from glacial meltwater may possibly act as main factors controlling the dynamics of phytoplankton communities in glacier retreat areas. The findings may also serve as fundamental data for better understanding the carbon cycle in Marian Cove.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Korean Journal of Environment and Ecology
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• v.18 no.1
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• pp.75-91
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• 2004

Biodiversity and seasonal community composition of benthic macroinvertebrates were studied from Upo wetlands in Gyeongsangnam-do, Korea, comprising Upo (4 sites), Mokpo (2 sites), Sajipo (1 site), Jjokjibeol (1 site), Yeobeol (1 site), and Topyeongcheon (2 sites) areas from October 2002 to August 2003. As a result, it was known that Upo wetlands retained relatively well-preserved littoral zones which may provide good habitats for benthic macroinvertebrates; however, frequent disturbances of littoral zones caused by flood were the major factor affecting on the survival and distribution of benthic macroinvertebrates in the areas. During the study period, a total of 135 species of benthic macroinvertebrates in 10 genera, 59 families, 16 orders, 7 classes, and 3 phyla were collected those of which are the highest degree of diversity of the taxa ever known in Korean wetlands: aquatic insects 103 spp. (Diptera 27 spp., Odonata 24 spp., Coleoptera 19 spp., Hemiptera 16 spp., Ephemeroptera 9 spp., Trichoptera 7 spp., and Collembola 1 sp.), Crustacea 2 spp., Mollusca 19 spp. (Gastropoda 12 spp. and Bivalvia 7 spp.), and Annelids 11 spp. (Oligocaeta 1 sp. and Hirudinea 10 spp.). Sajipo (St.G) and Jjokjibeol (St.H) areas yielded relatively larger numbers of species, 54 spp. and 53 spp., respectively, while more than 40 species occurred at most other sites. Based on quantitative sampling (0.5m${\times}$2m), aquatic insects (88.0％), particularly chironomids in Diptera (61.0％), occupied major proportion of the total individuals of benthic macroinvertebrates, while Mollusca (5.3％), Annelida (3.5％), and Crustacea (3.2％) occupied minor proportions. In standing water areas, diverse groups of benthic macroinvertebrates such as chironomids, demselflies, aquatic bugs, aquatic beetles, crustaceans, and gastropods were dominant in terms of individual number; in the running water areas, on the other hand, chironomids and baetid mayflies were dominant. However, gastropods, i.e. viviparids, were the dominant group of benthic macroinvertebrates in most study areas in terms of biomass. Dominance indices were 0.22-0.51 (mean$\pm$sd 0.42$\pm$0.09) in autumn, 0.31-0.96 (0.02$\pm$0.23) in winter, and 0.30-0.89 (0.57$\pm$0.18) in summer; diversity indices were 3.50-4.26 (3.80$\pm$0.24) in autumn,1.55-4.50 (3.10$\pm$1.01) in winter, and 1.35-3.77 (2.55$\pm$0.09) in summer. Highly movable or true aquatic benthic macroinvertebyates such as aquatic bugs, aquatic beetles, and gastropods recovered earlier after flood. In the study sites of Upo wetlands, Upo and Sajipo areas showed relatively higher values of average diversity index which may indicate a good habitat condition for benthic macroinvertebrates.

• Asia Marketing Journal
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• v.14 no.2
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• pp.157-176
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• 2012

Brand switching data frequently used in market structure analysis is adequate to analyze non- durable goods, because it can capture competition between specific two brands. But brand switching data sometimes can not be used to analyze goods like automobiles having long term duration because one of main assumptions that consumer preference toward brand attributes is not changed against time can be violated. Therefore a new type of data which can precisely capture competition among durable goods is needed. Another problem of using brand switching data collected from actual purchase behavior is short of explanation why consumers consider different set of brands. Considering above problems, main purpose of this study is to analyze market structure for durable goods with consideration set. The author uses exploratory approach and latent class clustering to identify market structure based on heterogeneous consideration set among consumers. Then the relationship between some factors and consideration set formation is analyzed. Some benefits and two demographic variables - age and income - are selected as factors based on consumer behavior theory. The author analyzed USA automotive market with top 11 brands using exploratory approach and latent class clustering. 2,500 respondents are randomly selected from the total sample and used for analysis. Six models concerning market structure are established to test. Model 1 means non-structured market and model 6 means market structure composed of six sub-markets. It is exploratory approach because any hypothetical market structure is not defined. The result showed that model 1 is insufficient to fit data. It implies that USA automotive market is a structured market. Model 3 with three market structures is significant and identified as the optimal market structure in USA automotive market. Three sub markets are named as USA brands, Asian Brands, and European Brands. And it implies that country of origin effect may exist in USA automotive market. Comparison between modal classification by derived market structures and probabilistic classification by research model was conducted to test how model 3 can correctly classify respondents. The model classify 97% of respondents exactly. The result of this study is different from those of previous research. Previous research used confirmatory approach. Car type and price were chosen as criteria for market structuring and car type-price structure was revealed as the optimal structure for USA automotive market. But this research used exploratory approach without hypothetical market structures. It is not concluded yet which approach is superior. For confirmatory approach, hypothetical market structures should be established exhaustively, because the optimal market structure is selected among hypothetical structures. On the other hand, exploratory approach has a potential problem that validity for derived optimal market structure is somewhat difficult to verify. There also exist market boundary difference between this research and previous research. While previous research analyzed seven car brands, this research analyzed eleven car brands. Both researches seemed to represent entire car market, because cumulative market shares for analyzed brands exceeds 50%. But market boundary difference might affect the different results. Though both researches showed different results, it is obvious that country of origin effect among brands should be considered as important criteria to analyze USA automotive market structure. This research tried to explain heterogeneity of consideration sets among consumers using benefits and two demographic factors, sex and income. Benefit works as a key variable for consumer decision process, and also works as an important criterion in market segmentation. Three factors - trust/safety, image/fun to drive, and economy - are identified among nine benefit related measure. Then the relationship between market structures and independent variables is analyzed using multinomial regression. Independent variables are three benefit factors and two demographic factors. The result showed that all independent variables can be used to explain why there exist different market structures in USA automotive market. For example, a male consumer who perceives all benefits important and has lower income tends to consider domestic brands more than European brands. And the result also showed benefits, sex, and income have an effect to consideration set formation. Though it is generally perceived that a consumer who has higher income is likely to purchase a high priced car, it is notable that American consumers perceived benefits of domestic brands much positive regardless of income. Male consumers especially showed higher loyalty for domestic brands. Managerial implications of this research are as follow. Though implication may be confined to the USA automotive market, the effect of sex on automotive buying behavior should be analyzed. The automotive market is traditionally conceived as male consumers oriented market. But the proportion of female consumers has grown over the years in the automotive market. It is natural outcome that Volvo and Hyundai motors recently developed new cars which are targeted for women market. Secondly, the model used in this research can be applied easier than that of previous researches. Exploratory approach has many advantages except difficulty to apply for practice, because it tends to accompany with complicated model and to require various types of data. The data needed for the model in this research are a few items such as purchased brands, consideration set, some benefits, and some demographic factors and easy to collect from consumers.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of the Korean Society of Food Culture
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• v.32 no.5
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• pp.383-393
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• 2017

This study was conducted to analyze the dietary lifestyle of local Chinese consumers and to classify dietary characteristics according to their dietary lifestyle factors and dietary behaviors. This investigation was conducted for 1 month from 1 January 2017 targeting 300 adult males and females living in China using the online survey company surveymonkey. Four factors relating to dietary lifestyle were identified, gourmet factor, healthy factor, convenience factor and economic factor, and these were grouped into 4 clusters according to their dietary lifestyle factor scores. Group 1, the gourmet economy group, showed a high percentage of living alone and a high frequency of eating out, but a relatively low percentage of three regular meals per day. Their dietary lifestyle was sensitive to gourmet factors and economic factors, but less sensitive to health and convenience factors. Group 2, the wide interest group, contained a high percentage of individuals in their 30s, as well as more highly educated individuals and a higher income than other groups. Because their dietary lifestyle scores tended to be higher than those of other groups, they sought a variety of new foods and gourmet meals for enjoyment of dining and life, as well as well-being food materials and foods related to health. Group 3, the health economic group, constituted a family-type consumer group with lower income level than the other groups. Members of this group were seeking health food and natural food in their dietary lifestyle and tended to pursue a high economic profit ratio when purchasing food. Finally, group 4 showed a relatively higher percentage of women over 30 and individuals with a college level or higher education than the other groups. This group was more interested in health and taste than price and convenience, and showed the highest LOHAS orientation among middle aged Chinese women. Moreover, members of this group directly utilized their knowledge regarding nutrition in real life.