• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.1
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• pp.42-54
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• 2016

It is highly anticipated that warming temperature resulting from global climate change will affect the phenological pattern of kiwifruit, which has been commercially grown in Korea since the early 1980s. Here, we present the potential impacts of climate change on the variations of flowering day of a gold kiwifruit cultivar, Haegeum, in the Jeonnam Province, Korea. By running six global climate models (GCM), the results from this study emphasize the uncertainty in climate change scenarios. To predict the flowering day of kiwifruit, we obtained three parameters of the 'Chill-day' model for the simulation of Haegeum: $6.3^{\circ}C$ for the base temperature (Tb), 102.5 for chill requirement (Rc), and 575 for heat requirement (Rh). Two separate validations of the resulting 'Chill-day' model were conducted. First, direct comparisons were made between the observed flowering days collected from 25 kiwifruit orchards for two years (2014-15) and the simulated flowering days from the 'Chill-day' model using weather data from four weather stations near the 25 orchards. The estimation error between the observed and simulated flowering days was 5.2 days. Second, the model was simulated using temperature data extracted, for the 25 orchards, from a high-resolution digital temperature map, resulting in the error of 3.4 days. Using the RCP 4.5 and 8.5 climate change scenarios from six GCMs for the period of 2021-40, the future flowering days were simulated with the 'Chill-day' model. The predicted flowering days of Haegeum in Jeonnam were advanced more than 10 days compared to the present ones from multi-model ensemble, while some individual models resulted in quite different magnitudes of impacts, indicating the multi-model ensemble accounts for uncertainty better than individual climate models. In addition, the current flowering period of Haegeum in Jeonnam Province was predicted to expand northward, reaching over Jeonbuk and Chungnam Provinces. This preliminary result will provide a basis for the local impact assessment of climate change as more phenology models are developed for other fruit trees.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.127-137
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• 2014

The main purpose of this study is to estimate tradial growth response and to predict the potential spatial distribution of major tree species(Pinus densiflora, Quercus mongolica, Quercus spp., Castanea crenata and Larix kaempferi) in South Korea, considering climate and topographic factors. To estimate radial growth response, $5^{th}$ National Forest Inventory data, Topographic Wetness Index (TWI) and climatic data such as temperature and precipitation were used. Also, to predict the potential spatial distribution of major tree species, RCP 8.5 Scenario was applied. By our analysis, it was found that the rising temperature would have negative impacts on radial growth of Pinus densiflora, Castanea crenata and Larix kaempferi, and positive impacts on that of Quercus mongolica, Quercus spp.. Incremental precipitation would have positive effects on radial growth of Pinus densiflora and Quercus mongolica. When radial growth response considered by RCP 8.5 scenario, it was found that the radial growth of Pinus densiflora, Castanea crenata and Larix kaempferi would be more vulnerable than that of Quercus mongolica and Quercus spp. to temperature. According to the climate change scenario, Quercus spp. including Quercus mongolica would be expected to have greater abundance than its present status in South Korea. The result of this study would be helpful for understanding the impact of climatic factors on tree growth and for predicting the distribution of major tree species by climate change in South Korea.

• Journal of Korea Spatial Information System Society
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• v.5 no.1 s.9
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• pp.49-63
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• 2003

Recently, two international standard organizations, ISO and OGC, have done the work of standardization for GIS. Current standardization work for providing interoperability among GIS DB focuses on the design of open interfaces. But, this work has not considered procedures and methods for designing GIS DB. Eventually, GIS DB has its own model. When we share the data by open interface among heterogeneous GIS DB, differences between models result in the loss of information. Our aim in this paper is to revise the design guidelines for geographic information databases in order to make consistent spatial data models, logical structures, and semantic structure of populated geographical databases. In details, we propose standard guidelines which convert ISO abstract schema into relation model, object-relation model, object-centered model, and geometry-centered model. Furthermore, we provide sample models for applying these guidelines in commercial GIS S/Ws. Building GIS DB based on design guidelines proposed in the paper has the following advantages: the interoperability among databases, the standardization of schema definitions, and the catalogue of GIS databases through.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1730-1738
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• 2011

Personal Rapid Transit (PRT) has emerged as a promising alternative transportation mode for transit-oriented sustainable communities by creating compact and walkable environments with competitive construction and operational costs. This study seeks to capture the changes in travel mode choice behavior in response to the introduction of PRT to travelers who have no previous experience of using it. A critical issue in modeling the PRT mode choice is how to capture travelers' perception and evaluation of the unexperienced travel mode. The data used come from questionnaire surveys, in which RP (Revealed Preference) and SP (Stated Preference) data were collected in relation to travel mode choices with and without PRT systems. The questionnaire was designed especially for mitigating the potential bias in favor of or against choosing PRT. In addition, an efficient approach was proposed to reduce the number of SP questions by avoiding the complex fractional factorial design which tends to make it difficult for respondents to keep their attention throughout the survey. The analysis results show that the proposed approach is able to realistically capture the effects of explanatory variables on the travel mode choice. Discrete choice models are developed to predict travelers' mode choices under different choice scenarios by varying PRT system specifications and operational characteristics. PRT patronages are projected for two different test sites using the developed PRT mode choice models.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.7-16
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• 2000

This paper is focused on verifying time-space repetition of the highway accident and finding the their causes and deterrents. We classify all months into several seasonal groups, develop the model for each seasonal group and analyze the results of these models for Joong-bu highway. The existence of seasonal effect is verified by the analysis or self-organizing map and the accident indices. Agglomerative hierarchical cluster analysis which is used to decide the seasonal groups in accordance with accident patterns, winter group, spring-fall group. and summer group. The accident features of winter group are that the accident rate is high but the severity rate is low. while those of summer group are that the accident rate is low but the severity rate is high. Also, the regression model which is developed to identify the accident Pattern or each seasonal group represents that the season-related factors, such as the amount of rainfall, the amount of snowfall, days of rainfall, days of snowfall etc. are strongly related to the accident pattern of evert seasonal group and among these factors the traffic volume, amount of rainfall. the amount of snowfall and days of freezing importantly affect the local accident Pattern. So, seasonal effect should be considered to the identification of high-risk road section. the development of descriptive and Predictive accident model, the resource allocation model of accident in order to make safety management plan efficient.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.921-932
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• 2013

Runoff data availability is a substantial factor for precise flood control such as flood frequency or flood forecasting. However, runoff depths and/or peak discharges for small watersheds are rarely measured which are necessary components for hydrological analysis. To compensate for this discrepancy, a lumped concept such as a Storage Function Method (SFM) was applied for the partitioned Choongju Dam Watershed in Korea. This area was divided into 22 small watersheds for measuring the capability of spatial extension of runoff data. The chosen total number of flood events for searching parameters of SFM was 21 from 1991 to 2009. The parameters for 22 small watersheds consist of physical property based (storage coefficient: k, storage exponent: p, lag time: $T_l$) and flood event based parameters (primary runoff ratio: $f_1$, saturated rainfall: $R_{sa}$). Saturated rainfall and base flow from event based parameters were explored with respect to inflow at Choongju Dam while other parameters for each small watershed were fixed. When inflow of Choongju Dam was optimized, Youngchoon and Panwoon stations obtained average of Nash-Sutcliffe Efficiency (NSE) were 0.67 and 0.52, respectively, which are in the satisfaction condition (NSE > 0.5) for model evaluation. This result is showing the possibility of spatial data extension using a lumped concept model.

• Journal of Korea Technology Innovation Society
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• v.13 no.2
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• pp.252-281
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• 2010

In this paper, we analyze the result of the Technology Level Evaluation of 'Biochip and biosensor (BB) Technology' consisted of 3 sub-categorized technologies; biochip sensing (BS), lab on a chip and high-efficient customized health care technology. As an analysis tool, authors used a delphi (a repeated survey) and dynamic methodology with technology growth model to overcome limits of previous evaluations. As a result, levels of BB were evaluated 51.5% (Korea) and 75.1% (US), and the technology gap between two countries was 6.1 yrs. In 2013, these levels were expected to change to 60.1% (Korea), 78.4% (US) and 4.3 yrs, respectively. In comparison with other biotechnology, the gap of BB was smaller and expected to catch up with US faster. In the case of sub-categorized technologies, they showed the smallest gap and would have faster catch-up speed than other sub-categorized technologies in the Biotechnology field. Based on the result of the survey, relative superiority of BB in Korea was originated from competent researchers and research fund, but weak basic science would be weak points. We think that BB's characteristic as an emerging technology and concentrated research activities on BB are additional strong points. This research proposes the supporting and supplemented points to promote the BB in Korea.

• Korean Journal of Food Science and Technology
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• v.46 no.4
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• pp.410-417
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• 2014

Berries and green tea are underutilized in the food industry despite their great potential as a functional food ingredients. The purpose of this study was to determine the extraction conditions under which total phenolic contents and antioxidant activities of berry and green tea extracts are maximized. Extracts produced using 0-80% ethanol and temperatures ranging from $5-65^{\circ}C$ were evaluated for total phenolic content (TP), as well as for DPPH and ABTS radical-scavenging activities by using response surface methodology. Both ethanol and temperature had significant effects (p<0.05). Ogaja extract produced at $67^{\circ}C$ by using 33% ethanol yielded maximum TP, ABTS, and DPPH values of 23.74 mg GAE/g, 19.77, and 25.04 mg VCE/g, respectively. Optimum conditions for mulberry and raspberry extraction were found to be $65^{\circ}C$ by using 69% and 40% ethanol, respectively. Mulberry and raspberry extracts had TP, DPPH, and ABTS values of 20.74 mg GAE/g, 23.55, and 35.44 mg VCE/g, and 26.08 mg GAE/g, 39.93, and 55.60 mg VCE/g, respectively. Green tea extraction at $57^{\circ}C$ by using 43% ethanol was found to be optimal, yielding TP, ABTS, and DPPH values of 101.15 mg GAE/g, 171.38, and 177.56 mg VCE/g, respectively.