• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.5
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• pp.25-34
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• 2009

The objective of this study is to evaluate the future potential climate and vegetation canopy change impact on a dam watershed hydrology. A $6,661.5\;km^2$ dam watershed, the part of Han-river basin which has the watershed outlet at Chungju dam was selected. The SWAT model was calibrated and verified using 9 year and another 7 year daily dam inflow data. The Nash-Sutcliffe model efficiency ranged from 0.43 to 0.91. The Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model3 (CGCM3) data based on Intergovernmental Panel on Climate Change (IPCC) SRES (Special Report Emission Scenarios) B1 scenario was adopted for future climate condition and the data were downscaled by artificial neural network method. The future vegetation canopy condition was predicted by using nonlinear regression between monthly LAI (Leaf Area Index) of each land cover from MODIS satellite image and monthly mean temperature was accomplished. The future watershed mean temperatures of 2100 increased by $2.0^{\circ}C$, and the precipitation increased by 20.4 % based on 2001 data. The vegetation canopy prediction results showed that the 2100 year LAI of deciduous, evergreen and mixed on April increased 57.1 %, 15.5 %, and 62.5% respectively. The 2100 evapotranspiration, dam inflow, soil moisture content and groundwater recharge increased 10.2 %, 38.1 %, 16.6 %, and 118.9 % respectively. The consideration of future vegetation canopy affected up to 3.0%, 1.3%, 4.2%, and 3.6% respectively for each component.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.109-121
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• 2007

In this study, an imputation model using circular probability distribution was developed in order to overcome problems of missing data from a traffic survey. The existing ad-hoc or heuristic, model-based and algorithm-based imputation techniques were reviewed through previous studies, and then their limitations for imputing missing traffic volume data were revealed. The statistical computing language 'R' was employed for model construction, and a mixture of von Mises probability distribution, which is classified as symmetric, and unimodal circular probability were finally fitted on the basis of traffic volume data at survey stations in urban and rural areas, respectively. The circular probability distribution model largely proved to outperform a dummy variable regression model in regards to various evaluation conditions. It turned out that circular probability distribution models depict circularity of hourly volumes well and are very cost-effective and robust to changes in missing mechanisms.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.14 no.6
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• pp.119-130
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• 2011

Land-use changes have made considerable impacts on humans and nature such as biodiversity and ecosystem services. It is recognized as important elements for land use planning and regional natural resources conservation to identify the major causes of land use changes and to predict a process of changes and effects. This study, by using a spatially explicit Dyna-CLUE model, analyzed correlations between driving factors, quantified location characteristics of different land use types using logistic regression analysis and examined future land use changes and its effects in Yangpyeong and Yeoju region. We expected land use changes based on the three scenarios with different future land demands and simulated future changes for spatial variations of land use for the 20 years. The outcomes shows that larger change was found in agricultural areas than forest areas, based on the change in built-up areas. The changes in forest areas, which were mainly occurred in edge area, were expected to affect a large impact on its ecotone. It was found to be the importance of the management of forest edge and the necessity of the environmentally sound and sustainable development in order to conserve natural resources of the region.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Korean journal of applied entomology
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• v.55 no.2
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• pp.139-148
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• 2016

A seasonal outlook for crop insect pests is most valuable when it provides accurate information for timely management decisions. In this study, we investigated probable tele-connections between climatic phenomena and pest infestations in Korea using a statistical method. A rice insect pest, brown planthopper (BPH), was selected because of its migration characteristics, which fits well with the concept of our statistical modelling - utilizing a long-term, multi-regional influence of selected climatic phenomena to predict a dominant biological event at certain time and place. Variables of the seasonal climate forecast from 10 climate models were used as a predictor, and annual infestation area for BPH as a predictand in the statistical analyses. The Moving Window Regression model showed high correlation between the national infestation trends of BPH in South Korea and selected tempo-spatial climatic variables along with its sequential migration path. Overall, the statistical models developed in this study showed a promising predictability for BPH infestation in Korea, although the dynamical relationships between the infestation and selected climatic phenomena need to be further elucidated.

• Horticultural Science & Technology
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• v.30 no.6
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• pp.650-656
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• 2012

The aims of the experiment, conducted over three years in the Central Europe field conditions, were (1) to investigate the effect of growing period (plantings in the middle and at the end of August: $1^{st}$ and $2^{nd}$ term, respectively) on yield and chemical composition of the non-heading Chinese cabbage (Brassica rapa var. chinensis) cultivars 'Taisai', 'Pak Choy White', and 'Green Fortune', and (2) to develop regression models to evaluate the changes in crop yields as a function of weather conditions. A highest marketable yield was obtained from 'Taisai' (65.71 and 77.20 $t{\cdot}ha^{-1}$), especially in the $2^{nd}$ term of production. Low yield, observed for 'Pak Choy White' was due to its premature bolting. Almost 39% ($1^{st}$ term) and 70% ($2^{nd}$ term) of plants of this cultivar formed inflorescence shoots before harvest. The highest dry matter level was observed in the leaf petioles of 'Taisai', while 'Green Fortune' was the most abundant of carotenoids and L-ascorbic acid. The content of soluble sugars was the lowest for 'Pak Choy White'. In a phase of harvest maturity, more of the analyzed constituents were gathered by plants from earlier plantings, and differences were as follows: 4.7% (dry matter), 26.3% (carotenoids) and 22.1% (L-ascorbic acid), in comparison to $2^{nd}$ term of production. Significant increase of soluble sugars level was observed for plants from later harvest. The regression model for marketable yield of Chinese cabbage cultivar 'Taisai' as a function of maximum air temperature can predict the yield with accuracy 68%. The models for yield or bolting of 'Pak Choy White', based on extreme air temperatures and sunshine duration, were more precise (98%). It should be pointed out that Taisai could be recommended for later growing period in Central Europe conditions with regard to maximum yield potential. 'Green Fortune' was notable for its uniform yielding. To obtained plants of higher nutritional value, earlier time of cultivation should be suggested. Described models can be successfully applied for an approximate simulation of Chinese cabbage yielding.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.1
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• pp.138-144
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• 2013

The automation technology for overlay welding is needed due to the occurrence of severe corrosion and abrasion on the surface of internal contact in different shape of fittings. In Korea, different shapes of fittings have been manufactured by using the imported equipment of overlay welding automation at some companies. Thus the research on the development of overlay welding automation system (in short, OWAS) for a large L-type tube is urgently needed. In this paper, the investigation is focused on the optimal design of a supporting base for the (currently developing) OWAS of large L-type tube. Specifically we assume that the base which supports the equipment during the process of overlay welding is loaded as self-weight in the direction of gravity through static analysis especially when it is rotated 180 degree on the OWAS. For optimal design of a supporting base for OWAS of large L-type tube, Solidworks(R) (for 3-dimensional modelling) and ANASYS Workbench(R) (for structural analysis) are incorporated so as to proceed an optimization routines based on Response Surface Method (RSM) and Design of Experiment (DOE). In more specific, DOE finds out major factors (or dimensions) of the supporting base by using MINITAB(R). Then the regression equations between design variables (the major factors of supporting base) and response variables (deformation, stress and safety factor for the supporting base), which will be resulted in by RSM, verify the major factors of DOE. In the next step, Central Composite Design (CCD) plans 20 simulations of ANASYS Workbench(R) and then figures out the optimal values of design variables which will be reflected on the manufacturing of supporting base. Finally welding experiment is conducted to figure out the influence of overlay welding quality in applying the optimized design values of supporting base to the actual OWAS.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Journal of Korea Water Resources Association
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• v.49 no.11
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• pp.951-959
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• 2016

This study was conducted to evaluate observed runoff data collected every 10 minutes at stream gauging stations in Jeju Island using a physically-based model, SWAT. The Hancheon watershed was selected as study area, and ephemeral stream algorithm suggested by previous research was incorporated into the model, which is able to simulate ephemeral runoff pattern of Jeju streams. Simulated runoff and runoff rates were compared to observations during 2008-2013, which showed 'very good' performance rating in Nash-Sutcliffe model efficiency (ME) and determination coefficient ($R^2$). Some observations had problems such that runoff rates were very high for some rainfall events with little amount of antecedent rainfall, and were very low or missing with much rainfall comparing to previous researches. Additionally, regression equation between precipitation and simulated runoff was generated with high degree of correlation. The equation can be utilized to simply predict reasonable runoff, or to investigate and complement the abnormal or missing data of observations on the assumption that modelling results were sufficiently reliable and satisfactory. As results, minimizing the error in calibrating the model by evaluation of observed data would be helpful to accurately model the rainfall-runoff characteristics and analyze the water balance components of watersheds in Jeju Island.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.