• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.6
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• pp.43-54
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• 2009

This study attempted to develop a model for selecting sites for ecologically effective, multi-functional wetlands during the environmental and ecological planning stage, prior to land use Planning. This model was developed with an emphasis upon the creation of a water circulation system for a newly-created city, dispersing and retaining the run-off that is increased due to urbanization and securing spaces to create wetlands that can promote urban biodiversity. A series of Precesses for selecting sites for wetland restoration and creation - watershed analysis, selection of evaluation items, calculation of weights, reparation of thematic maps and synthesis - were incorporated into the model. Its potentials and limitations were examined by applying it to the recently-planned WiRae New Community Development Area, which is located in the Seoul metropolitan region. At the watershed analysis stage, the site was divided into 13 sub-catchment areas. Inflow to watersheds including the area was $3,020,765m^3$ Run-off before and after development is estimated as $1,901,969m^3$ and $1,970,735{\sim}2,039,502m^3$, respectively. The total storage capacity required in the development area amounts to $68,766{\sim}137,533m^3$. When thematic maps were overlapped during the selection stage for wetland sites, 13 sub-catchment areas were prioritized for wetland restoration and creation. The locations and areas for retaining run-off showed that various types of wetlands, including retaining wetlands (area wetlands), riverine wetlands (linear wetlands) and pond wetlands (point wetlands), can be created and that they can be systematically connected. By providing a basic framework for the water circulation system plan of an entire city, it may be used effectively in the space planning stage, such as planning an urban eco-network through integration with greet areas. In order to estimate reasonable run-off and create an adequate water circulation system however, a feedback process following land use planning is required. This study strived to promote urban changes in a positive direction while minimizing urban changes in negative forms.

• Journal of the Korean Society for Marine Environment & Energy
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• v.9 no.3
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• pp.160-167
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• 2006

Harpacticoid copepod Tigriopus japonicus is widely distributed in coastal waters of Korea and plays important role in marine trophic structure as a first consumer. In this study, a series of experiment were conducted to test the potential of the species as a standard test species for marine bioassay. Tolerance on salinity and pH, sensitivity on the reference materials(copper sulfate and cadmium chloride) and response on the ocean dumping materials(waste sludge) we re tested to identify if the species satisfy the basic criteria as standard species for marine bioassay. The nauplius of the species($100{\sim}200{\mu}m$) showed wide tolerance on salinity with >90.0% survival rates exposed to $5.0{\sim}35.0psu$ for 48 h. Wide adaptability on pH's were also observed from 6.3 to 8.2 with >90.0% survival rates during the test. $LC_{50}$ values for copper sulfate and cadmium chloride were $3.6{\pm}0.7ppm,\;1.7{\pm}0.8ppm$, respectively. The variations in mortality between replicates were less than 10.0%. Comparison of $LC_{50}$ values indicated that T. japonicus nauplius was lower sensitive to copper sulfate than the most marine crustaceans included copepods, however, the sensitivity of test animal to cadmium chloride higher than the adults of copepod T. japonicus, Paracalanus parvus, and marine rotifer Brachinonus plicatilis. There were significant concentration-response relationship in the mortality of T. japonicus nauplius using the elutriates of three ocean dumping materials(industrial waste sludge). 48 h $LC_{50}$ values we re $31.1{\pm}1.1%$ for the elutriate of sludge from leather processing company and $54.4{\pm}15.1%$ for that of dye production company. Based on the above experimental results, bioassay using benthic harpacticoid T. japonicus nauplius must be a good estimation tool for marine ecotoxicological assessment of waste or chemicals. Wide tolerance on the salinity and pH, and significant linear relationship between concentration and response(mortality) supported the high potential of the species as a standard test species.

• The Journal of the Petrological Society of Korea
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• v.12 no.1
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• pp.16-31
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• 2003

The granites in the southern Gimcheon area can be divided into two parts, marginal hornblende biotite granodiorite (Mgd) and central biotite granodiorite to granite (Cgd). Mgd and Cgd are gray in color and display gradational contact relations and are mainly composed of coarse-grained and medium-grained rocks, respectively. Mgd has more frequent and larger mafic enclaves than Cgd, and the two granites partly show parallel foliation at thire contact with gneisses. From representative samples of the granites, K-Ar biotite ages of 197∼207 Ma were obtained. Considering the blocking temperature of biotite, it is suggested that the emplacement age of the granitic magma was probably late Triassic. The anorthite contents of plagioclases in Mgd display less variation than those of Cgd, indicating that Mgd crystallized within a narrow range of temperatures. In the Al$\_$total/-Mg diagram, the biotites from the granites plot within the subalkaline field, and the smooth slope indicates differentiation from a single magma. All amphiboles from the granites belong to magnesio-hornblende. The linear trends of major oxides, AFM and Ba-Sr-Rb indicate that Mgd and Cgd were fractionally differentiated from a single granitic magma body crystallizing from the margin inwards. The relations of modal (Qz+Af) vs. Op, K$_2$O vs. Na$_2$O, Fe$_2$ $O_3$ vs. FeO, Fe$\^$+3/(Fe$\^$+3/+Fe$\^$+2/) and K/Rb vs. Rb/Sr show that they belong to I-type and magnetite-series granitic rocks developed by the progressive melting products of fixed sources. REE data, normalized to chondrite value, have trends of enriched LREE and depleted HREE together with weakly negative Eu anomalies.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.4
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• pp.251-261
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• 2007

The wind-wave interaction around the Kyung-gi Bay, Korea, was studied using the observed data from ocean buoy at DeuckJeuck-Do from Jan. to Dec., 2005, and from waverider data at KeuckYeulBee-Do on Mar. 19-26 and May 23-28, 2005. Wind-driven surface waves and wave-driven wind speed decrease were estimated from the ocean buoy data, and the characteristics of wave spectrum response were also investigated from the waverider data for the wave developing and calm stages of sea surface, including the time series of spectrum pattern change, frequency trend of the maximum energy level and spectrum slope for the equilibrium state range. The wind speed difference between before and after considering the wave effect was about $2ms^{-1}$ (wind stress ${\sim}0.1Nm^{-2}$) for the wind speed range $5-10ms^{-1}$ and about $3ms^{-1}$ (wind stress ${\sim}0.4Nm^{-2}$) for the wind speed range $10-15ms^{-1}$. Correlation coefficient between wind and wave height was increased from 0.71 to 0.75 after the wave effect considered on the observed wind speed. When surface waves were generated by wind, the initial waves were short waves about 4-5 sec in period and become in gradual longer period waves about 9-10 sec. For the developed wave, the frequency of maximum energy was showed a constant value taking 6-7 hours to reach at the state. The spectrum slope for the equilibrium state range varied with an amplitude in the initial stage of wave developing, however it finally became a constant value 4.11. Linear correlation between the frictional velocity and wave spectrum for each frequency showed a trend of higher correlation coefficient at the frequency of the maximum energy level. In average, the correlation coefficients were 0.80 and 0.82 for the frequencies 0.30 Hz and 0.35 Hz, respectively.

• Radiation Oncology Journal
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• v.9 no.2
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• pp.185-195
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• 1991

Seventy four patients with pituitary adenoma received radiation therapy (RT) on the pituitary area using 6 MV linear accelerator during the past 7 years at the Division of Radiation Therapy, Kangnam St. Mary's Hospital, Catholic University Medical College. Thirty nine were men and 35 were women. The age ranged from 7 to 65 years with the mean being 37 years. Sixty five ($88\%$) patients were treated postoperatively and 9 ($12\%$) primary RT, To evaluate the effects of RT, we analyzed the series of endocrinologic studies with prolactin (PRL), growth hormone (GH), adrenocorticotrophic hormone (ACTH), leuteinizing hormone (LH), follicular stimulating hormone (FSH) and thyroid stimulating hormone (TSH) etc after RT. All but one with Nelson's syndrome showed abnormal neuroradiologic changes in the sella turcica with invasive tumor mass around supra- and/or parasella area. The patients were classified as 23 ($29\%$) prolactinomas and 20 ($26\%$) growth hormone (GH) secreting tumors, and 6 ($8\%$ ACTH secreting ones consisting of 4 Cushing's disease and 2 Nelson's syndrome. Twentynine ($37\%$) had nonfunctioning tumor and four ($5\%$) of those secreting pituitary tumors were mixed PRL-GH secreting tumors. The hormonal level in 15 ($65\%$) of 23 PRL and 3 ($15\%$) of 20 GH secreting tumors returned to normal by 2 to 3 years after RT, but five PRL and five GH secreting tumors showed high hormonal level requiring bromocriptine medication. Endocrinologic insufficiency developed by 3 years after RT in 5 of 7 panhypopituitarisms, 4 of seven hypothyroidisms and one of two hypogonadisms, respectively. Fifteen ($20\%$) patients were lost to follow up after RT.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.947-958
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• 2021

This study was conducted to provide basic data for crop monitoring by comparing and analyzing changes in reflectance and vegetation index by sensor of multi-spectral sensors mounted on unmanned aerial vehicles. For four types of unmanned aerial vehicle-mounted multispectral sensors, such as RedEdge-MX, S110 NIR, Sequioa, and P4M, on September 14 and September 15, 2020, aerial images were taken, once in the morning and in the afternoon, a total of 4 times, and reflectance and vegetation index were calculated and compared. In the case of reflectance, the time-series coefficient of variation of all sensors showed an average value of about 10% or more, indicating that there is a limit to its use. The coefficient of variation of the vegetation index by sensor for the crop test group showed an average value of 1.2 to 3.6% in the crop experimental sites with high vitality due to thick vegetation, showing variability within 5%. However, this was a higher value than the coefficient of variation on a clear day, and it is estimated that the weather conditions such as clouds were different in the morning and afternoon during the experiment period. It is thought that it is necessary to establish and implement a UAV flight plan. As a result of comparing the NDVI between the multi-spectral sensors of the unmanned aerial vehicle, in this experiment, it is thought that the RedEdeg-MX sensor can be used together without special correction of the NDVI value even if several sensors of the same type are used in a stable light environment. RedEdge-MX, P4M, and Sequioa sensors showed a linear relationship with each other, but supplementary experiments are needed to evaluate joint utilization through off-set correction between vegetation indices.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.40-49
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• 2019

Coarse woody debris (CWD), which is a component of the forest ecosystem, plays a major role in forest energy flow and nutrient cycling. In particular, CWD isolates carbon for a long time and is important in terms of slowing the rate of carbon released from the forest to the atmosphere. Therefore, this study measured the physiochemical characteristics and respiration rate ($R_{CWD}$) of CWD for Larix kaempferi and Pinus rigida in temperate forests in central Korea. In summer 2018, CWD samples from decay class (DC) I to IV were collected in the 14 forest stands. $R_{CWD}$ and physiochemical characteristics were measured using a closed chamber with a portable carbon dioxide sensor in the laboratory. In both species, as CWD decomposition progressed, the density ($D_{CWD}$) of the CWD decreased while the water content ($WC_{CWD}$) increased. Furthermore, the carbon concentrations did not significantly differ by DC, whereas the nitrogen concentration significantly increased and the C/N ratio decreased. The respiration rate of L. kaempferi CWD increased significantly up to DC IV, but for P. rigida it increased to DC II and then unchanged for DC II-IV. Accordingly, except for carbon concentration, all the measured characteristics showed a significant correlation with $R_{CWD}$. Multiple linear regression showed that $WC_{CWD}$ was the most influential factor on $R_{CWD}$. $WC_{CWD}$ affects $R_{CWD}$ by increasing microbial activity and is closely related to complex environmental factors such as temperature and light conditions. Therefore, it is necessary to study their correlation and estimate the time-series pattern of CWD moisture.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.