• Journal of Soil and Groundwater Environment
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• v.13 no.5
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• pp.57-73
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• 2008

Nitrate concentrations were measured up to 49 mg/L (as $NO_3$-N) and 22% of the samples exceeded drinking water standard in shallow and bedrock groundwater of the northern Nonsan area. Nitrate concentrations showed a significant difference among land use groups. To predict nitrate concentration in groundwater, multiple regression analysis was carried out using hydrogeologic parameters of soil media, topography and land use which were categorized as several groups, well depth and altitude, and field parameters of temperature, pH, DO and EC. Hydrogeologic parameters were quantified as area proportions of each category within circular buffers centering at wells. Regression was performed to all the combination of variables and the most relevant model was selected based on adjusted coefficient of determination (Adj. $R^2$). Regression using hydrogelogic parameters with varying buffer radii show highest Adj. $R^2$ at 50m and 300m for shallow and bedrock groundwater, respectively. Shallow groundwater has higher Adj. $R^2$ than bedrock groundwater indicating higher susceptibility to hydrogeologic properties of surface environment near the well. Land use and soil media was major explanatory variables for shallow and bedrock groundwater, respectively and residential area was a major variable in both shallow and bedrock groundwater. Regression involving hydrogeologic parameters and field parameters showed that EC, paddy and pH were major variables in shallow groundwater whereas DO, EC and natural area were in bedrock groundwater. Field parameters have much higher explanatory power over the hydrogeologic parameters suggesting field parameters which are routinely measured can provide important information on each well in assessment of nitrate contamination. The most relevant buffer radii can be applied to estimation of travel time of contaminants in surface environment to wells.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.2
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• pp.128-136
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• 2006

This study was conducted for developing the stability parameter in uncontrolled landfill by using a biomolecular investigation on the microbial community growing through leachate plume. Landfill J(which is in Cheonan) and landfill T(which is in Wonju) were chosen for this study among a total of 244 closed uncontrolled landfills. It addressed the genetic diversity of the microbial community in the leachate by 165 rDNA gene cloning using PCR and compared quantitative analysis of denitrifiers and methanotrophs with the conventional water quality parameters. From the BLAST search, genes of 47.6% in landfill J, and 32.5% in landfill T, respectively, showed more than 97% of the similarity where Proteobacteria phylum was most significantly observed. It showed that the numbers of denitrification genes, i.e. nirS gene and cnorB gene in the J site are 7 and 4 times higher than those in T site, which is well reflecting from a difference of site closure showing 7 and 13 years after being closed, respectively. In addition, the quantitative analysis on methane formation gene showed that J1 spot immediately bordering with the sources has the greatest number of methane formation bacteria, and it was decreased rapidly according to distribute toward the outer boundary of landfill. The comparative investigation between the number of genes, i.e. nirS gene, cnorB gene and MCR gene, md the conventional monitoring parameters, i.e. TOC, $NH_3-N,\;NO_3-N,\;NO_2-N,\;Cl^-$, alkalinity, addressed that more than 99% of the correlation was observed except for the $NO_3-N$. It was concluded that biomolecular investigation was well consistent with the conventional monitoring parameters to interpret their influences and stability made by leachate plume formed in downgradient around the uncontrolled sites.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.241-249
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• 2014

The continuous observations for atmospheric aerosol were carried out during 3 years (2009-2011) by using a multi-wavelength Raman lidar at the Gwangju Institute of Science and Technology (GIST), Korea ($35.11^{\circ}N$, $126.54^{\circ}E$). The particle depolarization ratios were retrieved from the observations in order to distinguish the Asian dust layer. The vertical information of Asian dust layers were used as input parameter for the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for analysis of its backward trajectories. The source regions and transport pathways of the Asian dust layer were identified. The most frequent source region of Asian dust in Korea was Gobi desert during observation period in this study. The statistical analysis on the particle depolarization ratio of Asian dust was conducted according to their transport route in order to retrieve the variation of optical properties of Asian dust during long-range transport. The transport routes were classified into the Asian dust which was transported to observation site directly from the source regions, and the Asian dust which was passed over pollution regions of China. The particle depolarization ratios of Asian dust which were transported via industrial regions of China was ranged 0.07-0.1, whereas, the particle depolarization ratio of Asian dust which was transported directly from the source regions to observation site were comparably higher and ranged 0.11-0.15. It is considered that the pure Asian dust particle from source regions were mixed with pollution particles, which is likely to spherical particle, during transportation so that the values of particle depolarization of Asian dust mixed with pollution was decreased.

• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.120-132
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• 2014

Purpose : To compare dynamic susceptibility contrast imaging, diffusion-weighted imaging, and susceptibility-weighted imaging (SWI) for the differentiation of tumor recurrence and delayed radiation therapy (RT)-related changes in patients treated with RT for primary brain tumors. Materials and Methods: We enrolled 24 patients treated with RT for various primary brain tumors, who showed newly appearing enhancing lesions more than one year after completion of RT on follow-up MRI. The enhancing-lesions were confirmed as recurrences (n=14) or RT-changes (n=10). We calculated the mean values of normalized cerebral blood volume (nCBV), apparent diffusion coefficient (ADC), and proportion of dark signal intensity on SWI (proSWI) for the enhancing-lesions. All the values between the two groups were compared using t-test. A multivariable logistic regression model was used to determine the best predictor of differential diagnosis. The cutoff value of the best predictor obtained from receiver-operating characteristic curve analysis was applied to calculate the sensitivity, specificity, and accuracy for the diagnosis. Results: The mean nCBV value was significantly higher in the recurrence group than in the RT-change group (P=.004), and the mean proSWI was significantly lower in the recurrence group (P<.001). However, no significant difference was observed in the mean ADC values between the two groups. A multivariable logistic regression analysis showed that proSWI was the only independent variable for the differentiation; the sensitivity, specificity, and accuracy were 78.6% (11 of 14), 100% (10 of 10), and 87.5% (21 of 24), respectively. Conclusion: The proSWI was the most promising parameter for the differentiation of newly developed enhancing-lesions more than one year after RT completion in brain tumor patients.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.46-53
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• 2010

Purpose: The static image of nuclear medicine study should be acquired without a motion, however, it is difficult to acquire static image without movement for the serious patients, advanced aged patients. These movements cause decreases in reliability for quantitative and qualitative analysis, therefore re-examination was inevitable in the some cases. Consequently, in order to improve the problem of motion artifacts, the authors substituted the dynamic acquisition technique for the static acquisition, using motion correction. Materials and Methods: A capillary tube and IEC body phantom were used. First, the static image was acquired for 60 seconds while the dynamic images were acquired with a protocol, 2 sec/frame${\times}$30 frames, under the same parameter and the frames were summed up into one image afterwards. Also, minimal motion and excessive motion were applied during the another dynamic acquisition and the coordinate correction was applied towards X and Y axis on the frames where the motion artifact occurred. But the severe blurred images were deleted. Finally, the resolution and counts were compared between the static image and the summed dynamic images which before and after applying motion correction, and the signal of frequency was analysed after frequency spatial domain was transformed into 2D FFT. Supplementary examination, the blind test was performed by the nuclear medicine department staff. Results: First, the resolution in the static image and summed dynamic image without motion were 8.32 mm, 8.37 mm on X-axis and 8.30 mm, 8.42 mm on Y-axis, respectively. The counts were 484 kcounts, 485 kcounts each, so there was nearly no difference. Secondly, the resolution in the image with minimal motion applying motion correction was 8.66 mm on X-axis, 8.85 mm on Y-axis and had 469 kcounts while the image without motion correction was 21.81 mm, 24.02 mm and 469 kcounts in order. So, this shows the image with minimal motion applying motion correction has similar resolution with the static image. Lastly, the resolution in the images with excessive motion applying motion correction were 9.09 mm on X-axis, 8.83 mm on Y-axis and had 469 kcounts while the image without motion correction was 47.35 mm, 40.46 mm and 255 kcounts in order. Although there was difference in counts because of deletion of blurred frames, we could get similar resolution. And when the image was transformed into frequency, the high frequency was decreased by the movement. However, the frequency was improved again after motion correction. In the blind test, there was no difference between the image applying motion correction and the static image without motion. Conclusion: There was no significant difference between the static image and the summed dynamic image. This technique can be applied to patients who may have difficulty remaining still during the imaging process, so that the quality of image can be improved as well as the reliance for analysis of quantity. Moreover, the re-examination rate will be considerably decreased. However, there is a limit of motion correction, more time will be required to successfully image the patients applying motion correction. Also, the decrease of total counts due to deletion of the severe blurred images should be calculated and the proper number of frames should be acquired.

• Journal of the Korean Society of Radiology
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• v.14 no.5
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• pp.669-676
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• 2020

The purpose of this study was to analyze the changes in the values of Hounsfield Unit (HU) according to the changes in monoenergy (keV) and dilution ratio of the contrast agent, using the spectral CT. Spectral CT was used as the testing device, while 20 cc syringe phantom was used to set a total of six dilution ratios of the contrast agent: 8:2, 7:3, 6:4, 5:5, 4:6, and 3:7. Here, the non-ionic iodine solution (350 mg/ml) was used as a contrast agent. The syringe axial image was reconstructed by adjusting the obtained data on nine MonoE levels; 40 keV, 45 keV, 50 keV, 55 keV, 60 keV, 65 keV, 70 keV, 75 keV, and 80 keV. The HU values were measured at the three points of the reconstructed syringe axial image. The measurements were taken 1,620 times in total. In the analysis of the HU values according to the changes in keV and dilution ratio of the contrast agent, the highest and lowest HU values were obtained from dilution ratio 8:2 and dilution ratio 3:7, respectively, across every MonoE in the comparison of HU according to dilution ratio per MonoE (p<0.05), while the highest and lowest HU values were obtained from 40 keV and 80 keV, respectively, across all dilution ratios in the comparison of HU according to MonoE per dilution ratio (p<0.05). For the correlation per each parameter, a negative correlation of -15.014 ± 0.298 was found for HU per keV (R²=0.519) and a negative correlation of -61.372 ± 3.608 was found for HU per dilution ratio (R²=0.152) (p<0.05). To conclude, an increase in keV or dilution ratio of the contrast agent was shown to decrease the HU, and the findings in this study are anticipated to serve as the basic data in the research of HU-related parameters in Spectral CT.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.3-32
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• 1995

The Highway Capacity Manual specifies procedures for evaluating intersection performance in terms of delay per vehicle. What is lacking in the current methodology is a comparable quantitative procedure for ass~ssing the safety-based level of service provided to motorists. The objective of the research described herein was to develop a computational procedure for evaluating the safety-based level of service of signalized intersections based on the relative hazard of alternative intersection designs and signal timing plans. Conflict opportunity models were developed for those crossing, diverging, and stopping maneuvers which are associated with left-turn and rear-end accidents. Safety¬based level-of-service criteria were then developed based on the distribution of conflict opportunities computed from the developed models. A case study evaluation of the level of service analysis methodology revealed that the developed safety-based criteria were not as sensitive to changes in prevailing traffic, roadway, and signal timing conditions as the traditional delay-based measure. However, the methodology did permit a quantitative assessment of the trade-off between delay reduction and safety improvement. The Highway Capacity Manual (HCM) specifies procedures for evaluating intersection performance in terms of a wide variety of prevailing conditions such as traffic composition, intersection geometry, traffic volumes, and signal timing (1). At the present time, however, performance is only measured in terms of delay per vehicle. This is a parameter which is widely accepted as a meaningful and useful indicator of the efficiency with which an intersection is serving traffic needs. What is lacking in the current methodology is a comparable quantitative procedure for assessing the safety-based level of service provided to motorists. For example, it is well¬known that the change from permissive to protected left-turn phasing can reduce left-turn accident frequency. However, the HCM only permits a quantitative assessment of the impact of this alternative phasing arrangement on vehicle delay. It is left to the engineer or planner to subjectively judge the level of safety benefits, and to evaluate the trade-off between the efficiency and safety consequences of the alternative phasing plans. Numerous examples of other geometric design and signal timing improvements could also be given. At present, the principal methods available to the practitioner for evaluating the relative safety at signalized intersections are: a) the application of engineering judgement, b) accident analyses, and c) traffic conflicts analysis. Reliance on engineering judgement has obvious limitations, especially when placed in the context of the elaborate HCM procedures for calculating delay. Accident analyses generally require some type of before-after comparison, either for the case study intersection or for a large set of similar intersections. In e.ither situation, there are problems associated with compensating for regression-to-the-mean phenomena (2), as well as obtaining an adequate sample size. Research has also pointed to potential bias caused by the way in which exposure to accidents is measured (3, 4). Because of the problems associated with traditional accident analyses, some have promoted the use of tqe traffic conflicts technique (5). However, this procedure also has shortcomings in that it.requires extensive field data collection and trained observers to identify the different types of conflicts occurring in the field. The objective of the research described herein was to develop a computational procedure for evaluating the safety-based level of service of signalized intersections that would be compatible and consistent with that presently found in the HCM for evaluating efficiency-based level of service as measured by delay per vehicle (6). The intent was not to develop a new set of accident prediction models, but to design a methodology to quantitatively predict the relative hazard of alternative intersection designs and signal timing plans.

• 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.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.421-428
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• 2009

Purpose: We have evaluated characteristics of adrenal masses incidentally observed in nonenhanced F-18 FDG PET/CT of the oncologic patients and the diagnostic ability of F-18 FDG PET/CT to differentiate malignant from benign adrenal masses. Materials and Methods: Between Mar 2005 and Aug 2008, 75 oncologic patients (46 men, 29 women; mean age, $60.8{\pm}10.2$ years; range, 35-87 years) with 89 adrenal masses incidentally found in PET/CT were enrolled in this study. For quantitative analysis, size (cm), Hounsfield unit (HU), maximum standardized uptake value (SUVmax), SUVratio of all 89 adrenal masses were measured. SUVmax of the adrenal mass divided by SUVliver, which is SUVmax of the segment 8, was defined as SUVratio. The final diagnosis of adrenal masses was based on pathologic confirmation, radiologic evaluation (HU<0 : benign), and clinical decision. Results: Size, HU, SUVmax, and SUVratio were all significantly different between benign and malignant adrenal masses.(P < 0.05) And, SUVratio was the most accurate parameter. A cut-off value of 1.0 for SUVratio provided 90.9% sensitivity and 75.6% specificity. In small adrenal masses (1.5 cm or less), only SUVratio had statistically significant difference between benign and malignant adrenal masses. Similarly a cut-off value of 1.0 for SUVratio provided 80.0% sensitivity and 86.4% specificity. Conclusion: F-18 FDG PET/CT can offer more accurate information with quantitative analysis in differentiating malignant from benign adrenal masses incidentally observed in oncologic patients, compared to nonenhanced CT.