• The Journal of Engineering Geology
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• v.30 no.4
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• pp.649-661
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• 2020

Cut-slope management system (CSMS) has been investigated all slopes on the road of the whole country to evaluate risk rating of each slope. Based on this evaluation, the decision-making for maintenance can be conducted, and this procedure will be helpful to establish a consistent and efficient policy of safe road. CSMS has updated the database of all slopes annually, and this database is constructed based on a basic and detailed investigation. In the database, there are two type of data: first one is an objective data such as slopes' location, height, width, length, and information about underground and bedrock, etc; second one is subjective data, which is decided by experts based on those objective data, e.g., degree of emergency and risk, maintenance solution, etc. The purpose of this study is identifying an data application plan to utilize those CSMS data. For this purpose, logistic regression, which is a basic machine-learning method to construct a prediction model, is performed to predict a judging-type variable (i.e., subjective data) based on objective data. The constructed logistic model shows the accurate prediction, and this model can be used to judge a priority of slopes for detailed investigation. Also, it is anticipated that the prediction model can filter unusual data by comparing with a prediction value.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.49-60
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• 2022

Underground urbanization appears to be a promising solution in response to the shortage of construction sites in the above-ground space. In this context, an accurate evaluation of a construction site ensures the long-term performance of geosystems. This study characterizes potential sites for complex plants built in underground space using geophysical methods (i.e., seismic refraction exploration and electrical resistivity survey) and in situ tests (i.e., standard penetration tests (SPTs) and downhole tests). SPTs are conducted in nine boreholes BH-1-BH-9 to estimate the groundwater level and vertical distribution of geological structures. The seismic refraction method enables us to obtain the elastic wave velocity and thickness of each soil layer for each cross-sectional area. An electrical resistivity survey conducted using the dipole array method provides the electrical resistivity profiles of the cross-sectional area. Data obtained using geophysical techniques are used to assess the classification of the soil layer and bedrock, particularly the fracture zone. This study suggests that geotechnical information using in situ tests and geophysical methods are useful references to design an underground complex plant construction.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4571-4584
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• 2022

The analysis of rapid flow transients in Reactor Coolant Pumps (RCP) is essential for a reactor safety study. An accurate and precise analysis of the RCP coastdown is necessary for the reactor design. The coastdown of RCP affects the coolant temperature and the colloidal crud in the primary coolant. A realistic and kinetic model has been used to investigate the behavior of activated colloidal crud in the primary coolant and steam generator that solves the pump speed analytically. The analytic solution of the non-dimensional flow rate has been determined by the energy ratio β. The kinetic energy of the coolant fluid and the kinetic energy stored in the rotating parts of a pump are two essential parameters in the form of β. Under normal operation, the pump's speed and moment of inertia are constant. However, in a coastdown situation, kinetic damping in the interval has been implemented. A dynamic model ACCP-SMART has been developed for System Integrated Modular and Advanced Reactor (SMART) to investigate the corrosion due to activated colloidal crud. The Fickian diffusion model has been implemented as the reference corrosion model for the constituent component of the primary loop of the SMART reactor. The activated colloidal crud activity in the primary coolant and steam generator of the SMART reactor has been studied for different equilibrium corrosion rates, linear increase in corrosion rate, and dynamic RCP coastdown situation energy ratio b. The coolant specific activity of SMART reactor equilibrium corrosion (4.0 mg s^-1) has been found 9.63×10^-3 µCi cm^-3, 3.53×10^-3 µC cm^-3, 2.39×10^-2 µC cm^-3, 8.10×10^-3 µC cm^-3, 6.77× 10^-3 µC cm^-3, 4.95×10^-4 µC cm^-3, 1.19×10^-3 µC cm^-3, and 7.87×10^-4 µC cm^-3 for ²⁴Na, ⁵⁴Mn, ⁵⁶Mn, ⁵⁹Fe, ⁵⁸Co, ⁶⁰Co, ⁹⁹Mo, and ⁵¹Cr which are 14.95%, 5.48%, 37.08%, 12.57%, 10.51%, 0.77%, 18.50%, and 0.12% respectively. For linear and exponential coastdown with a constant corrosion rate, the total coolant and steam generator activity approaches a higher saturation value than the normal values. The coolant and steam generator activity changes considerably with kinetic corrosion rate, equilibrium corrosion, growth of corrosion rate (ΔC/Δt), and RCP coastdown situations. The effect of the RCP coastdown on the specific activity of the steam generators is smeared by linearly rising corrosion rates, equilibrium corrosion, and rapid coasting down of the RCP. However, the time taken to reach the saturation activity is also influenced by the slope of corrosion rate, coastdown situation, equilibrium corrosion rate, and energy ratio β.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.429-439
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• 2009

When Catastrophic extreme flood occurs due to dam break, the response time for flood warning is much shorter than for natural floods. Numerical models can be powerful tools to predict behaviors in flood wave propagation and to provide the information about the flooded area, wave front arrival time and water depth and so on. But flood wave propagation due to dam break can be a process of difficult mathematical characterization since the flood wave includes discontinuous flow and dry bed propagation. Nevertheless, a lot of numerical models using finite volume method have been recently developed to simulate flood inundation due to dam break. As Finite volume methods are based on the integral form of the conservation equations, finite volume model can easily capture discontinuous flows and shock wave. In this study the numerical model using Riemann approximate solvers and finite volume method applied to the conservative form for two-dimensional shallow water equation was developed. The MUSCL scheme with surface gradient method for reconstruction of conservation variables in continuity and momentum equations is used in the predictor-corrector procedure and the scheme is second order accurate both in space and time. The developed finite volume model is applied to 2D partial dam break flows and dam break flows with triangular bump and validated by comparing numerical solution with laboratory measurements data and other researcher's data.

• The Journal of the Acoustical Society of Korea
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• v.26 no.7
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• pp.361-365
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• 2007

Aero-pulsation noise, generally caused by geometric asymmetry of a rotating device, is one of considerable sources of annoyance in passenger cars using the turbocharged diesel engine. Main source of this noise is the compressor wheel in the turbocharger system, and can be reduced by after-treatment devices such as silencers, but which may increase the manufacturing cost. More effective solution is to improve the geometric symmetry over all, or to control the quality of components by sorting out inferior ones. The latter is more simple and reasonable than the former in view of manufacturing. Thus, an appropriate discrimination method should be needed to evaluate aero-pulsation noise level at the production line. In this paper, we introduce the accurate method which can measure the noise level of aero-pulsation and also present its evaluation criteria. Besides verifying the reliability of a measurement system - a rig test system-, we analyze the correlation between the results from rig tests and those from vehicle tests. The gage R&R method is carried out to check the repeatability of measurements over 25 samples. From the result, we propose the standard specification which can discriminate inferior products from superior ones on the basis of aero-pulsation noise level.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.152-155
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• 2009

Purpose: The most widely accepted tool for follow up management of thyroid cancer patients is serum thyroglobulin (Tg) measurement, but its value is limited by the interference of anti-thyroglobulin antibodies (anti-Tg Ab). Recently thyroglobulin measurement in the wash out of fine-needle aspiration biopsy specimens (Tg-FNAB) is frequently used for differential diagnosis of recurrences/metastases. The aim of this study was the investigation of the diagnostic utility of Tg-FNAB compared with serum Tg. Materials and Methods: We enrolled 41 consecutive patients with thyroid cancer who were evaluated for Tg-FNAB between January 2007 and February 2008 retrospectively. We ruled out 6 patients who anti-Tg Ab positive (${\geq}$100 U/mL) in the RIA (BRAHMS anti-Tgn RIA 100Det; BRAHMS Aktiengesell schaft, Berlin, Germany). Serum Tg and Tg-FNAB were measured by immunoradiometric assay (BRAHMS Tg pluS RIA 100 Det; BRAHMS Aktienge sellschaft, Berlin, Germany). We evaluated for Tg-FNAB compared with serum Tg and corresponding cytological smear. To compare the values of the two the t-test was used. Results: Tg-FNAB values were significantly higher (median 1,060 ng/mL, range 0.2~434,000 ng/mL) than serum Tg (median 2.5 ng/mL, range 0.9~131 ng/mL) (p=0.0394). The rate of correspondence with Tg-FNAB between cytological result was 87.9% and 65.9% in the case of serum Tg. Tg-FNAB was positive in 28 (24 with positive and 4 with suspicious cytology). Of the remaining 13 patients with negative Tg-FNAB, 1 had suspicious and 12 had unsuspicious cytology. serum Tg was positive in 26 (17 with positive and 3 with suspicious and 6 with unsuspicious cytology), Of the remaining 15 patients with negative serum Tg, 8 was positive in cytological result and 1 had suspicious and 6 had unsuspicious cytology. Conclusions: Tg-FNAB measurement is more accurate with high sensitivity (87.9%) than serum Tg (65.9%). The Tg-FNAB was a useful predictor for detecting recurrences/metastases with serum Tg.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Geophysics and Geophysical Exploration
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• v.1 no.2
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• pp.127-135
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• 1998

The Born approximation is widely used for solving the complex scattering problems in electromagnetics. Approximating total internal electric field by the background field is reasonable for small material contrasts as long as scatterer is not too large and the frequency is not too high. However in many geophysical applications, moderate and high conductivity contrasts cause both real and imaginary part of internal electric field to differ greatly from background. In the extended Born approximation, which can improve the accuracy of Born approximation dramatically, the total electric field in the integral over the scattering volume is approximated by the background electric field projected to a depolarization tensor. The finite difference and elements methods are usually used in EM scattering problems with a 2D model and a 3D source, due to their capability for simulating complex subsurface conductivity distributions. The price paid for a 3D source is that many wavenumber domain solutions and their inverse Fourier transform must be computed. In these differential equation methods, all the area including homogeneous region should be discretized, which increases the number of nodes and matrix size. Therefore, the differential equation methods need a lot of computing time and large memory. In this study, EM modeling program for a 2D model and a 3D source is developed, which is based on the extended Born approximation. The solution is very fast and stable. Using the program, crosshole EM responses with a vertical magnetic dipole source are obtained and the results are compared with those of 3D integral equation solutions. The agreement between the integral equation solution and extended Born approximation is remarkable within the entire frequency range, but degrades with the increase of conductivity contrast between anomalous body and background medium. The extended Born approximation is accurate in the case conductivity contrast is lower than 1:10. Therefore, the location and conductivity of the anomalous body can be estimated effectively by the extended Born approximation although the quantitative estimate of conductivity is difficult for the case conductivity contrast is too high.

• Journal of Sasang Constitutional Medicine
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• v.27 no.2
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• pp.270-287
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• 2015

Objectives To evaluate the neuroprotective effects of modified Yuldahanso-tang (MYH) in a Parkinson's disease mouse model. Methods 1) Four groups (each of 8 rats per group) were used in this study. 2) The neuroprotective effect of MYH was examined in a Parkinson's disease mouse model. C57BL/6 mice treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, 30 mg/kg/day), intraperitoneal (i.p.) for 5 days. 3) The brains of 2 mice per group were removed and frozen at $-20^{\circ}C$, and the striatum-substantia nigra part was seperated. The protein volume was measured by Bradford method following Bio-Rad protein analyzing kit. Using mouse/Rat Dopamine ELISA Assay Kit. 4) The brains of 2 mice per group were separated and removed. TH-immunohistochemical was examined in the MPTP-induced Parkinson's disease mice to evaluate the neuroprotective effects of MYH on ST and SNpc. 5) Two mice out of each group were anesthetized and skulls were opened from occipital to frontal direction to take out the brains. The brains added TTC solution for 20 minutes for staining. 6) The water tank used for morris water maze test was filled with $28^{\circ}C$ water, and a round platform of 10cm in diameter was installed for mice to step on. The study was carried out once a day within 30 seconds, keep exercising to step on the platform in the pool. 7) The brains of two mice out of each group were fixed in 10% formaldehyde solution and paraphillin substance was infiltrated. They were fragmented by microtome, and observed under an optical microscope after Hematoxylin & Eosin staining. 8) A round acrylic cylinder with its upper side open was filled with clean water and depressive mouse models were forced to swim for 15 minutes. After 24 hours the animals were put in the same equipment for 5 minutes and were forced to swim. 9) The convenient, simple, and accurate high-performance liquid chromatography (HPLC) method was established for simultaneous determination of Neurotransmitters in MPTP-MYH group. Results 1) MYH possess Dopamine cell protective effect on MPTP-induced injury in striatum and substantia nigra pars compacta. 2) MYH inhibits the loss of tyrosine hydroxylase-immunoreacitive (TH-IR) cells in the striatum and substantia nigra pars compacta on MPTP-induced injury in C57BL/6 mice. 3) MYH possesses improvement effect on MPTP-induced memory deterioration in C57BL/6 mice through the reduction of prolongated Sort of lost time by MPTP injection using the Morris water maze test. 4) MYH possesses hippocampal neuron protective effect on MPTP-induced injury in C57BL/6 mice. 5) MYH possesses improvement effect on MPTP-induced motor behaviour deficits and depression in C57BL/6 mice through the reduction of prolongated losing motion by MPTP injection using the Forced swimming test. 6) MYH increases serotonin product amount on MPTP-induced injury in C57BL/6 mice. Conclusions This experiment suggests that the neuroprotective effect of MYH is mediated by the increase in Dopamin, TH-ir cell, Hippocampus and Serotonin. Furthermore, MYH essential oil may serve as a potential preventive or therapeutic agent regarding Parkinson's disease.