• Journal of the Korean Society for Railway
• /
• v.20 no.1
• /
• pp.64-75
• /
• 2017

The role of a track subgrade is to provide bearing capacity and distribute load transferred to lower foundation soils. Track subgrade soils are usually compacted by heavy mechanical machines in the field, such that sometimes they are attributed to progressive residual settlement during the service after construction completion of the railway track. The progressive residual settlement generated in the upper part of a track subgrade is mostly non-recoverable plastic deformation, which causes unstable conditions such as track irregularity. Nonetheless, up to now no design code for allowable residual settlement of subgrade in a railway trackbed has been proposed based on mechanical testing, such as repetitive triaxial testing. At this time, to check the DOC or stiffness of the soil, field test criteria for compacted track subgrade are composed of data from RPBT and field compaction testing. However, the field test criteria do not provide critical design values obtained from mechanical test results that can offer correct information about allowable permanent deformation. In this study, a test procedure is proposed for permanent deformation of compacted subgrade soil that is used usually in railway trackbed in the laboratory using repetitive triaxial testing. To develop the test procedure, an FEA was performed to obtain the shear stress ratio (${\tau}/{\tau}_f$) and the confining stress (${\sigma}_3$) on the top of the subgrade. Comprehensive repetitive triaxial tests were performed using the proposed test procedure on several field subgrade soils obtained in construction sites of railway trackbeds. A permanent deformation model was proposed using the test results for the railway track.

• Journal of Radiation Protection and Research
• /
• v.23 no.3
• /
• pp.175-184
• /
• 1998

In order to calculate dose rates from steam generators to be replaced from Kori unit 1 in 1998, radionuclide inventories inside steam generator were evaluated from smear test results and measured dose rates from S/G tubes withdrawn for the metallographical examination of damaged tubes. Based on the inventories, contact dose rates and dose rates at 1 m from the surface of a steam generator were calculated using the QAD-CG computer code. Contact dose rates ranged from 11.5 mR/hr at the bottom of channel head to 37.7 mR/hr at the middle of shell barrel, and showed no significant difference with dose rates at 1 m from the surface of steam generator. Shielding effects of lead and carbon steel were compared to provide basic shielding data. Lead shield showed excellent shielding effects. Dose rate at 1 m from the middle of S/G shell barrel decreased from 38.6 mR/hr to 15.5 mR/hr with the lead shield of 2 mm thickness. However, carbon steel showed a poor shielding effect even with the thickness of 2.0 cm. This can be explained with the great differences in the attenuation effect and buildup factor between lead and carbon steel for low energy photons.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.57 no.6
• /
• pp.21-33
• /
• 2015

Recently, the Korean government announced a new development plan for a large-scale greenhouse complex in reclaimed lands. Wind environments of reclaimed land are entirely different from those of inland. Many standard books for ventilation design didn't include qualitative standard for natural ventilation. In this study, natural ventilation rates were analyzed to suggest standard for ventilation design of venlo type greenhouse built on reclaimed land. CFD (Computational Fluid Dynamics) simulation models were designed according to the number of spans, wind conditions and vent openings. The wind profile at a reclaimed land was designed using ESDU (Engineering Sciences Data Unit) code. Using the designed CFD simulation model, ventilation rates were computed using mass flow rate and tracer gas decay method. Additionally computed natural ventilation rates were evaluated by comparing with ventilation requirements. As a result of this study, ventilation rates were decreased with increasing of the number of spans. Ventilation rates were linearly increased with increasing of wind speed. When the wind speed was $1.0\;m{\cdot}s^{-1}$, only side vent was open and wind direction was $45^{\circ}$, homogeneity of ventilation rate at 0~1 m height is the worst. Finally, chart for computing natural ventilation rate was suggested. The chart was expected to be used for establishing standard of ventilation design.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.671-679
• /
• 2008

The effect of Pre-cooled Turbojet Engine installation and nozzle exhaust jet on Hypersonic Turbojet EXperimental aircraft(HYTEX aircraft) were investigated by three-dimensional numerical analyses to obtain aerodynamic characteristics of the aircraft during its in-flight condition. First, simulations of wind tunnel experiment using small scale model of the aircraft with and without the rectangular duct reproducing engine was performed at M=5.1 condition in order to validate the calculation code. Here, good agreements with experimental data were obtained regarding centerline wall pressures on the aircraft and aerodynamic coefficients of forces and moments acting on the aircraft. Next, full scale integrated analysis of the aircraft and the engine were conducted for flight Mach numbers of M=5.0, 4.0, 3.5, 3.0, and 2.0. Increasing the angle of attack $\alpha$ of the aircraft in M=5.0 flight increased the mass flow rate of the air captured at the intake due to pre-compression effect of the nose shockwave, also increasing the thrust obtained at the engine plug nozzle. Sufficient thrust for acceleration were obtained at $\alpha=3$ and 5 degrees. Increase of flight Mach number at $\alpha=0$ degrees resulted in decrease of mass flow rate captured at the engine intake, and thus decrease in thrust at the nozzle. The thrust was sufficient for acceleration at M=3.5 and lower cases. Lift force on the aircraft was increased by the integration of engine on the aircraft for all varying angles of attack or flight Mach numbers. However, the slope of lift increase when increasing flight Mach number showed decrease as flight Mach number reach to M=5.0, due to the separation shockwave at the upper surface of the aircraft. Pitch moment of the aircraft was not affected by the installation of the engines for all angles of attack at M=5.0 condition. In low Mach number cases at $\alpha=0$ degrees, installation of the engines increased the pitch moment compared to no engine configuration. Installation of the engines increased the frictional drag on the aircraft, and its percentage to the total drag ranged between 30-50% for varying angle of attack in M=5.0 flight.

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

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.53-77
• /
• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.