• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1539-1546
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• 2016

Specific patterns of the hereditary breast and ovarian cancer (HBOC) syndrome are related to mutations in the BRCA1 gene. One hundred unrelated breast cancer patients were interviewed to obtain clinical symptoms and signs, pedigree and familial history of HBOC syndrome related cancer. Subsequently, data were calculated using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk prediction model. Patients with high score of BOADICEA were offered genetic testing. Eleven patients with high score of BOADICEA, 2 patients with low score of BOADICEA, 2 patient's family members and 15 controls underwent BRCA1 genetic testing. Mutation screening using PCR-HRM was carried out in 22 exons (41 amplicons) of BRCA1 gene. Sanger sequencing was subjected in all samples with aberrant graph. This study identified 10 variants in the BRCA1 gene, consisting of 6 missense mutations (c.1480C>A, c.2612C>T, c.2566T>C, c.3113A>G, c.3548 A>G, c.4837 A>G), 3 synonymous mutations (c.2082 C>T, c.2311 T>C and c.4308T>C) and one intronic mutation (c.134+35 G>T). All variants tend to be polymorphisms and unclassified variants. However, no known pathogenic mutations were found.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3477-3482
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• 2016

Background: Thyroid cancer is the most common endocrine malignancy, and exact causes remain unknown. The role of CYP450 1A1 (CYP1A1) in cancer initiation and progression has been investigated. The aim of this work was to analyze, for the first time, CYP1A1 gene expression and its relationship with several clinicopathological factors in Mexican patients diagnosed with thyroid cancer. Materials and Methods: Real-time PCR analysis was conducted on 32 sets of thyroid tumors and benign pathologies. Expression levels were tested for correlations with clinical and pathological data. All statistical analysis were performed using GraphPad Prism version 3.0 software. Results: We found that female gender was associated with thyroid cancer risk (P<0.05). A positive relationship was identified between CYP1A1 mRNA levels and the presence of chronic disease, alcohol use, tumor size, metastasis and an advanced clinical stage (P<0.05). Conclusions: The results suggest that CYP1A1 gene expression could be used as a marker for thyroid cancer.

• Journal of the Korea Institute of Building Construction
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• v.18 no.1
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• pp.59-65
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• 2018

Radon gas is a colorless, odorless, tasteless gas that occurs when uranium, a natural radioactive material in rocks and soils, collapses. 85% of the annual radiation exposure of the human body is due to natural radiation, of which 50% is radon. According to the US Environmental Protection Agency (EPA) survey, 62 out of 1,000 smokers and 7 out of 1,000 nonsmokers are exposed to lung cancer when exposed to radon gas for a long time. In order to reduce the risk of radon gas, activate carbon was used to fabricate matrix, and the pore properties and radon reduction properties were investigated. When the activate carbon was used, the radon gas concentration was drastically reduced and the graph was changed as the measurement period became longer. The pore distribution and microporous properties, which are one of the material properties of activate carbon, can be grasped.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.843-845
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• 2003

Shortest Path Problems are among the most studied network flow optimization problems, with interesting applications in various fields. One such field is the route determination service, where various kinds of shortest path problems need to be solved in location-based service. Our research aim is to propose a route technique in real-time locationbased service (LBS) environments according to user’s route preferences such as shortest, fastest, easiest and so on. Turn costs modeling and computation are important procedures in route planning. There are major two kinds of cost parameters in route planning. One is static cost parameter which can be pre-computed such as distance and number of traffic-lane. The other is dynamic cost parameter which can be computed in run-time such as number of turns and risk of congestion. In this paper, we propose a new cost modeling method for turn costs which are traditionally attached to edges in a graph. Our proposed route determination technique also has an advantage that can provide service interoperability by implementing XML web service for the OpenLS route determination service specification. In addition to, describing the details of our shortest path algorithms, we present a location-based service system by using proposed routing algorithms.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.35-44
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• 2019

The purpose of this study is to find out quantitative vulnerability assessment about COTS(Commercial Off The Shelf) O/S based I&C System. This paper analyzed vulnerability's lifecycle and it's impact. this paper is to develop a quantitative assessment of overall cyber security risks and vulnerabilities I&C System by studying the vulnerability analysis and prediction method. The probabilistic vulnerability assessment method proposed in this study suggests a modeling method that enables setting priority of patches, threshold setting of vulnerable size, and attack path in a commercial OS-based measurement control system that is difficult to patch an immediate vulnerability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.505-514
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• 2012

Because people usually establish their online social network based on their offline relationship, the social networks (i.e., the graph of friendship relationships) are often used to share contents. Mobile devices let it easier in these days, but it also increases the privacy risk such as access control of shared data and relationship exposure to untrusted server. To control the access on encrypted data and protect relationship from the server, M. Atallah et al. proposed a hop-based scheme in 2009. Their scheme assumed a distributed environment such as p2p, and each user in it shares encrypted data on their social network. On the other hand, it is very inefficient to keep their relationship private, so we propose an improved scheme. In this paper, among encrypted contents and relationships, some authenticated users can only access the data in distributed way. For this, we adopt 'circular-secure symmetric encryption' first. Proposed scheme guarantees the improved security and efficiency compared to the previous work.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.451-463
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• 2022

Whenever a mineshaft accidentally collapses, speedy risk assessment is both required and crucial. But onsite safety diagnosis by humans is reportedly difficult considering the additional risk of collapse of the unstable mineshaft. Generally, drones equipped with high-speed lidar sensors can be used for such inspection. However, the drone technology is restrictively applicable at very shallow depth, failing in mineshafts with depths of hundreds of meters because of the limit of wireless communication and turbulence inside the mineshaft. In previous study, a three-dimensional (3D) scanning system with a single channel lidar was fabricated and operated using towed cable in a mineshaft to a depth of 200 m. The rotation and pendulum movement errors of the measuring unit were compensated for by applying the data of inertial measuring unit and comparing the similarity between the scan data of the adjacent depths (Kim et al., 2020). However, the errors grew with scan depth. In this paper, a multi-channel lidar sensor to obtain a continuous cross-sectional image of the mineshaft from a winch system pulled from bottom upward. In this new approach, within overlapped region viewed by the multi-channel lidar, rotation error was compensated for by comparing the similarity between the scan data at the same depth. The fabricated system was applied to scan 0-165 m depth of the mineshaft with 180 m depth. The reconstructed image was depicted in a 3D graph for interpretation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.273-282
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• 2023

Probabilistic safety assessment (PSA) has been widely used to evaluate the seismic risk of nuclear power plants (NPPs). However, studies on seismic PSA for process plants, such as gas plants, oil refineries, and chemical plants, have been scarce. This is because the major disasters to which these process plants are vulnerable include explosions, fires, and release (or dispersion) of toxic chemicals. However, seismic PSA is essential for the plants located in regions with significant earthquake risks. Seismic PSA entails probabilistic seismic hazard analysis (PSHA), event tree analysis (ETA), fault tree analysis (FTA), and fragility analysis for the structures and essential equipment items. Among those analyses, ETA can depict the accident sequence for core damage, which is the worst disaster and top event concerning NPPs. However, there is no general top event with regard to process plants. Therefore, PSA cannot be directly applied to process plants. Moreover, there is a paucity of studies on developing fragility curves for various equipment. This paper introduces PSA for gas plants based on FTA, which is then transformed into Bayesian network, that is, a probabilistic graph model that can aid risk-informed decision-making. Finally, the proposed method is applied to a gas plant, and several decision-making cases are demonstrated.

• 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.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.658-672
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• 2022

The unique teaching and learning difficulties of speed-related units in elementary school science are mainly due to the student's lack of mathematical thinking ability and procedural knowledge on speed measurement, and curriculums and textbooks must be constructed with these in mind. To identify the implications of composing a new science curriculum and relevant textbooks, this study reviewed the structure and contents of the speed-related units of three curriculums from the 2007 revised curriculum to the 2015 revised curriculum and the resulting textbooks and examined their relevance in light of the literature. Results showed that the current content carries the risk of making students calculate only the speed of an object through a mechanical algorithm by memorization rather than grasp the multifaceted relation between traveled distance, duration time, and speed. Findings also highlighted the need to reorganize the curriculum and textbooks to offer students the opportunity to learn the meaning of speed step-by-step by visualizing materials such as double number lines and dealing with simple numbers that are easy to calculate and understand intuitively. In addition, this paper discussed the urgency of improving inquiry performance such as process skills by observing and measuring an actual object's movement, displaying it as a graph, and interpreting it rather than conducting data interpretation through investigation. Lastly, although the current curriculum and textbooks emphasize the connection with daily life in their application aspects, they also deal with dynamics-related content somewhat differently from kinematics, which is the main learning content of the unit. Hence, it is necessary to reorganize the contents focusing on cases related to speed so that students can grasp the concept of speed and use it in their everyday lives. With regard to the new curriculum and textbooks, this study proposes that students be provided the opportunity to systematically and deeply study core topics rather than exclude content that is difficult to learn and challenging to teach so that students realize the value of science and enjoy learning it.