• Journal of Genetic Medicine
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• v.7 no.1
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• pp.59-66
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• 2010

Purpose: Quantitative fluorescent polymerase chain reaction (QF-PCR) allows for the rapid prenatal diagnosis of common aneuploidies. The main advantages of this assay are its low cost, speed, and automation, allowing for large-scale application. However, despite these advantages, it is not a routine method for prenatal aneuploidy screening in Korea. Our objective in the present study was to validate the performance of QF-PCR using short tandem repeat (STR) markers in a Korean population as a means for rapid prenatal diagnosis. Material and Methods: A QF-PCR assay using an Elucigene kit (Gen-Probe, Abingdon, UK), containing 20 STR markers located on chromosomes 13, 18, 21, X and Y, was performed on 847 amniotic fluid (AF) samples for prenatal aneuploidy screening referred for prenatal aneuploidy screening from 2007 to 2009. The results were then compared to those obtained using conventional cytogenetic analysis. To evaluate the informativity of STR markers, the heterozygosity index of each marker was determined in all the samples. Results: Three autosomes (13, 18, and 21) and X and Y chromosome aneuploidies were detected in 19 cases (2.2%, 19/847) after QF-PCR analysis of the 847 AF samples. Their results are identical to those of conventional cytogenetic analysis, with 100% positive predictive value. However, after cytogenetic analysis, 7 cases (0.8%, 7/847) were found to have 5 balanced and 2 unbalanced chromosomal abnormalities that were not detected by QF-PCR. The STR markers had a slightly low heterozygosity index (average: 0.76) compared to those reported in Caucasians (average: 0.80). Submicroscopic duplication of D13S634 marker, which might be a unique finding in Koreans, was detected in 1.4% (12/847) of the samples in the present study. Conclusion: A QF-PCR assay for prenatal aneuploidy screening was validated in our institution and proved to be efficient and reliable. However, we suggest that each laboratory must perform an independent validation test for each STR marker in order to develop interpretation guidelines of the results and must integrate QF-PCR into the routine cytogenetic laboratory workflow.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.532-539
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• 2004

Purpose: There has been a renewal of interest in Macrophage migration inhibitory factor (MIF), especially correlation in pathogenesis of sepsis by many infectious diseases and in regulation of host inflammatory and immune response. We developed immunoradiometric assay (IRMA) to determine serum human MIF concentration. Materials and Methods: The IRMA system utilizes solid phase bound monoclonal anti-recombinant human MIF (rhMIF) antibody as a capture antibody, biotinylated polyclonal anti-rhMIF antibody as a detector antibody. We applied with rhMIF that concentration of standard solutions increased from 0 ng/ml to 100 ng/ml. We used $^{125}I$-streptavidin (SA) as radiotracer to determination of rhMIF concentration. Streptavidin was labeled with $^{125}I$ by Chloramine-T method and $^{125}I$-SA was purified by ultracentrifugation. $^{125}I$-SA stability was evaluated by ITLC analysis at $4^{\circ}C$ and room temperatures until 60days. To validate IRMA system for MIF, we experimented intra-assay and inter-assay coefficients of variation, recovery test and dilution test. Results: Radiolabeling yield of $^{125}I$-SA was 87% and purified $^{125}I$-SA retained above 99% radiochemical purity. $^{125}I$-SA showed above 93% stability in $4^{\circ}C$ until 60days that it is good for immunoradiometric assay as radiotracer. Plotted standard dose response curve showed that increased concentration of rhMIF linearly correlated (R2=0.99) with bound radioactivity of $^{125}I$-SA. The highest intra- and inter-assay coefficients of variation were 5.5% and 7.6%, respectively. The average of recovery of MIF in samples was 102%. In dilution test, linear response curves were obtained (R2=0.97). Conclusion: Radioimmunoassay using $^{125}I$-SA as radiotracer thought to be useful for the determination of serum MIF concentration, and further, its data will be used to evaluate the correlation between clinical significance and serum MIF concentration in patients with various inflammatory diseases.

• Korean Journal of Food Science and Technology
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• v.41 no.4
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• pp.458-463
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• 2009

To establish and validate a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the rapid and accurate quantitation of diarrhetic shellfish poisoning (DSP) toxins, we compared the results from different mobile phases and columns used for their analysis and collision energies for MS/MS experiments. Clear peaks of okadaic acid (OA) and dinophysistoxin-1 (DTX1) were obtained by using a mobile phase comprising aqueous acetonitrile containing 2 mM ammonium formate and 50 mM formic acid. The collision energies were optimized to facilitate the most sensitive detection for each toxin, namely, OA, DTX1, pectenotoxin-2 (PTX2), or yessotoxin (YTX). Further, the maximum ion response was obtained at a collision energy of 48 V for OA and DTX1. We compared the analytical performance of $C_8$ and $C_{18}$ columns. A wide range of toxins namely, OA, DTX1, PTX2, and YTX, except DTX3, were detected by both the columns. Although DTX3 was only detected by the $C_8$ column, we found that the $C_{18}$ column was also suitable for the quantitation of OA and DTX1 the toxins responsible for inducing diarrhea. The limit of detection of OA and DTX1 by the established LC-MS/MS conditions was 1 ng/g, and the limit of quantitation of the toxins under the same conditions was 3 ng/g. The process efficiencies were 91-118% for oysters (Crassostrea gigas) and 96-117% for mussels (Mytilus galloprovincialis) further, we observed no significant effect of matrix during the ionization process in LC-MS/MS. The comparison between mouse bioassay (MBA) and LC-MS/MS yielded varying results because low concentrations of OA and DTX1 were detected by LC-MS/MS in some shellfish samples, which provided positive results on MBA for DSP. The analysis time required by MBA for DSP analysis can be reduced by LC-MS/MS.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.163-174
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• 2012

CBI(Composite Burn Index) developed by USDA Forest Service is a index to measure burn severity based on remote sensing. In Korea, the CBI has been used to investigate the burn severity of fire sites for the last few years. However, it has been an argument on that CBI is not adequate to capture unique characteristics of Korean forests, and there has been a demand to develop KCBI(Korean Composite Burn Index). In this regard, this study aimed to develop KCBI by adjusting the CBI and to validate its applicability by using remote sensing technique. Uljin and Youngduk, two large fire sites burned in 2011, were selected as study areas, and forty-four sampling plots were assigned in each study area for field survey. Burn severity(BS) of the study areas were estimated by analyzing NDVI from SPOT images taken one month later of the fires. Applicability of KCBI was validated with correlation analysis between KCBI index values and NDVI values and their confusion matrix. The result showed that KCBI index values and NDVI values were closely correlated in both Uljin (r = -0.54 and p<0.01) and Youngduk (r = -0.61 and p<0.01). Thus this result supported that proposed KCBI is adequate index to measure burn severity of fire sites in Korea. There was a number of limitations, such as the low correlation coefficients between BS and KCBI and skewed distribution of KCBI sampling plots toward High and Extreme classes. Despite of these limitations, the proposed KCBI showed high potentials for estimating burn severity of fire sites in Korea, and could be improved by considering the limitations in further studies.

• Korean Security Journal
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• no.42
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• pp.393-422
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• 2015

The school is committed to the role of public education for the effective development of student academic achievement and intellectual ability. It is also a space to expand the range of care and understanding for others with as a member of the academic community as well. However, the reality of our country schooling has been pointed out that if a lot about the lack of character education related to the attitude of life care for others and feel a sense of responsibility. An individual is not necessarily emotional intelligence There are side out, as well as grow into adults, schools are obliged to teach a variety of methods associated with it. Nevertheless, education is not of the country beyond the formal excessive administrative work or other activities due to the process of character education to neglect, including the work of teachers. Therefore, students brought the expansion of the act that should not, and eventually lead to serious social issues that directly harm others, such as school violence. Therefore, students brought the expansion of the act that should not, and eventually lead to serious social issues that directly harm others, such as school violence. This study is based on an act of juvenile delinquency and criminology education was to refine the concept of toughness and validate the relationship between school violence through empirical research. Accordingly, from July 1, 2013 September 31, 2013 to 277 high schools across the country are attending the third year of the schools available for non-response and analysis of the students who participated in the admission and simulated typical presentation of K University in Gyeonggi-do not judge students in the final analysis, except for the data and data from a total of 1045 patients were utilized. As a result, many schools have experienced violence, male student work can be applied to a lot of rock school violence was experienced. Also, a lot of experience can be applied to a healthy student rock school violence, anger-control and empathy, this is considered a low student showed consciousness experienced school violence exerted.

• Journal of Chest Surgery
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• v.42 no.6
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• pp.749-756
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• 2009

Background: Vascular ring is a rare anomaly of the aortic arch. We did surgical repair procedures on 16 cases of vascular ring over the past 12 years. This article reviews our results. Material and Method: Between 1995 and 2007, 16 patients (5 with double aortic arch, 7 with right aortic arch-left ligamentum, 4 with pulmonary artery sling) underwent surgical repair. Mean age at the time of the operation were as follows: double aortic arch, $5.7{\pm}5.5$ years; right aortic arch-left ligamentum, $6.1{\pm}13.4$ years; pulmonary artery sling, $2.9{\pm}2.6$ years. Five patients (71%) with right aortic arch-left ligamentum had an associated Kommerell's diverticulum. Two patients (40%) with double aortic arch, 2 patients (28.6%) with right aortic arch-left ligament and 4 patients (100%) with pulmonary artery sling had associated airway stenosis. Cardiac anomalies were present in 8 of 16 patients. Result: There was no peri-operative or post-operative mortality. The mean hospital stay was $27.1{\pm}38.2$ days. None of our patients underwent reoperation. Conclusion: Vascular ring is rare, but, it needs surgical correction. It is important to suspect the diagnosis and to validate with echocardiography. Preoperative and postoperative computed tomography and bronchoscopy are useful to evaluate the airway and surrounding structures.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.627-636
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• 2020

Debris flow disasters primarily occur in mountainous terrains far from cities. As such, they have been underestimated to cause relatively less damage compared with other natural disasters. However, owing to urbanization, several residential areas and major facilities have been built in mountainous regions, and the frequency of debris flow disasters is steadily increasing owing to the increase in rainfall with environmental and climate changes. Thus, the risk of debris flow is on the rise. However, only a few studies have explored the characteristics of flooding and reduction measures for debris flow in areas designated as steep slopes. In this regard, it is necessary to conduct research on securing independent disaster prevention technology, suitable for the environment in South Korea and reflective of the topographical characteristics thereof, and update and improve disaster prevention information. Accordingly, this study aimed to calculate the amount of debris flow, depending on disaster prevention performance targets for regions designated as steep slopes in South Korea, and develop an independent model to not only evaluate the impact of debris flow but also identify debris barriers that are optimal for mitigating damage. To validate the reliability of the two-dimensional debris flow model developed for the evaluation of debris barriers, the model's performance was compared with that of the hydraulic model. Furthermore, a 2-D debris model was constructed in consideration of the regional characteristics around the steep slopes to analyze the flow characteristics of the debris that directly reaches the damaged area. The flow characteristics of the debris delivered downstream were further analyzed, depending on the specifications (height) and installation locations of the debris barriers employed to reduce the damage. The experimental results showed that the reliability of the developed model is satisfactory; further, this study confirmed significant performance degradation of debris barriers in areas where the barriers were installed at a slope of 20° or more, which is the slope at which debris flows occur.