• Nuclear Medicine and Molecular Imaging
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• v.43 no.4
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• pp.301-308
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• 2009

Purpose: The aim of this study was to evaluate the usefulness of FDG-PET/CT as follow up imaging tool in patients with endometrial cancer after therapy. Material and Methods: One hundred one patients with endometrial cancer who underwent FDG PET/CT after the treatment of this disease were included in this study population (25-79 yr old, Mean age 50.6 yr old) and all these patients also performed various laboratory and imaging studies such as serum tumor marker, CT or MRI. The lesions having increased focal FDG uptake were classified into benign, equivocal, and malignant one according to their pattern and activity. Tumor recurrence was confirmed by histopathological results and other clinical and imaging data. Results: Among the 19 patients with 30 malignant or equivocal hot uptakes, 11 of 14 patients supposed to be malignant finding in PET/CT were proved to be tumor recurrence, while one of 5 patients with equivocal lesions were recurred malignancy, Two false negative cases were turned out to be peritoneal carcinomatosis, Estimated sensitivity, specificity and accuracy of PET/CT for diagnosis of recurrence in endometrial carcinoma after treatment were 86 %, 92 % and 91 %, respectively. Positive and negative predictive values in the same issue were 63% and 98%, respectively. Conclusion: FDG-PET/CT is useful for regular work up of endometrial carcinoma after the treatment because of its high negative predictive value as well as high sensitivity and specificity.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.319-343
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• 2009

This paper considers some kinds of socio-spatial constraints and strategies for overcoming them which immigrant workers in Korea have experienced in their work-place and life-space, with an analysis of questionnaire data and of direct interview materials on them. Though they appear somewhat satisfactory or positive with their work-place, this can be seen as a hypocritical or false attitude rather than a real one: they are forced to work with long hours (more than 70 hours per week) and rigid controls in the other' territory. Their daily life-spaces also are severe: they can be hardly embedded in an existing community with a sense of place due to serious institutional and interaction constraints, even though they seem to have a basic mobility to survive in life-spaces. In order to escape or alleviate such local constraints, they try to constitute multi-scalar (local, trans-regional, and transnational) networks, and to find informations and means to resolve or cope with them. However, this kind of endeavors of immigrant workers to make a trans-national network and social space has a limitation for them to be free entirely from constraints, which might be strengthened with a lack of geographical knowledge of them. Then immigrant workers in Korea live ineluctably with not only hybrid national identity but also with disturbed local identity in an aliened workplace and life-spaces.

• The Korean Journal of Pesticide Science
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• v.9 no.4
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• pp.292-302
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• 2005

Condition of Ion-Trap gas chromatography-mass spectrometry (GC-MS) for rapid screening of 206-pesticides residues in agricultural foodstuffs was optimized. As applying a wide-bore column (10 m${\times}$0.53 mm, DF 0.25 um) connected with a fused silica restrictor (0.6 m${\times}$0.1 mm), a significant retention time reduction was obtained. Additionally, the shape of peaks was sharper and higher than classical GC's and GC-MS's, which allowed lower detection limits. To easily manage many spectral data, both of Electron Ionization(EI) and Chemical Ionization(CI) techniques were adopted in screening procedure. At the following steps, MS-MS technique were used to confirm screened analytes in complicated matrices.

• The Korean Journal of Financial Management
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• v.25 no.1
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• pp.51-84
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• 2008

We investigate the signaling effect of repurchase preceding new equity issue using Korean data. In a short time span, firms announce stock repurchases and equity offerings. The proximity of two events in Korean firms indicates that those are not independent of each other. In this paper, we test the signaling effect of repurchase on equity offerings on the two measures. One is announcement effect, which is measured as CAR(0, +2). The other is the effectiveness which is measured as CAR(0, +30) because the price movement during this window influences on the price of new issues. Previous studies that stock repurchase convey positive signal to equity offerings-Billet and Xue(2004) and Jung(2004)-construct sample without the limit of time interval between two events. This causes the unclear relation between those because of the long time interval. In this study we consider only samples of being within one year each other to reduce this problem and clarify the signal of repurchase on equity offerings. Korean firms are allowed to repurchase own shares with two different method. One is direct repurchase as same as open market repurchase. The other is stock stabilization fund and stock trust fund which trust company or bank buy and sell their shares on the behalf of firms. Generally, the striking different characteristic between direct repurchase and indirect repurchase is following. Direct repurchase is applied by more strict regulation than indirect repurchase. Therefore, the direct repurchase is more informative signal to the equity offering than the indirect repurchase. We construct two sample firms- firms with direct repurchase preceding-equity offerings and indirect repurchase-preceding equity offering, and one control firms-equity offerings only firms-to investigate the announcement effect and the effectiveness of repurchases. Our findings are as follows. Direct repurchase favorably affect the price of new issues favorably. CAR(0, +2) of firms with direct repurchase is not different from that of equity offerings only firms but CAR(0, +30) is higher than that of equity offerings only firms. For firms with indirect repurchase and equity offerings, Both the announcement effect and the effectiveness does not exist. Jung(2004) suggest the possibilities of how indirect stock repurchase can be regarded as one of unfair trading practices on based on the survey results that financial managers of some of KSE listed firms have been asked of their opinion on the likelihood of the stock repurchase being used in unfair trading. This is not objective empirical evidence but opinion of financial managers. To investigate whether firms announce false signal before equity offerings to boost the price of new issues, we calculate the long-run performance following equity offerings. If firms have announced repurchase to boost the price of new issues intentionally, they would undergo the severe underperformance. The empirical results do not show the severer underperformance of both sample firms than equity offerings only firms. The suggestion of false signaling of repurchase preceding equity offerings is not supported by our evidence.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. 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, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. 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 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• 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 Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.