• Journal of Life Science
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• v.25 no.1
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• pp.84-92
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• 2015

Interferon inducible transmembrane protein (IFITM) family genes have been implicated in various cellular processes such as the homotypic cell adhesion functions of IFNs and cellular anti-proliferative activities. The present study aimed to investigate whether the polymorphisms of the IFITM2 and IFITM5 genes are associated with susceptibility to UC. We identified a total of thirteen polymorphisms (eleven SNPs and two variations) in the IFITM2 gene and twelve polymorphisms (eleven SNPs and one variation) in the IFITM5 gene, by the direct sequencing method. Genotype analysis in the IFITM2 and IFITM5 SNPs was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and Taq-Man probe analysis, and the haplotype frequencies of IFITM2 and IFITM5 SNPs for multiple loci were estimated using the expectation maximization (EM) algorithm. The genotype and allele frequencies of IFITM2 SNPs, as well as IFITM5 SNPs, in UC patients were not significantly different from those of the healthy controls. We also analyzed the combined frequencies of rs77537847 of IFITM1, rs909097 of IFITM2, and rs56069858 of IFITM5 in the UC patients and the healthy controls. Although the distribution of the major combined genotype frequency did not differ significantly between the healthy controls and the UC patients, the GGT combined frequency in the healthy controls was significantly different from that in the UC patients (P=0.002). This result suggests that the combined genotype of the IFITMs polymorphisms may be associated with a susceptibility to UC and could be a useful genetic marker for UC.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.3 s.106
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• pp.249-258
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• 2006

In this letter, we propose a novel printed helix antenna for RFID reader in UHF band. The printed strip line of the antenna is first wound up outside a polygonal shaped layer and then the winding continues on an inner layer to control the overall gain and the radiation pattern. In addition, the winding pitch angles on each layer have either negative or positive values resulting in the broad CP bandwidth. The detail structure of the antenna was optimized using Pareto genetic algorithm(GA), so as to obtain excellent performances for RFID reader antennas. The optimized two-layered polygonal helix was fabricated on the cardboard of a flexible substrate and the performances were measured and compared with the simulations. The fabricated antenna was made up of copper tape which can adhere to a flexible cardboard and had 21.4 % matching bandwidth, 31.9 % CP bandwidth, readable range of $5.5m^2$ with kr=3.2. Also based on the current distribution of the strip line of the antenna and sensitivity of the antenna bents points, we confirmed that the antenna has the quarter-wave transformer near the feed for the broad matching bandwidth and radiates the traveling wave for the broad CP bandwidth using the bent strip line.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of Life Science
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• v.13 no.3
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• pp.298-307
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• 2003

To figure out the conserved genes and newly added genes at each phylogenetic level of Archaea, COG (clusters of orthologous groups of proteins) algorithm was applied. The number of conserved genes within 9 species of Archaea was 340 and that of 8 species of Euryarchaeota was 388. Many of conserved 265 COGs, which are specific to Archaea and absent in Bacteria and S. cerevisiae, were concerned with 'information storage and processing' (94 COG, 35.5%) and 'metabolism' (82 COG, 30.9%). COGs related to these functions were assumed as highly conserved and permit peculiar life form to Archaea. It seemed that there was some difference in 'nucleotide transport and metabolism' and there was little difference in 'information storage and processing' between Euryarchaeota and Crenarchaeota. Marine-origin Euryarchaeota showed different conserved COGs with terrestrial Euryarchaeota. Conserved COGs, related to carbohydrate transport and metabolism and others, were different between marine- and terrestrial-origin Euryarchaeota. Hence it was assumed that their physiology might be different. This study may help to understand the origin and conserved genes at each phylogenetic level of marine-origin Euryarchaeota and may help in the mining of useful genes in marine Archaea as Manco et al. (Arch. Biochem. Biophy. 373, 182 (2000)).

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.137-144
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• 2004

Algorithms for estimating breeding values on several categorical data by using latent variables with threshold conception were developed and showed. Thresholds on each categorical trait were estimated by Newton’s method via gradients and Hessian matrix. This algorithm was developed by way of expansion of bivariate analysis provided by Quaas(2001). Breeding values on latent variables of categorical traits and observations on linear traits were estimated by preconditioned conjugate gradient(PCG) method, which was known having a property of fast convergence. Example was shown by simulated data with two linear traits and a categorical trait with four categories(CE=calving ease) and a dichotomous trait(SB=Still Birth) in threshold animal mixed model(TAMM). Breeding value estimates in TAMM were compared to those in linear animal mixed model (LAMM). As results, correlation estimates of breeding values to parameters were 0.91${\sim}$0.92 on CE and 0.87${\sim}$0.89 on SB in TAMM and 0.72~0.84 on CE and 0.59~0.70 on SB in LAMM. As conclusion, PCG method for estimating breeding values on several categorical traits with linear traits were feasible in TAMM.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.12
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• pp.471-480
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• 2016

Recently, the damage of cyber attack toward infra-system, national defence and security system is gradually increasing. In this situation, military recognizes the importance of cyber warfare, and they establish a cyber system in preparation, regardless of the existence of threaten. Thus, the study of Intrusion Detection System(IDS) that plays an important role in network defence system is required. IDS is divided into misuse and anomaly detection methods. Recent studies attempt to combine those two methods to maximize advantagesand to minimize disadvantages both of misuse and anomaly. The combination is called Hybrid IDS. Previous studies would not be inappropriate for near real-time network environments because they have computational complexity problems. It leads to the need of the study considering the structure of IDS that have high detection rate and low computational cost. In this paper, we proposed a Hybrid IDS which combines C4.5 decision tree(misuse detection method) and Weighted K-means algorithm (anomaly detection method) hierarchically. It can detect malicious network packets effectively with low complexity by applying mutual information and genetic algorithm based efficient feature selection technique. Also we construct upgraded the the hierarchical structure of IDS reusing feature weights in anomaly detection section. It is validated that proposed Hybrid IDS ensures high detection accuracy (98.68%) and performance at experiment section.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.445-454
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• 2022

Ice accretion on the aircraft components, such as wings, fuselage, and empennage, can occur when the aircraft encounters a cloud zone with high humidity and low temperature. The prevention of ice accretion is important because it causes a decrease in the aerodynamic performance and flight stability, thus leading to fatal safety problems. In this study, a shape design optimization of a multi-element airfoil is performed to minimize the amount of ice accretion on the high-lift device including leading-edge slat, main element, and trailing-edge flap. The design optimization framework proposed in this paper consists of four major parts: air flow, droplet impingement and ice accretion simulations and gradient-free optimization algorithm. Reynolds-averaged Navier-Stokes (RANS) simulation is used to predict the aerodynamic performance and flow field around the multi-element airfoil at the angle of attack 8°. Droplet impingement and ice accretion simulations are conducted using the multi-physics computational analysis tool. The objective function is to minimize the total mass of ice accretion and the design variables are the deflection angle, gap, and overhang of the flap and slat. Kriging surrogate model is used to construct the response surface, providing rapid approximations of time-consuming function evaluation, and genetic algorithm is employed to find the optimal solution. As a result of optimization, the total mass of ice accretion on the optimized multielement airfoil is reduced by about 8% compared to the baseline configuration.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.157-161
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• 2011

Although many researches have been carried out concerning the watershed division in natural areas, it has not been researched for the urban watershed division. If the boundary between two urban areas is indistinct because no natural distinction or no administrative division is between the areas, the boundary between the urban areas that have the different outlets (multi-outlet urban watershed) is determined by only designer of sewer system. The suggested urban watershed division model (UWDM) determines the watershed boundary to reduce simultaneously the peak outflows at the outlets of each watershed. Then, the UWDM determines the sewer network to reduce the peak outflow at outlet by determining the pipe connecting directions between the manholes that have the multi-possible pipe connecting directions. In the UWDM, because the modification of the sewer network changes the superposition effect of the runoff hydrographs in sewer pipes, the optimal sewer layout can reduce the peak outflow at outlet, as much as the superposition effects of the hydrographs are reduced. Therefore, the UWDM can optimize the watershed distinction in multi-outlet urban watershed by determining the connecting directions of the boundary-manholes using the genetic algorithm. The suggested model was applied to a multi-outlet urban watershed of 50.3ha, Seoul, Korea, and the watershed division of this model, the peak outflows at two outlets were decreased by approximately 15% for the design rainfall.

• 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 Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.