• Journal of Industrial Technology
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• v.15
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• pp.217-222
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• 1995

In this paper, the importance of the size effects on the strength ratio is demonstrated by numerical results. The rate of decrease of tensile strength is for glass fiber, based on the experience of a composite manufacturing specialist. For other material, similar procedure may be used until detailed test result on such material is available. The strength criteria used is that of Tsai-Wu for stress space. The factors influencing the ratio are, reducing the tensile strength alone or both tensile and compression strengths, selection of the normalized interaction term, that is, the generalized Von Mises criterion or the Hill's criterion, and the status of applied stresses. Some of the numerical results are presented for a guideline for the furture study.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.4 no.2
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• pp.1-6
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• 2008

Very High Temperature Gas Cooled Reactor (VHTR) has been selected as a high energy heat source for nuclear hydrogen generation. The VHTR can produce hydrogen from heat and water by using a thermo-chemical process or from heat, water, and natural gas by steam reformer technology. A co-axial double-tube primary hot gas duct (HGD) is a key component connecting the reactor pressure vessel and the intermediate heat exchanger (IHX) for the VHTR. In this study, a preliminary design analysis for the primary HGD of the nuclear hydrogen system was carried out. These preliminary design activities include a determination of the size, a strength evaluation and an appropriate material selection. The determination of the size was undertaken based on various engineering concepts, such as a constant flow velocity model, a constant flow rate model, a constant hydraulic head model, and finally a heat balanced model.

• Journal of Dental Anesthesia and Pain Medicine
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• v.17 no.3
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• pp.231-233
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• 2017

Wolf-Hirschhorn syndrome is a rare hereditary disease that results from a 4p chromosome deletion. Patients with this syndrome are characterized by craniofacial dysgenesis, seizures, growth delay, intellectual disability, and congenital heart disease. Although several cases have been reported, very little information is available on anesthetic management for patients with Wolf-Hirschhorn syndrome. We encountered a case requiring anesthetic management for a 2-year-old girl with Wolf-Hirschhorn syndrome. The selection of an appropriately sized tracheal tube and maintaining intraoperatively stable hemodynamics might be critical problems for anesthetic management. In patients with short stature, the tracheal tube size may differ from what may be predicted based on age. The appropriate size ( internal diameter ) of tracheal tubes for children has been investigated. Congenital heart disease is frequently associated with Wolf-Hirschhorn syndrome. Depending on the degree and type of heart disease, careful monitoring of hemodynamics is important.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.201-210
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• 2003

In this paper, we propose a new binocular stereo correspondence method by maximizing a fitness formulated by integrating two constraints of edge similarity and disparity smoothness simultaneously. The proposed stereopsis focusing to measure distances to leading vehicles on roads uses intensity gradients as matching attribute. In contrast to the previous work of area-based stereo matching, in which matching unit is a pixel, the matching unit of the proposed method becomes an area itself which is obtained by selecting a series of pixels enclosed by two pixels on the left and right boundaries of an object. This approach allows us to cope with real-time processing and to avoid window size selection problems arising from conventional area-based stereo.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Korean Management Science Review
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• v.32 no.2
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• pp.125-143
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• 2015

When studying the film industry, researchers have seldom addressed the dynamic interaction between marketing information and word of mouth in the motion picture industry mainly because of the limitation of traditional research methodologies. This study explores integration and competition among important variables influencing on audience's choice on movie selection, particularly by using a new method of agent-based modeling including competitive environment. Decision process of moviegoer composed of transition probability based on multinomial logit model, considering marketing and box-office information, critique, and word of mouth from other moviegoers. After validating the fitness of market share among released movies, this study conducted a set of simulation experiments considering several variables such as market size, change of weight between variables, and movie performance under competition. Propositions are derived from the simulation results is also suggested for future research.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.449-466
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• 2006

We propose to use variable selection methods based on penalized regression for pruning decision tree ensembles. Pruning methods based on LASSO and SCAD are compared with the cluster pruning method. Comparative studies are performed on some artificial datasets and real datasets. According to the results of comparative studies, the proposed methods based on penalized regression reduce the size of boosting ensembles without decreasing accuracy significantly and have better performance than the cluster pruning method. In terms of classification noise, the proposed pruning methods can mitigate the weakness of AdaBoost to some degree.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.7 s.361
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• pp.59-69
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• 2007

In this paper, we propose a MIMO-OFDMA (Multi Input Multi Output-Orthogonal Frequency Division Multiple Access) system based on Grassmannian beamforming for performance improvement of downlink real-time traffic transmission in harsh channel conditions with low CIR (Carrier-to-Interference Ratio). In the proposed system to reduce feedback information for the beamforming, we also apply Grassmannian Beamforming. Furthermore, we propose antenna selection scheme which performs the beamforming with more useful transmit antennas. In the proposed system, the optimal combination of transmit antennas with maximum MRT (Maximum Ratio Transmission) beamforming gain, is selected. Simulation results reveal that the proposed MIMO-OFDMA system achieves significant improvement of spectral efficiency in low CIR region as compared to a typical open-loop MIMO-OFDMA system using pseudo-orthogonal space time block code.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.43-52
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• 2007

As the number of public Web Services increases, there will be many services with the same functionality. These services. however, will vary in their QoS properties, such as price, response time and availability, and it is very important to choose a best service while satisfying given QoS constraints. This paper brings parallel branching and response time constraint of business processes into focus and investigates several service selection plans based on multidimensional multiple choice Knapsack model. Specifically. proposed in the paper are a plan with response time constraints for each execution flow, a plan with a single constraint over the whole service types and a plan with a constraint on a particular execution path of a composite Web Services. Experiments are conducted to observe the performance of each plan with varying the number of services, the number of branches and the values of response time constraint. Experimental results show that reducing the number of candidate services using Pareto Dominance is very effective and the plan with a constraint over the whole service types is efficient in time and solution quality for small to medium size problems.