• Wind and Structures
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• v.27 no.6
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• pp.381-397
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• 2018

Nonlinear behavior in fluid-structure interaction (FSI) of bridge decks becomes increasingly significant for modern bridges with increasing spans, larger flexibility and new aerodynamic deck configurations. Better understanding of the nonlinear aeroelasticity of bridge decks and further development of reduced-order nonlinear models for the aeroelastic forces become necessary. In this paper, the amplitude-dependent and neutral angle dependent nonlinearities of the motion-induced loads are further highlighted by series of computational fluid dynamics (CFD) simulations. An effort has been made to investigate a semi-analytical time-domain model of the nonlinear motion induced loads on the deck, which enables nonlinear time domain simulations of the aeroelastic responses of the bridge deck. First, the computational schemes used here are validated through theoretically well-known cases. Then, static aerodynamic coefficients of the Great Belt East Bridge (GBEB) cross section are evaluated at various angles of attack, leading to the so-called nonlinear backbone curves. Flutter derivatives of the bridge are identified by CFD simulations using forced harmonic motion of the cross-section with various frequencies. By varying the amplitude of the forced motion, it is observed that the identified flutter derivatives are amplitude-dependent, especially for $A^*_2$ and $H^*_2$ parameters. Another nonlinear feature is observed from the change of hysteresis loop (between angle of attack and lift/moment) when the neutral angles of the cross-section are changed. Based on the CFD results, a semi-analytical time-domain model for describing the nonlinear motion-induced loads is proposed and calibrated. This model is based on accounting for the delay effect with respect to the nonlinear backbone curve and is established in the state-space form. Reasonable agreement between the results from the semi-analytical model and CFD demonstrates the potential application of the proposed model for nonlinear aeroelastic analysis of bridge decks.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.1
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• pp.313-332
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• 2009

Cross-language text categorization(CLTC) can classify documents automatically using training set from other language. In this study, collections appropriated for CLTC were extracted from KTSET. Classification performance of various CLTC methods were compared by SVM classifier using machine translation. Results showed that the classification performance in the order of poly-lingual training method, training-set translation and test-set translation. However, training-set translation could be regarded as the most useful method among CLTC, because it was efficient for machine translation and easily adapted to general environment. On the other hand, low performance was shown to be due to the feature reduction or features with no subject characteristics, which occurred in the process of machine translation of CLTC.

• Journal of Technologic Dentistry
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• v.31 no.3
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• pp.67-85
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• 2009

This study aims to provide the research for dental technician's stress prevention and management with basic materials by understanding dental technician's psychosocial stress level and examining relevant factors. The subject of this study is 255 dental technologists who work mainly in Seoul Gyeonggi district for a month of April of 2009 and I conducted cross-sectional study through self administered survey. The contents of survey include general feature, occupational feature, health behavior feature. I used Karasek's Job Content Questionnaire, JCQ and Psychosocial well-being index, PWI-SF as means of measurement. To compare the level of dental technician's psychosocial stress, I conducted t-test and ANOVA and I measured the factors that are related with psychosocial stress symptom with step by step multiple regressive analysis. According to the result of Cronbach's a value which is yielded to verify the reliability of means of measurement, the reliability of concept is sufficient. The detailed result of this study is as follows. 1. According to the result of analyzing the stress symptom in accordance with general feature and occupational feature, those dental technologists who are older and not married, graduate from junior college, have lower position, work at university hospital or general hospital show lower stress(p<0.05). There is no difference in the level of psychosocial stress with regard to duty related feature, period of service, daily average working hours, monthly average pay. 2. With regard to health behavior feature, those dental technologists who control weight better and have meal more regularly show lower stress(p<0.05). Those dental technicians who smoke, drink liquid and take a suitable sleep show low stress but the difference does not have significance statistically. 3. With regard to the factors of stress in the workplace, those dental technicians who have lower duty related requirement, have higher duty related control ability, have higher social support, have less instability of employment and have less workload and physical burden show lower stress(p<0.05). 4. According to the result of analyzing the factors that influence dental technologist's stress symptom, social support has the most enormous influence on stress symptom. Unstable employment, regular exercise, regular eating, daily average sleeping hours and technological capacity are also important in this order. According to the result of this study, those dental technicians who have higher social support, less instability of employment, do exercise more regularly, take enough sleep more soundly and have higher technological capacity show lower psychosocial stress symptom. Therefore, to adjust appropriately the dental technician's stress and properly maintain and improve the dental technician's mental health, effective management plan that enables dental technicians to maintain smooth human relationships for dental technicians should be sought. In addition, heath education and health management for dental technicians should be given more thoroughly so that they can establish desirable health behavior in daily life.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.3
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• pp.173-180
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• 1992

A feature typical of natural rivers is the bend. The purpose of this study is to examine hydraulic and morphometric characteristics in channel bend reach by the deterministic approach. Cross section shape factor, "As" is suggested for a new cahracteristic factor of channel bend reach analysis. The variation of this new factor along the river reach showed the location of the concentration of the force due to the current all over the reach, that is curved or not. Some general meander factors are used for correlation with new factor suggested, and the applicability of "As" is verified. The range R/W values are concentrated 2~4, the meanning of this value can be regarded to the warning for bank erosion or breaking. And this paper dealt with prediction of cross section bed shape variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3756-3777
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• 2019

Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.155-164
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• 2016

Algal blooms not only destroy fish habitats but also diminish biological diversity of ecosystem which results into water quality deterioration of 4 major rivers in South Korea. The relationship between algal bloom and environmental factors had been analyzed through the cross-correlation function between concentration of chlorophyll a and other environmental factors. However, time series of cross-correlations can be affected by the stochastic structure such auto-correlated feature of other controllers. In order to remove external effect in the correlation analysis, the pre-whitening procedure was implemented into the cross correlation analysis. The modeling process is consisted of a series of procedure (e.g., model identification, parameter estimation, and diagnostic checking of selected models). This study provides the exclusive correlation relationship between algae concentration and other environmental factors. The difference between the conventional correlation using raw data and that of pre-whitened series was discussed. The process implemented in this paper is useful not only to identify exclusive environmental variables to model Chl-a concentration but also in further extensive application to configure causality in the environment.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.227-235
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• 2000

Agricultural products are easily deformable its shape because of some external forces. However, these force behavior is difficult to measure quantitatively. Until now, many researches on the mechanical property was performed with various methods such as material testing, chemical analysis and non-destructive methods. In order to investigate force behavior on the cellular unit of agricultural products, electro-microscope based 3D image processing method will contribute to analysis of plant cells behavior. Before image measurement of plant cells, plant sample was cut off cross-sectioned area in a size of almost 300-400 ${\mu}$ m units using the micron thickness device, and some of preprocessing procedure was performed with fixing and dyeing. However, the wall structure of plant cell is closely neighbor each other, it is necessary to separate its boundary pixel. Therefore, image merging and shrinking algorithm was adopted to avoid disconnection. After then, boundary pixel was traced through thinning algorithm. Each image from the electro-microscope has a information of x,y position and its height along the z axis cross sectioned image plane. 3D image was constructed using the continuous image combination. Major feature was acquired from a fault image and measured area, thickness of cell wall, shape and unit cell volume. The shape of plant cell was consist of multiple facet shape. Through this measured information, it is possible to construct for structure shape of unit plant cell. This micro unit image processing techniques will contribute to the filed of agricultural mechanical property and will use to construct unit cell model of each agricultural products and information of boundary will use for finite element analysis on unit cell image.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.571-579
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• 2005

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.32 no.5
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• pp.14-25
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• 2000

In this investigation, a wavelet transform analysis was used to decompose beta-radiographic formation images into spectral and spatial components. Conventional formation analysis may use spectral analysis, based on Fourier transformation or variance vs. zone size, to describe the grammage distribution of features such as flocs, streaks and mean fiber orientation. However, these methods have limited utility for the analysis of statistically stationary data sets where variance is not uniform with position, e.g. paper machine CD profiles (especially those that contain streaks). A continuous wavelet transform was used to analyze formation data arrays obtained from radiographic imaging of handsheets and cross machine paper samples. The response of the analytical method to grammage, floc size distribution, mean fiber orientation an sensitivity to feature localization were assessed. From wavelet analysis, the change in scale of grammage variation as a function of position was used to demonstrate regular and isolated differences in the formed structure.

• 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.