• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.9
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• pp.81-92
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• 1998

A new hybrid technique using several sub-populations having completely different evolutionary behaviors is proposed to increase the possibility to quickly find the global optimum of continuous optimization problem. It has three sub-populations. Two NPOSA algorithms showing good performance in the problem having a rugged fitness function are applied to two sub-populations and a self-adaptive evolutionary algorithm to the other sub-population. Sub-populations evolve in different manners and the interaction among these sub-populations lead to the global optimum quickly. The efficiency of this technique is verified through benchmark test functions. Finally, the algorithm with three sub-populations has been applied to seek for the optimal camera calibration parameters. After an error function has been defined using measured feature points of a calibration block, it has been shown that the algorithm searches for the camera parameters that minimize the error function.

• Korean Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.57-67
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• 1998

Genetic programming (GP) is an extension of a genetic algoriths paradigm, deals with tree structures representing computer programs as individuals. In recent, there have been many research activities on applications of GP to various engineering problems including system identification, data mining, function approximation, and so forth. However, standard GP suffers from the lack of the estimation techniques for numerical parameters of the GP tree that is an essential element in treating various engineering applications involving real-valued function approximations. Unlike the other research activities, where nonlinear optimization methods are employed, I adopt the use of a weighted linear associative memory for estimation of these parameters under GP algorithm. This approach can significantly reduce computational cost while the reasonable accurate value for parameters can be obtained. Due to the fact that the GP algorithm is likely to fall into a local minimum, the GP algorithm often fails to generate the tree with the desired accuracy. This motivates to devise a group of additive genetic programming trees (GAGPT) which consists of a primary tree and a set of auxiliary trees. The output of the GAGPT is the summation of outputs of the primary tree and all auxiliary trees. The addition of auxiliary trees makes it possible to improve both the teaming and generalization capability of the GAGPT, since the auxiliary tree evolves toward refining the quality of the GAGPT by optimizing its fitness function. The effectiveness of this approach is verified by applying the GAGPT to the estimation of the principal dimensions of bulk cargo ships and engine torque of the passenger car.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.586-591
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• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.43-48
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• 2014

Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.53-62
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• 2009

Lee[15] examined quantity discount contracts between a manufacturer and a retailer in a stochastic, two-period inventory model where quantity discounts are provided based on the previous order size. During the two periods, the retailer faces stochastic (truncated Poisson distributed) demands and he/she places orders to meet the demands. The manufacturer provides for the retailer a price discount for the second period order if its quantity exceeds the first period order quantity. In this paper we extend the above two-period model to a k-period one (where k < 2) and propose a stochastic nonlinear mixed binary integer program for it. In order to make the program tractable, the nonlinear term involving the sum of truncated Poisson cumulative probability function values over a certain range of demand is approximated by an i-interval piecewise linear function. With the value of i selected and fixed, the piecewise linear function is determined using an evolutionary algorithm where its fitness to the original nonlinear term is maximized. The resulting piecewise linear mixed binary integer program is then transformed to a mixed binary integer linear program. With the k-period model developed, we suggest a solution procedure of receding horizon control style to solve n-period (n < k) order decision problems. We implement Lee's two-period model and the proposed k-period model for the use in receding horizon control style to solve n-period order decision problems, and compare between the two models in terms of the pattern of order quantities and the total profits. Our computational study shows that the proposed model is superior to the two-period model with respect to the total profits, and that order quantities from the proposed model have higher fluctuations over periods.

• Fashion & Textile Research Journal
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• v.21 no.4
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• pp.468-479
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• 2019

This study used a three-dimensional-analysis method to quantitatively analyze the change of clothing appearance according to a gusset added to bodice and sleeve patterns for men in their twenties. Comparing six different patterns, the study found that patterns P1 and P2 with little gusset did not have a large difference in the strain map, and pattern P6 had no gusset in the motion of raising the arm $28^{\circ}$ to the side (M1). When the arm was raised $45^{\circ}$ to the side (M2), the P1 pattern had the smallest deformation, and only the P5 pattern had a large deformation from the neck to the armhole area. In contrast, except for in the P3 pattern, large wrinkles formed in the front and back when the arm was raised above $158^{\circ}$ (M3) from the side of the waist to the armpit. In addition, P3 had the smallest change in the hem of the bodice and sleeves. However, the appearance of P2, P3, and P5 was excellent when the arm was moved forward (M4), and the P2 and P5 patterns were the smallest at the bodice and sleeve hem. The P6 pattern showed the least fitness in terms of function. In the case of raising the arm, there was a strong correlation between gusset size and motion function, but when the motion of the arm changed, the motion function did not improved just by changing the ease size.

• Journal of Life Science
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• v.26 no.1
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• pp.129-139
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• 2016

Exercise increases the expression and interaction of major neurotrophic factors such as brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1 (IGF-1), and vascular endothelial growth factor (VEGF) at both central and peripheral tissues, which contributes to improved brain and neural plasticity and cognitive function. Previous findings have been to understand the effect of light or moderate intensity aerobic exercise on neurotrophic factors and cognitive function, not that of high intensity aerobic exercise. However, recent findings suggest that high intensity interval training is a safe, less time-consuming, efficient way to improve cardiorespiratory fitness and weight control, thus American College of Sport Medicine (ACSM)’s guidelines for exercise prescription for various adult populations also recommend the application of high intensity interval training to promote their overall health. High intensity interval training also enhances the expression of BDNF, IGF-1, and VEGF at the brain and peripheral tissues, which improves cognitive function. Increased frequency of intermittent hypoxia and increased usage of lactate as a supplementary metabolic resource at the brain and neural components are considered a putative physiological mechanism by which high intensity interval training improves neurotrophic factors and cognitive function. Therefore, future studies are required to understand how increased hypoxia and lactate usage leads to the improvement of neurotrophic factors and what the related biological mechanisms are. In addition, by comparing with the iso-caloric moderate continuous exercise, the superiority of high intensity interval training on the expression of neurotrophic factors and cognitive function should be demonstrated by associated future studies.

• Journal of Dental Rehabilitation and Applied Science
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• v.30 no.1
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• pp.16-22
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• 2014

Purpose: The purpose of this study was to assess the marginal and mesial fitness of zirconia single copings and 3-unit fixed partial dentures (FPDs) manufactured with an identical model. Materials and Methods: An epoxy model in which the maxillary right 2nd premolar is lost and maxillary 1st premolar and 2nd molar are formed as abutments was manufactured and scanned by using a laser scanner. A ten units of zirconia single copings were manufactured for maxillary 1st premolar and 2nd molar, respectively and the same number of 3-unit FPDs were manufactured. For the measurements of fitness, the manufactured silicone replicas were divided into four parts and the fitness were measured by digital microscope at measurement points (P1, P2, P3, P4 and P5) of each plane. The measured gaps were classified into three categories: marginal gap (MG, P1), axial gap (AG, average of P2 and P3), occlusal gap (OG, average of P4 and P5). Results: The ranges of MG, AG and OG for single copings were 18.47 - 40.54 ${\mu}m$, 39.73 - 73.61 ${\mu}m$ and 116.90 - 134.69 ${\mu}m$, respectively. The ranges of MG, AG and OG for 3-unit FPDs were 45.95 - 87.44 ${\mu}m$, 23.78 - 57.00 ${\mu}m$ and 99.89 - 131.06 ${\mu}m$, respectively. Conclusion: The result of the study shows that the MGs for 3-unit FPDs were higher than those of single copings, though they are within the range of clinical acceptance, indicating that the use of more homogeneous zirconia block and modification of sintering processes are needed to ensure the prevention of increase of gap in 3-unit FPDs.

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.6
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• pp.1062-1070
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• 1996

Concept of the comfort and fitness has become a major concern in the basic function of the ready-made clothes. Until now, ready-made clothes were not made by on the basis of the bodytype, but by the body size only. This research was performed to classify and characterize the bodytypes of Korean adult males. Sample size was 1290 subjects and their age range was from 19 to 54 years old. 15 variables from the photographic data of 1112 subjects were applied to analyse the bodytype of th\ulcorner lower part of trunk. Data were analyzed by the multivariate method, especially factor and cluster analysis. The groups forming a cluster can be subdivided into 5 sets by crosstabulation extracted by the hierarchical cluster analysis. 5 bodytypes classified by the photographic sources could be combined with the anthropcmetric data and were demonstrated with 5 silhouette. Type 2 and 3 in the lower part of trunk were dominant and were composed of the majority of 56.8% of the subjects. Bodytypes of Korean males were influenced by the degree of posture erectness and of curvature of the front side of the body in waist and abdomen.