• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.39-50
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• 2015

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

• Korean Journal of Agricultural Science
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• v.32 no.2
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• pp.151-167
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• 2005

In this study, certain factors influencing cherry tomato were estimated using system equations. In addition, the amount of influence to income from each factor was estimated from both direct and indirect effects. Based on OLS(Ordinary Least Squares) estimation, path analysis and factor analysis were employed to overcome multicollinearity problems. Data used in this study is interviewed cross sectional data of 65 cherry tomato producing farm in Chungnam-do area. Average age of the producers is 46.5. Average year of the production is 8 years. Average farm size, productivity, and income are 1,123 pyong, 7,439kg/10a, 8,112,000won/10a, respectively. The business performance of the sample farms were above average, in terms of the diagnosis by "Standard Business Diagnosis for Cherry tomato". To identify the factors influencing productivity, 15, 19, and 25 independent variables were selected for the dependent variables of yield, price(quality), and business cost, respectively. Finally, yield, quality, and business cost variables were set as independent variables to explain income as dependent variable. As a result of main factor analysis, 10, 12, 15, and 16 factors were identified as main factors for yield, quality, business cost, and income, respectively.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.145-154
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• 2020

The purpose of this study is to analyze how the problems caused by overtourism affect the quality of life of Jeju residents and their perceptions of the overtourism phenomenon by exploring related factors for future policy implications. In the research model, each independent factor related to tourists affects the quality of life of residents, and the mediation variable (QoL) ultimately agrees with overtourism. This study uses Partial Least Squares-Structural Equation Modeling (PLS-SEM), which is less influenced by the sample size. The research is based on 360 questionnaires. The test results showed that cultural factors affected the QoL statistically at 1% significance level, and economic factors were significant at 5%. The quality of life variable affects the agreement of overtourism (p-value 1% significance level). An indirect effect analysis on whether each independent factor affects the overtourism factor through the parameter of the QoL of the residents showed that the cultural factor at 5% level statistically affected it, and economic factors were significant at 10%. In conclusion, we recommend implementing both economic and cultural factors to reduce the negative perception of overtourism for the policy planning. Further research in multiple aspects should be continued to overcome the vulnerability of the Island destination tourism.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.225-236
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• 2020

This paper examines empirically the net impact of residual state ownership on privatized firm efficiency in the transitional context of Vietnam. Vietnamese privatization has its own characteristics. Instead of mass and full privatization, Vietnam has chosen a partial and gradual path. Thus, it is important to assess the net impact of residual state ownership on privatized firms during the post-privatization period. This study employs stochastic frontier analysis to investigate the association between residual state ownership and the efficiency of privatized firms, using a sample of all privatized firms that are listed on the Vietnamese stock exchanges over the period from 2007 to 2017. Also, two-stage least squares regression is incorporated into the model to deal with potential endogeneity issues. Our study provides evidence that state ownership should not be considered as a pure source of agency problems. Indeed, the net impact of residual state ownership on privatized firm efficiency is non-monotonic, and the relationship between residual state ownership and privatized firm efficiency is under an inverted U-shape. A moderate level (less than 50%) of residual state ownership might be beneficial to privatized firm efficiency whereas too much state ownership is detrimental to the efficiency of privatized firms.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.40-49
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• 1995

This paper presents the performance and problems in analysis method and testing system of Electronic Speckle Pattern Interferometry (ESPI) method, in measuring two - dimensional in-plane displacement. The anyalysis result of measurement by ESPE is quite comparable to that tof measurement by strain gauge method. This implies that the method of ESPE is a very effective tool in non-contact two-dimensional in-plane strain analysis. But there is a controversal point, measurment error. This error is discussed to be affected not by ESPE method itself, but by its analysis scheme of the interference fringe, where the first-order interpolation has been applied to the points of strain measured. In this case, it is turned out that the more errors would be occurred in the large interval of fringe. And so this paper describes a computer method for drawing when the height is available only for some arbitrary collection of points. The method is based on a distance-weighted, last- squares approximation technique with the weight varying with the distance of the data points.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.117-137
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• 2010

Iris recognition is a biometric technique which uses unique iris patterns between the pupil and sclera. The advantage of iris recognition lies in high recognition accuracy; however, for good performance, it requires the diameter of the iris to be greater than 200 pixels in an input image. So, a conventional iris system uses a camera with a costly and bulky zoom lens. To overcome this problem, we propose a new method to restore a low resolution iris image into a high resolution image using a single image. This study has three novelties compared to previous works: (i) To obtain a high resolution iris image, we only use a single iris image. This can solve the problems of conventional restoration methods with multiple images, which need considerable processing time for image capturing and registration. (ii) By using bilinear interpolation and a constrained least squares (CLS) filter based on the degradation model, we obtain a high resolution iris image with high recognition performance at fast speed. (iii) We select the optimized parameters of the CLS filter and degradation model according to the zoom factor of the image in terms of recognition accuracy. Experimental results showed that the accuracy of iris recognition was enhanced using the proposed method.

• Management & Information Systems Review
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• v.7
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• pp.427-450
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• 2001

This research suggests the estimation methodology of Logistics. This paper elucidates the main problems associated with estimation in the regression model. We review the methods for estimating the parameters in the model and introduce a modified procedure in which all models are fitted and combined to construct a combination of estimates. The resulting estimators are found to be as efficient as the maximum likelihood (ML) estimators in various cases. Our method requires more computations but has an advantage for large data sets. Also, it enables to detect particular features in the data structure. Examples of real data are used to illustrate the properties of the estimators. The backgrounds of estimation of logistic regression model is the increasing logistic environment importance today. In the first phase, we conduct an exploratory study to discuss 9 independent variables. In the second phase, we try to find the fittest logistic regression model. In the third phase, we calculate the logistic estimation using logistic regression model. The parameters of logistic regression model were estimated using ordinary least squares regression. The standard assumptions of OLS estimation were tested. The calculated value of the F-statistics for the logistic regression model is significant at the 5% level. The logistic regression model also explains a significant amount of variance in the dependent variable. The parameter estimates of the logistic regression model with t-statistics in parentheses are presented in Table. The object of this paper is to find the best logistic regression model to estimate the comparative accurate logistics.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.513-517
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• 2006

Autonomous Underwater Vehicles plays specific role in underwater investigation. Generally, this kind of vehicles will move along a planned path for sea bottom or underwater installations inspections, search for mineral deposits along shelves, seeking lost items including bottom mines or for hydrographic measurements. A crucial barrier for it remains the possibility of precise determination of their underwater position. Commonly used radionavigation systems do not work in such circumstances or do not guarantee the required accuracies. In the paper some new solution is proposed on the assumption that it is possible to increase the precision by certain processing of a combination of measurements conducted by means of different techniques. Objective of the paper is the idea of navigation of AUV which consists of two phases: firstly a trip of AUV along pre-planned route and after that postprocessed transformation of collected data in post-processing mode. During the processing of collected data the modern adjustment methods have been applied, mainly estimation by means of least squares and M-estimation. Application of these methods should be associated with the measuring and geometric conditions of navigational tasks and thus suited for specific scientific and technical problems of underwater navigation. The first results of computer aided investigation will be presented and the basic scope of these application and possible development directions will be indicated also. The paper is prepared as an partial results of the works carried out within a framework of the research Project: 'Improvement of the Precise Underwater Vehicle Navigation Methods' financed by the Polish Ministry of Education and Science (No 0 T00A 012 25).