• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.165-171
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• 2003

Visual search in an unstructured search field is a fruitful research area for computational modeling. Search models that describe relationship between search time and probability of target detection have been used for prediction of human search performance and provision of ideal goals for search training. Until recently, however, most of models were focused on detecting a single target in a search field, although, in practice, a search field includes multiple targets and search models for multiple targets may differ from search models for a single target. This study proposed a random search model for multiple targets, generalizing a random search model for a single target which is the most typical search model. To test this model, human search data were collected and compared with the model. This model well predicted human performance in visual search for multiple targets. This paper also proposed how to determine optimal stopping time in multiple-target search.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.7
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• pp.923-927
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• 2008

The objective of this study was to compare random regression model and multiple trait animal model estimates of the (co) variance of total sperm cells over the active lifetime of AI boars. Data were provided by Smithfield Premium Genetics (Rose Hill, NC). Total number of records and animals for the random regression model were 19,629 and 1,736, respectively. Data for multiple trait animal model analyses were edited to include only records produced at 9, 12, 15, 18, 21, 24, and 27 months of age. For the multiple trait method estimates of genetic and residual variance for total sperm cells were heterogeneous among age classifications. When comparing multiple trait method to random regression, heritability estimates were similar except for total sperm cells at 24 months of age. The multiple trait method also resulted in higher estimates of heritability of total sperm cells at every age when compared to random regression results. Random regression analysis provided more detail with regard to changes of variance components with age. Random regression methods are the most appropriate to analyze semen traits as they are longitudinal data measured over the lifetime of boars.

• Human Ecology Research
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• v.58 no.4
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• pp.539-560
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• 2020

This study presents work-family multiple-role planning by female university students as a new approach to worklife balance. Accordingly, this study examines university years as a key time frame during which students establish their career paths. This study integrates the social cognitive career theory and the planned behavior theory to design and evaluate a model that explains the work-family multiple-role planning process; in addition, it develops an optimal model to predict the intentions of female university students in work-family multiple-role planning. This study has conducted a structural survey with 500 female university students. After inspecting the data, the responses of 435 participants were used in the data analysis (SEM) with SPSS 21.0 and AMOS 21.0. The findings include the following. First, suitability of predictive model presents a satisfying fit. The major factors in this study's model (parental support, subjective norms, attitudes toward multiple-role planning, career decision self-efficacy, and outcome expectations) are verified as direct and indirect predictors of the work-family multiple-role planning intent of female university students. Second, the strongest predictive factor for the work-family multiple-role planning intent is the social environment factor (subjective norms), indicating that the influence of social pressure on intent is relatively large. The predictive model formulated under this study's integrated theoretical framework supplements existing research that focused on attitudes toward multiple-role planning as well as provides a more profound theoretical foundation on which work-family multiple-role planning behaviors can be better understood.

• Journal of the military operations research society of Korea
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• v.7 no.2
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• pp.63-99
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• 1981

The generalized goal decomposition model proposed by Ruefli as a single period decision model is presented for the purpose of a review and extended to make a multiple period planning model. The multiple period planning model in the three level organization is formulated with, linear goal deviations by introducing the goal programming method. Dynamic formulation using the generalized goal decomposition model for each single period problem is also presented. An iterative search algorithm is presented as an appropriate solution method of the dynamic formulation of the multiple period planning model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.660-667
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• 2003

In this study, a Multiple Objective Mixed Integer Programming (MOMIP) Model is developed for sewer rehabilitation planning by considering cost, inflow/infiltration. A sewer rehabilitation planning model is required to decide the economic life of the sewer by considering trade-off between cost and inflow/infiltration. And it is required to find the optimal rehabilitation timing, according to the cost effectiveness of each sewer rehabilitation within the budget. To develop such a model, a multiple objective mixed integer programming model is formulated based on network flow optimization. The network is composed of state nodes and arcs. The state nodes represent the remaining life and the arcs represent the change of the state. The model consider multiple objectives which are cost minimization and minimization of inflow/infiltration. Using the multiple objective optimization, the trade-off between the cost and inflow/infiltration is presented to the planner so that a proper sewer rehabilitation plan can be selected.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2E
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• pp.100-106
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• 2010

Multiple-model speech recognizer has been shown to be quite successful in noisy speech recognition. However, its performance has usually been tested using the general speech front-ends which do not incorporate any noise adaptive algorithms. For the accurate evaluation of the effectiveness of the multiple-model frame in noisy speech recognition, we used the state-of-the-art front-ends and compared its performance with the well-known multi-style training method. In addition, we improved the multiple-model speech recognizer by employing N-best reference HMMs for interpolation and using multiple SNR levels for training each of the reference HMM.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2340-2344
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• 2003

This paper considers a tracking filter algorithm which can track a maneuvering target. Multiresolutional Interacting Multiple Model (MRIMM) algorithm is proposed to reduce computational burden. In this paper multiresolutional state space model equation and multiresolutional measurement equation are derived by using wavelet transform. This paper shows the outline of MRIMM algorithm. Simulation results show that MRIMM algorithm maintains a good tracking performance and reduces computational burden.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.1
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• pp.29-43
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• 2012

We design a DEA-AR model using multiple regression analysis with new methods which limit weights. When there are multiple input and single output variables, our model can be used, and the weights of input variables use the regression coefficient and coefficient of determination. To verify the effectiveness of the new model, we evaluate the efficiency of the Regional Corporations in Korea. Accordance with statistical analysis, it proved that there is no difference between the efficiency value of the DEA-AR using AHP and our DEA-AR model. Our model can be applied to a lot of research by substituting DEA-AR model relying on AHP in the future.

• Ocean and Polar Research
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• v.37 no.2
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• pp.161-177
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• 2015

This study analyzes the stock assessment of yellow croaker caught mainly by the Korean stow net and gill net fisheries focusing on single species and multiple fisheries. This study standardizes fishing efforts for the two fisheries using the general linear model and uses a surplus production model based on the exponential growth model. The Clarke Yoshimoto Pooley model estimates a maximum sustainable yield(MSY), an allowable biological catch(ABC), fishing efforts for MSY($E_{MSY}$) and for ABC($E_{ABC}$). The bio-economic model is used to estimate the maximum economic yield(MEY) and fishing efforts for MEY($E_{MSY}$). Also, the study employs an economic analysis to estimate the economic interaction between stow net and gill net fisheries. The economic analysis shows the profit accruing to the two fisheries from estimated ABC. Finally, the study compares TACs based on single species and single fishery to TAC based on single species and multiple fisheries. The study proposes that the TAC assessment is necessary for single species and multiple fisheries in order to preserve resources.