• Journal of the military operations research society of Korea
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• v.22 no.1
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• pp.30-42
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• 1996

This study deals with an optimal firecar location and allocation models under uncertain enemy attack at the airforce base. It allocates just one firecar on the runaway and the other firecars on the headquarter of firecar company in usual situation. It is possible for several facilities at the airforce base to be attacked simultaneously by missiles enemy air attacker, other things under uncertain enemy attack. We formulate two stochastic LP location-allocation models to deal with uncertainty. One model is to locate all firecars on one site like present situation. We generate a new firecar location with a weighted average method. We call this model "centralized allocation model". The other model is to distribute firecars on several possible sites. We call this model "distributed allcoation model". Finally, we compare two models with computer experimentations on 8 airforce bases.on 8 airforce bases.

• 한국연소학회:학술대회논문집
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• 1997.06a
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• pp.113-122
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• 1997

The conditional moment closure formulation considering the molecular and turbulent diffusion is derived. A simplified solution procedure is proposed to reduce the computational burden due to the increased dimensionality of the conditionally averaged variables. A conditionally averaged variable is expressed as a linear weighted average of the two extremes, 'no reaction' and 'equilibrium' states. The modified elliptic-type conditional moment closure formulation is implemented to simulate a two dimensional nonpremixed mixing layer reacting flow. Results show good agreement for the conditional averages of the species concentration in Bilger et al.

• Journal of the Korean Statistical Society
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• v.18 no.2
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• pp.118-124
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• 1989

In the one-way random effect model, we often estimate the variance components by the ANOVA method and then estimate the population mean. Whe there are only two distint group sizes, the conventional mean estimator is represented as a weighted average of two normal means with weights being the function of variance component estimators. In this paper, we will study a method which can compute the exact variance of the mean estimator when we set the negative variance component estimate to zero.

• Journal of the military operations research society of Korea
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• v.15 no.1
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• pp.28-43
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• 1989

Squadron crew scheduling problems can be defined as the assignment of crews to flights consistent with safety regulations and squadron policy. In this paper, the daily crew scheduling problems are formulated as zero-one interger programs known as generalized assignment problems. The objective function is to maximize the weighted mission interval to improve the crew performance. Flight schedules using the 0-1 integer model are compared with manual schedules. The results of the study show that the average crew performance is improved.

• Korean Journal of Hydrosciences
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• v.7
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• pp.125-137
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• 1996

The subject of the paper is the selection of the number and location of raingauge stations among existing ones for the computation of mean areal precipitation and for use as input of real-time flow prediction models. The weighted average method developed by National Weather Service was used to compute MAP over the Boone River basin in Iowa with a 40 year daily data set. Two different searching methods were used to find local optimal solutions. An operational rainfall-runoff model was used to determine the optimal location and number of stations for flow prediction.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• ETRI Journal
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• v.31 no.2
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• pp.237-239
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• 2009

A whole-body voxel model of a 7-year-old male volunteer was developed from 384 axial magnetic resonance images (MRIs). The MRIs were acquired with intervals of 3 mm for the entire body in a body coil. In order to reduce the MRI acquisition time for the child, the repetition and echo times under T1 weighted image were chosen to be 566 ms and 8 ms, respectively. The MRIs were classified according to 30 types of tissues with known electrical parameters. The developed voxel model was adjusted to the physical average of 7-year-old Korean boys. The body weight of the adjusted model, calculated with the mass tissue densities, is within a 6% difference from the 50th percentile weight.

• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.170-178
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• 2012

Dipterocarps or Dipterocarpaceae is a commercially important timber producing and dominant keystone tree family in the rain forests of Borneo. Borneo's landscape is changing at an unprecedented rate in recent years which affects this important biodiversity. This paper attempts to model the natural occurrence (distribution including those areas with natural forests before being converted to other land uses as opposed to current distribution) of dipterocarp species in Sarawak which is important for forest biodiversity conservation and management. Local modeling method of Inverse Distance Weighting was compared with commonly used statistical method (Binary Logistic Regression) to build the best natural distribution models for three genera (12 species) of dipterocarps. Database of species occurrence data and pseudoabsence data were constructed and divided into two halves for model building and validation. For logistic regression modeling, climatic, topographical and edaphic parameters were used. Proxy variables were used to represent the parameters which were highly (p>0.75) correlated to avoid over-fitting. The results show that Inverse Distance Weighting produced the best and consistent prediction with an average accuracy of over 80%. This study demonstrates that local interpolation method can be used for the modeling of natural distribution of dipterocarp species. The Inverse Distance Weighted was proven a better method and the possible reasons are discussed.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.625-632
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• 2020

In the diagnosis of lung cancer, the tumor size is measured by the longest diameter of the tumor in the entire slice of the CT. In order to accurately estimate the size of the tumor, it is better to measure the volume, but there are some limitations in calculating the volume in the clinic. In this study, we propose an algorithm to segment lung cancer by applying a custom loss function that combines focal loss and dice loss to a U-Net model that shows high performance in segmentation problems in chest CT images. The combination of values of the various parameters in custom loss function was compared to the results of the model learned. The purposed loss function showed F1 score of 88.77%, precision of 87.31%, recall of 90.30% and average precision of 0.827 at α=0.25, γ=4, β=0.7. The performance of the proposed custom loss function showed good performance in lung cancer segmentation.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.381-399
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• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.