• The Journal of Information Systems
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• v.19 no.3
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• pp.59-73
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• 2010

Along with advance of information technology, value of information is growing much more than ever. And nearly all organizations pay great attentions to information security to protect their own important informations against every kind of hazardous accidents. Therefore, organizations want to select best information security system among many possible alternatives. For this purpose, several fuzzy AHP decision making approaches can be utilized. In this study, we consider a number of qualitative and quantitative factors to evaluate security systems and then apply three fuzzy AHP approaches for simple case to compare the results from three approaches. We find that final decision depends on both fuzzy AHP methods and degree of fuzziness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.72-83
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• 2005

Recall aims to remove the products hazardous to consumers or users from the commerce. However, a recall with a poor decision making procedure could results in disaster to corporations. Therefore, recall managers should establish a proper recall plan in advance to minimize the damage to business. The purpose of the study is to propose a computerized recall management system(RMSys) to handle recall process systematically and timely manners. RMSys, a recall decision-making procedures software, consists of two different modules such as recall decision-making module and recall procedure module. RMSys on the basis of the world wide web is designed to be compatible to ERP(Enterprise Resources Panning). RMSys could play a role as a management support system to help the corporations recall the hazardous products with minimum efforts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.385-405
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• 2019

High Efficiency Video Coding (HEVC) enables significantly improved compression performance relative to existing standards. However, the advance also requires high computational complexity. To accelerate the intra prediction mode decision, a minimum risk Bayesian classification framework is introduced. The classifier selects a small number of candidate modes to be evaluated by a rate-distortion optimization process using the sum of absolute Hadamard transformed difference (SATD). Moreover, the proposed method provides a loss factor that is a good trade-off model between computational complexity and coding efficiency. Experimental results show that the proposed method achieves a 31.54% average reduction in the encoding run time with a negligible coding loss of 0.93% BD-rate relative to HEVC test model 16.6 for the Intra_Main common test condition.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.21-28
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• 2022

All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.

• Journal of Korea Port Economic Association
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• v.21 no.1
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• pp.1-21
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• 2005

It has been more recent trends in container trade to make bogger ship from shipowners that many more parties concerned are getting involved. Well, it is natural to swift these situations if we have looked into container trade market in present time, which a lot of trade volumes has increased in world economy. Thus, supply side of shipping service needs to employ more capacity in the shipping market, then newbuilding may be compulsory options, that is deployment of larger ships. To cope with market situations as able shipowner, some alternatives can be also adopted, such as newbuilding, chartering and securing the space by strategic alliance. But whatever he does, shipowner has to keep in mind to prepare for the future. This is much more important factor considered to make investment decision in case of newbuilding and then he can make more efficient decision as well. However, there has been a little problems arisen due to larger ship employed on the trade route, which is linked with seaport, shipping companies and freight rates as well. Although shipowner decides to build new larger vessel as one of corporate strategic decision, there are many questions to be considered in advance. Therefore, in order to take more efficient decision, shipowner has to take into an account various situations surrounded, and then it can lead truly thoughtful decision making process.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.70-83
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• 2007

Most of works for skyline queries have focused on static data objects. With the advance in mobile applications, however, the need of continuous skyline queries for moving objects has been increasing. To process continuous skyline queries, the 4-phased decision method of skyline regions has been proposed recently. However, it is not feasible for a large number of data because of the high cost of computing skyline regions. To solve this problem, this paper first provides a theoretical analysis of the 4-phased decision method. Then we propose a progressive decision method of skyline regions for the 4-phased decision method, which consists of a distance-based pruning and an extent shrinking of region decision lines. The proposed method can efficiently reduce the cost of the decision of skyline region in the 4-phased decision method. This paper also presents the experimental results to show the effectiveness of the proposed method.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.5
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• pp.271-279
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• 2023

Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3C
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• pp.271-278
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• 2006

Rate-constrained coding is one of the many coding-efficiency oriented tools of H.264/AVC, but mode decision process of RDO(Rate distortion optimization) requires high computational complexity. Many fast mode decision algorithms have been proposed to reduce the computational complexity of mode decision. In this paper, we propose two algorithms for reduction of mode decision in H.264/AVC, which are the fast reference frame selection and selective intra prediction mode decision. Fast reference frame selection is efficient for inter predication and selective intra prediction mode decision can effectively reduce excessive calculation load of intra prediction mode decision. The simulation results showed that the proposed methods could reduce the encoding time of the overall sequences by 44.63% on average without any noticeable degradation of the coding efficiency.

• Journal of the Korean Statistical Society
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• v.20 no.1
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• pp.67-76
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• 1991

The empirical Bayes version involves ″independent″ repetitions(a sequence) of the component decision problem. With the varying sample size possible, these are not identical components. However, we impose the usual assumption that the parameters sequence $\theta$=($\theta$$_1$, $\theta$$_2$, …) consists of independent G-distributed parameters where G is unknown. We assume that G $\in$ g, a known family of distributions. The sample size $N_i$ and the decisin rule $d_i$ for component i of the sequence are determined in an evolutionary way. The sample size $N_1$ and the decision rule $d_1$$\in$ $D_{N1}$ used in the first component are fixed and chosen in advance. The sample size $N_2$and the decision rule $d_2$ are functions of *see full text($\underline{X}^1$equation omitted), the observations in the first component. In general, $N_i$ is an integer-valued function of *see full text(equation omitted) and, given $N_i$, $d_i$ is a $D_{Ni}$/-valued function of *see full text(equation omitted). The action chosen in the i-th component is *(equation omitted) which hides the display of dependence on *(equation omitted). We construct an empirical Bayes decision rule for estimating normal mean and show that it is asymptotically optimal.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.663-673
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• 2011

In the face of accelerating biodiversity loss and its significance in our coexistence with nature, biodiversity is becoming more crucial in sustainable development perspective. To estimate biodiversity in the future which provides valuable information for decision making system especially in the national level, a quantitative approach must be studied forehand as a baseline of the present status. In this study, we developed a large-scale map of Plant Species Richness (PSR, typical indicator of biodiversity) for Young-dong and Pyung-chang provinces. Due to the accessibility of appropriate data and advance of modelling techniques, reduction of variables without deteriorating the predictive power is considered by applying Genetic algorithm. In addition, a number of Correctly Classified Instances (CCI) with 10-fold cross validation which indicates the predictive power, was carried out for evaluation. This study, as a fundamental baseline, will be beneficial in future land work as well as ecosystem restoration business or other relevant decision making agenda.