• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.417-425
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• 2003

H.264, which is the latest video coding standard of both ITU-T(International Telecommunication Union-Telecommunication standardization sector) and MPEG(Moving Picture Experts Group), adopts new video coding tools such as variable block size motion estimation, multiple reference frames, quarter-pel motion estimation/compensation(ME/MC), 4${\times}$4 Integer DCT(Discrete Cosine Transform), and Rate-Distortion Optimization, etc. These new video coding tools provide good coding of efficiency compared with existing video coding standards as H.263, MPEG-4, etc. However, these new coding tools require the increase of encoder complexity. Therefore, in order to apply H.264 to many real applications, fast algorithms are required for H.264 coding tools. In this paper, when encoder MacroBlock(MB) mode is decided by rate-distortion optimization tool, fast mode decision algorithm by skipping variable block size ME/MC and spatial-predictive coding, which occupies most encoder complexity, is proposed. In terms of computational complexity, the proposed method runs about 4 times as far as JM(Joint Model) 42 encoder of H.264, while the PSNR(peak signal-to-noise ratio)s of the decoded images are maintained.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Journal of the Korean Solar Energy Society
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• v.24 no.4
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• pp.55-63
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• 2004

The purpose of this study is to development of life cycle cost analysis methodology of HVAC system for decision maker. The results of this study are as follows; maintenance/management, equipment construction, planning/design, and demolition/sell phases (1) To develop the cost breakdown structure for LCC in HVAC system, this study apply the method of additional pertinent level, title, CBS number, block number and variable index. (2) LCC analysis order of HVAC system compose four phase. (3) Life cycle costing influence diagram can bring us to make the most efficient decision through a visual graphical diagram that is shown relationship among variables and that decision maker traces easily from life cycle cost analysis situation.

• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.93-100
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• 2020

The purpose of this study was to examine the relationship between career decision-making self-efficacy, character strength, and gratitude as a factor influencing the happy life of adults. Subjects were 89 adults in university and graduate school. SPSS 23.0 and PROCESS Macro were used for data analysis. Significant correlations were found between career decision-making self-efficacy, character strength, gratitude, and happy life. The direct effect of career decision-making self-efficacy, an independent variable, on happy life, a dependent variable, was not significant. Character strength and gratitude, which are mediators, were significant. Therefore, the indirect effect and the total effects showed significant results. Career decision-making self-efficacy does not directly predict a happy life, but if it mediates character strengths and gratitude, it means that a happy life can be predicted.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.267-276
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• 2004

Much of previous attention on researches of the decision tree focuses on the splitting criteria and optimization of tree size. Nowadays the quantity of the data increase and relation of variables becomes very complex. And hence, this comes to have plenty number of unnecessary node and leaf. Consequently the confidence of the explanation and forecasting of the decision tree falls off. In this research report, we propose some decision tree algorithms considering the interaction of predictor variables. A generic algorithm, the k-1 Algorithm, dealing with the interaction with a combination of all predictor variable is presented. And then, the extended version k-k Algorithm which considers with the interaction every k-depth with a combination of some predictor variables. Also, we present an improved algorithm by introducing control parameter to the algorithms. The algorithms are tested by real field credit card data, census data, bank data, etc.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.417-425
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• 2020

This paper investigates the potential impact of external factors on bid/no-bid decision for supervision consultant service on construction project in Hanoi, the capital of Vietnam. The data used in this study are secondary data taken from annual reports and sourced from the Department of Public Procurement, Ministry of Planning and Investment in Hanoi. Besides, to identify the impact of external factors on bid/no-bid decision for supervision consultant service on construction project in Hanoi, the study collected data from 272 qualified questionnaires from bidders doing business in Hanoi. Cronbach's Alpha, EFA and regression model are used to explore the impact of each independent variable on bid/no-bid decision for supervision consultant service on construction project. The results show that three external determinants, including Project characteristic (PC), Legal codes (LC) and Competition (C) are affecting bid/no-bid decision for supervision consultant service on construction project in Hanoi. Among them, Project characteristic (PC) and Legal codes (LC) have positive relationships with bid/no-bid decision for supervision consultant service on construction project, whereas Competition (C) negatively affects bid/no-bid decision for supervision consultant service on construction project. It means the more contractors actually participate in a bid, the less bid/ no-bid decisions are made.

• IE interfaces
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• v.12 no.4
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• pp.551-556
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• 1999

In this paper, we compare the performance of three data mining classification algorithms(neural network, decision tree, logistic regression) in consideration of various characteristics of categorical input and output data. $2^{4-1}$. 3 fractional factorial design is used to simulate the comparison situation where factors used are (1) the categorical ratio of input variables, (2) the complexity of functional relationship between the output and input variables, (3) the size of randomness in the relationship, (4) the categorical ratio of an output variable, and (5) the classification algorithm. Experimental study results indicate the following: decision tree performs better than the others when the relationship between output and input variables is simple while logistic regression is better when the other way is around; and neural network appears a better choice than the others when the randomness in the relationship is relatively large. We also use Taguchi design to improve the practicality of our study results by letting the relationship between the output and input variables as a noise factor. As a result, the classification accuracy of neural network and decision tree turns out to be higher than that of logistic regression, when the categorical proportion of the output variable is even.

• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.163-189
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• 2010

The purpose of this research is to develop a decision-making model for the calculation of the National Disaster Management System's standard prevention workforce quota. The final purpose of such model is to support in arranging a rationally sized prevention workforce for local authorities by providing information about its calculation in order to support an effective and efficient disaster management administration. In other words, it is to establish and develop a model that calculates the standard disaster prevention workforce quota for basic local governments in order to arrange realistically required prevention workforce. In calculating Korea's prevention workforce, it was found that the prevention investment expenses, number of prevention facilities, frequency of flood damage, number of disaster victims, prevention density, and national disaster recovery costs have positive influence on the dependent variable when the standard prevention workforce was set as the dependent variable. The model based on the regression analysis-which consists of dependent and independent variables-was classified into inland mountainous region, East coast region, Southwest coastal plain region to reflect regional characteristics for the calculation of the prevention workforce. We anticipate that the decision-making model for the standard prevention workforce quota will aid in arranging an objective and essential prevention workforce for Korea's basic local authorities.