In this paper, among multi-criteria decision making (MCDM) procedures, the ELECTRE(Elimination and (Et) Choice Translating Reality)-III is revised to solve the supplier selection problem where both a criteria evaluation matrix and a decision matrix are linguistically evaluated by a group of decision makers. Final weights are obtained through subjective weights from criteria matrix and weights of the entropy from decision matrix. In addition, the fuzzy theory for dealing with ambiguity of linguistic variables is incorporated to develop the new procedure of the ELECTRE-III.
The proposed procedure is tested through the numerical example which was used in the previous research with the fuzzy TOPSIS (technique for order preference by similarity to ideal solution). The results of the proposed procedure are compared to validate the outranking orders of suppliers. On the other hand, sensitivity analyses for the five cases with different subjective weights are carried to investigate the changes in the outranking orders of suppliers. In conclusion, the fuzzy ELECTRE-III procedure with entropy weights considering subjective judgement seems to be useful in the supplier selection problem.

The purpose of this study is to determine the optimum conditions of the extrusion process. Extrusion tube which is the main material of the intravenous set is produced in the extrusion process. In this paper, design of experiments is used to derive the regression model of the tensile strength and the transmittance rate for extrusion tube. The output variables were selected as tensile strength and transmittance rate, the input variables were selected as cylinder temperature, die temperature, lip die temperature and work room temperature. The experiments were conducted as full factorial experiments by 2-level, 4-factors. Through the design of experiments, we have analyzed the main effects and interaction effects of the input variables on the tensile strength and the transmittance rate of the extrusion tube and derived a predictive model. The result was that the tensile strength under the optimal condition was improved by 15% compared to those before improvement.

Hydrogen is an essential element as an energy carrier in the hydrogen economy. In order to mass-produce hydrogen, a High Temperature Gas-cooled Reactor (HTGR) cogeneration system coupled with hydrogen production process and electricity generation has been considered as the most effective method to produce hydrogen. Even if an HTGR cogeneration system is technically capable of supplying a mass amount of hydrogen, it is uncertain that the HTGR cogeneration system can compete with other hydrogen production technologies. Therefore, it is important to evaluate the economic competitiveness of the HTGR cogeneration system. In this paper we developed a process flow diagram for the cogeneration system to maximize hydrogen productivity in Steam Methane Reforming (SMR) hydrogen production processes in conjunction with electrical generation systems in a 350MWt HTGR. Economic analysis and ripple effect estimation were conducted for the self-developed process model using Benefit to Cost ratio (BC ratio) and an inter-industry table. The results of the quantitative analysis show that the HTGR cogeneration system is economically competitive and can be used as basic data for commercial HTGR construction in the future.

It can be seen that a lot of deeper researches have to be done in that the pollution that occurs in the toilet can propagate through the air. The purpose of this study is to investigate the behavior of contaminated liquid droplets generated by using the toilet in a toilet and to find out the method to prevent the spread of contamination. In this paper, multi-zone simulation was performed to find out the diffusion of contamination and 1its control method. This paper shows evaluation results in order to remove the contamination from the toilet. The fan installed in the toilet should be operated for 30 minutes and it should be operated for at least 90 to 120 minutes for complete removal. Therefore, it was found out that the exhaust fan was turned on for more than 30 minutes to prevent pollution by fixing the habit of simultaneously turning off the lights and the exhaust fan while leaving the toilet after using the toilet.

In this paper, we propose a recurrent neural network in order to predict the time series of the gas energy consumption in a conditioning equipment. We, in particular, characterize the time series data by using various method and find that the data contain a non-linear correlation. Based on the finding that the time series of the gas consumption contained a considerable amount of the non-linear correlation, we adopt the Elman neural network as a non-linear model for the prediction of the gas consumption. By tuning the parameters in the model, we demonstrate that the proposed model predicted the gas consumption with the relative errors and the average errors less than 1% and 0.5N㎥/h - 3.2N㎥/h respectively. The results of this study can be used to the gas energy management system in terms of the effective control of the conditioning energy in the dry process.

The cluster analysis is an exploratory data analysis process of finding the characteristics of each group by dividing the objects constituting the data into several clusters having similar characteristics. This method maximizes homogeneity within a cluster and maximizes heterogeneity among different clusters. The criterion for selecting the cluster analysis algorithm depends on the attributes of the objects that constitute the data. If the object attributes of the data are a mixture of continuous and categorical types, then k-prototypes algorithm which is hybrid version of k-means algorithm that deals with continuous attributes and k-modes algorithm that deals with categorical attributes can be used. For the use of the k-prototypes clustering method, the weight for the distances calculated in the categorical property should be determined. In this paper, we propose a method to find the weight using the bootstrap method based on stability and investigate the applicability of the proposed method through simulation.