• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.1
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• pp.70-77
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• 2001

This paper present a fuzzy mxlel of describing reacton features for ozon contactor in advanced water treatn-ent systems. Input and output variables are chosen by considenng the object of ozon processing and several parameters related to management of water quahty. Dissolved organic carbon concentration, $UV_{254}$ absorptIon and $KM_NO_4$ consumption are proposed as common variables in input and outp.lt variables. Furthermore own concentration, raw water's temperature and contact time are suggested as input variables, Membership hmctions for input variables have triangular type share and the grades in each lrembership function are determined by investigating process data gathered at pilot planl The decision parts of fuzzy model have linear combination form of input variables and coefficients included in such linear equations are computedd with process clata in the sense of least square error Also fuzzy trodel suggested in this paper is partitioned by 3 independent fuzzy rnxlels using the characteristics of having no interactions armng output variables. As a result, such fuzzy mxlel has rrerits in computation and comprehension. According to simulatIon results, fuzzy moIel's outputs give almost similar data to process output under same input conditions.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.181-185
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• 1999

In this paper, we apply AHP method to select long range technology alternative for KEPCO. The input data was gathered by the questionnaire surveys answered by experts. The effectiveness and validity of the method were tested by comparing the results with those of the current selection procedure. We show that the proposed method can outperform the current method and can applied to the real decision making process of the electric power company.

• IE interfaces
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• v.13 no.2
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• pp.166-170
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• 2000

In this paper, we apply AHP method to select long range technology alternatives for KEPCO. The input data was gathered by the questionnaire surveys answered by experts. The effectiveness and validity of the method were tested by comparing the results with those of the current selection procedure. We show that the proposed method can outperform the current method and can be applied to the real decision making process of the electric power company.

• Journal of Engineering Education Research
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• v.13 no.4
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• pp.51-59
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• 2010

In order to manage the lectures efficiently in the university and improve the educational outcome, the process is needed that make diagnosis of the present educational outcome of each classes on a lecture and find factors of educational outcome. In most studies for finding the factors of the efficient lecture, statistical methods such as association analysis, regression analysis are used usually, and recently decision tree analysis is employed, too. The decision tree analysis have the merits that is easy to understand a result model, and to be easy to apply for the decision making, but have the weaknesses that is not strong for characteristic of input data such as multicollinearity. This paper indicates the weaknesses of decision tree analysis, and suggests the experimental solution using multiple decision tree algorithm to supplement these problems. The experimental result shows that the suggested method is more effective in finding the reliable factors of the educational outcome.

• Journal of Industrial Technology
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• v.20 no.A
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• pp.131-138
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• 2000

A past monthly data is not faithful so much for a short term. But, the stochastic generation technique was provide of a long-term data. Thus this study is used a data which generated a monthly inflow amounts data by Thomas-Fiering model. This model is needed a certain process which determination of distribution, decision of continuous durability, etc. It was generated a inflow data every one month as Thomas-Fiering method. The generated inflow data was used input data for a monthly cumulative analysis. This analysis obtained a storage capacities which would be required during droughts having various return periods. It was presented a equation of fitting regression that was carried out regression analysis of 5, 10, 20, 50 years period.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.74-81
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• 2006

In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear discriminant functions. Toward the end, we develop a new $l_1$-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming (MILP) model. Given a finite number of discriminant functions as an input, the proposed model considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear decision surface that best separates the data on hand. Hence, exploiting powerful MILP solvers, the model efficiently analyzes any given data set for its piecewise nonlinear separability. The classification of four sets of artificial data demonstrates the aforementioned strength of the proposed model. Classification results on five machine learning benchmark databases prove that the data separation via the proposed MILP model is an effective supervised learning methodology that compares quite favorably to well-established learning methodologies.

• Journal of Distribution Science
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• v.15 no.6
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• pp.37-46
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• 2017

Purpose - The paper aims to propose an optimization model for supporting the buyer-seller negotiations. We consider the price, quality, and delivery as evaluation criteria, also recognized as objectives for negotiation. Research design, data, and methodology - The methodology used in this paper involves the input-oriented DEA with the inverse optimization. Under the existence of several potential suppliers, the price would be considered to be the decision variable to conclude the negotiation so as to meet the desired level of the quality and delivery. The data set for six suppliers with three criteria is examined by the proposed approach. Results - We present the decision aid model by displaying the price spectrum as the changes of desired output levels. It overcomes the shortcomings from previous researches mainly based on the discrete types of scenario generations. This approach shows that the obtained results help the buyer understand the trade-offs between price and performance when he/she considers the negotiation. Conclusions - The paper contributes to the numerical models for buyer-supplier negotiation in that the model for the supplier evaluation and selection is closely linked with the model for negotiation. In addition, it eliminates the unrealistic negotiation strategy, and provides the negotiation strategies that the buyer would not shift the burden on suppliers by maintaining the current efficiency.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.85-90
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• 2021

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models' validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.331-336
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• 2004

In order to strengthen the competitiveness of port against calling for the huge vessel and reducing the shipping service time, the productivity of container terminal must be improved. This productivity variously results according to the kinds of productivity evaluation model, input elements like yard, equipment, employee, facility, etc,. But, it is discussed that the productivity is measured by partial productivity evaluation model or general input elements. Therefore, we measured for the productivity of the container terminal using the Developed the data Envelopment Analysis (DEA), which is developed in order to evaluate the relative efficiency of decision making units - it's difficult to clear cause and effect between input and output. We measured the whole productivity of container terminal in Busan according to decision of the correct input elements. And we investigated the change of the productivity measurement result according to input elements, presents more accurate productivity evaluation model in container terminal.