• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.2060-2066
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• 2002

The closed-loop state and input observer is a pole-placement type observer and estimates unknown state and input variables simultaneously. Pole-placement type observers may have poor transient performance with respect to ill-conditioning factors such as unknown initial estimates, round-off error, etc. For the robust transient performance, the effects of these ill-conditioning factors must be minimized in designing observers. In this paper, the transient performance of the closed-loop state and input observer is investigated quantitatively by considering the error bounds due to ill-conditioning factors. The performance indices are selected from these error bounds and are related to the observer robustness with respect to the ill -conditioning factors. The closed-loop state and input observer with small performance indices is considered as a well-conditioned observer from the transient perspective.

• IE interfaces
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• v.22 no.1
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• pp.44-55
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• 2009

We propose a method for selection of input-output factors in DEA. It is designed to select better combinations of input-output factors that are well suited for evaluating substantial performance of DMUs. Several selected DEA models with different input-output factors combinations are evaluated, and the relationship between the computed efficiency scores and a single performance criterion of DMUs is investigated using decision tree. Based on the results of decision tree analysis, a relatively better DEA model can be chosen, which is expected to well represent the true performance of DMUs. We illustrate the effectiveness of the proposed method by applying it to the efficiency evaluation of 101 listed companies in steel and metal industry.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.75-90
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• 1998

The improper use of input and output factors in DEA has a critical and negative impact on the efficiency measurement and the discernment of decision making units(DMUs) : hence the proper selection Process of the factors should precede the actual applications of DEA. In this paper, we propose a new approach to selecting proper factors based on Tofallis' partial efficiency evaluation method(1996). With the approach, the factors aye clustered by measuring their respective partial efficiencies and analyzing the rank correlations of them. The method and procedure we propose in this paper are then applied to measure the efficiencies of the public libraries in Seoul District area, and the results show that the proposed approach can provide meaningful information to improve discernment of the DMUs while using less number of input factors (and less information). The proposed method can be effectively used in the situation where the number of the DMUs to be considered is relatively small compared to the number of available input and output factors, which usually lessens the power to identify the inefficient units in DEA.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1122-1137
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• 2021

PM_2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.

• Journal of Integrative Natural Science
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• v.8 no.4
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• pp.293-304
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• 2015

This study examined the factors which have influence on the welfare facilities for the elderly and analyzes their efficiency. It investigated theoretical studies and preceding studies and divided the efficiency evaluation factors into input and output factors. Input factors included budget, the number of workers and clients and facility area and output factors were operation management, the number of clients, profitability and welfare for the elderly. To sum up the analysis results of evaluation factors of welfare facilities for the elderly, the analysis of relative importance of input showed that budget was most important. As a result of analyzing the relative importance among detailed items, balance sheet and professional manpower were highest. Input factors by facility types showed that the budget for utility facilities and living facilities were highest. In output factors, utility facilities and living facilities were highest in management systematization and welfare for the elderly, respectively. In efficiency evaluation, utility facilities for the elderly showed 100% of efficiency in CCR and BCC models. In welfare facilities for the elderly, while CCR model showed 100% of efficiency in facility types A, C, D, and F, the efficiency was low in facility B (79.89%), E (77.14%), and G (80.72%). In BCC model, facility E was low as 78.69%. In efficiency comparison between utility facilities and living facilities for the elderly welfare, the efficiency of utility facilities for the elderly welfare was higher. Therefore, this study investigated the efficiency of welfare facilities for the elderly as its main purpose and presented policy suggestions based on the research results as the alternative.

• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.123-131
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• 2003

This paper considers a six sigma project for reducing the defects rate of the welding process in manufacturing firms. The project follows a disciplined process of five macro phases. define, measure, analyze, improve and control(DMAIC). The need of customers is used to identify critical to quality(CTQ) of project. And a process map is used to identify process input factors of CTQ. Four key process input factors are selected by using an input factor evaluation of teams; an interval of welding, an abrasion, an electric current and a moving freely. DOE is utilized for finding the optimal process conditions of the three key process input factors. Another one key input factor improved to welding machine. The six sigma level of defects rate becomes a 2.01 from a 1.61 at the beginning of the project.

• Ocean and Polar Research
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• v.33 no.3
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• pp.291-301
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• 2011

To investigate major factors controlling variations in water quality, principal component analysis and cluster analysis were used to analyze data sets of 12 parameters measured at 23 sampling stations of Jinhae Bay during winter and spring. Principal component analysis extracted three major factors controlling variations of water quality during winter and spring. In winter, major factors included freshwater input, polluted material input, and biological activity. Whereas in spring they were polluted material input, freshwater input, and suspended material input. The most distinct difference in the controlling factors between winter and spring was that the freshwater input was more important than the polluted material input in winter, but the polluted material input was more important than the freshwater input in spring. Cluster analysis grouped 23 sampling stations into four clusters in winter and five clusters in spring respectively. In winter, the four clusters were A (station 5), B (stations 1, 2), C (station 4), and D (the remaining stations). In spring, the five clusters included A (station 5), B (station 1), C (station 3), D (station 6), and E (the remaining stations). Intensive management of the water quality of Masan and Hangam bays could improve the water quality of Jinhae Bay since the polluted materials were mainly introduced into Jinhae Bay through Masan and Hangam bays.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.23-35
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• 2017

A nonlinear static procedure considering work-energy principle and global failure mechanism to estimate base shears of reinforced concrete (RC) frame-type structures is presented. The relative energy equation comprising of elastic vibrational energy, plastic strain energy and seismic input energy is obtained. The input energy is modified with a factor depending on damping ratio and ductility, and the energy that contributes to damage is obtained. The plastic energy is decreased with a factor to consider the reduced hysteretic behavior of RC members. Given the pre-selected failure mechanism, the modified energy balance equality is written using various approximations for modification factors of input energy and plastic energy in scientific literature. External work done by the design lateral forces distributed to story levels in accordance with Turkish Seismic Design Code is calculated considering the target plastic drift. Equating the plastic energy obtained from energy balance to external work done by the equivalent inertia forces considering, a total of 16 energy-based base shears for each frame are derived considering different combinations of modification factors. Ductility related parameters of modification factors are determined from pushover analysis. Relative input energy of multi degree of freedom (MDOF) system is approximated by using the modal-energy-decomposition approach. Energy-based design base shears are compared with those obtained from nonlinear time history (NLTH) analysis using recorded accelerograms. It is found that some of the energy-based base shears are in reasonable agreement with the mean base shear obtained from NLTH analysis.

• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.140-152
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• 2016

It is very important for the competitiveness and sustainable management of enterprises that the rapid changes in the managerial environments quickly and accurately are responded. For example, the large-scale investment accompanied by bad alternatives in accordance with misunderstanding of the managerial environments yields the huge cost and effort to modify and improve. In firm management, the quality of products and the productivity are influenced by changes of the endogenous factors yielded in manufacturing process and the exogenous factors as market, etc. These changes include not only changes in 4M (man, machine, material, method) but also those in the market, competitors, and technologies in the process of commodification, i.e., first, such disturbances make dispersion of the process big and odd. By Shewhart chart it can be checked that the process monitored is control-in or out. Business administration executes activities for input stabilization by monitoring changes in 4Ms, comparing with the standards, and taking measures for any abnormality. Second, TRM (technology road map) is to prospect product deployment and technological trend by predicting technologies in the competitive environment as the market, and to suggest the future directions of business. So, TRM must be modified and improved according to DR (design review) stages and changes in mass-production like input material change. Therefore, a role of TRM in input stabilization for reducing cost and man-hour is important. This study purposed to suggest that the environment changes are classified into endogenous factors and exogenous factors in production process, and then, quality and productivity should be stabilized efficiently through connection between TRM and input stabilization, and to prove that it is more effective for the display industry to connect TRM with input stabilization rather than to use TRM separately.