• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2712-2714
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• 1999

The small signal model for input series-output parallel connected converter system employing charge control together with input capacitor voltage feedback loop is developed. From the model developed, the effect of input capacitor voltage feedback loop to the system stability and outer loop compensator design is analyzed. Theoretical results and simulation show that input capacitor voltage feedback loop has no critical effects on the system stability, so the system can be reduced to a equivalent single module for the stability analysis and outer loop compensator design.

• Korean Management Science Review
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• v.22 no.1
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• pp.1-13
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• 2005

This paper proposed a quantitative evaluation method to measure the operational effectiveness of the local military manpower administrations. The proposed method compared the relative operational effectiveness level for 12 local military manpower administrations in Korea.. The method used the IDEA (imprecise Data Envelopment Analysis) model which Is able to measure relative operational effectiveness level, and also used two input variables (labor cost, operational cost) and three output variables (number of military applicants, number of civil application approved & processed, management accuracy level). Through the model output analysis, we presented the relative effectiveness scores, the reason for non-effectiveness, and the relationship between non-effective ness level and input/output variables for each local military manpower administration. We also presented a few recommendations how to improve the effectiveness level on particular local military manpower administration.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.449-455
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.188-197
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• 2012

DEA 모델중 널리 사용되는 교차평가모델(cross efficiency model)은 가중치에 제한을 두지 않고 어떤 특정분야에 탁월한 성과를 내는 DMU(Decision Making Unit)보다는 보다 전반적인 분야에서 두각을 나타내는 DMU를 선발함으로써 많은 연구자들이 DEA문헌에서 적용하여 왔다. 본 연구에서는 이러한 교차평가모델이 실제에 있어서는 암묵적으로 고정 가중치를 사용한다는 것과 동일한 결과를 나타낸다는 것을 분석적으로 밝혔다(one input, multi output case). 또한 multi-input, multi-output case의 경우에도 overall performer의 cluster에 근접한 대다수 DMU의 경우에는 고정 가중치를 사용한 경우와 거의 차이가 없음을 보였다. 교차평가 모델에 적용된 변수의 가중치를 보다 명확히 함으로써 연구자들이 모델의 평가결과를 이해하는데 도움이 될 수 있을 것이다. 또한 교차 평가의 가중치 도식을 더 명확히 보여주기 위해 biplot을 제안한다.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.786-789
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• 2002

A recurrent artificial neural network (ANN) model is developed to simulate hydrocarbon recovery process at petroleum-contaminated site. The groundwater extraction rate, vacuum pressure, and saturation hydraulic conductivity are selected as the input variables, while the cumulative hydrocarbon recovery volume is considered as the output variable. The experimental data fer establishing the ANN model are from implementation of a multiphase flow model for dual phase remediation process under different input variable conditions. The complex nonlinear and dynamic relationship between input and output data sets are then identified through the developed ANN model. Reasonable agreements between modeling results and experimental data are observed, which reveals high effectiveness and efficiency of the neural network approach in modeling complex hydrocarbon recovery behavior.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.50-58
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• 2017

For nonlinear control system design, there are many studies based on TSK fuzzy model. However, TSK fuzzy modelling needs nonlinear dynamic equations of the object system or a data set fully distributed in input-output space. This paper proposes an modelling technique using only step input response data. The technique uses also the genetic algorithm. The object systems in this paper are nonlinear to control input variable or output variable. In the case of nonlinear to control input, response data obtained with several step input values are used. In the case of nonlinear to output, step input response data and zero input response data are used. This paper also presents a fuzzy controller design technique from TSK fuzzy model. The effectiveness of the proposed techniques is verified with numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1883-1887
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• 1991

Identification theory for linear discrete system has been presented by a great many reference, but research works for identification of continuous-time system are less than preceding identification. In fact, a great man), systems for engineering are continuous-time systems, hence, research for identification of continuous-time system has important meaning. This paper offers the following results: 1. Corresponding relations for the parameters of continuous-time model and discrete model may be shown, when single input-output system has general characteristic roots. 2. To do identification of single variable continuity linear system with stability constraints from samples of input-output data, it is necessary to use optimization with stability constraints. 3. Main results of this paper may be explained by a simple example.

• Journal of Power System Engineering
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• v.13 no.2
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• pp.71-82
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• 2009

This paper presents a real-time haptic rendering method for a realistic force feedback in a remote environment with varying communication time-delay. The remote environment is assumed as a virtual environment based on a computer graphics, for example, on-line shopping mall, internet game and cyber-education. The properties of a virtual object such as stiffness and viscosity are assumed to be unknown because they are changed according to the contact position and/or a penetrated depth into the object. The DARMAX model based output estimator is proposed to trace the correct impedance of the virtual object in real-time. The output estimator is developed on the input-output relationship. It can trace the varying impedance in real-time by virtue of P-matrix resetting algorithm. And the estimator can trace the correct impedance by using a white noise that prevents the biased input-output information. Realistic output forces are generated in real-time, by using the inputs and the estimated impedance, even though the communication time delay and the impedance of the virtual object are unknown and changed. The generated forces trace the analytical forces computed from the virtual model of the remote environment. Performance is demonstrated by experiments with a 1-dof haptic device and a spring-damper-based virtual model.

• Economic Analysis
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• v.26 no.4
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• pp.65-97
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• 2020

The purpose of this paper is to analyze the effects of exports on Korea's employment and to decompose driving factors of change in Korea's employment embodied in exports (EEX). This study uses a multi-regional input-output (MRIO) and structural decomposition analysis (SDA) for empirical analysis, and uses a dataset of World Input-Output Tables (WIOTs) and Socio-Economic Accounts (SEAs) from the World Input-Output Database (WIOD). The main findings of the empirical results are summarized as follows. First, Korea's EEX continues to increase and Korea's share of EEX compared to total employment shows an upward trend. However, Korea's employment inducement coefficient of value-added exports showed a downward trend during the 2000-2014 period. Second, final demand from three countries (China, the United States, and the Rest of the World (RoW)) has affected a significant portion of Korea's EEX. Finally, from the results of the SDA, the effect of changes in final demand was the most important driving factor for the increase in Korea's EEX. Based on the results of this empirical analysis, this study discusses useful policy implications that could increase domestic employment in Korea.