• 한국초전도저온공학회:학술대회논문집
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• 한국초전도저온공학회 2003년도 추계학술대회 논문집
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• pp.146-149
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• 2003

In this Work, we have studied about an RSFQ 2$\times$2 crossbar switch. The circuit was designed, simulated, and laid out for mask fabrication The switch cell was composed of a splitter a confluence buffer, and a switch core. An RSFQ 2$\times$2 crossbar switch was composed of 4 switch cells, a switch control input to select the cross and bar, data input, and data outputs. When a pulse was input to the switch control input to select the cross or bar the route of the input data was determined, and the data was output at the proper output port. We simulated and optimized the switch-element circuit and 2$\times$2 crossbar switch, by using Xic and Julia. We also performed the mask layout of the circuit by using Xic and Lmeter.

• 한국지능시스템학회논문지
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• 제27권1호
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• pp.50-58
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• 2017

비선형 시스템 제어에 TSK 퍼지 모델을 이용하는 연구가 많다. 그러나 비선형 시스템을 TSK 퍼지 모델로 표현하기 위해서는 대상 시스템의 비선형 동특성 방정식을 알고 있거나 시스템으로부터 실험으로 입출력 공간에 충분히 분포된 데이터를 얻을 수 있어야 한다. 본 논문에서는 비선형 시스템의 TSK 퍼지 모델링에 계단 입력 응답과 GA만을 이용하는 방법을 제안한다. 연구 대상인 시스템은 제어 입력에 비선형인 경우와 출력에 비선형인 두 경우로 하였다. 제어 입력에 비선형인 경우 여러 입력 값에 대한 계단 입력 응답 데이터들로 모델링 하며, 제어 입력에 비선형인 경우에는 계단 입력 응답 데이터와 제어 입력 값이 0인 경우 응답 데이터를 이용하여 모델링 한다. 또한 제안한 방법으로 구해진 TSK 퍼지 모델로부터 퍼지 제어기를 설계하는 방법도 제시한다. 제안한 방법들을 예제들에 적용하여 모의 실험한 결과 원하는 제어 결과를 얻을 수 있었다.

• 산업경영시스템학회지
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• 제20권44호
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• pp.129-134
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• 1997

The purpose of this study is to estimate the distribution rate of R&D input on R&D output in major manufacturing industrial sector. The distribution rate is estimated on time-series data for the period 1980 to 1996. The data used in this study can be divided into the two categories. 1) R&D output data (Patent, Utility) 2) R&D input data (R&D expenditure, R&D workers) The raw data of R&D expenditure is transformed into R&D stock. And the specific production function is used to represent the interaction between R&D input and output. The production function shows the maximum rate of R&D output that can be achieved by certain given, technologically possible, R&D input combinations. The main findings can be summarized as follows. 1) There was a diminishing return between R&D input and output$(\alpha+\beta<1). 2) R&D output growth was more affected by R&D expenditures than R&D workers. 3) R&D workers were more contributed highly to Patent granted than Utility model.

• 한국측량학회지
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• 제16권2호
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• pp.291-297
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• 1998

일반적으로 수치지도제작을 수행하기 위해서는 종이에 인쇄된 원도를 컴퓨터상에서 작업할 수 있는 수치자료로 변환하는 자료입력과정과 작업이 끝난 자료를 출력하는 도면출력과정에 입출력장비가 이용된다. 본 연구에서는 수치지도 작업과정에 수반되는 입력장비중 벡터형태의 자료를 직접적으로 생성할 수 있는 장점에 의해 부분 도화된 수정도화 원도의 입력시 주로 이용되는 디지타이저와, 작업의 용이성 에 의해 주로 원도의 입력과정에 이용되는 스캐너에 대해 작업방법 및 입출력장비 특성에 따른 위치오차를 분석하였으며, 출력장비에 따른 위치오차분석에서는 플로터방식과 출력도면의 재질에 따른 위치정확도를 분석하였다.

• 수산경영론집
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• 제42권1호
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• pp.85-96
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• 2011

We have done the input-output analysis to see the over all impact of flounder industry of Jeju region on the domestic economy of Korea. To do the input-output analysis, we have constructed the data set for the input-output table by using the existing data set in the "2003 input- output table of Jeju regional area" published by the joint work of Jeju branch of Korea bank and the Jeju Development Institute, together with some raw data provided by Jejudo Marine Fish-Culture Cooperative. We have also produced input coefficient of flounder industry by making flounder industrial sector exogenous, separated from intermediate demand. To summarize our empirical results, the inducement effect of production, value added, and employment of Jeju flounder aquaculture industry are 300 billion won, 116 billion won and 1,800 people respectively. In conclusion, the results of this study suggest flounder industry of Jeju region contributes powerfully to not only Jeju economy but also all over the Korea economy.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.111-114
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• 1997

Generally, fuzzy models have the capability of dividing input space into several subspaces. compared to liner ones. But hitherto suggested fuzzy modeling algorithms not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem. this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently than conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space. the method of principal component is used. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.107-114
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• 1999

This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.201-209
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• 2013

For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.