• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.5-101
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• 2001

A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step. However, the state estimation in subspace identification gives a biased estimate and there is model mismatch when the controller is applied to a nonlinear process. To overcome such difficulties, we add integral action to a linear input/output data-based predictive controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation(BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor the proposed controller demonstrates.

• Proceedings of the Korea Institute of Applied Superconductivity and Cryogenics Conference
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• 2003.10a
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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.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.2
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• pp.291-297
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• 1998

In order to accomplish the digital map production I/O devices should be used which are used for data input procedure to convert original paper map(hardcopy) data into computer compatible digital map data, and for the mapsheet output procedure of worked out data. For the input device, digitizer and scanner are most frequently used. Digitizer has possibility of direct production of digital data, and are mainly used for input procedure of partly plotted source map. In contrary, scanner is rather easy to operate the instrument, so that is widely used for the input procedure of original sheet map. In this study, to extract the input device characteristics, some kinds of digitizers and scanners were cheesed and used for the positional error analysis through the operational method and types of instruments. Also for the output device characteristics, some kinds of plotter and materials are used and compared to analyze the positional error through the instrumental types and output sheet materials.

• The Journal of Fisheries Business Administration
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• v.42 no.1
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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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• v.10 no.1
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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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.