• Journal of Electrical Engineering and Technology
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• v.6 no.3
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• pp.423-430
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• 2011

Multi-input multi-output (MIMO) dynamic matrix control (DMC) technique is applied to control steam temperatures in a large-scale ultrasupercritical once-through boiler-turbine system. Specifically, four output variables (i.e., outlet temperatures of platen superheater, finish superheater, primary reheater, and finish reheater) are controlled using four input variables (i.e., two spray valves, bypass valve, and damper). The step-response matrix for the MIMO DMC is constructed using the four input and the four output variables. Online optimization is performed for the MIMO DMC using the model predictive control technique. The MIMO DMC controller is implemented in a full-scope power plant simulator with satisfactory performance.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.1
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• pp.41-47
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• 1997

A geographical resource analysis support system (GRASS) was incorporated to an input and output processor for the agricultural nonpoint source pollution (AGNPS) model. The resulting interface system, GIS-AGNPS was a user-friendly, menu-driven system. GIS-AGNPS was developed to automatically process the input and output data from GIS-based data using GRASS and Motif routines. GIS-AGNPS was consisted of GISAGIN which was an input processor for the AGNPS model, GISAGOUT a output processor for the AGNPS and management submodel. The system defines an input data set for AGNPS from attributes of basic and thematic maps. It also provides with editing modes so that users can adjust and detail the values for selected input parameters, if needed. The post-processor at the system displays graphically the outputs from AGNPS, which may he used to identify areas significantly contributing nonpoint source pollution loads.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.157-165
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• 1995

In this paper the comparison between the neural networks and traditional approaches as nonlinear system identification methods are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. Computer simulation for an analytic dynamic model of a single input single output nonlinear system has been done for all the chosen models. Model validation for the obtained models also has been done with testing inputs of the sinusoidal, ramp and the noise ramp.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.401-406
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• 2004

In this paper, a new time-domain adaptive predistortion scheme is proposed to compensate for the nonlinearity of high power amplifiers (HPA) in OFDM systems. A complex Wiener-Hammerstein model (WHM) is adopted to describe the input-output relationship of unknown HPA with linear dynamics, and a power series model with memory (PSMWM) is used to approximate the HPA expressed by WHM. By using the PSMWM, the compensation input to HPA is calculated in a real-time manner so that the linearization from the predistorter input to the HPA output can be attained even if the nonlinear input-output relation of HPA is uncertain and changeable. In numerical example, the effectiveness of the proposed method is confirmed and compared with the identification method based on PSMWM.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1707-1714
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• 2006

In order to enhance sustainable war fighting capabilities, it is important to maintain a good ammunition support system. In this paper, we evaluate the performance of Ammunition companies using Imprecise Data Envelopment Analysis (IDEA)-BCC and IDEA-Additive model, which can deal with imprecise data in DEA. In order to select a list of input and output variables, we used a multiple regression analysis. We could choose input variables that have significant effects on the output performance with stepwise regression model. From the regression analysis, the number of soldiers, officers, and ammunition warehouses were selected as the input variables. Seven out of sixteen Ammunition companies were found to be inefficient by the IDEA-BCC model. And using IDEA-Additive model, we could identify the input excess and the output shortfall in reaching at a point on the efficiency frontier.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.157-172
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• 2020

This study attempts to analyze trade flows between Vietnam and China in order to understand the mutual influence of bilateral trade relations. China is a country with the world's leading economic potential. China and Vietnam are neighboring countries sharing a border of 1,281 km. Trade relations between the two countries are a necessity and, with a right policy, are beneficial to both. Vietnam has a trade deficit with China. This situation is exacerbated by the continuing rise in the gap. Vietnam trade deficit from China was USD12.5 billion in 2010, increasing to USD24 billion in 2018. Data are extracted from the 2015 national input-output tables of Vietnam and China as well as Vietnam Household Living Standard Survey statistics. The research identified 36 sectors of bilateral input-output trade between Vietnam and China. A bilateral output-input model is applied to analyze how final demand and use of input in the production of this country induces output and value added of the other country. The results show that China benefits more from Vietnam's production and consumption than Vietnam does. Vietnam's inter-sector structure does not stimulate domestic production due to the absence of supporting products as inputs in the production process.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.301-314
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• 2007

The aim of this paper is to deal with a method to construct a separate fuzzy regression model with crisp input and fuzzy output data using a best response function for the center and the width of the predicted output. Also we introduce the crisp mean and variance of the predicted fuzzy value and also give some examples to compare a performance of the proposed fuzzy model with various other fuzzy regression model.

• Journal of Power System Engineering
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• v.7 no.1
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• pp.74-81
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• 2003

A system identification is to estimate the mathematical model on the base of input output data and to measure the output in the presence of adequate input for the controlled system. In the traditional system control field, most identification problems have been thought as estimating the unknown modeling parameters on the assumption that the model structures are fixed. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input output case with the observed noise. We suggest the adjusted least squares method as a consistent estimation method in the system identification in the case where there is observed noise only in the output. In this paper the adjusted least squares method has been developed from the least squares method and the efficiency of the estimating results was confirmed by the generating data with the computer simulations.

• 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.