• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4692-4697
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• 2014

A micro-grid including photovoltaic source and Li battery has been installed and operated for several years at the campus of USF and been used as a test bed. Photovoltaic power source has been strongly influenced by the location, weather and climate of the installed area. To compensate for the uncertainty of photovoltaic source's power output, a Li battery is connected directly to the photovoltaic source and supplies electric power to the grid. The Li battery is operated to supply power output to the grid according to the charging or discharging mode of the battery based on the average power output of the photovoltaic source, which is calculated from the monitored data for several years. The grid of the photovoltaic and Li battery system is operated as a severe loading condition and the operating characteristics of PV source and Li battery cells are analyzed in detail.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.143-151
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• 2005

This paper considers a robust decentralized $H_\infty$ control problem for uncertain large-scale interconnected systems. The uncertainties are assumed to be time-invariant, norm-bounded, and exist in subsystems. A design method based on the bounded real lemma is developed for a dynamic output feedback controller, which is reduced to a feasibility problem for a nonlinear matrix inequality (NMI). It is proposed to solve the NMI iteratively by the idea of homotopy, where some of the variables are fixed alternately on each iteration to reduce the NMI to a linear matrix inequality (LMI). A decentralized controller for the nominal system is computed first by imposing structural constraints on the coefficient matrices gradually. Then, the decentralized controller is modified again gradually to cope with the uncertainties. A given example shows the efficiency of this method.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.54-67
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• 2003

In this paper, a novel estimation technique for a robust adaptive control scheme is presented for a class of uncertain nonlinear systems with a general set of uncertainty. For a class of introduced more extended semi-strict feedback forms which generalize the systems studied in recent years, a novel estimation technique is proposed to estimate the states of the fully nonlinear unmodeled dynamics without stringent conditions. With the introduction of powerful functions, the estimation error can be tuned to a desired small region around the origin via the estimator parameters. In addition, with some effective functions, a modified adaptive backstepping for dynamic uncertainties is presented to drive the output to an arbitrarily small region around the origin by an appropriate choice of the design parameters. With our proposed schemes, we can remove or relax the assumptions of the existing results.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.454-459
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• 2004

This paper considers the problem of satellite's orbit control and a solution based in Linear Matrix Inequalities (LMI) is proposed for the case of Low Earth Orbiters (LEO). In particular, the modelling procedure and the algorithm for control law synthesis are tested using as study case the European Gravity Field and Ocean Circulation Explorer satellite (GOCE), to be launched by the European Space Agency (ESA) in the year 2006. The scientific objective of this space mission is the recovering of the Earth gravity field with high accuracy (less than 10${\mu}m$/${\mu}m$) and spatial resolution (better than 100km). In order to meet these scientific requirements, the orbit control must guarantee stringent specifications in terms of environmental disturbances attenuation (atmospheric drag forces) even in presence of high levels of model uncertainty.

• Journal of Industrial Technology
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• v.25 no.B
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• pp.149-157
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• 2005

The power performance monitoring system for a small class of wind turbine is established. The wind turbine power performance characteristics are determined by measured power curve and the estimated annual energy production (AEP). The measured power curve is determined by collecting simultaneous measurements of wind speed and power output at the test site under varying wind conditions. In order to determine the power performance characteristics of the wind turbine accurately, the data are of sufficient quantity and quality shall be corrected according to defined criteria. In this study, the 6kW wind turbine made by Germany Inventus GmbH is examined.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.35-44
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• 1997

In this paper, a an algorithm that detects the endocardial boundary, expanding the region from endocardial cavity using fuzzy inference, is proposed. This algorithm decides the ventricular cavity by fuzzy inference in process of searching each pixel from the inside of left ventricle in echocardial image and expands it. Uncertainty and fuzziness exists in decision of endocardial boundary. Therefore, we convert the lingustic representation of mean, standard deviation, and threshold value that are characteristic variables of endocardial boundary to fuzzy input and output variables. And, we extract proposed method is robuster to noise than radial searching method that is highly dependent on center position. To prove the similarity of detected boundary by fuzzy nference, we used the measures of SIZE, correlation coefficient, MSD, and RMSE and had acquired reasonable results.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.95-104
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• 1997

We consider controller design problem to enhance shift quality for automatic transmission. A dynamic modeling related to shifting (mainly 2-3 up-shift) is constructed and nonlinear robust controllers are designed to reduce output torque during shifting. Suggesting a new hydraulic circuit enabling the direct clutch drive, the control activity is extended and more implementable than the conventional design. The designed robust controllers overcome the unmodeled dynamics and the uncertainty embending in the system. Moreover, the dynamic effect between the clutch pressure and the PWM valve duty is considered via singular perturbation technique.