• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.459-487
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• 2011

A/R CDM project has properties such as irreversibility and uncertainty that Real Option Analysis can be applied to its modelling. This study tries to model A/R CDM using Real Option under CER price uncertainty, and conducts empirical test with the Posco A/R CDM Project case. For precise comparison and decision-making, l-CER's expected present value is calculated from the Spot CER price. As a result, the critical value of the project is lower than the expected l-CER price, which means that the decision to invest made by the project owner is profitable. We can also find out that the level and the range of the discount rate, where is applied to, affect the result; the critical value of the project and the decision-making.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.156-156
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• 2018

The subbasin spatial scale can affect a hydrological simulation result. The objective of this study was to investigate an appropriate subbasin spatial scale for reproducing the different flow phases with the Soil and Water Assessment Tool (SWAT). Moreover, this study addressed the total hydrologic model uncertainty using the Generalized Likelihood Uncertainty Estimation (GLUE) method. The hydrologic modelling uncertainty analysis revealed that the courser subbasin spatial scale provided a relatively better coverage of most of the observations by the 95PPU. On the other hand, the finer subbasin spatial scale produced the best single simulation output closer to the observation. Moreover, most of the observed high flows were enveloped by the 95PPU while this did not happen for the low flows. The overall average performance improvement through an appropriate subbasin spatial scale for reproducing the different flow phases in the Yongdam and Gilgelabay watersheds were found to be 36% and 53%, respectively. It is, therefore, a worth that to put more effort in reproducing the different flow phases by investigating an appropriate subbasin spatial scale to improve our understanding about the frequency and magnitude of the different flow phases.

• Analytical Science and Technology
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• v.18 no.6
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• pp.475-482
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• 2005

The expression of uncertainty applied to the chemical analysis is highly recommended with increasing demands upon the systematic quality assurance and control(QA/QC) with ISO 17025. For the quantification of quality source, 7 major common sources of uncertainty, normally contributing to the quality of the chemical analysis, were selected from QA/QC literatures of chemical analysis. They were classified into repeatability, drift, uncertainty in standards, linearity of calibration, homogeneity, stability of sample, and matrix effect. And, the quantification of the sources by means of measurement uncertainty was proposed as a prerequisite steps for QA/QC. Examples applied to the quantification procedures of modelling, combination and expression of standard uncertainty for the 7 major common sources were presented as a reference guide for QA/QC in chemical analysis.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.12
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• pp.1228-1235
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• 2014

Input impedance of UHF RFID tag chip is needed to design a tag. In determining the chip impedance, direct measurement method is adopted commonly. In this paper, problems generated from fixtures that interface between tag chip and coaxial-oriented measurement instrument are investigated and the result of the problems is shown, when the direct measurement method is applied. As an alternative to the method, a modeling method is proposed and its validity and accuracy are shown.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.912-915
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• 1996

The computed-torque method (CTM) shows good trajectory tracking performance in controlling robot manipulator if there is no disturbance or modelling errors. But with the increase of a payload or the disturbance of a manipulator, the tracking errors become large. So there have been many researches to reduce the tracking error. In this paper, we propose a new control algorithm based on the CTM that decreases a tracking error by generating new reference trajectory to the controller. In this algorithm we used the concept of sliding mode theory and fuzzy system to reduce chattering in control input. For the numerical simulation, we used a 2-link robot manipulator. To simulate the disturbance due to a modelling uncertainty, we added errors to each elements of the inertia matrix and the nonlinear terms and assumed a payload to the end-effector. In this simulation, proposed method showed better trajectory tracking performance compared with the CTM.

• IE interfaces
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• v.18 no.2
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• pp.184-192
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• 2005

In recent research there has been intense interest in understanding how real option valuation (ROV) approaches might usefully complement conventional discounted cash flow (DCF) techniques. However, investment decision makers in a real world have been worried about adopting the ROV approaches mainly because of difficulty in technically understanding the theory of the ROV approaches as indicated by many researchers. With this difficulty in mind, we propose the opportunity cost model as another discrete-time model to value a deferral option. The main advantage of observing a real options value in terms of the opportunity cost concept is to provide a technique for practitioners to estimate a wide range of real options values without sticking to a financial option modelling. The fundamental ground for developing the opportunity cost model proposed in this paper lies in the work of dissecting the structure of the real options value into three categories: capital gain, expected opportunity loss, and expected opportunity gain. At the end of the paper, we will present a short illustrative example to demonstrate the applicability of the model.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1205-1207
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• 1996

The computed-torque method (CTM) shows good trajectory tracking performance in controlling robot manipulator if there is no disturbance or modelling errors. But with the increase of a payload or the disturbance of a manipulator, the tracking errors become large. So there have been many researchs to reduce the tracking error. In this paper, we propose a new control algorithm based on the CTM that decreases a tracking error by generating new reference trajectory to the controller. In this algorithm we used a fuzzy system based on the rule bases. For the numerical simulation, we used a 2-link robot manipulator. To simulate the disturbance due to a modelling uncertainty, we added errors to each elements of the inertia matrix and the nonlinear terms and assumed a payload to the end-effector. In the simulations of several cases, our method showed better trajectory tracking performance compared with the CTM.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1615-1625
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• 2013

Modelling and simulating the wind power intermittent behaviour are the basis of the planning and scheduling studies concerning wind power integration. The wind power outputs are evidently correlated in space and time and bring challenges in characterizing their behaviour. This paper provides a methodology to model and simulate the clustered wind power considering its spatio-temporal correlations using the theory of copula. The sampling approach captures the complex spatio-temporal connections among the wind farms by employing a conditional density function calculated using multidimensional copula function. The empirical study of real wind power measurement shows how the wind power outputs are correlated and how these correlations affect the overall uncertainty of clustered wind power output. The case study validates the simulation technique by comparing the simulated results with the real measurements.