• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.76-84
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• 1998

The requirements of the accuracy, the high speed and the stability are very important factors in the defect-detection sytem for the texture. In this paper, we describe a novel scheme of the defect detection using a statistical behavior of defect patterns. Some prior knowledge as to the characteristics of flaws is that the defects are consistently distributed in the space and the noise are randomly generated. An empirical knowledge is adapted for the binarization and the determination process of defects in textured image. Since the process of the determination exclude the segmentations or delineation steps, we are able to meet the speed requirements. We show the validity of the scheme through the simulation of textured images.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.175-189
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• 2019

The problem of the estimation of quantitative sensitive variable using the item sum technique (IST) on successive occasions has been discussed. IST difference, IST regression, and IST general class of estimators have been proposed to estimate quantitative sensitive variable at the current occasion in two occasion successive sampling. The proposed new estimators have been elaborated under Trappmann et al. (Journal of Survey Statistics and Methodology, 2, 58-77, 2014) as well as Perri et al. (Biometrical Journal, 60, 155-173, 2018) allocation designs to allocate long list and short list samples of IST. The properties of all proposed estimators have been derived including optimum replacement policy. The proposed estimators have been mutually compared under the above mentioned allocation designs. The comparison has also been conducted with a direct method. Numerical applications through empirical as well as simplistic simulation has been used to show how the illustrated IST on successive occasions may venture in practical situations.

• 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.

• Earthquakes and Structures
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• v.3 no.1
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• pp.17-35
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• 2012

Simplified equations for fundamental period of vibration of skeletal structures provided by most seismic design provisions suffer from the absence of any associated confidence levels and of any reference to their empirical basis. Therefore, such equations may typically give a sector of designers the false impression of yielding a fairly accurate value of the period of vibration. This paper, although not addressing simplified codes equations, introduces a set of mathematical equations utilizing the theory of error propagation and First-Order Second-Moment (FOSM) techniques to determine bounds on the relative error in theoretically calculated fundamental period of vibration of skeletal structures. In a complementary step, and for verification purposes, Monte Carlo simulation technique has been also applied. The latter, despite involving larger computational effort, is expected to provide more precise estimates than FOSM methods. Studies of parametric uncertainties applied to reinforced concrete frame bents - potentially idealized as SDOF systems - are conducted demonstrating the effect of randomness and uncertainty of various relevant properties, shaping both mass and stiffness, on the variance (i.e. relative error) in the estimated period of vibration. Correlation between mass and stiffness parameters - regarded as random variables - is also thoroughly discussed. According to achieved results, a relative error in the period of vibration in the order of 19% for new designs/constructions and of about 25% for existing structures for assessment purposes - and even climbing up to about 36% in some special applications and/or circumstances - is acknowledged when adopting estimates gathered from the literature for relative errors in the relevant random input variables.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.3
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• pp.103-113
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• 2018

Tide-surge characteristics of the West/South domestic coasts were analyzed with a tool of EST (empirical simulation technique). As a result, stations of Incheon, Gunsan, Mokpo and Busan are categorized as tide-dominant coasts, while Yeosu, Tongyoung and Busan are as surge-dominant coasts. In the tide-dominant coasts, extreme sea level of less than 50-yr frequency is formed without typhoon-surge, while only 10-yr extreme sea level is formed in the surge-dominant coasts. As the results of casual condition of extreme sea level formation considering the relative degree of surge on tide, the regional characteristics were detected also. Three methods for estimating the design tide level were compared. The AHHW method shows an unrealistic outcomes of the concern of over estimate design. Furthermore, the probability distribution function method has been concerned as causing missing data if a huge typhoon occurs in a neap tide or a low tide. To cope with these drawbacks, the applicability of the EST method is proved to be suitable especially in tide-dominant coasts.

• Journal of the Korea Society for Simulation
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• v.29 no.3
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• pp.49-55
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• 2020

A goal of improving projectile is to increasing achievable range. The shape of a projectile is generally selected on the basis of combined aerodynamics and structural considerations. The choice of body, nose and boattail shape has a large effect on aerodynamic design. One of the main design factors that affect projectile configuration is aerodynamic drag. The aerodynamic drag refers to the aerodynamic force that acts opposite to the relative motion of a projectile. An investigation was made to predict the effects of nose, boattail and body shapes on the aerodynamic characteristics of projectiles using a semi-empirical technique. A parametric study is conducted which includes different projectile geometry. Performance predictions of achievable range are conducted using a trajectory simulation model. The potential of extending the range of a projectile using optimization of projectile configuration is evaluated. The maximum range increase is achieved due to the combination of optimal body shapes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.1
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• pp.52-59
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• 1999

Vegetation canopy plays an important role in $CO_2$/$H_2$O exchange between the biosphere and the atmosphere by controlling leaf stomata. In this study, rice (Oryza sativa L.), a staple crop in Asia was investigated to formulate its single leaf model of photosynthesis and stomatal conductance. Photosynthesis and stomatal conductance were measured with a portable infrared gas analyzer system. Other plant and meteorological variables were also measured. To evaluate empirical constants in this biochemical leaf model, nonlinear least squares technique was used. The maximum catalytic activity of enzyme and the maximum rate of electron transport were $ 100\mu$㏖ $m^{-2}$ $s^{-1}$ and $140 \mu$㏖ m$^{-2}$ s$^{-1}$ (@ 35$^{\circ}C$), respectively. The empirical constants, m and b, associated with stomatal conductance model were 9.7 and $0.06 m^{-2}$ $s^{-1}$ , respectively. On a leaf scale, agreements between the modeled and the measured values of photosynthesis and stomatal conductance were on average within 20%, and the simulation of diurnal variation was also satisfactory On a canopy scale, the Simple Biosphere model(SiB2) was tested using the derived parameters. The modeled energy fluxes were compared against the micrometeorologically measured fluxes over a rice canopy. Agreements between the modeled and the measured values of net radiation, sensible heat and latent heat fluxes, and $CO_2$ flux (i.e., net canopy photosynthesis) were on average within 25%.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Smart Structures and Systems
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• v.33 no.3
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.