• Journal of Astronomy and Space Sciences
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• v.24 no.1
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• pp.31-38
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• 2007

We have investigated the evolutionary status of 85 Peg within the framework of standard evolutionary theory. 85 Peg has been known to be a visual and spectroscopic binary system in the solar neighborhood. In spite of the accurate information of the total mass (${\sim}1.5M_{\odot}$) and the distance (${\sim}12pc$) from the HIPPARCOS parallax, it has been undetermined an individual mass, therefore the evolved status of the system. Moreover, the coupled uncertainties of chemical composition and age, make matters worse in predicting an evolutionary status of the system. Nevertheless, we computed the various possible models for 85 Peg, and then calibrated stellar parameters by adjusting to the recent observational data. Our modelling computation has included recently updated input physics and stellar theory such as opacity, equation of state, and chemical diffusion. Through a statistical assessment, we have derived a confident parameter set as the best solution which minimized $X^{2}$ within the observational error domain. Most of all, we found that 85 Peg is not a binary system but a triple system with an unseen companion 85 Peg $B_{b}\;{\sim}0.16M_{\odot}$. The aim of the present paper is (1) to provide a complete modelling of the stellar system based on the evolutionary theory, and (2) to constrain the physical dimensions such as mass, metallicity and age.

• Structural Engineering and Mechanics
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• v.21 no.5
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• pp.539-551
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• 2005

Finite elements based on isoparametric formulation are known to suffer spurious stiffness properties and corresponding stress oscillations, even when care is taken to ensure that completeness and continuity requirements are enforced. This occurs frequently when the physics of the problem requires multiple strain components to be defined. This kind of error, commonly known as locking, can be circumvented by using reduced integration techniques to evaluate the element stiffness matrices instead of the full integration that is mathematically prescribed. However, the reduced integration technique itself can have a further drawback - rank deficiency, which physically implies that spurious energy modes (e.g., hourglass modes) are introduced because of reduced integration. Such instability in an existing stiffness matrix is generally detected by means of an eigenvalue test. In this paper we show that a knowledge of the dimension of the solution space spanned by the column vectors of the strain-displacement matrix can be used to identify the instabilities arising in an element due to reduced/selective integration techniques a priori, without having to complete the element stiffness matrix formulation and then test for zero eigenvalues.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.539-550
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• 2021

Purpose: The purpose of this study is to propose a multivariate CUSUM control chart that can detect the out-of-control state fast while monitoring the cross- and auto- correlated multivariate time series data. Methods: We first build models to estimate the observation data and calculate the corresponding residuals. After then, a multivariate CUSUM chart is applied to monitor the residuals instead of the original raw observation data. Vector Autoregression and Artificial Neural Net are selected for the modelling, and Separated-MCUSUM chart is selected for the monitoring. The suggested methods are tested under a number of experimental settings and the performances are compared with those of other existing methods. Results: We find that Artificial Neural Net is more appropriate than Vector Autoregression for the modelling and show the combination of Separated-MCUSUM with Artificial Neural Net outperforms the other alternatives considered in this paper. Conclusion: The suggested chart has many advantages. It can monitor the complicated multivariate data with cross- and auto- correlation, and detects the out-of-control state fast. Unlike other CUSUM charts finding their control limits by trial and error simulation, the suggested chart saves lots of time and effort by approximating its control limit mathematically. We expect that the suggested chart performs not only effectively but also efficiently for monitoring the process with complicated correlations and frequently-changed parameters.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.718-723
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• 2017

When structural analysis modelling methods of practical fields are investigated, a slab is generally modeled by a finite element mesh using plate elements and a shear wall is modeled using a shell element or wall element for 3-D structural analysis. The point worthy of notice in this practice is that a shear wall is modelled using only one wall or shell element divided by floors and column lines to produce structural models. The modeling method like this can cause analysis errors according to the type of computer programs in use, and these errors reduce the reliability of the analysis results. Therefore, to secure the reliability of structural analysis, studies of the causes of errors and finding reasonable modeling methods are necessary. In this study, the causes of analysis errors according to the modelling methods of a shear wall, which are used in practical fields, were investigated and some considering matters for modelling a shear wall are presented to reduce the analysis errors on these analysis results.

• Journal of Forest and Environmental Science
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• v.32 no.3
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• pp.280-290
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• 2016

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size $20m{\times}20m$, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area ($m^2$), site index (m), relative spacing and percentile positions of dbh at $24^{th}$, $63^{rd}$, $76^{th}$ and $93^{rd}$ (i.e. $P_{24}$, $P_{63}$, $P_{76}$ and $P_{93}$) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion ($AIC_c$) and larger coefficient of determination ($R^2$). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. $P_{24}$ and $P_{93}$) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.2
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• pp.115-126
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• 2017

There is an increasing concern over arsenic (As) contamination in rice. This study was conducted to develope a prediction model for As uptake by rice based on the physico-chemical properties of soil. Soil and brown rice samples were collected from 46 sites in paddy fields near three different areas of closed mines and industrial complexes. Total As concentration, soil pH, Al oxide, available phosphorus (avail-P), organic matter (OM) content, and clay content in the soil samples were determined. Also, 1.0 N HCl, 1.0 M $NH_4NO_3$, 0.01 M $Ca(NO_3)_2$, and Mehlich 3 extractable-As in the soils were measured as phytoavailable As concentration in soil. Total As concentration in brown rice samples was also determined. Relationships among As concentrations in brown rice, total As concentrations in soils, and selected soil properties were as follows: As concentration in brown rice was negatively correlated with soil pH value, where as it was positively correlated with Al oxide concentration, avail-P concentration, and OM content in soil. In addition, the concentration of As in brown rice was statistically correlated only with 1.0 N HCl-extractable As in soil. Also, using multiple stepwise regression analysis, a modelling equation was created to predict As concentration in brown rice as affected by selected soil properties including soil As concentration. Prediction of As uptake by rice was delineated by the model [As in brown rice = 0.352 + $0.00109^*$ HCl extractable As in soil + $0.00002^*$ Al oxide + $0.0097^*$ OM + $0.00061^*$ avail-P - $0.0332^*$ soil pH] ($R=0.714^{***}$). The concentrations of As in brown rice estimated by the modelling equation were statistically acceptable because normalized mean error (NME) and normalized root mean square error (NRMSE) values were -0.055 and 0.2229, respectively, when compared with measured As concentration in the plant.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.412-421
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• 2018

The Line array sonar consisting of several hydrophones increases array gain and improves the performance for detecting the direction of the target compared to single hydrophone. However, line array sonar produces the bearing error that makes it difficult to determine the bearing of incoming source signal due to the relation between bearing angle of target and vertical angle of multipath signals. Vertical angles of multipath are varied with the geometry of receiver and target and various underwater environments, therefore it is necessary to consider the bearing error to estimate accurately the bearing of the target. In this study, acoustic modelling was performed to understand the effect of multipath signals on the target signal. The errors of bearing angle estimated from the bottom bounced signals are calculated with several environment. In addition, the expected bearing line, as a function of source-receiver range, compensated for the bearing error is predicted from the estimated bearing angle.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.61-72
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• 2013

The relationship between export growth and economic growth in developing countries has been one of the main issues in the growth theory field. Many of empirical studies have been done during the last three decades in order to investigate the export-led growth hypothesis using either time-series or cross-sectional data mainly in developing countries. This paper applies cointegration and error correction models to test causal relationship between export growth and economic growth in the Korean 8 manufacturing industries using the industrial time-series quarterly data over 1975-2010. The export-output relationship is tested by including industrial capital stock and the industrial labor force as exogenous variables. The cointegration and error-correction modelling technique with industrial export and output data have showed the strong evidence that there is a bi-directional causality between industrial export and industrial output in 6 manufacturing industries except wood & pulp and nonmetallic industries.

• Smart Structures and Systems
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• v.33 no.2
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• pp.119-131
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• 2024

Drift angle is a significant index for diagnosing post-event structures. A common way to estimate this drift response is by using modal parameters identified under natural excitations. Although the modal parameters of shear structures cannot be identified accurately in the real environment, the identification error has little impact on the estimation when measurements from several floors are used. However, the estimation accuracy falls dramatically when there is only one accelerometer. This paper describes the susceptibility of single sensor identification to modelling error and simulations that preliminarily verified this characteristic. To make a robust evaluation from measurements of one floor of shear structures based on imprecisely identified parameters, a novel scheme is devised to approximately correct the mode shapes with respect to fictitious frequencies generated with a genetic algorithm; in particular, the scheme uses constrained minimization to take both the mathematical aspect and the realistic aspect of the mode shapes into account. The algorithm was validated by using a full-scale shear building. The differences between single-sensor and multiple-sensor estimations were analyzed. It was found that, as the number of accelerometers decreases, the error rises due to insufficient data and becomes very high when there is only one sensor. Moreover, when measurements for only one floor are available, the proposed method yields more precise and appropriate mode shapes, leading to a better estimation on the drift angle of the lower floors compared with a method designed for multiple sensors. As well, it is shown that the reduction in space complexity is offset by increasing the computation complexity.