• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.436-445
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• 2011

In this paper, equivalent properties of 2-2 mode piezocomposites were studied. Variation of the properties of 2-2 mode piezocomposites was analyzed by the finite element method, and the result was compared with experimental measurement data to confirm the validity of the analysis. The equivalent properties of a single phase material to represent the piezocomposite composed of PZT-5H and polymer were derived by the asymptotic averaging method. Accuracy of the derived equivalent properties was enhanced by minimizing the discrepancy between the impedance spectra of full 2-2 piezocomposite and equivalent single phase material resonators of various vibration modes by the least square method. The equivalent properties of 2-2 piezocomposites derived in this study can be utilized to the design of diverse acoustic sensors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Korean Journal of Food Science and Technology
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• v.25 no.2
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• pp.178-184
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• 1993

The procedure using equivalent time at reference temperature has been assessed for the estimation of kinetic parameters with experimental data. Kinetic studies of nonenzymatic browning reaction in model and food system were carried out with linearly increasing temperature method. These kinetic parameters, n, $k_{ref}$ and $E_a$ of the systems were evaluated from original data in one step by nonlinear least square regression. The one step procedure yielded efficiently accurate parameter estimation. Computer simulated data with the kinetic models were well consistent with experimental data (average correlation coefficient=0.96).

• KIEAE Journal
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• v.7 no.4
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• pp.147-152
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• 2007

Numerous non-destructive tests(NDT) to assess the safety of real structures have been developed. System identification(SI) techniques using dynamic responses and behaviors of structural systems become an outstanding issue of researchers. However the conventional SI techniques are identified to be non-practical to the complex and tall buildings, due to limitation of the availability of an accurate data that is magnitude or location of external loads. In most SI approaches, the information on input loading and output responses must be known. In many cases, measuring the input information may take most of the resources, and it is very difficult to accurately measure the input information during actual vibrations of practical importance, e.g., earthquakes, winds, micro seismic tremors, and mechanical vibration. However, the desirability and application potential of SI to real structures could be highly improved if an algorithm is available that can estimate structural parameters based on the response data alone without the input information. Thus a technique to estimate structural properties of building without input measurement data and using limited response is essential in structural health monitoring. In this study, shaking table tests on three-story plane frame steel structures were performed. Out-put only model analysis on the measured data was performed, and the dynamic properties were inverse analyzed using least square method in time domain. In results damage detection was performed in each member level, which was performed at story level in conventional SI techniques of frequency domain.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.173-183
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• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.4
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• pp.347-353
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• 2003

Lately, Digital Photogrammetry has been increasingly applied to various hightech industries and becomes one of more interesting focuses of study than ever. Thus, this study aims to develop a Road Horizontal Alignment Information System by means of digital photogrammetry. Data acquired from digital photographic techniques were processed using Delphi, an object oriented programming language to develop a computer aided program, that allows us to build the information on Road Horizontal Alignment(Beginning Point of Curve, Ending Point of Curve, Radius, Intersection Point). The developed program could maximize visualization for better analysis compared with traditional programs because it utilizes many image data. Comparing with data from traditional horizontal alignment extraction programs based on the principle of least square method, the data acquired by Horizontal Alignment Information and kinematic GPS showed out of the developed road information systems the improved accuracy of IP value up to about 2m in the direction of X, Y axes, where the accuracy of curve radius(R) becomes enhanced up to about 2.5 m.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.700-711
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• 2022

In this study, deepwater design waves were estimated for 16 wave directions and various return periods based on statistical analysis of extreme waves observed for more than 20 years at three stations (Chilbal-do, Geomun-do, Donghae). These values were compared with design waves estimated based on the omni-directional wave data. The Weibull distribution was used as the probability distribution function whose parameters were determined by the least square method. The Kolmogorov-Smirnov test was applied for the goodness of fit test. Notably, the directional design waves were smaller than the omni-directional design wave for every wave direction. The maximum 50-year wave heights for directional sectors were 7.46 m (NNE), 12.05 m (S), and 9,59 m (SSW) at Chilbal-do, Geomun-do and Donghae whereas those for uni-directional wave data were 7.91 m, 13.82 m and 10.38 m, respectively. This implied possible under-estimation of the deepwater design waves for 16 wave directions being currently used in the design of offshore and coastal structures.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.245-254
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• 2020

This study investigates the impact of the country's governance on the revenue efficiency in the banking sectors of 42 Islamic banks in 15 countries offering Islamic banking and financial services. Technical efficiencies of individual Islamic banks were analyzed using the Data Envelopment Analysis method. The Ordinary Least Square estimation method is employed to examine the impact of country supervision and regulation on the technical efficiency of Islamic banks. With robustness check, the study assesses the impact of bank regulations and supervision on the efficiency of Islamic banks operating in different regions. The empirical findings suggest that supervisory power, activity restrictions, and private monitoring positively influence the efficiency of Islamic banks. On the other hand, we observe a negative impact of capital requirement on Middle East and North Africa (MENA) countries. The findings indicate that supervisory power, activity restrictions, and private monitoring positively influence the efficiency of Islamic banks in Asia, but vice versa on capital requirement in MENA countries. This study will contribute to the body of knowledge by assessing the types of reforms in bank regulations and supervision that work best for Islamic banks in order to increase the level of efficiency and the level of regulations and supervision of Islamic banks.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.208-220
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• 2016

Purpose: Fusel oil is a potent volatile aroma compound found in many alcoholic beverages. At low concentrations, it makes an essential contribution to the flavor and aroma of fermented alcoholic beverages, while at high concentrations, it induced an off-flavor and is thought to cause undesirable side effects. In this work, we introduce Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid and nondestructive technique for the quantitative determination of fusel oil in the Korean alcoholic beverage "soju". Methods: FT-NIR transmittance spectra in the 1000-2500 nm region were collected for 120 soju samples with fusel oil concentrations ranging from 0 to 1400 ppm. The calibration and validation data sets were designed using data from 75 and 45 samples, respectively. The net analyte signal (NAS) was used as a preprocessing method before the application of the partial least-square regression (PLSR) and principal component regression (PCR) methods for predicting fusel oil concentration. A novel variable selection method was adopted to determine the most informative spectral variables to minimize the effect of nonmodeled interferences. Finally, the efficiency of the developed technique was evaluated with two different validation sets. Results: The results revealed that the NAS-PLSR model with selected variables ($R^2_{\upsilon}=0.95$, RMSEV = 100ppm) did not outperform the NAS-PCR model (($R^2_{\upsilon}=0.97$, RMSEV = 7 8.9ppm). In addition, the NAS-PCR shows a better recovery for validation set 2 and a lower relative error for validation set 3 than the NAS-PLSR model. Conclusion: The experimental results indicate that the proposed technique could be an alternative to conventional methods for the quantitative determination of fusel oil in alcoholic beverages and has the potential for use in in-line process control.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.3
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• pp.295-304
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• 2008

This paper comprises two specific objectives. The first is to examine the applicability of Ordinary Kriging interpolation(OK) to finite element method that is based on variogram modeling in conjunction with different allowable limits of separation distance. The second is to investigate the accuracy according to theoretical variograms such as polynomial, Gauss, and spherical models. For this purpose, the weighted least square method is applied to obtain the estimated new stress field from the stress data at the Gauss points. The weight factor is determined by experimental and theoretical variograms for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. The validity of the proposed approach has been tested by analyzing two numerical examples. It is noted that the numerical results by Gauss model using 25% allowable limit of separation distance show an excellent agreement with theoretical solutions in literature.