• Journal of the Korean Solar Energy Society
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• v.38 no.5
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• pp.11-25
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• 2018

The Ln-least method is commonly used to estimate the Weibull parameters from the observed wind speed data. In previous studies, the bin method has been used to calculate the cumulative frequency distribution for the Ln-least method. The purpose of this study is to obtain better performance in the Ln-least method by applying probability plotting position(PPP) instead of the bin method. Two types of the wind speed data were used for the analysis. One was the observed wind speed data taken from three sites with different topographical conditions. The other was the virtual wind speed data which were statistically generated by a random variable with known Weibull parameters. Also, ten types of PPP formulas were applied which were Hazen, California, Weibull, Blom, Gringorten, Chegodayev, Cunnane, Tukey, Beard and Median. In addition, in order to suggest the most suitable PPP formula for estimating Weibull parameters, two accuracy tests, the root mean square error(RMSE) and $R^2$ tests, were performed. As a result, all of PPPs showed better performances than the bin method and the best PPP was the Hazen formula. In the RMSE test, compared with the bin method, the Hazen formula increased estimation performance by 38.2% for the observed wind speed data and by 37.0% for the virtual wind speed data. For the $R^2$ test, the Hazen formula improved the performance by 1.2% and 2.7%, respectively. In addition, the performance of the PPP depended on the frequency of low wind speeds and wind speed variability.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.2
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• pp.426-434
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• 2015

In this paper we proposed a dada-aided Doppler estimation method for underwater acoustic communication. The training sequence is non-dedicate, hence it can be designed for Doppler estimation as well as channel equalization. We assume the channel has been equalized and consider only flat-fading channel. First, based on the training symbols the theoretical received sequence is composed. Next the least square principle is applied to build the objective function, which minimizes the error between the composed and the actual received signal. Then an iterative approach is applied to solve the least square problem. The proposed approach involves an outer loop and inner loop, which resolve the channel gain and Doppler coefficient, respectively. The theoretical performance bound, i.e. the Cramer-Rao Lower Bound (CRLB) of estimation is also derived. Computer simulations results show that the proposed algorithm achieves the CRLB in medium to high SNR cases.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.294-298
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• 2009

In this study, we proposes the method to improve the quality of the image through the edge extraction more delicately. Our method is named ESII(Edge Sensitive Image Interpolation) and doesn't use the fixed parameter of the interpolation kernel. However, it changes the parameter of pixel which is interpolated to the high definition image using the proper information from the surrounding pixels. It reconstructs the image by using the LSE(Least Square Error) and determining the pixel values to make the CME(Camera Modelling Error) minimized. Compared to the conventional methods, suggested method shows the higher quality of subjective and objective image definition and lessons the computational complexity by separating the image into 1-D data.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.873-876
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• 2005

The nonlinear characteristics of a damper is directly related to the car behavior and performance, both for handling and comfort. So considering the nonlinear characteristics of a damper such as hysteresis is important to analyze the dynamic characteristics of a car suspension. In this study, a mathematical nonlinear dynamic damper model based on experimental data is devised to estimate the nonlinear parameters of a NEW EF-SONATA damper using the least square method. The devised nonlinear dynamic damper model is used to analyze the reaction force of a NEW EF-SONATA suspension using ADAMS. The simulation results are good agreement with the experimental data than those of the linear model.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.6
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• pp.98-104
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• 2002

In this study, we have developed an algorithm by attaching a camera at the end-effector of industrial six-axis robot in order to determine position and orientation of the camera system from cartesian coordinates. Cartesian coordinate as a starting point to evaluate for suggested algorithm, it was easy to confront increase of orientation vector for a linear line point that connects two points from coordinate space applied by recursive least square method which includes previous data result and new data result according to increase of image point. Therefore, when the camera attached to the end-effector has been applied to production location, with a calibration mask that has more than eight points arranged, this simulation approved that it is possible to determine position and orientation of cartesian coordinates of camera system even without a special measuring equipment.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• Bulletin of the Society of Naval Architects of Korea
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• v.27 no.2
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• pp.87-95
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• 1990

Conventional methods to estimate natural frequencies and damping ratios of structures from measured response time series obtained during impact tests are reviewed. Maximum Entropy Method and Least Square Prony Method are introduced to alleviate the inherent limitation of the conventional methods. The performance of the methods are explored through computer simulation. As an example of application, they are applied to the time series obtained from an anchor drop-and-snup test of a container ship and the result is compared to that of conventional FFT method. As a result of the computer simulation, it is found that Maximum Entropy Method is very efficient to estimate natural frequencies of structures when two neighboring natural frequencies are close enough and short data records are only available, but it is not a reliable estimator for damping ratios. And it is also found that Least Square Prony Method is efficient to estimate the natural frequencies and damping ratios of highly damped structural system, but the estimation efficiency of damping ratios is significantly deteriorated in the presence of significant additive noise.

• Journal of the Korean Chemical Society
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• v.23 no.2
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• pp.104-110
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• 1979

A new least square method for analysis of the whole time course of enzyme-catalyzed reactions is presented. This method requires only a programmable calculator with small capacity and is applicable to both uninhibited reactions and reactions inhibited by products or added compounds. This method fits the data to the nonlinear plot of substrate concentration vs. time, and, consequently, estimates the kinetic parameters better than the least square method based on linearly transformed equations.

• Journal of Industrial Technology
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• v.25 no.B
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• pp.157-164
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• 2005

Two methods, the bin averaging method and least square method, are often used in calibrating wind turbine test sites. The objective of this work was to determine a better method to predict the wind speed at wind turbine installing point. The calibration was done at the test site on a complex terrain located in Daegwallyeong, Korea. It was performed for two different cases based on the IEC 61400-12 power performance measurement standard. The wind speeds averaged for 10 minutes ranged between 4 m/s and 16 m/s. The wind-direction bins of each meteorological mast were 10 degrees apart, and only the bins having data measured for more than 24 hours were employed for the test site calibration. For both cases, the two methods were found to yield almost same results which estimated real wind speed very closely.