• Journal of Internet Computing and Services
• /
• v.6 no.6
• /
• pp.187-197
• /
• 2005

In this paper, we propose a method to estimate camera motion parameter based on invariant point features, Typically, feature information of image has drawbacks, it is variable to camera viewpoint, and therefore information quantity increases after time, The LM(Levenberg-Marquardt) method using nonlinear minimum square evaluation for camera extrinsic parameter estimation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum, In order to complement these shortfalls, we, first proposed constructing feature models using invariant vector of geometry, Secondly, we proposed a two-stage calculation method to improve accuracy and convergence by using 2D homography and LM method, In the experiment, we compared and analyzed the proposed method with existing method to demonstrate the superiority of the proposed algorithms.

• Journal of Radiation Protection and Research
• /
• v.29 no.3
• /
• pp.157-163
• /
• 2004

Atmospheric dispersion modelling has been widely used to predict the fate and transport of radioactive or toxic materials released from nuclear facilities which is an unlikely accidental event. To improve the forecasting performance of the dispersion model, it is required that source rate and dispersion characteristics must be defined appropriately. Generally, source term of the radioactive materials is much uncertain at the early phase of an accidental event. In this study, we computed the source rate with the experimental field data monitored at the Yeoung-Kwang nuclear site and obtained the optimal source rate to minimize the errors between the measured concentrations and the computed ones by the Gaussian plume model. Computed source term showed a good result within 24% of the artificially released source rate.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.31 no.12
• /
• pp.1180-1187
• /
• 2007

In the present work, topology optimization method using adaptive inner-front level set method is presented. In the conventional level set based topology optimization method, there exists an incapability for inner-front creation during optimization process. In this regard, as a new attempt to avoid and to overcome the limitation, an inner-front creation algorithm is proposed. In the inner-front creation algorithm, the strain energy density of a structure along with volume constraint is considered. Especially, to facilitate the inner-front creation process during the optimization process, the inner-front creation map which corresponds to the discrete valued function of strain energy density is constructed. In the evolution of the level set function during the optimization process, the least-squares finite element method (LSFEM) is employed. As an application to shell structures, the lightweight design of doubly curved shell and segmented mirror is carried out.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.3
• /
• pp.38-44
• /
• 1997

In this study, wer derived LMF-Sato, LMSF-Sato complex blind equalization algorithms for complex data. And then, the convergence rates, the convergence characteristics at the steady state and the stability of the proposed LMF and LMSF blind equalization algorithms are compared with those of LMS-Sato blind equalization algorithm. In simulations with 16-QAM data, LMF-Sato and LMSF-Sato algorithms showed better performance comparing with LMS-Sato algorithm generally. When the initial estimation errors of the weights of the equalizer are large, LMF-Sato algorithm showed ill characteristic in stability. However, LMSF-Sato algorithm has good covergence characteristics and preserves robustness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.06a
• /
• pp.406-409
• /
• 2007

This paper proposes an algorithm that predicts current electrostatic charge in a factory. The algorithm based on LSM(Least Square Method) dynamically takes the number of sample while calculating the value of electrostatic charge. The simulation results show that the proposed algorithm gains 73.18161 standard deviation with 95% trust probability and is better than conventional algorithm. We design the electrostatic prediction embedded system based on pxa255 with the proposes algorithm.

• The Korean Journal of Applied Statistics
• /
• v.31 no.2
• /
• pp.229-239
• /
• 2018

This paper introduces how to adopt the concept of temporal hierarchies to forecast time series data. Similarly as in hierarchical cross-sectional data, temporal hierarchies can be constructed for any time series data by means of non-overlapping temporal aggregation. Reconciliation forecasts with temporal hierarchies result in more accurate and robust forecasts when compared with the independent base and bottom-up forecasts. As an empirical example, we forecast traffic accident counts with temporal hierarchies and observe that reconciliation forecasts are superior to the base and bottom-up forecasts in terms of forecast accuracy.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.2
• /
• pp.341-353
• /
• 2013

In many practical machine learning and data mining applications, unlabeled data are inexpensive and easy to obtain. Semi-supervised learning try to use such data to improve prediction performance. In this paper, a semi-supervised regression method, semi-supervised kernel ridge regression estimation, is proposed on the basis of kernel ridge regression model. The proposed method does not require a pilot estimation of the label of the unlabeled data. This means that the proposed method has good advantages including less number of parameters, easy computing and good generalization ability. Experiments show that the proposed method can effectively utilize unlabeled data to improve regression estimation.

• Progress in Medical Physics
• /
• v.15 no.1
• /
• pp.39-44
• /
• 2004

The energy distributions for clinically used electron beams from measured and calculated mono energetic depth dose values were calculated. The energy distributions having the minimum difference between the measured and reduced values of depth dose are determined by iterations based on least square method. The nominal energies of 6, 9, 12, 15 MeV clinical electron beams were examined. The Monte Carlo depth dose calculations with determined energy distributions were peformed to evaluate those distributions. In a comparison of the calculated and measured depth dose data, the standard errors are estimated within $\pm$ 3% from surface to R$_{80}$ depth and within $\pm$4% from the surface to near the range for all electron beams. This can be practically applied to determine the energy distributions for clinically used electron beams.

• Communications for Statistical Applications and Methods
• /
• v.18 no.5
• /
• pp.625-636
• /
• 2011

This paper proposes two parametric entropy estimators, the minimum variance unbiased estimator and the maximum likelihood estimator, for the lognormal distribution for a comparison of the properties of the two estimators. The variances of both estimators are derived. The influence of the bias of the maximum likelihood estimator on estimation is analytically revealed. The distributions of the proposed estimators obtained by the delta approximation method are also presented. Performance comparisons are made with the two estimators. The following observations are made from the results. The MSE efficacy of the minimum variance unbiased estimator appears consistently high and increases rapidly as the sample size and variance, n and ${\sigma}^2$, become simultaneously small. To conclude, the minimum variance unbiased estimator outperforms the maximum likelihood estimator.

• Nuclear Engineering and Technology
• /
• v.28 no.1
• /
• pp.72-78
• /
• 1996

A computer program COVAFIT has been developed and applied to the evaluation of experimental cross sections for MeV energy incident particles. The program utilizes weighted least-square linear regression method with high-order polynomials derived in this study. Meeting the growing demand for the treatment of covariances in nuclear data, it deals with the variance and covariance data provided along with experimental cross sections and yields those for the evaluated ones. The evaluated results on two sets of neutron total cross section of oxygen and three sets of proton cross section for $C^{11}$ production reactions confirm the methodology formulated in and the applicability of the program.