• Bulletin of the Korean Chemical Society
• /
• v.33 no.9
• /
• pp.3017-3024
• /
• 2012

The geometrical parameters, vibrational frequencies, and binding energies for $(H_2O_3)_n$ (n = 1-5) have been investigated using various quantum mechanical techniques. The possible structures of the clusters (n = 2-5) are fully optimized and the binding energies are predicted using energy differences at each optimized geometry. The harmonic vibrational frequencies are also determined and zero-point vibrational energies (ZPVEs) are considered for the better prediction of the binding energy. The best estimation of the binding energy for the dimer is 8.65 kcal/mol. For n = 2 and 3, linear structures with all trans forms of the HOOOH monomers are predicted to be the lowest conformations in energy, while the cyclic structures with all cis-HOOOH monomers are preferable structures for n = 4 and 5.

• Asian-Australasian Journal of Animal Sciences
• /
• v.14 no.11
• /
• pp.1511-1515
• /
• 2001

A prediction of 305 d milk production from early records using an empirical Bayes method (EBM) was performed. The EBM was compared with the best predicted estimation (BPE), test interval method (TIM), and the linearized Wood's model (LWM). Daily milk yields were obtained from 606 first lactation Japanese Holstein cows in three herds. From each file of 305 daily records, 10 random test day records with an interval of approximately one month were taken. The accuracies of these methods were compared using the absolute difference (AD) and the standard deviation (SD) of the differences between the actual and the estimated 305 d milk production. The results showed that in the early stage of the lactation, EBM was superior in obtaining the prediction with high accuracy. When all the herds were analyzed jointly, the AD during the first 5 test day records were on average 373, 590, 917 and 1,042 kg for EBM, BPE, TIM, and LWM, respectively. Corresponding SD for EBM, BPE, TIM, and LWM were on average 488, 733, 747 and 1,605 kg. When the herds were analyzed separately, the EBM predictions retained high accuracy. When more information on the actual lactation was added to the prediction, TIM and LWM gradually achieved better accuracies. Finally, in the last period of the lactation, the accuracy of both of the methods exceeded EBM and BPM. The AD for the last 2 samples analyzing all the herds jointly were on average 141, 142, 164, and 214 kg for LWM, TIM, EBM, and BPE, respectively. In the current practices of collecting monthly records, early prediction of future milk production may be more accurate using EBM. Alternatively, if enough information of the actual lactation is accumulated, TIM may obtain better accuracy in the latter stage of lactation.

• Journal of Forest and Environmental Science
• /
• v.33 no.4
• /
• pp.315-321
• /
• 2017

This study was carried out to estimate the parameters of stem taper functions, to figure out the best taper model by species, and to compare with previous studies by species, targeting on the stemmed tree samples collected from the Korean red pine (Pinus densiflora), Korean white pine (Pinus koraiensis), and Japanese larch (Larix kaempferi ) stands in Gangwon and North Gyeongsang provinces of South Korea. The seven widely used models were applied in this study, and Muhairwe 1999 model for Korean red pine and Korean white pine and Kozak 2002 model for Japanese larch were evaluated as the best model for each species according to the fit statistics and the predicted stem form comparison. In addition, the predicted diameter was suitably fitted when comparing the previous studies, and the values were more appropriate following stem taper according to neiloid, paraboloid, and cone parts by species. Consequently, the estimation of this study was considered to represent the stem taper well. When comparing stem taper of three species, the diameter was largest in Korean white pine. Overall, the taper models of this study are judged to be useful for estimating stem form and volume computation of Korean red pine, Korean white pine, and Japanese larch.

• Journal of KIISE:Computer Systems and Theory
• /
• v.26 no.8
• /
• pp.966-974
• /
• 1999

완전 탐색 블록 정합 알고리즘(FBMA)은 다양한 움직임 추정 알고리즘 중 최상의 움직임 추정을 할 수 있으나, 방대한 계산량이 실시간 처리의 적용에 장애 요소이다. 본 논문에서는 완전 탐색 블록 정합 알고리즘에 비해 더 낮은 계산량과 유사한 화질을 가지는 새로운 고속 움직임 추정 알고리즘을 제안한다. 제안한 방법에서는 공간적인 상관성을 이용함으로써 적절한 탐색 영역의 크기를 예측할 수 있다. 현재 블록의 움직임 추정을 위하여 이웃 블록이 가지고 있는 움직임과 탐색 영역의 크기를 이용하여 현재 블록의 탐색 영역을 적응적으로 변화시키는 방법이다. 이 예측값으로 현재 블록의 탐색 영역 크기를 결정한 후, FBMA와 같이 이 영역 안의 모든 화소점들에 대하여 현재 블록을 정합하여 움직임 벡터를 추정한다. 컴퓨터 모의 실험 결과 계산량 측면에서 제안 방법이 완전 탐색 블록 정합 알고리즘보다 50%정도 감소하였으며, PSNR 측면에서는 0.08dB에서 1.29dB 정도 감소하는 좋은 결과를 얻었다.Abstract Full search block-matching algorithm (FBMA) was shown to be able to produce the best motion compensated images among various motion estimation algorithms. However, huge computational load inhibits its applicability in real applications. A new motion estimation algorithm with lower computational complexity and good image quality when compared to the FBMA will be presented in this paper. In the proposed method, The appropriate search area can be predicted by using the temporal correlation between neighbouring blocks. For motion estimation of the current block, it is the method changing adjustably search area of current block by using motion and search area size of the neighbouring block. After deciding search area size of the current block with this predicted value, we estimate motion vector that matching current block like the FBMA for every pixel in this area. By the computer simulation the computation amount of the proposed method can be greatly decreased about 50% than that of the FBMA and the good result of the PSNR can be attained.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.2
• /
• pp.20-28
• /
• 2003

Recently we need a faster and more accurate motion vector search algorithm for ASIC(Application Specific IC) or small systems. Block motion estimation using Full Search(FS) algorithm provides the best visual quality and PSNR, but it requires intensive computations. The previously proposed fast algorithms reduced the number of computations by limiting the number of searching locations. This is accomplished at the expense of less accuracy of motion estimation and gives rise to an appreciably higher SAD(Sum of Absolute Difference) for motion compensated images. In this paper we exploit the spatial correlation of motion vectors and present a fast motion estimation scheme which uses the predicted motion vector(PMV). The PMV scheme is more clear and simpler than the previously proposed algorithms which also use adjacent motion vectors. Simulation results with standard video sequences show that the PMV scheme is faster and more accurate than other algorithms such as Nearest-Neighbors Search(NNS) algorithm.

• Communications for Statistical Applications and Methods
• /
• v.27 no.5
• /
• pp.547-568
• /
• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• Journal of Korean Society on Water Environment
• /
• v.29 no.1
• /
• pp.29-35
• /
• 2013

Recently, various Best Management Practices (BMPs) have been applied at a field to reduce soil erosion. Hourly Runoff and Sediment Model for Best Management Practices (HRSM4BMP) model could be used to evaluate soil erosion reduction for various agricultural BMPs at fields. Runoff and sediment yield from source areas have to be predicted with greater accuracies to evaluate sediment reduction efficiently with BMPs. To achieve this, the best parameters related with runoff and sediment modules of the HRSM4BMP model should be identified with proper calibration processes. Although manual calibration is often utilized in calibrating runoff and sediment using the HRSM4BMP, objective calibration method would be recommended. The purpose of the study was to develop an automatic calibration tool of the HRSM4BMP model with PARASOL method. This automatic calibration tool was applied to Bangdongri, Chuncheon-si to evaluate its calibration performance. The $R^2$, NSE and RMSE value for runoff estimation were 0.92, 0.92, $0.3m^3$, and for sediment yield estimation were 0.94, 0.94, 0.0027 kg. As shown in this result, automatic calibration tool of HRSM4BMP model would be used to determine the best parameters and can be used to simulate runoff and sediment yield with acceptable accuracies.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.3
• /
• pp.18-27
• /
• 2009

In this paper, we propose a frame rate conversion algorithm using adaptive search-based motion estimation (ME). The proposed ME method uses recursive search, 3-step search, and single predicted search as candidates for search strategy. The best method among the three candidates is adaptively selected on a block basis according to the predicted motion type. The adaptation of the search method improves the accuracy of the estimated motion vectors while curbing the increase of computational load. To support the proposed ME method, an entire image is divided into three regions with different motion types. Experimental results show that the proposed FRC method achieves better image quality than existing algorithms in both subjective and objective measures.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.5
• /
• pp.56-65
• /
• 2009

Stereoscopic images consist of the left image and the right image. Thus, stereoscopic images have much amounts of data than single image. Then an efficient image compression technique is needed, the DPCM-based predicted coding compression technique is used in most video coding standards. Motion and disparity estimation are needed to realize the predicted coding compression technique. Their performing algorithm is block matching algorithm used in most video coding standards. Full search algorithm is a base algorithm of block matching algorithm which finds an optimal block to compare the base block with every other block in the search area. This algorithm presents the best efficiency for finding optimal blocks, but it has very large computational loads. In this paper, we have proposed fast disparity estimation algorithm using motion and disparity vector information of the prior frame in stereo image coding. We can realize fast disparity vector estimation in order to reduce search area by taking advantage of global disparity vector and to decrease computational loads by limiting search points using motion vectors and disparity vectors of prior frame. Experimental results show that the proposed algorithm has better performance in the simple image sequence than complex image sequence. We conclude that the fast disparity vector estimation is possible in simple image sequences by reducing computational complexities.

• Transactions of Materials Processing
• /
• v.27 no.1
• /
• pp.28-36
• /
• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.