• Journal of the Korean Wood Science and Technology
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• v.43 no.3
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• pp.311-319
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• 2015

Near infrared (NIR) reflectance spectroscopy was employed to develop moisture content prediction model of pitch pine (Pinus rigida), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), yellow poplar (Liriodendron tulipifera) wood below fiber saturation point. NIR reflectance spectra of specimens ranging from 1000 nm to 2400 nm were acquired after humidifying specimens to reach several equilibrium moisture contents. To determine the optimal moisture contents prediction model, 5 mathematical preprocessing methods (moving average (smoothing point: 3), baseline, standard normal variate (SNV), mean normalization, Savitzky-Golay $2^{nd}$ derivatives (polynomial order: 3, smoothing point: 11)) were applied to reflectance spectra of each specimen as 8 combinations. After finishing mathematical preprocessings, partial least squares (PLS) regression analysis was performed to each modified spectra. Consequently, the mathematical preprocessing methods deriving optimal moisture content prediction were 1) moving average/SNV for pitch pine and red pine, 2) moving average/SNV/Savitzky-golay $2^{nd}$ derivatives for Korean pine and yellow poplar. Every model contained three principal components.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.533-542
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• 2021

The main objective of this article is to investigate the existence of the lunar effect during the full moon period (FM period) and the new moon period (NM period) on the selected Islamic stock market returns and volatilities. For this purpose, the Ordinary Least Squares model, Autoregressive Conditional Heteroscedasticity model, Generalised Autoregressive Conditional Heteroscedasticity model and Generalised Autoregressive Conditional Heteroscedasticity-in-Mean model are employed using the mean daily returns data between January 2010 and December 2019. Next, the log-likelihood, Akaike Information Criterion and Schwarz Information Criterion value are analyzed to determine the best models for explaining the returns and volatility of returns. The empirical results have deduced that, during the NM period, excluding Malaysia, the total mean daily returns for all of the selected countries have increased mean daily returns in contrast to the mean daily returns during the FM period. The volatility shocks are intense and conditional volatility is persistent in all countries. Subsequently, the volatility behavior tends to have lower volatility during the FM period and NM period in the Islamic stock market, except Malaysia. This article also concluded that the ARCH (1) model is the preferred model for stock returns whereas GARCH-M (1, 1) is preferred for the volatility of returns.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1670-1676
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• 2018

Objective: This study was conducted to enable on-line prediction of primal and commercial cut weights in Korean slaughter pigs by AutoFom III, which non-invasively scans pig carcasses early after slaughter using ultrasonic sensors. Methods: A total of 162 Landrace, Yorkshire, and Duroc (LYD) pigs and 154 LYD pigs representing the yearly Korean slaughter distribution were included in the calibration and validation dataset, respectively. Partial least squares (PLS) models were developed for prediction of the weight of deboned shoulder blade, shoulder picnic, belly, loin, and ham. In addition, AutoFom III's ability to predict the weight of the commercial cuts of spare rib, jowl, false lean, back rib, diaphragm, and tenderloin was investigated. Each cut was manually prepared by local butchers and then recorded. Results: The cross-validated prediction accuracy ($R^2cv$) of the calibration models for deboned shoulder blade, shoulder picnic, loin, belly, and ham ranged from 0.77 to 0.86. The $R^2cv$ for tenderloin, spare rib, diaphragm, false lean, jowl, and back rib ranged from 0.34 to 0.62. Because the $R^2cv$ of the latter commercial cuts were less than 0.65, AutoFom III was less accurate for the prediction of those cuts. The root mean squares error of cross validation calibration (RMSECV) model was comparable to the root mean squares error of prediction (RMSEP), although the RMSECV was numerically higher than RMSEP for the deboned shoulder blade and belly. Conclusion: AutoFom III predicts the weight of deboned shoulder blade, shoulder picnic, loin, belly, and ham with high accuracy, and is a suitable process analytical tool for sorting pork primals in Korea. However, AutoFom III's prediction of smaller commercial Korean cuts is less accurate, which may be attributed to the lack of anatomical reference points and the lack of a good correlation between the scanned area of the carcass and those traits.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.32-38
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• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.691-698
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• 2003

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-l, we may compute the updated estimate of this vector at iteration n upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RLS algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the B times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.11
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• pp.1746-1753
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• 2007

Steers (n = 335) of known genetic background from four fundamentally different growth types were subjected to two production systems to study differences in cooking loss (CL), tenderness, and chemical composition. Growth types were animals with genetic potential for large mature weight-late maturing (LL), intermediate mature weight-late maturing (IL), intermediate mature weight -early maturing (IE), and small mature weight-early maturing (SE). Each year, in a nine-year study, calves of each growth type were weaned and five steers of each growth type were developed on pasture or feedlot and harvested at approximately 20 and 14 mo of age, respectively. Data collected were CL and Warner-Bratzler shear force (WBS) for the Longissimus dorsi (LM), Psoas major (PS), and Quadriceps femoris (QF) muscles. Chemical composition was also determined from the right fore- and hindquarter. Data were analyzed using least squares analysis of variance for unequal subclass numbers. The beef growth $type{\times}production$ system interaction was significant for CL and WBS of the LM and ash in the lean trim of the forequarter. Growth types of LL and IL had greater (p<0.05) mean percentage CL in the PS and QF muscles than did IE and SE steers. Growth type LL had the highest (p<0.05) mean for both moisture and protein in the fore- and hindquarters; while SE had the lowest numerical mean value for moisture and protein in the fore- and hindquarters. Shear force of the PS did not differ (p>0.05) among steers of the four growth types. Increasing challenges to the cattle feeding industry may dictate that pasture development play a larger role in future production regimes. Producers should strive to match genetic growth type with available resources in order to remain viable and continue producing a quality product.

• Journal of Technologic Dentistry
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• v.38 no.3
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• pp.217-224
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• 2016

Purpose: This study aimed to generate 3-D occlusal curvatures and evaluate the relationship between the occlusal curvatures and mandibular occlusal morphology factors. Methods: Mandibular dental casts from 25 young adult Korean were scanned as a virtual dental models with a 3-D scanner(Scanner S600, Zirkonzahn, Italy). The curve of Spee, curve of Wilson, and Monson's sphere were generated by fitting a circle/sphere to the cusp tips using a least-squares method. The mandibular mesiodistal cusp inclination, buccolingual cusp inclination, and tooth wear parameters were measured on the prepared virtual models using RapidForm2004(INUS technology INC, Seoul, Korea). Wilcoxon signed-rank test was performed to test side difference, and Spearman's rank correlation coefficients were investigated to verify the correlation between occlusal curvatures and correlated factors (a=0.05). Results: The mean radii of curve of Spee were $83.09{\pm}33.94$ in the left side and $79.00{\pm}28.12mm$ in the right side. The mean radii of curve of Wilson were $66.82{\pm}15.87mm$ in the mesial side and $47.87{\pm}9.40mm$ in the distal side with significantly difference between mesiodistal sides(p<0.001). The mean radius of Monson's sphere was $121.85{\pm}47.11mm$. Most of the cusp inclination parameters showed negative correlation for the radius of Monson' sphere(p<0.05). Especially, the buccolingual cusp inclinations in mesial side of molar showed high correlation coefficients among the factors(p<0.05). Conclusion: The radius of Monson's sphere was greater than the classical 4-inch values, and the buccolingual cusp inclinations in mesial side of molar can be considered as one of the main factors correlating with the radius of Monson's sphere.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1577-1580
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• 2004

This paper proposes a design and implementation of transversal adaptive digital filter using LMS (Least Mean Squares) adaptive algorithm. The filter structure is based on Distributed Arithmetic (DA) which is able to calculate the inner product by shifting and accumulating of partial products and storing in look-up table, also the desired adaptive digital filter will be multiplierless filter. In addition, the hardware implementation uses VHDL (Very high speed integrated circuit Hardware Description Language) and synthesis using FLEX10K Altera FPGA (Field Programmable Gate Array) as target technology and uses Leonardo Spectrum and MAX+plusII program for overall development. The results of this design are shown that the speed performance and used area of FPGA. The experimental results are presented to demonstrate the feasibility of the desired adaptive digital filter.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.365-370
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• 2000

A sliding mode fuzzy control (SMFC) algorithm is applied to design a controller for a benchmark problem on a wind- excited building. The structure is a 76-story concrete office tower with a height of 306 meters, hence the wind resistance characteristics are very important for the serviceability as well as the safety. A control system with an active tuned mass damper is assumed to be installed on the top floor. Since the structural acceleration is measured only at ,limited number of locations without measurement of the wind force, the structure of the conventional continuous sliding mode control may have the feed-back loop only. So, an adaptive least mean squares (LMS) filter is employed in the SMFC algorithm to generate a fictitious feed-forward loop. The adaptive LMS filter is designed based on the information of the stochastic characteristics of the wind velocity along the structure. A numerical study is carried out. and the performance of the present SMFC with the ,adaptive LMS filter is investigated in comparison with those of' other control, of algorithms such as linear quadratic Gaussian control, frequency domain optimal control, quadratic stability control, continuous sliding mode control, and H/sub ∞///sub μ/, control, which were reported by other researchers. The effectiveness of the adaptive LMS filter is also examined. The results indicate that the present algorithm is very efficient .

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1449-1462
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• 2013

We present a novel approach to producing facial caricature with Simpson cartoon style based on Moving Least Squares (MLS). We take advantage of employing the caricature stylization rule of caricature artist, Justin. Our method allows Simpson-style cartoon character similar to user's features by using Justin's technique, which is a set of caricature stylization rules. Our method transforms input photo image into Simpson style caricature by using MLS approximation. The unique characteristics of user in the photo can be detected by comparing to the mean face feature and the input face feature extracted by AAM(Active Appearance Model). To exaggerate the detected unique characteristics, we set up the exaggeration rules using Justin's technique. In addition, during the cartooning process, user's hairs and accessories are used to the deformed image to make a close resemblance. Our method preserves the reliable and stylized caricature through the exaggeration rules of the actual caricature artist's techniques. From this study, we can easily create a Simpson-style cartoon caricature to resemble user's features by combining a caricature with existing cartoon researches.