• Korean Journal of Computational Design and Engineering
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• v.11 no.1
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• pp.1-10
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• 2006

This paper addresses B-spline curve approximation of a set of ordered points to a specified toterance. The important issue in this problem is to reduce the number of control points while keeping the desired accuracy in the resulting B-spline curve. In this paper we propose a new method for error-bounded B-spline curve approximation based on adaptive selection of dominant points. The method first selects from the given points initial dominant points that govern the overall shape of the point set. It then computes a knot vector using the dominant points and performs B-spline curve fitting to all the given points. If the fitted B-spline curve cannot approximate the points within the tolerance, the method selects more points as dominant points and repeats the curve fitting process. The knots are determined in each step by averaging the parameters of the dominant points. The resulting curve is a piecewise B-spline curve of order (degree+1) p with $C^{(p-2)}$ continuity at each knot. The shape index of a point set is introduced to facilitate the dominant point selection during the iterative curve fitting process. Compared with previous methods for error-bounded B-spline curve approximation, the proposed method requires much less control points to approximate the given point set with the desired shape fidelity. Some experimental results demonstrate its usefulness and quality.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.79-89
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• 2019

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.293-301
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• 1994

In this paper we propose a variable selection criterion minimizing influence curve in regression, and compare it with other criteria such as $C_p$(Mallows 1973) and adjusted coefficient of determination. Examples and extension to the generalized linear models are given.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.52 no.4
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• pp.281-289
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• 2016

Spinyhead sculpin Dasycottus setiger, a species of cold water fish, is distributed along the eastern coastal waters of Korea. A series of fishing experiments was carried out in the waters near Uljin from June, 2002 to November, 2004, using the experimental monofilament gill nets of different mesh sizes (82.2, 89.4, 104.8, and 120.2 mm) to describe the selectivity of the gill net for the fish. The SELECT (Share Each Length's Catch Total) analysis with maximum likelihood method was applied to fit the different functional models (normal, lognormal, and bi-normal models) for selection curves to the catch data. The bi-normal model with the fixed relative fishing intensity was selected as the best-fit selection curve by AIC (Akaike's Information Criterion) comparison. For the best-fit selection curve, the optimum relative length (the ratio of fish total length to mesh size) with the maximum efficiency and the selection range ($R_{50%,large}-R_{50%,small}$) of 50% retention were obtained as 2.363 and 0.851, respectively. The ratios of body girth to mesh perimeter at 100% retention where the selection curve of each mesh size represented the optimum total length were calculated as the range of 0.86 ~ 0.87.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.287-299
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• 2022

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

• Animal Bioscience
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• v.37 no.5
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• pp.839-851
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• 2024

Objective: The main goal of our current study was to improve the growth curve of meat animals by decreasing the birth weight while achieving a finishing weight that is the same as that before selection but at younger age. Methods: Random regression model was developed to derive various selection indices to achieve desired gains in body weight at target time points throughout the fattening process. We considered absolute and proportional gains at specific ages (in weeks) and for various stages (i.e., early, middle, late) during the fattening process. Results: The point gain index was particularly easy to use because breeders can assign a specific age (in weeks) as a time point and model either the actual weight gain desired or a scaled percentage gain in body weight. Conclusion: The point gain index we developed can achieve the desired weight gain at any given postnatal week of the growing process and is an easy-to-use and practical option for improving the growth curve.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.30 no.3
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• pp.161-171
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• 1994

Mesh selection analysis for the trawl net were carried out at the Southern Korean Sea and the East China Sea during the 1992-1994 years by the training ship Seabada of the National Fisheries University of Pusan, using A type (51.2mm), B type (70.2mm), C type (77.6mm), D type (88.0mm) square mesh cod-ends. The fishing trials were made using bottom trawl of the trouser type cod-end with cover net. Selection curves and selection parameters were calculated by a logistic model for Sphyraena pinguis, Pampus argenteus, Trachurus japonicus, Pagrus major, Callanthias japonicus, Trichiurus lepturus. The results obtained are summarized as follows: 1. Red barracuda : Selection range and fifty percent selection length in the A type was 115.8mm, 292.8mm, respectively. 2. Harvest fish : Each selection range and fifty percent selection length in the B. C. and D type was 37.7mm, 113.8mm : 40.1mm, 131.7mm and 64.8mm, 148.6mm, respectively. Selection parameters of master curve were : slope, 3.81 : intercept, -6.4. Optimum mesh size was 89.3mm. 3. Horse mackerel : Each selection range and fifty percent selection length in the A, B, C and D type was 43.0mm, 148.3mm : 60.7mm, 183.2mm, 214.5mm and 91.4mm, 254.9mm, respectively. Selection parameters of master curve were : slope 2.30 : intercept, -6.4. Optimum mesh size was 66.8mm. 4. Red seabrem : Selection range and fifty percent selection length in the D type was 42.7mm, 203.4mm, respectively. 5. Yellowsail red bass : Selection range and fifty percent selection length in the A type was 84.0mm, 110.6mm, respectively. 6. Hair tail : Each selection range and fifty percent selection length in the A, B and C type was 59.7mm, 176.0mm : 100.9mm, 250.7mm and 178.6mm, 307.0mm, respectively. Selection parameters of master curve were : slope, 1.54 : intercept, -5.4. Optimum mesh size was 57.5mm.

• Journal of Korean Society for Quality Management
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• v.23 no.1
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• pp.95-105
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• 1995

The error structure of nonlinearized technological growth models, such as, the Pearl curve, the Gompertz curve and the Wei bull growth curve, has zero mean and a constant variance over time. Transformed models, however, like the linearized Fisher-Pry model. the linearized Gompertz growth curve, and the linearized Weibull growth curve have increasing variance from t = 0 to the inflection point.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.138-142
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• 2014

This research presents an A* based algorithm which can be applied to Unmanned Ground Vehicle self-navigation in order to make the driving path smoother. Based on the grid map, A* algorithm generated the path by using straight lines. However, in this situation, the knee points, which are the connection points when vehicle changed orientation, are created. These points make Unmanned Ground Vehicle continuous navigation unsuitable. Therefore, in this paper, B-spline curve function is applied to transform the path transfer into curve type. And because the location of the control point has influenced the B-spline curve, the optimal control selection algorithm is proposed. Also, the optimal path tracking speed can be calculated through the curvature radius of the B-spline curve. Finally, based on this algorithm, a path created program is applied to the path results of the A* algorithm and this B-spline curve algorithm. After that, the final path results are compared through the simulation.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.4
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• pp.247-259
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• 1993

The fishing experiment was carried out by the training ship Saebada in order to analyse the mesh selectivity for trawl cod-end, in the Southern Korea Sea and the East China Sea from June. 1991 through August, 1992. The trawl cod-end used in this experiment has the trouser type of cod-end with cover net. and the mesh selectivity was examined for the five kinds of the opening of mesh in its cod-end part. A total of 163 hauls, of which having mesh size 51.2mm ; A 89, 70.2mm ; B 54, 77.6mm ; C 55, 88.0mm ; D 52 and 111.3mm ; E 20 were used respectively. Selection curves and selection parameters were calculated by using a logistic function, S=1/(1+exp super(-(aL+b)) ). The mesh election master curves were estimated by S=1/(1+exp super(-[a(L/M)+$\beta$]) ). and the optimum mesh size were calculated with (L/M) sub(50) of master curve. In these cases 'a' and '$\alpha$' are slope, 'b' and '$\beta$' are intercept. 'L' is body length of the target species of fishes, 'M' is the mesh size, and 'S' denotes mesh selectivity. In this report, the four species of compressed form fishes were taken analized according to fish shape, and 'S' denotes mesh selectivity. In this report, the four species of compressed form fishes were taken analized according to fish shape, and the results obtained are summarized as follows: 1. Red seabream Pagrus major(Temminct et Schlegel) and yellow porgy Dentex tumifrons(Temminct et Schlegel) ; Selection rate in each mesh size of A, B, C, D and E were 99.7%, 97.5%, 91.4%, 76.7% and 57.8% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 2.65 and -28.62, 4.40 and -77.73, 2.31 and -46.99, and their selection factors were 1.39, 2.10, 1.83 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.05 and -5.65 respectively, and (L/M) sub(50) was 1.85. The optimum mesh size of Red seabream was 141mm. 2. Filefish Thamnaconus modestus (Gunther) ; Selection rate in each mesh size of A, B, C, D and E were 99.6%, 98.3%, 91.2%, 80.0% and 48.6% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 5.82 and -55.10, 2.92 and -36.90, 3.91 and -63.09, and their selection factors were 1.35, 1.44, 1.45 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.02 and -4.32 respectively, and (L/M) sub(50) was 1.43. The optimum mesh size was 129mm. 3. Target dory Zeus faber Valenciennes ; Selection rate in each mesh size of A, B, C, D and E were 99.7%, 100%, 83.2%, 91.6% and 65.0% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 3.85 and -32.46, 4.19 and -57.38, 2.45 and -40.03, and their selection factors were 1.09, 1.56, 1.47 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 2.64 and -3.53 respectively, and (L/M) sub(50) was 1.34. The optimum mesh size was 127mm. 4. Butterfish Psenopsis anomala (Temminct et Schlegel) ; Selection rate in each mesh size of A, B, C, D and E were 99.2%, 34.1%, 46.5%, 14.3% and 2.4% respectively. Selection parameters 'a' and 'b' of mesh sizes B, C and D were 5.35 and -71.70, 5.07 and -69.25, 3.31 and -62.06 and their selection factors were 1.91, 1.75, 2.13 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.16 and -6.24 respectively, and (L/M) sub(50) was 1.98. The optimum mesh size was 71mm.