• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.39 no.3
• /
• pp.174-180
• /
• 2003

This study investigated the size selectivity of the round traps for greenling (Hexagrammos otakii) in the western sea of Korea. The selection curve for the greenling from the experiments on Oct. 2000 and Ar. 2001 was fitted by Kitahara's method to a polynomial equation and two parameter logistic selection curve. The selectio curve of the latter was more reasonable than that of the former. The equation of selectivity curve obtained using a logistic function with least square method was , s(R)=1/1+exp(-1.1169R+6.4565), where R=1/m, and 1 and m are total length and mesh size, respectively. The size selectivity curve showed that the current regulated mesh size(35mm) in case of the round trap was close to the L50 (37.0mm) of the selection curve for the biological minimum length (21.4cm) of the greenling.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.30 no.3
• /
• pp.172-181
• /
• 1994

The mesh selectivity of diamond and suare mesh cod-ends at the Southern Korean Sea and the East China Sea were compared for Pampus argenteus, Trachurus japonicus, Trichiurus lepturus. Selection trials were carried out using diamond and square mesh cod-end by trouser type cod-end with cover net. of which the mesh cod-end has four types : A(51.2mm), B(70.2mm), C(77.6mm), D(88.0mm). Selection curves and selection parameters were calculated using a logistic model. The results obained are summarized as follows : 1. Harvest fish : In B. C and D type selection range and fifty percent selection length of the square mesh were about 21mm, 11mm : 12mm, 18mm and 34mm, 5mm higher than those of the diamond mesh, respectively. Selection factor of master curve for the diamond mesh was 1.54 and for the square mesh was 1.68. The optimum mesh size for the diamond mesh was 97.4mm and for the square mesh was 89.3mm, the difference was 8.1mm. 2. Horse mackerel : In A type, selection range was nearly the same for the diamond and the square mesh, but fifty percent selection length of the square mesh was 43mm higher than the diamond mesh. In B. C and D type, selection range and fifty percent selection length of the square mesh were about 6mm, 3mm : 24mm, 21mm and 11mm, 42mm higher than those of the diamond mesh, respectively. Selection factor of master curve for the diamond mesh was 2.37, for the square mesh was 2.77. The optimum mesh size for the diamond mesh was 78.1mm and for the square mesh was 66.8mm, the difference was 11.3mm. 3. Hair tail : In A, B and C type, selection range of the square mesh was about 34mm, 8mm, 60mm higher than those of the diamond mesh. Fifty percent selection length for the diamond mesh was about 5mm, 7mm, 8mm higher than that of the square mesh. Selection factor of master curve for the diamond mesh was 3.11, for the square mesh was 3.48. The optimum mesh size for the diamond mesh was 64.3mm and for the square mesh was 57.5mm, the difference was 6.8mm.

• Journal of Nutrition and Health
• /
• v.23 no.7
• /
• pp.504-512
• /
• 1990

In experiment 1, dietary self-selection of the 3 macronutrients, protein, fat, and carbohydrate were examined in male rats given 3 food cups of 80% carbohydrate, 80% protein, and 70% fat diets simultaneously. All the rats showed normal growth pattern and organ weight, which means they have ability to select just right kinds and amounts of nurients in order to support their growth and development. Mean values of caloric intake, body weight gain, serum lipid values and empty carcass compositions were not significantly differ between the upper and lower quartile groups of fat proportion of empty carcass compared to the lower quartile group(LF). Same feeding design was employed in experiment 2 where the effect of mild electric stress on food selection was studied. The rats in both control and electric stress group revealed a normal growth curve and organ weights. The rats in both control and electric stress group revealed a normal growth curve and organ weights. The stress group showed higher caloric intake and body weight gain than control group, but no significant effects of stress on serum and empty carcass components was found. Even though normal rats seemed to select macronutrients according to their physiolosical needs, there were individual differences in food selection whether they were exposed to stress or not. Therefore life long individual food selection pattern may have a great influence on nutritional status and chronic degenerative diseases of eldery, and on aging process.

• Journal of Korean Society for Quality Management
• /
• v.25 no.1
• /
• pp.44-55
• /
• 1997

In order to determine procedures for a, pp.opriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected according to data characteristics. Three different growth curve models were fitted onto data sets in an attempt to determine which growth curve models achieved the best forecasts for types of growth data. The analysis of the results gives rise to an a, pp.oach for selecting a, pp.opriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the goodness of fit test.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.9
• /
• pp.59-65
• /
• 1995

The cam is used to a main component in a variety of automatic machines and instruments. To meet the demand of a complicated operation and of reducing weight for automatic machine, Curve Blending Technology, in which each of the basic curves suitable for individual interval is connected, is used for the cam design. In the curve blending, it is necessary to select appropriate elementary curve for each interval and to confirm the dynamic continuity at connecting points between adjoining elementary curves. This paper represented the elementary curve selection method to select an appropriate curve for each interval, and executed computation for the follower displacement and angular displacement of each interval. The paper made an analysis and examine closely for elementary curves to synthesizing curve blending, and it performed dynamic conditions clearly at every points on the cam motions. Therefore the curve blending technology presented by the paper turned into easier work.

• Journal of Animal Science and Technology
• /
• v.57 no.5
• /
• pp.19.1-19.6
• /
• 2015

The objective of this study was to estimate variance components and genetic parameters for growth curve parameters in Guilan sheep. Studied traits were parameters of Brody growth model which included A (asymptotic mature weight), B (initial animal weight) and K (maturation rate). The data set and pedigree information used in this study were obtained from the Agricultural Organization of Guilan province (Rasht, Iran) and comprised 8647 growth curve records of lambs from birth to 240 days of age during 1994 to 2014. Marginal posterior distributions of parameters and variance components were estimated using TM program. The Gibbs sampler was run 300000 rounds and the first 60000 rounds were discarded as a burn-in period. Posterior mean estimates of direct heritabilities for A, B and K were 0.39, 0.23 and 0.039, respectively. Estimates of direct genetic correlation between growth curve parameters were 0.57, 0.03 and -0.01 between A-B, A-K and B-K, respectively. Estimates of direct genetic trends for A, B and K were positive and their corresponding values were $0.014{\pm}0.003$ (P < 0.001), $0.0012{\pm}0.0009$ (P > 0.05) and $0.000002{\pm}0.0001$ (P > 0.05), respectively. Residual correlations between growth curve parameters varied form -0.52 (between A-K) to 0.48 (between A-B). Also, phenotypic correlations between growth curve parameters varied form -0.49 (between A-K) to 0.47 (between A-B). The results of this study indicated that improvement of growth curve parameters of Guilan sheep seems feasible in selection programs. It is worthwhile to develop a selection strategy to obtain an appropriate shape of growth curve through changing genetically the parameters of growth model.

• Geomechanics and Engineering
• /
• v.25 no.1
• /
• pp.1-16
• /
• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.313-316
• /
• 2004

Model selection is the process that sets up the regularization parameter in the support vector machine or regularization network by using the external methods such as general cross validation or L-curve criterion. This paper suggests that the regularization parameter can be obtained simultaneously within the learning process of neural networks without resort to separate selection methods. In this paper, extended kernel method is introduced. The relationship between regularization parameter and the bias term in the extended kernel is established. Experimental results show the effectiveness of the new model selection method.

• Structural Engineering and Mechanics
• /
• v.53 no.5
• /
• pp.881-896
• /
• 2015

In this study, a method for fatigue performance estimation of deepwater steel catenary riser (SCR) under short-term vortex-induced vibration was investigated for selected S-N curves. General tendency between S-N curve capacity and fatigue performance was analysed. SCRs are generally used to transport produced oil and gas or to export separated oil and gas, and are exposed to various environmental loads in terms of current, wave, wind and others. Current is closely related with VIV and it affects fatigue life of riser structures significantly. In this regards, the process of appropriate S-N curve selection was performed in the initial design stage based on the scale of fabrication-related initial imperfections such as welding, hot spot, crack, stress concentration factor, and others. To draw the general tendency, the effects of stress concentration factor (SCF), S-N curve type, current profile, and three different sizes of SCRs were considered, and the relationship between S-N curve capacity and short-term VIV fatigue performance of SCR was derived. In case of S-N curve selection, DNV (2012) guideline was adopted and four different current profiles of the Gulf of Mexico (normal condition and Hurricane condition) and Brazil (Amazon basin and Campos basin) were considered. The obtained results will be useful to select the S-N curve for deepwater SCRs and also to understand the relationship between S-N curve capacity and short-term VIV fatigue performance of deepwater SCRs.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.40 no.4
• /
• pp.247-254
• /
• 2004

The mesh selectivity of the drum net fish trap for elkhorn sculpin(Alcichthys alcicornis) in the estern sea of Korea was described. The selection curve for the elkhorn sculpin caught from the experiments between June 2003 and December 2003 was by SELECT(Share Each Length Class's Catch Total)model and by Kitahaa's method to a polynomial equation and two parameter logistic selection curve. The selection curve by SELECT model showed to be equal probability of entrance of the elkhorn sculpin in the large(55mm) and small(20mm) mesh traps by minimum AIC (Akaike Information Criteria). The equation of selectivity curve obtained by Kitahara's method using a logistic function with least square method was $s(R)\;=\;\frac{1}{1+exp(-0.3545R+2.141)$, where R=1/m, and/and m are total length and mesh size, respectively. The mesh selectivity curve showed that the current regulated mesh size(35mm) for the trap was corresponded to 21.4cm in the $L_{50}$of the selection curve for the elkhorn sculpin.