• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.411-422
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• 2014

A classification ensemble method aggregates different classifiers obtained from training data to classify new data points. Voting algorithms are typical tools to summarize the outputs of each classifier in an ensemble. WAVE, proposed by Kim et al. (2011), is a new weight-adjusted voting algorithm for ensembles of classifiers with an optimal weight vector. In this study, when constructing an ensemble, we applied the WAVE algorithm on the double-bagging method (Hothorn and Lausen, 2003) to observe if any significant improvement can be achieved on performance. The results showed that double-bagging using WAVE algorithm performs better than other ensemble methods that employ plurality voting. In addition, double-bagging with WAVE algorithm is comparable with the random forest ensemble method when the ensemble size is large.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.255-264
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• 2016

Classification is a predictive modeling for a categorical target variable. Various classification ensemble methods, which predict with better accuracy by combining multiple classifiers, became a powerful machine learning and data mining paradigm. Well-known methodologies of classification ensemble are boosting, bagging and random forest. In this article, we assume that decision trees are used as classifiers in the ensemble. Further, we hypothesized that tree size affects classification accuracy. To study how the tree size in uences accuracy, we performed experiments using twenty-eight data sets. Then we compare the performances of ensemble algorithms; bagging, double-bagging, boosting and random forest, with different tree sizes in the experiment.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.32 no.1
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• pp.33-41
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• 1996

In order to obtain the most favourable classification system for fishing gears, the problems in the existing systems were investigated and a new system in which the fishing method was adopted as the criterion of classification and the kinds of fishing gears were obtained by exchanging the word method into gear in the fishing methods classified newly for eliminating the problems was established. The new system to which the actual gears are arranged is as follows ; (1)Harvesting gear \circled1Plucking gears : Clamp, Tong, Wrench, etc. \circled2Sweeping gears : Push net, Coral sweep net, etc. \circled3Dredging gears : Hand dredge net, Boat dredge net, etc. (2)Sticking gears \circled1Shot sticking gears : Spear, Sharp plummet, Harpoon, etc. \circled2Pulled sticking gears : Gaff, Comb, Rake, Hook harrow, Jerking hook, etc. \circled3Left sticking gears : Rip - hook set line. (3)Angling gears \circled1Jerky angling gears (a)Single - jerky angling gears : Hand line, Pole line, etc. (b)Multiple - jerky angling gears : squid hook. \circled2Idly angling gears (a)Set angling gears : Set long line. (b)Drifted angling gears : Drift long line, Drift vertical line, etc. \circled3Dragged angling gears : Troll line. (4)Shelter gears : Eel tube, Webfoot - octopus pot, Octopus pot, etc. (5)Attracting gears : Fishing basket. (6)Cutoff gears : Wall, Screen net, Window net, etc. (7)Guiding gears \circled1Horizontally guiding gears : Triangular set net, Elliptic set net, Rectangular set net, Fish weir, etc. \circled2Vertically guiding gears : Pound net. \circled3Deeply guiding gears : Funnel net. (8)Receiving gears \circled1Jumping - fish receiving gears : Fish - receiving scoop net, Fish - receiving raft, etc. \circled2Drifting - fish receiving gears (a)Set drifting - fish receiving gears : Bamboo screen, Pillar stow net, Long stow net, etc. (b)Movable drifting - fish receiving gears : Stow net. (9)Bagging gears \circled1Drag - bagging gears (a)Bottom - drag bagging gears : Bottom otter trawl, Bottom beam trawl, Bottom pair trawl, etc. (b)Midwater - drag gagging gears : Midwater otter trawl, Midwater pair trawl, etc. (c)Surface - drag gagging gears : Anchovy drag net. \circled2Seine - bagging gears (a)Beach - seine bagging gears : Skimming scoop net, Beach seine, etc. (b)Boat - seine bagging gears : Boat seine, Danish seine, etc. \circled3Drive - bagging gears : Drive - in dustpan net, Inner drive - in net, etc. (10)Surrounding gears \circled1Incomplete surrounding gears : Lampara net, Ring net, etc. \circled2Complete surrounding gears : Purse seine, Round haul net, etc. (11)Covering gears \circled1Drop - type covering gears : Wooden cover, Lantern net, etc. \circled2Spread - type covering gears : Cast net. (12)Lifting gears \circled1Wait - lifting gears : Scoop net, Scrape net, etc. \circled2Gatherable lifting gears : Saury lift net, Anchovy lift net, etc. (13)Adherent gears \circled1Gilling gears (a)Set gilling gears : Bottom gill net, Floating gill net. (b)Drifted gilling gears : Drift gill net. (c)Encircled gilling gears : Encircled gill net. (d)Seine - gilling gears : Seining gill net. (e)Dragged gilling gears : Dragged gill net. \circled2Tangling gears (a)Set tangling gears : Double trammel net, Triple trammel net, etc. (b)Encircled tangling gears : Encircled tangle net. (c)Dragged tangling gears : Dragged tangle net. \circled3Restrainting gears (a)Drifted restrainting gears : Pocket net(Gen - type net). (b)Dragged restrainting gears : Dragged pocket net. (14)Sucking gears : Fish pumps.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.251-258
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• 2003

A support vector machine (SVM) is supposed to provide a good generalization performance, but the actual performance of a actually implemented SVM is often far from the theoretically expected level. This is largely because the implementation is based on an approximated algorithm, due to the high complexity of time and space. To improve this limitation, we propose ensemble of SVMs by using Bagging (bootstrap aggregating) and Boosting. By a Bagging stage each individual SVM is trained independently using randomly chosen training samples via a bootstrap technique. By a Boosting stage an individual SVM is trained by choosing training samples according to their probability distribution. The probability distribution is updated by the error of independent classifiers, and the process is iterated. After the training stage, they are aggregated to make a collective decision in several ways, such ai majority voting, the LSE(least squares estimation) -based weighting, and double layer hierarchical combining. The simulation results for IRIS data classification, the hand-written digit recognition and Face detection show that the proposed SVM ensembles greatly outperforms a single SVM in terms of classification accuracy.

• Korean Journal of Agricultural Science
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• v.40 no.2
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• pp.107-113
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• 2013

We tested fruits quality and skin coloration of two pear cultivar 'Wonhwang' and 'Whasan' produced by bagging with different kinds of paper bags in the Cheonan area, Chungnam Province, Korea. The fruits were bagged at 35, 45, 55 or 65 days after full bloom (DAFB) with 3 kinds of double layer paper bag which consisted of grey color, yellow color and newspaper for outer layer. The great extent of light interruption was observed in grey colored outer paperbag among three kinds of paperbag which showed only 0.46% of lowest light transmittance when compared with 43.7% and 40.0% of yellow paper and newspaper, respectively. The development of skin redness ($a^*$) increased with the delay of bagging time in two pear cultivars. But the fruits treated at 65 DAFB showed uneven coloration and excessive development of redness which represented low fruit external appearance value. Uniform flesh firmness was attained at the fruits bagged with grey outer color paperbag regardless of bagging time, although the fruit quality indices including soluble solids and titratable acidity did not change significantly by using different kind of fruit bags and bagging time in two pear cultivar. Based on our results, it was explained that the grey-colored fruit bag had positive effect on the development of skin coloration without any detrimental effect on fruit quality factors in 'Wonhwang' and 'Whasan' pears.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.3
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• pp.47-53
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• 2001

This paper investigates the characteristics of interlaminar fracture toughness of foam core sandwich structures under opening mode by using the double cantilever beam (DCB) specimens which are Carbon/Epoxy and foam core composites. Instead of using a DCB specimen of symmetric geometry, a non-symmetric DCB specimen was used to calculate the interlaminar fracture toughness. Three approaches for calculating the energy release rate(G$\sub$IC/) were used and fracture toughness of foam core sandwich structures made by autoclave, vacuum bagging and hotpress were compared. Experiment, analysis using nonlinear beam bending theory, and numerical work by FEM methods were performed. Bonding surface compensation and equivalent moment of inertia were used to calculate the energy release rate in nonlinear analytical work. Conclusions of experimental, nonlinear analytical and FEM methods were compared. It is, also, shown that the vacuum bagging forming can substitute the method of autoclave without serious loss of Mode I energy release rate(G$\sub$I/).

• Journal of the Korean Society for Precision Engineering
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• v.17 no.9
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• pp.81-86
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• 2000

This paper was carried out to investigate the characteristics of interlaminar fracture toughness of foam core sandwich structures under opening loading mode by using the double cantilever beam (DCB) specimens in Carbon/Epoxy and foam core composites. instead of using symmetric geometry of DCB specimen non-symmetric DCB specimen was used to calculate the interlaminar fracture toughness. Three approaches for calculating the energy release rate({{{{ {G }_{IC } }}}}) were compared. Fracture toughness of foam core sandwich structures by autoclave vacuum bagging and hotpress were compared and analyzed. Experiment nonlinear beam bending FEM method were performed. Suggested bonding surface compensation and equivalent area inertia moment was used to calculate the energy release rate in nonlinear analytical results. The conclusions among experimental nonlinear analytical and FEM results was observed. The vacuum bagging method was shown to be able to substitute method in stead of autoclave without serious loss of Mode I energy release rate({{{{ {G }_{IC }}}}}) to be able to substitute method in stead of autoclave without serious loss of Mode I energy release rate({{{{ {G }_{IC }}}}}).

• Structural Engineering and Mechanics
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• v.72 no.3
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• pp.305-312
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• 2019

This paper investigates the effect of utilizing vacuum bagging process to enhance the bond behavior between fiber reinforced polymer (FRP) composites and concrete substrate. Sixty specimens were prepared and tested using double-shear bond test. The effect of various parameters such as vacuum, fiber type, and FRP sheet length and width on the bond strength were investigated. The experimental results revealed that utilizing vacuum leads to improve the bond behavior between FRP composites and concrete. Both the ultimate bond forces and the maximum displacements were enhanced when applying the vacuum which leads to reduction in the amount of FRP materials needed to achieve the required bond strength compared with the un-vacuumed specimens. The efficiency of the enhancement in bond behavior due to vacuum highly depends on the fiber type; using carbon fiber showed higher enhancement in the bond strength compared to the glass fiber when vacuum was applied. On the contrary, specimens with glass fiber showed higher enhancement in the maximum slippage compared to specimens with carbon fibers. Utilizing vacuum does not affect the debonding failure modes but lead to increase in the amount of attached concrete on the surface of the debonded FRP sheet.

• Advances in pediatric surgery
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• v.14 no.1
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• pp.88-93
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• 2008

Esophageal atresia with double tracheoesophageal fistula is a very rare anomaly and is difficulty to diagnose preoperatively. We treated a full term baby with esophageal atresia with double tracheoesophageal fistula. At the first operation, only the distal tracheoesophageal fistula was identified and ligated. When the upper esophageal pouch was opened, intermittent air leakages in sequence with positive bagging were noticed. However, intraoperative bronchoscopy did not identify a fistula in the proximal pouch, and the operation was completed with end to end anastomosis of the esophagus. On the $7^{th}$ postoperative day, esophagography showed another tracheoesophageal fistula proximal to the esophageal anastomosis. A wire was placed in the fistula preoperatively under bronchoscopy. At the 2nd operation through the same thoracotomy incision the proximal fistula was identified and ligated. On the $12^{th}$ postoperative day, esophagography showed neither stricture nor leakage.

• Composites Research
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• v.31 no.6
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• pp.304-310
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• 2018

Autoclave process has been remaining as one of the most robust and stable process in fabricating structural composite part of aerospace industry. It has lots of advantages, however exhibits some disadvantages or limitations in capital investment and operation. Recently, there have been various Out-of-Autoclave process being researched and developed to overcome those limitations. In this study, laminate specimens were fabricated using LCM (Liquid Composite Molding) process, regarded as one of potential OoA process. DB (Double bagging), CAPRI (Controlled Atmospheric Pressure Resin Infusion), VAP (Vacuum Assisted Process) and Autoclave process were used for laminate specimens. Void content, Thickness, Tg (Glass Transition Temperature), ILSS (Interlaminar Shear Strength) and Flexural strength properties were evaluated for comparison. It is verified that Autoclave based specimen has uniform thickness distribution, the lowest void content and outstanding mechanical properties. And, CAPRI based specimen exhibits relatively good physical and mechanical properties over DB and VAP based specimen and comparable mechanical properties with autoclave based specimen.