• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.87-94
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• 2004

The feature subset selection is used as a preprocessing step of a teaming algorithm. If collected data are irrelevant or redundant information, we can improve the performance of learning by removing these data before creating of the learning model. The feature subset selection can also reduce the search space and the storage requirement. This paper proposed a new feature subset selection algorithm that is using the heuristic function based on entropy to evaluate the performance of the abstracted feature subset and feature selection. The ACS algorithm was used as a search method. We could decrease a size of learning model and unnecessary calculating time by reducing the dimension of the feature that was used for learning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11C
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• pp.1066-1072
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• 2007

In this paper, we propose a fast multiple reference frame selection method for motion estimation and compensation in video coding. Reference frames selected as an optimal reference frame by variable block sizes motion estimation have the statistical characteristic that was based on block size. Using the statistical characteristic, reference frames for smaller block size motion estimation can be selected from reference frame which was decided as an optimal one for the upper layer block size. Simulation results show that the proposal method decreased the computations about 60%. Nevertheless, PSNR and bit rate were almost same as the performances of original H.264 multiple reference motion estimation.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.3
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• pp.284-291
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• 2014

Finely-striate buccinum Buccinum striatissimum, a species of whelks, is caught mainly by pot in the eastern coastal waters of Korea. In order to determine the size selectivity of pot for the species, comparative fishing experiments were conducted near Yeongil Bay from June to September in 2003 using the dome-shaped pots with different five mesh sizes (17.1, 24.8, 35.3, 39.8, and 48.3 mm). The parameters of logistic equation were estimated by the SELECT (Share Each Length's Catch Total) method based on a multinomial distribution. The model with the estimated split parameter was found to fit the catch data best. The master selection curve was estimated to be s (R)=exp (13.044R-16.438)/[1 + exp (13.044R-16.438)], where R is the ratio of shell height to mesh size. The relative shell height of 50% retention was 1.260, and the selection range was 0.168. Enlargement in mesh size of the pot allows more small-sized whelks to escape.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10A
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• pp.760-768
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• 2009

In this paper, we propose the energy efficient transmission parameter selection method for Wireless Personal Area Network (WPAN) system which is applied to e-Navigation system considering various ship models environment. An appropriate selection of transmission parameters of HR-WPAN system is very essential to be considered for saving WPAN devices' energy consumption, when HR-WPAN system is applied to ship area network (SAN). Therefore, we propose an energy consumption model for a ship area network employing IEEE 802.15.3 based CSMA/CA HR-WPAN model and analyze the effect of transmission parameter selection on the performance of energy consumption. In particular, the path loss is the major performance decision parameter for the SAN employing HR-WPAN system, since it varies according to the material of shipbuilding such as steel(for large ship), FRP(for medium size ship) and compound wood(for small ship). Thus, we analyze and demonstrate that the proper transmission parameter selection of transmit power, PHY data rate and fragment size for each ship model could guarantee energy efficiency.

• Journal of Biosystems Engineering
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• v.34 no.1
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• pp.8-14
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• 2009

When a farmer buys a tractor, the power size of a tractor is determined by various factors such as farm size, farmer's age, farming type, topographical area of farm. Relationships between tractor selection and these factors were found. Three regression models were developed to analyze the relationship. Those models were an OLS-1 model (based on 567 samples having tractors), an OLS-2 model, and a Tobit model (both based on the 1,941 samples). Regression analysis results showed that farm size and farmer's age affected selection of power size for all models at an 1% significance level. It was also shown that some farming types also had significant relationships with the tractor power size. Upland cultivating farmers and livestock farmers had larger tractors than rice cultivating farmers, while orchard farmers had smaller tractors. As for the topographical area, only middle area had significant difference with plain area. Farmers who had a rice-transplanter or a combine had larger tractors than those who didn't.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.325-337
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• 1993

This paper propose two test statistics which enable us to proceed the variable selection in Fisher's linear discriminant function for the case of heterogeneous discrimination with equal training sample size. Simultaneous confidence intervals associated with the test are also given. These are exact and approximate results. The latter is based upon an approximation of a linear sum of Wishart distributions with unequal scale matrices. Using simulated sampling experiments, powers of the two tests have been tabulated, and power comparisons have been made between them.

• Journal of Ocean Engineering and Technology
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• v.17 no.2
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• pp.77-84
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• 2003

The excessive cost of building ships causes the instability of payability, which manage poorly fishing vessels longer than 24m. As a result, an officer evades embarkation and a vicious circle is repeated. In this study, the optimal engine horsepower system for fishing vessels longer than 24m was invented to develop the most efficient engine horsepower, and also a database program for the most efficient engine horsepower has been developed based on the type of and their size.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.27-44
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• 2008

Owl-based ontology is useful to realize the context-aware services which are composed of the distributed and self-configuring modules. Many ontology-based inference engines are developed to infer useful information from ontology. Since these engines show the uniqueness in terms of speed and information richness, it's difficult to ensure stable operation in providing dynamic context-aware services, especially when they should deal with the complex and big-size ontology. To provide a best inference service, the purpose of this paper is to propose a novel methodology of context-aware engine selection in a contextually prompt manner Case-based reasoning is applied to identify the causality between context and inference engined to be selected. Finally, a series of experiments is performed with a novel evaluation methodology to what extent the methodology works better than competitive methods on an actual context-aware service.