• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.130-137
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• 2006

In this paper, we propose a method of selecting new types of rectangle features that are suitable for facial expression recognition. The basic concept in this paper is similar to Viola's approach, which is used for face detection. Instead of previous Haar-like features we choose rectangle features for facial expression recognition among all possible rectangle types in a 3${\times}$3 matrix form using the AdaBoost algorithm. The facial expression recognition system constituted with the proposed rectangle features is also compared to that with previous rectangle features with regard to its capacity. The simulation and experimental results show that the proposed approach has better performance in facial expression recognition.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1153-1156
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• 2003

In this paper, we present a reducing power consumption of a scheduling for module selection under the time constraint. Traditional high-level synthesis do not allow reuse of complex, realistic datapath component during the task of scheduling. On the other hand, the proposed scheduling of reducing power consumption is able to approach a productivity of the design the low power to reuse which given a library of user-defined datapath component and to share of resource sharing on the switching activity in a shared resource. Also, we are obtainable the optimal the scheduling result in experimental results of our approach various HLS benchmark environment using chaining and multi-cycling in the scheduling techniques..

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.104-107
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• 1996

This paper focuses on a automation selection of optimal cutting conditions and cycle time for multi-spindle metal cutting machines based on machining parameters and tool change schemes which are the two most common terms used in the metal cutting. In this research we used two step generative approach, step 1 is mathematical modeling for the selection fo optimal cutting conditions and the other is GMDH-Type modeling to estimate the system performance evaluation. We developed computer programs for these models and the fitting manufacturing examples are applied to this model and it was shown that the proposed approach has a good potential and offers a valuable tools to analyse the metal cutting system.

• International Journal of Contents
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• v.8 no.4
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• pp.56-63
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• 2012

In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.

• Journal of Communications and Networks
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• v.17 no.4
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• pp.428-439
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• 2015

In this study, we present an approach for reducing the information inaccuracy of existing star aggregation based on bypass links when there are multi-constraint QoS parameters in asymmetric networks. In our approach, bypass links with low similarity are selected. Links that are not chosen as bypass links are included in each group depending on the star's link characteristics. Moreover, each link group is aggregated differently according to the similarity of the links that make up the group. The selection of a bypass link by using link similarity reduces the existing time complexity of O($N^3$) to O(N) by virtue of the simplification of the selection process. In addition, the adaptive integration according to the characteristics of the links in each group is designed to reduce the information inaccuracy caused by static aggregation. Simulation results show that the proposed method maintains low information distortion; specifically, it is 3.8 times lower than that of the existing method, even when the number of nodes in a network increases.

• IE interfaces
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• v.16 no.1
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• pp.34-43
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• 2003

The Korean government has been working to reduce national logistics costs since the early 1990s. One of the major plans instituted by the government was to build five complex freight terminals using ICDs(inland container depots) as hub inland terminals. This paper explains the process and the methods adopted for site selection, with special focus on the Youngnam Freight Terminal. Among nineteen developable sites in Youngnam region, four candidates were selected based on the Map Overlay Approach under several criteria, including land acquisition costs, proximity to major highways and railroads, suitability of the terrain, etc. In this study, we used the AHP to select the best site among the four sites.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.87-95
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• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate RP machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify RP machines that the users consider. After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of RP machines.

• ETRI Journal
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• v.30 no.1
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• pp.89-100
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• 2008

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable-feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable-feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable-feature selection achieves better performance than the conventional feature recombination system with reliable-feature selection.