• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2007.11a
• /
• pp.37-38
• /
• 2007

• Journal of KIISE:Software and Applications
• /
• v.35 no.2
• /
• pp.91-104
• /
• 2008

Service-Oriented Computing (SOC) is the development method that published services are selected and composed at runtime to deliver the expected functionality to service clients. SOC should get maximum benefits not only supporting business agility but also reducing the development time. Services are selected and composed at runtime to improve the benefits. However, current programming language, SOC platforms, business process modeling language, and tools support either manual selection or static binding of published services. There is a limitation on reconfiguring and redeploying the business process to deliver the expected services to each client. Therefore, dynamic selection is needed for composing appropriate services to service clients in a quick and flexible manner. In this paper, we propose Dynamic Selection Handler (DSH) on ESB. we present a design method of Dynamic Selection Handler which consists of four components; Invocation Listener, Service Selector, Service Binder and Interface Transformer. We apply appropriate design patterns for each component to maximize reusability of components. Finally, we describe a case study that shows the feasibility of DSH on ESB.

• Journal of the Korea Society for Simulation
• /
• v.18 no.1
• /
• pp.17-25
• /
• 2009

This paper suggests fuzzy-based Segment-Boost method and an effective method for face recognition using the fuzzy-based Segment-Boost. Fuzzy-based Segment-Boost eliminates the limitations of Segment-Boost, and it guarantees improved learning performance and the stability of the performance. By using the fuzzy theory, fuzzy-based Segment-Boost optimizes the selection number of sub-vectors, and leads the optimized learning performance. The fuzzy controller designed in this paper measures learning performance of the fuzzy-based Segment-Boost, and it controls the selection number of sub-vectors by inferring the optimized selection number. The simulation results show that the fuzzy controller inferred the selection number which is very approximate to the true optimized value. As a result, fuzzy-based Segment-Boost showed higher face recognition rate than compared boosting methods and it preserves the velocity of feature selection as fast as that of Segment-Boost. From the experimental results, it was proved that fuzzy-based Segment-Boost has improved and stable performances of learning, feature selection and face recognition.

• Journal of Technologic Dentistry
• /
• v.29 no.1
• /
• pp.35-48
• /
• 2007

The purpose of this study was to evaluate shade selection using conventional visual assessment in vivo natural teeth and porcelain-fused-to-metal (PFM) crown. Maxillary central incisors, lateral incisors and canines of one hundred twenty four college women were used as vivo natural teeth. Fifty one PFM crown for maxillary central incisor fabricated by dental laboratory were used as experimental materials. Using Vitapan Classical Shade Guides, shade selection of natural teeth was measured by each college woman and shade selection of PFM crown was measured by three ceramists with more than ten years career. Both natural teeth and PFM crown shade selection were measured through Shade Eye-Ex. From the shade selection comparing, following results were obtained. The results were as follows: 1. The shade matching identification of natural teeth between the shade selection using Vitapan Classical Shade Guides and the shade selection using Shade Eye-Ex was 27.4% in maxillary central incisor, 13.7% in lateral incisor and 18.5% in canine. 2. Among the shade selection of PFM crown by three ceramists, the shade evaluation of three ceramists were same only in ten cases. In twenty case, those of two ceramists were same. 3. The shade matching identification of PFM crown between the shade selection using Vitapan Classical Shade Guides and the shade selection using Shade Eye-Ex was 38.6% in average. These results suggest that the shade selection using conventional visual assessment should be dealt with care in clinic and need a credible method for shade matching color.

• Journal of the Semiconductor & Display Technology
• /
• v.14 no.2
• /
• pp.9-16
• /
• 2015

In this paper, selection criterions on selection diversity are researched. The diversity is applied to the multiple antenna system based on wireless access in vehicular environment (WAVE) standard for rapid varying channel. Least squares (LS) based decision feedback equalizer (DFE) are used for channel equalization. Received signal is regenerated by means of the decision feedback path. In the selection diversity, the regenerated signal as well as the received signal is selected according to selection criterion. The decision feedback algorithm can follow the fast speed of WAVE fading channel. To control the tracking speed of the time-varying channel, simple low pass filter is used. Finally, the estimated channel value recovers the distorted payloads. Signal power before automatic gain control (AGC) in analog stage can be used as a selection criterion. In the digital stage, signal power after AGC, noise power after AGC, signal to noise ratio after AGC and cross-correlation method can be used as selection criterions. According to the simulation results, the performance of the selection diversity is improved in comparison with that of the combining diversity for the WAVE fading channel.

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

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.4
• /
• pp.63-71
• /
• 2016

This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.5
• /
• pp.901-908
• /
• 2010

A geometrical description method is proposed to represent the process of the forward selection and backward elimination methods among many variable selection methods for multiple regression models. This graphical method shows the process of the forward selection and backward elimination on the first and second quadrants, respectively, of half circle with a unit radius. At each step, the SSR is represented by the norm of vector and the extra SSR or partial determinant coefficient is represented by the angle between two vectors. Some lines are dotted when the partial F test results are statistically significant, so that statistical analysis could be explored. This geometrical description can be obtained the final regression models based on the forward selection and backward elimination methods. And the goodness-of-fit for the model could be explored.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.105-115
• /
• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• Journal of Korean Home Economics Education Association
• /
• v.10 no.1
• /
• pp.41-57
• /
• 1998

The purpose of this study was to make a selection criteria for Home Economics textbooks in the middle school. For this purpose, first, the criteria were out by collecting, analyzing and synthesizing the literature. Second, questionnaire survey of the 6 selection criteria was performed. Questionnaire sent to Home Economics teachers of 401 middle school selected by systematic random sapling, 233 questionnaire were received and 220 questionnaire were analyzed for this study. As a statistical tool, SPSSWIN was used to analyze frequency, mean, standard deviation, and factor analysis. The research findings were as follows ; 1. Now for kinds of Home Economics textbooks are mainly used. At that time when textbooks were selected, these selections were made upon deliberation with the teachers in charge and in future this method will be desirable. Most home economics teachers realize that the selection criteria is needed to improve the objectivity of textbook selection. 2. As a result of making factor analysis, the selection criteria were revised that 52 items in 7 categories were chosen as textbook criteria plan. They consist of 5 items related to the outward form of textbook, 5 items related to the learning materials in textbook, 10 items related to the composition of textbook units, 11 items related to the guiding contents of textbook, 7 items related to the subject of experiment.practice, 9 items related to the composition of picture, photograph and diagram. and 7 items related to the use of instructional-learning method.