• The Research Journal of the Costume Culture
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• v.20 no.3
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• pp.330-346
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• 2012

The purpose of this study is to link traditional patterns to modern clothing. Of all the traditional patterns, the cloud pattern was chosen for use during the development of a modern one-piece dress design. This study is based upon document searches, including research papers. Through these searches, it investigates the symbolic meaning, historical change, and formation of the cloud pattern. Based on this investigation, the study attempts to modernize the cloud pattern and apply the modernized patterns to the design of a one-piece dress. The design procedure includes three sub-processes: selection, arrangement, and color-scheme. The selection process was divided in two: first, the original form of the cloud pattern was hand-drawn using tracing paper: second, the form of the pattern was edited using Adobe Photoshop. The arrangement of the pattern was made through the checklist conception method, containing the following functions: expand, reset, repeat, and overlap. For the color-scheme of the pattern, Roy Lichtenstein's(1923~1997) work was selected, and the colors in his work were adopted when dyeing the rest of the one-piece dress as well as the cloud features. In conclusion, six modern designs of the one-piece dress were created by using one of Korea's traditional patterns - the cloud pattern. Therefore, this study can offer invaluable suggestions for multifaceted research on how to come up with design concepts which apply Korea's traditional patterns to clothing design.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.2
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• pp.81-100
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• 2014

The purpose of this study is to link traditional patterns to mordern clothing. Of all the traditional patterns, the fish pattern was chosen for use during the development of a creative one-piece design for woman and shirts design for man. This study is based upon document searches, including research papers. Through these searches, it investigates the symbolic meaning, formation of the fish pattern and the creative design process. Based on this investigation, the study attempts to modernize the fish pattern and apply the fish patterns to the design of a one-piece dress and shirts. The design procedure includes three sub-processes: selection, arrangement and color-scheme. The selection process, the form of the pattern was edited using Adobe Photoshop. The arrangement of the pattern was made through the checklist conception method, containing the following functions: expand, reset, repeat, omission and dismantling. For the color-scheme of the pattern, Paul Klee(1879-1940)work was selected, and the colors in his work were adopted when dyeing the rest of the one-piece dress and shirts as well as fish features. In conclusion, six designs of the one-pieces dress and shirts were created by using one of Korea's traditional patterns-the fish pattern. This kind of study not only let the world know about the unique beauty of nations but also helps to inspire people who has a profession in design, by suggesting design development process based on design competitiveness improvement factor from fusion of tradition and modern.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2215-2220
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• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.171-179
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• 1997

Genetic Algorithms face an inherent conflict between exploitation and exploration. Exploitation refers to taking advantage of information already obtained in the search. Exploration show that a pattern in bits coupled with another pattern elsewhere in the string is more effective. In this paper shows that the selection method has a major impact on the balance between exploitation and exploration. A more heavy-handed approach seeks to exploit the available information. If decisions must be made quickly, especially those in real-time trading environments, then quicker convergence through exploitation may be more desirable. Also this paper we present some theoretical and empirical the selection method in genetic algorithms for a GA-hard problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.191-194
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• 2002

In this paper, a new GA-based methodology with information granules is suggested for construction of the fuzzy classifier. We deal with the selection of the fuzzy region as well as two major classification problems-the feature selection and the pattern classification. The proposed method consists of three steps: the selection of the fuzzy region, the construction of the fuzzy sets, and the tuning of the fuzzy rules. The genetic algorithms (GAs) are applied to the development of the information granules so as to decide the satisfactory fuzzy regions. Finally, the GAs are also applied to the tuning procedure of the fuzzy rules in terms of the management of the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example-the classification of the Iris data, is provided.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.141-149
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• 2023

Repacked mobile apps constitute about 78% of all malware of Android, and it greatly affects the technical ecosystem of Android. Although many methods exist for repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern (CKPS: LBP) Feature extraction-based Hashing is proposed for the identification of repacked android applications through the visual similarity, which is a notable feature of repacked applications. The results from the experiment prove that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it proved that the proposed CKPS: LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95124 applications and also the computational time was 0.91 seconds within which a user could get the decision of whether the app repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods, and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.597-598
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• 2011

In this paper, the spectrum hole prediction scheme was proposed for the cognitive radio networks using the primary user's traffic pattern. Using the channel prediction, the collision probability with primary users can be reduced and the system throuthput can be improved. Simulation result shows that the proposed method can enhance the throughput and reduce the interference to the primary user below the desired threshold.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.589-598
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• 2015

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.