• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.53-57
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• 2003

Chaos theory is a study researching the irregular, unpredictable behavior of deterministic and non-linear dynamical system. The interpretation using Chaos makes us evaluate characteristic existing in status space of system by tine series, so that the extraction of Chaos characteristic understanding and those characteristics enables us to do high precision interpretation. Therefore, This paper propose the new method which is adopted in extracting character features and recognizing characters using the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from input character images. And their feature is converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of character image after calculating Box-counting dimension, Natural Measure, information bit and information dimension which are meant fractal dimension. Finally, character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An experimental result shows 99% character classification rates for 2,350 Korean characters (Hangul) using proposed method in this paper.

• Cross-Cultural Studies
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• v.37
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• pp.383-400
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• 2014

Synonym means that the conceptual meaning of the word is the same or similar while other meanings or function of language difference may exist. That is two or more identified names correspond with one sense and have the words with minor difference. Words with synonym relation are a set of same meaning but conceptual area or emotional color, language function can be identified. Therefore, the core research of synonym is the difference analysis and in general difference analysis is progress in the three aspects of Meaning, Pragmatic, and Semantic. However, the difference analysis is the most important. In this paper, the set of meaning item of synonym word 'Attack' is 'zheng', 'fa', 'tao', 'qin', 'xi'. We compare the meaning of five verbs and analyze the difference and characteristics.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• 한국연소학회:학술대회논문집
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• 1997.06a
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• pp.163-173
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• 1997

This article presents an application of a large-scale structural mixing model (Broadwell et al. 1984) to the blowout of turbulent reacting jets discharging perpendicularly into an unconfined cross air-flow. In an analysis of a common stability curve, a plausible explanation can be made that the phenomenon of blowout is related only to the mixing time scale of the two flows. The most notable observation is that the blowout distance is traced at fixed positions at all times according to the velocity ratio R. Measurements of the lower blowout limits in the liftable flame agree qualitatively with the blowout parameter ${\varepsilon}$, proposed by Broadwell et al. Good agreement between the results calculated by a modified blowout parameter ${\varepsilon}^'$ and experimental results confirms the important effect of a large-scale structure in specifying the stabilization feature of blowouts.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2815-2817
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• 2001

This study describes the emotion classification using two different feature extraction methods for four-channel EEG signals. One of the methods is linear prediction analysis based on AR model. Another method is cross-correlation coefficients on frequencies of ${\theta}$, ${\alpha}$, ${\beta}$ bands. Using the linear predictor coefficients and the cross-correlation coefficients of frequencies, the emotion classification test for four emotions, such as anger, sad, joy, and relaxation is performed with a neural network. Comparing the results of two methods, it seems that the linear predictor coefficients produce the better results than the cross-correlation coefficients of frequencies for-emotion classification.

• Computers and Concrete
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• v.32 no.3
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.155-162
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• 2016

In recent years, hoping the interaction of individuals and rather than software development process and tools, and customers want software that works first, rather than a comprehensive document, in cooperation with the customer, rather than the developer negotiate a contract, to each other stick to the plan I think even more so than the value that corresponds to the change. In view of this, software development is given the autonomy and motivation to project team rather than process-oriented and have a passion and vision and human relations oriented management approach is required. In recent years, increasing the productivity benefits of agile development processes, improved quality, efficiency and customer satisfaction as is demonstrated in the methodology selected to promote the project, attention was given to the experts. Contemporary demands with regard to the methodology chosen to meet your needs, in this paper in the organization, and to solve the problems of product-based Cross functional team proposed methodology Feature Team model, this model is an organizational Cross functional team and the team is not the outcome (product) basis, were examined for the model that points to progress the development across multiple product as a functional unit, value-plan through the driven agile technique-based model and proposed a difference. And the domain analysis, required extraction by conventional JAD (joint application development) meeting the targets for the object-oriented modeling, in modeling and organize, review, aware in advance and the UML Structure and Behavior Diagrams and proposed to proceed with the project.

• International Journal of Safety
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• v.5 no.1
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• pp.37-41
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• 2006

The major objective of the statistical analysis about industrial accidents is to determine the safety factors so that it is possible to prevent or decrease the number of future accidents by educating those who work in a given industrial field in safety management. So far, however, there exists no quantitative method for evaluating danger related to industrial accidents. Therefore, as a method for developing quantitative evaluation technique, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze features of industrial accidents, a retrospective analysis was performed on 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work was chosen from data related to manufacturing businesses during a three-year period ($2002{\sim}2004$) in Korea. This study used AnswerTree of SPSS and the analysis results enabled us to determine the most important variables that can affect injured people such as the occurrence type, the company size, and the time of occurrence. Also, it was found that the classification system adopted in the present study using QUEST algorithm is quite reliable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1689-1705
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• 2023

To deal with the potential XSS vulnerabilities in the source code of the power communication network, an XSS vulnerability detection method combining the static analysis method with the dynamic testing method is proposed. The static analysis method aims to analyze the structure and content of the source code. We construct a set of feature expressions to match malignant content and set a "variable conversion" method to analyze the data flow of the code that implements interactive functions. The static analysis method explores the vulnerabilities existing in the source code structure and code content. Dynamic testing aims to simulate network attacks to reflect whether there are vulnerabilities in web pages. We construct many attack vectors and implemented the test in the Selenium tool. Due to the combination of the two analysis methods, XSS vulnerability discovery research could be conducted from two aspects: "white-box testing" and "black-box testing". Tests show that this method can effectively detect XSS vulnerabilities in the source code of the power communication network.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.183-187
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• 2005

In this paper, we propose a heuristic method to select features using a Two-Phase Markov Blanket-based (TPMB) algorithm. The first phase, filtering phase, of TPMB algorithm works by filtering the obviously redundant features. A non-linear correlation method based on Information theory is used as a metric to measure the redundancy of a feature [1]. In second phase, approximating phase, the Markov Blanket (MB) of a system is estimated by employing the concept of cross entropy to identify the MB. We perform experiments on microarray data and report two popular dataset, AML-ALL [3] and colon tumor [4], in this paper. The experimental results show that the TPMB algorithm can significantly reduce the number of features while maintaining the accuracy of the classifiers.