• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.159-162
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• 1998

Speech synthesis coding is classified into three categories: waveform coding, source coding and hybrid coding. To obtain the synthetic speech with high quality, the synthesis by waveform coding is desired. However, it is difficult to apply waveform coding to synthesis by syllable or phoneme unit, because it does not divide the speech into excitation and formant component. Thus it is required to alter the excitation in waveform coding for applying waveform coding to synthesis by rule. In this paper we propose a new pitch alteration method that minimizes the spectrum distortion by using the behavior of cepstrum. This method splits the spectrum of speech signal into excitation spectrum and formant spectrum and transforms the excitation spectrum into cepstrum domain. The pitch of excitation cepstrum is altered by zero insertion or zero deletion and the pitch altered spectrum is reconstructed in spectrum domain. As a result of performance test, the average spectrum distortion was below 2.29%.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2003.11a
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• pp.69-76
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• 2003

In this paper, a novel speed control system that implements the fuzzy logic controller(FLC) is proposed. Fuzzy controller is shown more excellent efficency than a conventional controllers in the strength aspect and non-linear controller using IF-THEN rule which can control without process the accurate mathematical modeling about induction motor. But we cannot expect that conventional fuzzy controller divide equally the space of input and output parameter and use the certain shape of triangle membership function. Therefore to develop the efficiency of conventional fuzzy controller, We need to scale the range of membership functions. In this study, proposed fuzzy controller has the ability controlling scale of membership functions using by output scaling factor.

• Asian Journal of Business Environment
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• v.5 no.4
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• pp.67-72
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• 2015

Purpose - This aim of this study is to examine how conglomerates in Korea have evolved from the perspective of institutional economics. The growth of the economy, dominated by large conglomerates, is projected in light of the dynamic equilibrium between government and capitalists. Research design, data, and methodology - The historical formation of big business groups is examined in chronological order. For the analysis, we divide the assessment into three different eras: Japanese colonial rule, liberation up to the civil war, and the fast growing period since the military coup. Each period is viewed as a dynamic equilibrium that is shaped by economic agents. Results and Conclusion - Despite the rise of modern commerce during the colonial era, contemporary conglomerates came into being with the "enemy property" allotted by the government. Around the civil war, the government coexisted with prototype conglomerates through foreign aid. As the external aid decreased, the system could not be sustained anymore, thus the military coup took place. The reinstated strong bond between government and the conglomerates has shaped the forms of the modern conglomerates thereafter.

• The Journal of Learner-Centered Curriculum and Instruction (JLCCI)
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• v.22 no.1
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• pp.171-208
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• 2018

This article applied the single case study method to analyze how the KEPCO managed the Miryang Transmission Tower conflict and how it affected the stakeholders and the village community. In order to resolve the conflict and continue with the construction, the KEPCO utilized a conflict management strategy that involved force, compensation and legal action. The KEPCO dismantled the protest camp through unilateral administrative enforcement to suppress the opposing residents. Moreover, individual compensation, strictly prohibited in the past, was used to reach a quick agreement to complete the construction. Although the KEPCO managed to increase consensus and finish the construction through its conflict management strategy, it also brought the conflict to the community, causing a division among its members. Additionally, how the KEPCO charged the different opposing stakeholders and brought them to the KEPCO camp resembled the divide and rule strategy. Such conflict management methods that excluded the residents, has broken the relations among the residents and destroyed the long sustained community. This study thoroughly analyzed the authoritative business promotion and conflict management strategies, and the conflict and division the village community has experienced. Follow-up studies need to comprehensively analyze the conflicts around the Miryang 765kV Transmission Tower construction by analyzing the intervention of the central government and its role in the planning and construction of the transmission tower.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.671-678
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• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.969-982
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• 2016

Recently, the researches to discovery drug candidates from natural herbs have received considerable attention. In human body, enzyme mostly metabolize the compounds of natural herbs. In this study, we analysis the enzyme interactions using assoication mining. We get this data from BRENDA(: BRaunschweig ENzyme DAtabase) system. Based on enzyme interaction model, we divide the metabolites into substrate metabolites, product metabolites, inhibitor metabolites, and activating metabolites. We then compose substrate metabolite transaction, product metabolite transaction with each metabolites and enzyme interaction transaction with all metabolites. Also we take account of organism for each transactions. We mine frequent metabolites and patterns from six transactions using association rule mining. And we analysis the relationship among metabolites. As a result, we identify the distributions and patterns of metabolites consist in enzyme interactions. We found that metabolites include in only substrate are identified and have very low supports. This results can be useful to develop the effective metabolism prediction model for compounds of natural herbs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.13-18
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• 2017

With the advancement of internet technology, the importance of data protection is gaining more attention. As a possible data protection solution, we propose a novel video encryption method using complemented maximum-length cellular automata (C-MLCA). The first step for encryption is to use 90/150 CA rule to generate a transition matrix T of a C-MLCA state followed by a 2D C-MLCA basis image. Then, we divide the video into multiple frames. Once, we perform exclusive-OR operation with the split frames and the 2D basis image, the final encrypted video can be obtained. By altering values of pixel, the fundamental information in visualizing image data, the proposed method provides improved security. Moreover, we carry out some computational experiments to further evaluate our method where the results confirm its feasibility.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2633-2635
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• 2003

In this study, we propose a fuzzy polynomial neural networks (FPNN) and a genetically optimized fuzzy polynomial neural networks(GoFPNN) for identification of non-linear system. GoFPNN architecture is designed by a FPNN based on fuzzy set and its structure and parameters are optimized by genetic algorithms. A fuzzy neural networks(FNN) based on fuzzy set divide into two structures that is simplified inference structure and linear inference structure. The proposed FPNN is resulted from integration and extension of simplified and linear inference structure of FNN. The consequence structure of the FPNN consist of polynomials represented by networks using connection weights for rules. The networks comprehend simplified(Type 0), linear (Type 1), and quadratic(Type 3) inferences. The proposed FPNN can select polynomial type of consequence part for each rule. Therefore, proposed scheme can offer flexible structure design capability for a system characteristics. Moreover, GAs is applied to networks structure and parameters tuning of proposed FPNN, and its efficient application method is discussed, these subjects are result in GoFPNN that is optimal FPNN. To evaluate proposed model performance, a numerical experiment is carried out.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2012-2030
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• 2021

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.