• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.9-15
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• 2001

In this paper we investigate context dependent acoustic models to improve the performance of the Korean speech recognition . The algorithm are using the Korean phonological rules and decision tree, By Successive State Splitting(SSS) algorithm the Hidden Merkov Netwwork(HM-Net) which is an efficient representation of phoneme-context-dependent HMMs, can be generated automatically SSS is powerful technique to design topologies of tied-state HMMs but it doesn't treat unknown contexts in the training phoneme contexts environment adequately In addition it has some problem in the procedure of the contextual domain. In this paper we adopt a new state-clustering algorithm of SSS, called Phonetic Decision Tree-based SSS (PDT-SSS) which includes contexts splits based on the Korean phonological rules. This method combines advantages of both the decision tree clustering and SSS, and can generated highly accurate HM-Net that can express any contexts To verify the effectiveness of the adopted methods. the experiments are carried out using KLE 452 word database and YNU 200 sentence database. Through the Korean phoneme word and sentence recognition experiments. we proved that the new state-clustering algorithm produce better phoneme, word and continuous speech recognition accuracy than the conventional HMMs.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.639-662
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• 2012

The purpose of this study is to develop the evaluation model of gifted curriculum in visual art. For this purpose, first, it discusses about what kinds of issues raised about gifted education in visual art. Second, it critically reviews the evaluation models of gifted curriculum, and investigates the suitable model for developing curriculum evaluation model of gifted in visual art. Third, it suggests the appropriate perspective and evaluation model of gifted curriculum in visual art. Along with the change in the concept of creativity, recent studies on gifted education in visual art concentrate that gifted learners who have the potential find their own way of creating art. Also they emphasize the contextual implementation which recognizes the significance of interaction among field, domain and individual. Based of these inquiry, existing evaluation models of gifted curriculum have limitations in suitability as a evaluation model of gifted curriculum in visual art. This study suggests that the curriculum evaluation of visual art gifted programs should be approached from the decision-making perspective. Also it develops the conceptual framework and the evaluation model of gifted curriculum in visual art based on the CIPP model, which is the representative model of decision-making approach. It concludes with its implications and the discussion about the role of evaluators.

• Investigative Magnetic Resonance Imaging
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• v.2 no.1
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• pp.58-66
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• 1998

Purpose : This paper introduces a new three dimensional magnetic Resonance Image classification which is based on Mar kov Random Field-Gibbs Random Field with a line model. Material and Methods : The performance of the Gibbs Classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at the local neighborhood level. This usually involves the construction of a line model for the image. In this paper we construct a line model for multisignature images based on the differential of the image which can provide an a priori estimate of the unobservable line field, which may lie in regions with significantly different statistics. the line model estimated from the original image data can in turn be used to alter the values of the interaction parameters of the Gibbs Classifier. Results : MRF-Gibbs classifier for volumetric MR images is developed under the condition that the domain of the image classification is $E^{3}$ space rather thatn the conventional $E^{2}$ space. Compared to context free classification, MRF-Gibbs classifier performed better in homogeneous and along boundaries since contextual information is used during the classification. Conclusion : We construct a line model for multisignature, multidimensional image and derive the interaction parameter for determining the energy function of MRF-Gibbs classifier.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.1-11
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• 2017

The importance of data curation is increasingly recognized as the need of data reuse drastically grows. Due to recent data explosion, scientists invest almost 90% of their efforts in the retrieval and collection of data needed to their study. In this paper, we deal with the development and application of a collaborative environment for community-driven data curation which is essential to enhance scientific data reusability and citability. The collaborative scientific data curation environment focuses on the cross-linking between data (or data collections) and their associated literatures to capture and organize inter-relations among research results in a specific domain. Also, plenty of contextual information is provided as metadata in order to support users in understanding data. The cross-linking has been realized by using DOI system to guarantee global accessibility to data and their relationships to literatures. The curation environment has been adopted to build a community-driven curated DB by a globally well-known intrinsically-disorderd protein research group. The curated DB will drastically reduce researchers' efforts to retrieve and collect the data required for scientific discovery.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.