• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.70-75
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• 2001

This paper proposes a syllabic segmentation method for the korean continuous speech. This method are formed three major steps as follows. (1) labeling the vowel, consonants, silence units and forming the Token the sequence of speech data using the segmental parameter in the time domain, pitch, energy, ZCR and PVR. (2) scanning the Token in the structure of korean syllable using the parser designed by the finite state automata, and (3) re-segmenting the syllable parts witch have two or more syllables using the pseudo-syllable nucleus information. Experimental results for the capability evaluation toward the proposed method regarding to the continuous words and sentence units are 73.5％, 85.9％, respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.101-107
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• 2010

The aim of this paper is to come up with a methodology of constructing an efficient model for multiple representations which can manage and reconcile real-time data about large-scale roads in Vector Domain. In other words, we suggested framework based on a bottom-up approach, which is allowed to integrate data from the network of the lowest level sequentially and perform automated matching in order to produce variable-scale map. Finally, we applied designed multi-LoD model to in-vehicle application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.512-518
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• 2013

A resource allocation algorithm has a high impact on user satisfaction as well as the ability to accommodate and process services in a distributed cloud computing. In other words, service rejections, which occur when datacenters have no enough resources, degrade the user satisfaction level. Therefore, in this paper, we propose a resource allocation algorithm considering the cloud domain's remaining resources to minimize the number of service rejections. The resource allocation rate based on Q-Learning increases when the remaining resources are sufficient to allocate the maximum allocation rate otherwise and avoids the service rejection. To demonstrate, We compare the proposed algorithm with two previous works and show that the proposed algorithm has the smaller number of the service rejections.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.47-61
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• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.8-14
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• 2009

In the research field of speech recognition, pinpointing the endpoints of speech utterance even with the presence of background noise is of great importance. These noise present during recording introduce disturbances which complicates matters since what we just want is to get the stationary parameters corresponding to each speech section. One major cause of error in automatic recognition of isolated words is the inaccurate detection of the beginning and end boundaries of the test and reference templates, thus the necessity to find an effective method in removing the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two linear time-domain measurements: the short-time energy, and short-time zero-crossing rate. They perform well for clean speech but their precision is not guaranteed if there is noise present, since the high energy and zero-crossing rate of the noise is mistaken as a part of the speech uttered. This paper proposes a novel approach in finding an apparent threshold between noise and speech based on Lyapunov Exponents (LEs). This proposed method adopts the nonlinear features to analyze the chaos characteristics of the speech signal instead of depending on the unreliable factor-energy. The excellent performance of this approach compared with the conventional methods lies in the fact that it detects the endpoints as a nonlinearity of speech signal, which we believe is an important characteristic and has been neglected by the conventional methods. The proposed method extracts the features based only on the time-domain waveform of the speech signal illustrating its low complexity. Simulations done showed the effective performance of the Proposed method in a noisy environment with an average recognition rate of up 92.85% for unspecified person.

• Journal of The Korean Association For Science Education
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• v.34 no.6
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• pp.571-581
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• 2014

This study aims to investigate how high school teachers and students perceive the purpose of science learning. Participants were high school science teachers and students from one hundred and sixty high schools nationwide, which were selected through proportional stratified sampling method. Teachers and students responded on open-ended questionnaires about the purpose of science learning. The data were analyzed using the semantic network analysis method. Our study illustrates three major finding: First, teachers recognized the intrinsic value related to cognitive domain as the more important purpose of science learning, while students recognized the extrinsic value related to personal usefulness domain as more important. Second, teachers' responses were significantly different depending on the teaching career. Beginning teachers believed both intrinsic and extrinsic values were equally important, while experienced teachers believed the cognitive domain about understanding of scientific knowledge was more important than intrinsic values. In other words, the differences in perception of the purpose of science between teachers and students, the experienced teachers is greater than the beginning teachers. Finally, students' responses were different depending on their academic track. Humanity major students recognized that learning science made their everyday-life easier while science major students recognized that learning science should be related to their future careers. In conclusion, the results of this study is expected to be of use as the basic data to identify the characteristic of teachers and students related to science.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.30-39
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• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• The Journal of Korean Association of Computer Education
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• v.14 no.2
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• pp.83-94
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• 2011

In the biomedical domain, the longest matching method is frequently used for recognizing named entity written in the literature. This method uses a dictionary as a resource for named entity recognition. If there exist appropriated dictionary about target domain, the longest matching method has the advantage of being able to recognize the entities of target domain quickly and exactly. However, the longest matching method is difficult to recognize the enumerated named entities, because these entities are frequently expressed as being omitted some words. In order to resolve this problem, we propose the partial matching method using a dictionary. The proposed method makes several candidate entities on the assumption that the ellipses may be included. After that, the method selects the most valid one among candidate entities through the optimization algorithm. We tested the longest and partial matching method about HLA entities: HLA gene, antigen, and allele entities, which are frequently enumerated among biomedical entities. As preparing for named entity recognition, we built two new resource, extended dictionary and tag-based dictionary about HLA entities. And later, we performed the longest and partial matching method using each dictionary. According to our experiment result, the longest matching method was effective in recognizing HLA antigen entities, in which the ellipses are rare, and the partial matching method was effective in recognizing HLA gene and allele entities, in which the ellipses are frequent. Especially, the partial matching method had a high F-score 95.59% about HLA alleles.

• Journal of Korean Library and Information Science Society
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• v.54 no.1
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• pp.143-165
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• 2023

This study aims to propose a scaffolding strategy that can be applied to the information search process by using Kuhlthau's ISP model, which presented a design and implementation strategy for the mediation role in the learning process. To this end, the relevant literature was reviewed to categorize scaffolding strategies, and impressions were collected from the students surveys after providing 150 middle school students in the Daejeon area with the project class to which the scaffolding strategy based on the ISP model was applied. The collected data were processed into a form suitable for analysis through data preprocessing for word frequencies to be extracted, and topic analysis was performed using STM (Structural Topic Modeling). First, after determining the optimal number of topics and extracting topics for each stage of the ISP model, the extracted topics were classified into three types: cognitive domain-macro perspective, cognitive domain-micro perspective, and emotional domain perspective. In this process, we focused on cognitive verbs and emotional verbs among words extracted through text mining, and presented a scaffolding strategy model related to each topic by reviewing representative document cases. Based on the results of this study, if an appropriate scaffolding strategy is provided at the ISP model stage, a positive effect on learners' self-directed task solving can be expected.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.