• Korean Journal of Child Studies
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• v.20 no.2
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• pp.125-138
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• 1999

This study explored mother-infant interactions during reading of picture books with and without printed words. The interactions of 40 mother-infant dyads(??) were video-taped while mothers read the books aloud to their 1- or 2-year-old child at home. When reading the books with words, mothers used more "where" questions and gave more feedback to the non-verbal behavior of the infants. Mothers gave more labels, descriptions, predictions, and "what", "function/activity" questions when reading the wordless book. The infants used more nonverbal answers reading the book with words. The mothers of the 2-year-olds used more "what", "function/activity" questions, and gave feedback to their verbal behaviors. The 2-year-olds used more imitation, verbal answers, and comments. For the mothers of the 2-year-olds, the interaction with the wordless book led to more attention-recruiting and bridging. For the children, however, reading the wordless book led to more labels, questions, and comments.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.17-26
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• 1998

Shortest path problem in road network with turn penalties and prohibitions frequently arises from various transportation optimization models. In this paper, we propose a new algorithm for the shortest Path problem with turn prohibitions and delays. The proposed algorithm maintains distance labels of arcs, which is similar to labels of nodes of Dijkstra's algorithm. Fibonacci heap implementation of the proposed algorithm solves the problem in O(mn ＋ mlogm). We provide a new insight in transforming network with turn penalties and prohibitions into another network in which turn penalties and prohibitions are implicitly considered. The proposed algorithm is implemented using new data structure and compared with Ziliaskopoulos' algorithm. Computational results show that the proposed algorithm is very efficient.

• Kyungpook Mathematical Journal
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• v.55 no.2
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• pp.267-277
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• 2015

In this paper we investigate the properties of (a, d)-vertex antimagic total labeling of a digraph D = (V, A). In this labeling, we assign to the vertices and arcs the consecutive integers from 1 to |V|+|A| and calculate the sum of labels at each vertex, i.e., the vertex label added to the labels on its out arcs. These sums form an arithmetical progression with initial term a and common difference d. We show the existence and non-existence of (a, d)-vertex antimagic total labeling for several class of digraphs, and show how to construct labelings for generalized de Bruijn digraphs. We conclude this paper with an open problem suitable for further research.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.213-214
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• 2016

This paper intends to analyze the roles of regulations and certifications within the construction market that affect the effective recycling and application methods of construction waterproofing industries. Certifications, eco-labels, green certification patents, and new excellent technologies obtained in construction waterproofing industries are studied. In accordance to the study results, it was determined that, a total of 38 items obtained eco-labels with effective recycling as the theme, 10 items with green certifications, and 8 items with New Excellent Technologies.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.1-7
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• 2018

Reference string recognition is to extract individual reference strings from a reference section of an academic article, which consists of a sequence of reference lines. This task has been attacked by heuristic-based, clustering-based, classification-based approaches, exploiting lexical and layout characteristics of reference lines. Most classification-based methods have used sequence labeling to assign labels to either a sequence of tokens within reference lines, or a sequence of reference lines. Unlike the previous token-level sequence labeling approach, this study attempts to assign different labels to the beginning, intermediate and terminating tokens of a reference string. After that, post-processing is applied to identify reference strings by predicting their beginning and/or terminating tokens. Experimental evaluation using English and German reference string recognition datasets shows that the proposed method obtains above 94% in the macro-averaged F1.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1670-1673
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• 2002

For pattern recognition on high-dimensional data, such as images, the dimensionality reduction as a preprocessing is effective. By dimensionality reduction, we can (1) reduce storage capacity or amount of calculation, and (2) avoid "the curse of dimensionality" and improve classification performance. Popular tools for dimensionality reduction are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Independent Component Analysis (ICA) recently. Among them, only LDA takes the class labels into consideration. Nevertheless, it, has been reported that, the classification performance with ICA is better than that with LDA because LDA has restriction on the number of dimensions after reduction. To overcome this dilemma, we propose a new dimensionality reduction technique based on an information theoretic measure for difference of distribution. It takes the class labels into consideration and still it does not, have restriction on number of dimensions after reduction. Improvement of classification performance has been confirmed experimentally.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.241-246
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• 2013

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

• MALSORI
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• no.35_36
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• pp.131-143
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• 1998

This study describes an algorithm for the F0 contour generation system for Korean sentences and its evaluation results. 400 K-ToBI labeled utterances were used which were read by one male and one female announcers. F0 contour generation system uses two classification trees for prediction of K-ToBI labels for input text and 11 regression trees for prediction of F0 values for the labels. Evaluation results of the system showed 77.2% prediction accuracy for prediction of IP boundaries and 72.0％ prediction accuracy for AP boundaries. Information of voicing and duration of the segments was not changed for F0 contour generation and its evaluation. Evaluation results showed 23.5Hz RMS error and 0.55 correlation coefficient in F0 generation experiment using labelling information from the original speech data.

• Kyungpook Mathematical Journal
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• v.56 no.1
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• pp.41-46
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• 2016

A graph G of order n has prime cordial labeling if its vertices can be assigned the distinct labels 1, $2{\cdots}$, n such that if each edge xy in G is assigned the label 1 in case the labels of x and y are relatively prime and 0 otherwise, then the number of edges labeled with 0 and the number of edges labeled with 1 differ by at most 1. In this paper, we give a complete characterization of complete graphs which are prime cordial and we give a prime cordial labeling of the closed helm ${\bar{H}}_n$, and present a new way of prime cordial labeling of $P^2_n$. Finally we make a correction of the proof of Theorem 2.5 in [12].

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.145-152
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• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.