• Journal of the Korean Society of Clothing and Textiles
• /
• v.44 no.5
• /
• pp.1019-1035
• /
• 2020

This study examines the millennial generation, who express themselves and share information on social media after experiencing constantly changing 'hot places' (places of interest) in contemporary cities, with the goal of analyzing space consumption behaviors. Data were collected via an Instagram crawler application developed with Python 3.4 administered to 19,262 posts using the term 'hot places' from November 1 and December 15, 2019. Issues were derived from a text mining technique using Textom 2.0; in addition, semantic network analysis using Ucinet6 and the NetDraw program were also conducted. The results are as follows. First, a frequency analysis of keywords for hot places indicated words frequently found in nouns were related to food, local names, SNS and timing. Words related to positive emotions felt in experience, and words related to behavior in hot places appeared in predicate. Based on importance, communication is the most important keyword and influenced all issues. Second, the results of visualization of semantic network analysis revealed four categories in the scope of the definition of "hot place": (1) culinary exploration, (2) atmosphere of cafés, (3) happy daily life of 'me' expressed in images, (4) emotional photos.

• Korean Journal of Child Studies
• /
• v.20 no.3
• /
• pp.199-216
• /
• 1999

The 121 five-year-old Korean subjects of this study were divided in 3 groups based on their experience in learning a foreign language (English). A battery of tests was administered to measure spoken and written language ability and the 3 metalinguistic domains of phonological, semantic, and syntactic awareness. Spoken language ability was positively correlated with semantic and syntactic awareness. The relative importance of each metalinguistic domain varied with level of written language development. Phonological awareness was the only predictor of decoding. Syntactic awareness and phonological awareness were significant variables in sentence comprehension. Metalinguistic ability was a better predictor of written language development than spoken language ability. Foreign language learning experience had an effect on syntactic awareness: low experience was superior to no experience, but high experience was not superior to low experience.

• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.184-200
• /
• 2020

Similarity searching is a basic issue in information processing because of the large size of formal contexts and their complicated derivation operators. Recently, some researchers have focused on knowledge reduction methods by using granular computing. In this process, suitable information granules are vital to characterizing the quantities of attributes and objects. To address this problem, a novel approach to obtain an entropy-weighted concept lattice with inclusion degree and similarity distance (ECLisd) has been proposed. The approach aims to compute the combined weights by merging the inclusion degree and entropy degree between two concepts. In addition, another method is utilized to measure the hierarchical distance by considering the different degrees of importance of each attribute. Finally, the rationality of the ECLisd is validated via a comparative analysis.

• Journal of Information Processing Systems
• /
• v.16 no.4
• /
• pp.820-831
• /
• 2020

Extractive document summarization aims to select a few sentences while preserving its main information on a given document, but the current extractive methods do not consider the sentence-information repeat problem especially for news document summarization. In view of the importance and redundancy of news text information, in this paper, we propose a neural extractive summarization approach with joint sentence semantic clipping and selection, which can effectively solve the problem of news text summary sentence repetition. Specifically, a hierarchical selective encoding network is constructed for both sentence-level and document-level document representations, and data containing important information is extracted on news text; a sentence extractor strategy is then adopted for joint scoring and redundant information clipping. This way, our model strikes a balance between important information extraction and redundant information filtering. Experimental results on both CNN/Daily Mail dataset and Court Public Opinion News dataset we built are presented to show the effectiveness of our proposed approach in terms of ROUGE metrics, especially for redundant information filtering.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.808-813
• /
• 2022

This study presents the problems of data imbalance, varying difficulties across target objects, and small objects in construction object segmentation for far-field monitoring and utilize compound loss functions to address it. Construction site scenes of assembling scaffolds were analyzed to test the effectiveness of compound loss functions for five construction object classes---workers, hardhats, harnesses, straps, hooks. The challenging problem was mitigated by employing a focal and Jaccard loss terms in the original loss function of LinkNet segmentation model. The findings indicates the importance of the loss function design for model performance on construction site scenes for far-field monitoring.

• Journal of Korea Multimedia Society
• /
• v.12 no.7
• /
• pp.1032-1042
• /
• 2009

This research is for early extraction and utilization of semantic information from the tags in tagged Web image retrieval. Generally, users attach a tag to a Web image with little thought of the order, up to over 100 ones. In this paper, we suggest a method of selecting prior tags based on their importance when tagged images are uploaded, and using them in image retrieval. Ideas came from the recognition of the important tags which give a better description of the image as the tags sharing more semantic information with other tags of the same image. This method includes calculation of relation scores between tags based on WordNet and multilevel search of tagged images with the scores. For evaluation, we compared the suggested method and other retrieval methods searching images with simple matching of tags to a given keyword. As the results, we found the superiority of our method in precision and recall rate.

• Journal of the Korea Convergence Society
• /
• v.6 no.3
• /
• pp.127-134
• /
• 2015

In this paper, we design a semantic-based syllabus template including learning ontologies. A syllabus has been considered as a important blueprint of teaching in universities. However, the current syllabus has no importance in real world because most of all syllabus management systems provide simple functionalities such as, creation, modification, and retrieval. In this paper, our approach consists of definition of hierarchical structure of syllabus and semantic relationships of syllabuses, formalization of learning goals, learning activity, and learning evaluation using Bloom's taxonomy and design of learning subject ontologies for improving the usability of syllabus. We prove the correctness of our proposed methods according to implementing a real syllabus for JAVA programing course and experiments for retrieving syllabuses.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.12
• /
• pp.2607-2613
• /
• 2012

The results of inherent knowledge based generic summarization are influenced by the composition of sentence in document set. In order to resolve the problem, this papser propses a new generic document summarization which uses clustering of semantic feature of document and coherence of document cluster. The proposed method clusters sentences using semantic feature deriving from NMF(non-negative matrix factorization), which it can classify document topic group because inherent structure of document are well represented by the sentence cluster. In addition, the method can improve the quality of summarization because the importance sentences are extracted by using coherence of sentence cluster and the cluster refinement by re-cluster. The experimental results demonstrate appling the proposed method to generic summarization achieves better performance than generic document summarization methods.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.1 s.45
• /
• pp.9-15
• /
• 2007

As the information of the internet increased, importance of sematic web for collecting and integration of these informations to support decision making of some group or ordinary people are growing as well. Basis structure that composes semantic web is ontology and languages like XML, RDF/RDF schema and OWL are basis means that compose ontology schema. When composes and manages Ontology schema, one of the important consideration point is that schema is changed as times go by. Therefore, change of domain of schema, change of data concept or change of relation between resource etc. are reflected in the ontology system. In this study, we suggest semantic web schema management skill in terms of version management. We categorized version change forms and created version graph for checking of version transition. With created version graph, we define transitivity rule and propose schema tag for detail application which enables extending of applicable version schema.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.8
• /
• pp.243-252
• /
• 2023

Satellite imageries are at a greatest importance for land cover examining. Numerous studies have been conducted with satellite images and uses semantic segmentation techniques to extract information which has higher altitude viewpoint. The device which is taking these images must employee wireless communication links to send them to receiving ground stations. Wireless communications from a satellite are inevitably affected due to transmission errors. Evidently images which are being transmitted are distorted because of the information loss. Current semantic segmentation techniques are not made for segmenting distorted images. Traditional image enhancement methods have their own limitations when they are used for satellite images enhancement. This paper proposes an auto-encoder based image pre-enhancing method for satellite images. As a distorted satellite images dataset, images received from a real radio transmitter were used. Training process of the proposed auto-encoder was done by letting it learn to produce a proper approximation of the source image which was sent by the image transmitter. Unlike traditional image enhancing methods, the proposed method was able to provide more applicable image to a segmentation model. Results showed that by using the proposed pre-enhancing technique, segmentation results have been greatly improved. Enhancements made to the aerial images are contributed the correct assessment of land resources.