• Journal of the Korean Institute of Landscape Architecture
• /
• v.42 no.2
• /
• pp.65-76
• /
• 2014

Green Infrastructure(GI) is suggested to recover urban water circulation system as a newly conceptual alternative methodology by Korean landscape field in recent years. In this context, the study considers the essential meaning of GI. The methodology of this study is literature review with 47 published papers which were peer-reviewed in international journals in the recent 5 years. These papers were collected from online database and academic archives. The main analysis targets are definition sentences about GI. The each sentences were interpreted by semantic structure between verbs and objects in the definition sentences. As the results, it figured out 5 aims('Provide', 'Improve', 'Produce', 'Conserve', 'Reduce'), 4 objects('Humanistic', 'Environmental', 'Ecological', 'Hydrological') and 3 spaces('Object space', 'Technically available spaces', 'Object or technically available spaces'). The '5 aims' connected with the elements of '4 objects' based on the '3 spaces'. The elements was connected to the '5 aims' via single form or 2~3 forms of the essential meaning networks of GI. The study provides 83 meaning networks to use landscape architecture planning and urban planning.

• The Journal of the Korea Contents Association
• /
• v.18 no.4
• /
• pp.428-443
• /
• 2018

Recently, many firms have been more interested in how the competitiveness of the supply chain can be enhanced, rather than that of the respective company. The purpose of this study is not only to investigate the relationships among the trust between companies, the supply chain integration, and the supply chain performance, but also to demonstrate how the firm size and the absorptive capacity can moderate these relationships. To conduct the hypothesis test including the causal relations between two factors and the moderating effects, 111 data were collected through a survey. As a result, the supply chain performance was positively affected by the supply chain integration as well as the trust between the companies. In addition, the trust had a significant effect on the supply chain performance. The result of the moderating effect of firm size indicates that, compared with a larger group, a smaller group has a stronger relationship between the trust and the supply chain performance, while the relationship between the supply chain integration and the supply chain performance is much stronger in the large group than the small group. As for the moderating effect of absorptive capacity, a higher absorptive group has stronger relationships between the trust and supply chain integration, and supply chain integration and supply chain performance than a lower absorptive group. The findings would provide significant implications for supply chain partners with different sizes and absorptive capacity.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.28 no.3
• /
• pp.217-240
• /
• 2017

The purpose of this study was to investigate the semantic network analysis to understand image contents and to examine the degree to which words, word clusters contributed to the formation of semantic map within image contents. For this research, from 1993 until 2016 the field of the image contents were collected for a total of 2,624 cases papers. The word appeared in Title analyzed the social network by using the R program of Big Data. The results were as follows: First, The field of image contents is based on researches related to 'image', 'media' and 'contents'. Second, there is a three-step flow ('education' -> 'media' -> 'contents') of research in the field of image contents. Third, researches related to 'broadcasting', 'digital', 'technology', and 'production' were continuously carried out. Finally, There were new research subjects for each regime.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.6
• /
• pp.663-667
• /
• 2010

A simple approach to semantic document-retrieval is to measure document similarity based on the bag-of-words representation, e.g., cosine similarity between two document vectors. However, such a syntactic method hardly considers the semantic similarity between documents, often producing semantically-unsound search results. We circumvent such a problem by combining supervised machine learning techniques with ontology information based on Markov logic. Specifically, Markov logic networks are learned from similarity-tagged documents with an ontology representing the diverse relationship among words. The learned Markov logic networks, the ontology, and the training documents are applied to the semantic document-retrieval task by inferring similarities between a query document and the training documents. Through experimental evaluation on real world question-answering data, the proposed method has been shown to outperform the simple cosine similarity-based approach in terms of retrieval accuracy.

• Journal of the Korea Convergence Society
• /
• v.12 no.1
• /
• pp.29-35
• /
• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.24-29
• /
• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.1
• /
• pp.25-31
• /
• 2014

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules; these rules are formulated by language experts and consisted of lexical items. Such grammar checkers, unfortunately, show low recall which is detection ratio of errors in the document. In order to resolve this shortcoming, a new error-decision rule-generalization method that utilizes the existing KorLex thesaurus, the Korean version of Princeton WordNet, is proposed. The method extracts noun classes from KorLex and generalizes error-decision rules from them using the Tree Cut Model and information-theory-based MDL (minimum description length).

• Journal of the Korean Society for Library and Information Science
• /
• v.39 no.4
• /
• pp.119-138
• /
• 2005

The purpose of this study is to analyze and systematize the semantic relationships in knowledge organization systems(KOS) . For this purpose, Classification systems, thesaurus, subject headings, semantic networks, ontology, databases were analyzed in terms of the semantic relationships between terms. Also, various kinds of the terminological relationships not only in the current KOS but in the theoretical researches were collected and analyzed. In addition, six proposals were suggested for the organized system of the terminological relationships for the future uses.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2006.06a
• /
• pp.229-233
• /
• 2006

기계번역(Machine Translaton, MT), 다국어 정보 검색, 의미 정보 검색 등에 대한 연구는 시소러스, 지식베이스, 사전 검색, 의미망, 코퍼스등과 같은 다양한 방법으로 이루어지고 있다. 시맨틱 웹이 등장과 시맨틱 웹 기반 기술의 발전에 따라 위 연구들을 시맨틱 웹에 적용시킬 필요성도 제안되었다. 특히 한국어 시소러스, 워드넷(WordNet), 전자 세종 사전, 가도까와(Kadokawa) 시소러스와 같은 지식베이스가 개발되었으나 활용 분야에 따라 그 구축 방법론이 다르게 적용되어, 위 연구에 효과적으로 통용될 수 있는 지식베이스는 실질적으로 구축되지 못한 실정이다. 따라서 본 연구에서는 세종 사전과 가도까와 시소러스, 한/일 기계 번역 사전 그리고 전문 용어 사전을 기반으로 한국어와 일본어 지식베이스를 위한 사전 온톨로지 서버를 정의하여 의미 정보를 구성하고, Semantic Web Rule Markup Language (이하 SWRL)을 이용해 구문 정보 규칙을 정의한다. 그리고 SWRL 기반 정방향 추론 엔진을 이용하여 번역에 필요한 추론 엔진을 구성하고 문장 구문형성 규칙 추론 엔진을 통해 사용자에게 한국어와 일본어의 문장 구성 변환을 제공한다. 본 연구는 현재 기계 번역이 갖고 있는 다의성, 술부 어순의 차이, 경어체 등 아직 해결해야 할 많은 부분들에 대한 해결 방안으로서 시맨틱 웹 기반 기술과의 활용방안을 제시하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.2
• /
• pp.198-205
• /
• 2011

GPS, ARPA, AIS, NAVTEX, VHF as modern aids-to-navigation equipments improve the safe navigation and help to reach a reduction in marine accidents by providing images, numeric values, texts, audio-based information for mates, However, we also noticed that it's complicate and difficult for a mate to acquire and analyze such information from these devices while he should devote himself to bridge watchkeeping especially in the urgent situation. Language is another way to get information and free the eyes and hands, so, to solve the problem above, we are trying to propose a new aids-to-navigation system, which can understand and merge multimedia marine safety information, analyze the situation and provide the necessary information in language. In this paper, we try to suggest a meaning interpretation model for supporting linguistic navigation safety information.