• Korean Journal of Computational Design and Engineering
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• v.20 no.2
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• pp.159-170
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• 2015

Radio-Frequency Identification (RFID) or Near Field Communication (NFC) technology has many advantages over other visual interfaces since it does not require line-of-sight alignment, can identify multiple tags simultaneously, and does not destroy the integrity of original objects. In addition, smart devices such as smartphone and smartpad have NFC/RFID readers which can provide mobile and natural interactions with digital and physical contents. Augmented reality has an excellent visual interaction capability with digital contents in a real environment by embedding digital contents into the physical world. In this paper, we propose a new approach to digital content interactions using RFID/NFC-based tangible interfaces in augmented reality environments that utilize invisible interfaces in addition to marker-based visual interfaces. By combining the advantages of invisible and visual interfaces, more intuitive interactions with digital contents can be provided, which can remove the difficulty of using typical AR paddles that are widely used in AR interactions. In particular, a semantic AR ontology is defined to provide more convenient interactions. Through the semantic ontology-based inferencing, physical querying and filtering are effectively supported. We will show the effectiveness and advantage of the proposed approach by demonstrating implementation results.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.1-9
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• 2009

Many techniques have been proposed to store efficiently and query XML data. One way achieving this goal is using relational database by transforming XML data into relational format. But most researches only transformed content and structure of XML schema. Although they transformed semantic constrainment of XML schema, they did not all of semantics. In this paper, we propose a systematic technique for extracting semantic constrainment from XML schema and storing method when the extracting result is transformed into relational schema without any lost of semantic constrainment. The transforming algorithm is used for extracting and storing semantic constrainment from XML schema and it shows how extracted information is stored according to schema notation. Also it provides semantic knowledges that are needed to be confirmed during the transformation to ensure a correct relation schema. The technique can reduce storage redundancy and can keep up content and structure with integrity constraints.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.32-36
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• 2003

An equivalent schema matching among several different source schemas is very important for information integration or mining on the XML based World Wide Web. Finding most similar source schema corresponding mediated schema is a major bottleneck because of the arbitrary nesting property and hierarchical structures of XML DTD schemas. It is complex and both very labor intensive and error prune job. In this paper, we present the first complex matching of XML schema, i.e. XML DTD. The proposed method captures not only schematic information but also integrity constraints information of DTD to match different structured DTD. We show the integrity constraints based hierarchical schema matching is more semantic than the schema matching only to use schematic information and stored data.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.48-50
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• 1999

본 논문은 공간데이터와 비공간데이터를 통합처리하는 공간 데이터베이스 시스템에서 데이터의 의미적 무결성을 보장하는 확장된 공간 데이터 모델을 설계한다. 공간 데이터베이스 시스템에서 다루는 단순 객체가 아닌 추상화된 복합 객체로 다양한 유도 데이터에 대한 의미적 무결성을 데이터베이스 시스템 내부에서 효율적으로 유지해야 하며, 공간 데이터의 의미적 무결성 제약 조건을 사용자에 의해 정의할 수 있어야 한다. 본 논문에서는 공간 데이터베이스에서 사용하는 공간 데이터에 대한 무결성 정보를 술어 논리 형태로 표현하고 유지할 수 있는 무결성 공간 데이터 모델 (ISRDM: Integrity supported Spatial-Relational Data Model)을 제안한다. 제안된 공간 데이터 모델은 하부 단계 저장 구조, 개념적 데이터 표현 단계, 무결성 표현 단계가 독립적으로 구성되는 다단계 구조로 기존의 공간 데이터베이스 시스템을 용이하게 확장하고 다양한 응용 요구에 대해 유연하게 대처할 수 있도록 설계한다.

• Journal of KIBIM
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• v.9 no.4
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• pp.31-40
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• 2019

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.53-63
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• 2011

Many techniques have been proposed to store and query XML data efficiently. One way achieving this goal is using relational database by transforming XML data into relational format. It is important to transform schema to preserve the content, the structure and the constraints of the semantics information of the XML document. Especially, key constraints are an important part of database theory. Therefore, the proposal technique has considered the semantics of XML as expressed by primary keys and foreign keys. And, the proposal technique can preserve not only XML data constraints but also the content and the structure and the semantics of XML data thru transformation process. Transforming information is the content and the structure of the document(the parent-child relationship), the functional dependencies, semantics of the document as captured by XML key and keyref constraints. Because of XML schema transformation ensures that preserving semantic constraints, the advantages of these transformation techniques do not need to use the stored procedure or trigger which these data ensures data integrity in the relational database. In this paper, there is not chosen the ID/IDREF key which supported in DTD, the inheritance relationship, the implicit referential integrity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.277-288
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• 2021

Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.

• Smart Structures and Systems
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• v.30 no.1
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• The Research Journal of the Costume Culture
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• v.32 no.1
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• pp.38-57
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• 2024

This study utilizes social big data to investigate the factors influencing the awareness, attitude, and behavior toward vegan fashion consumption among global and Korean consumers. Social media posts containing the keyword "vegan fashion" were gathered, and meaningful discourse patterns were identified using semantic network analysis and sentiment analysis. The study revealed that diverse factors guide the purchase of vegan fashion products within global consumer groups, while among Korean consumers, the predominant discourse involved the concepts of veganism and ethics, indicating a heightened awareness of vegan fashion. The research then delved into the factors underpinning awareness (comprehension of animal exploitation, environmental concerns, and alternative materials), attitudes (both positive and negative), and behaviors (exploration, rejection, advocacy, purchase decisions, recommendations, utilization, and disposal). Global consumers placed great significance on product-related information, whereas Korean consumers prioritized ethical integrity and reasonable pricing. In addition, environmental issues stemming from synthetic fibers emerged as a significant factor influencing the awareness, attitude, and behavior regarding vegan fashion consumption. Further, this study confirmed the potential presence of cultural disparities influencing overall awareness, attitude, and behavior concerning the acceptance of vegan fashion, and offers insights into vegan fashion marketing strategies tailored to specific cultures, aiming to provide vegan fashion companies and brands with a deeper understanding of their consumer base.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1187-1190
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• 1993

The problem of the optimization of fuzzy relational models for dealing with (non-fuzzy) numerical data is investigated. In this context, interfaces optimization assumes particular importance, becoming a determinant factor in what concerns the overall model performance. Considering this, several scenarios for building fuzzy relational models are presented. These are: (i) optimizing I/O interfaces in advance (independently from the linguistic part of the model); (ii) optimizing I/O interfaces in advance and allowing that their optimized parameters may change during the learning of the linguistic part of the model; (iii) build simultaneously both interfaces and the linguistic subsystem; and (iv) build simultaneously both linguistic subsystem and interfaces, now subject to semantic integrity constraints. As linguistic subsystems, both a basic type and an extended versions of fuzzy relation equations are exploited in each one of these scenarios. A comparative analysis of the differ nt approaches is summarized.