• Journal of KIISE:Databases
• /
• 제32권2호
• /
• pp.111-119
• /
• 2005

Electronic catalog contains ich semantics associated with products, and serves as a challenging practical domain for ontology application. Ontology is concerned with the nature and relations of being. It can play a crucial role in e-commerce as a formalization of e-Catalogs. Description Logics provide a theoretical core for most of the current ontology languages. In this paper, we present an ontological model of e-Catalogs in DL. We take an Extended Entity Relationship approach for conceptual modeling method, and present the fundamental set of modeling constructs and corresponding description language representation for each construct. Additional semantic knowledge can be represented directly in DL. Our modeling language stands within SHIQ(d) which is known reasonably practical with regard to its expressiveness and complexity. We illustrate sample scenarios to show how our approach may be utilized in modeling e-Catalogs, and also implement the scenarios through a DL inference tool to see the practical feasibility.

• Journal of the Korea Society of Computer and Information
• /
• 제15권1호
• /
• pp.23-30
• /
• 2010

With the advancement of information technology and increasing diversity in medical field, there are ongoing researches on ontology based intelligent medical system in Oriental medicine field. Intelligent diagnostic support system uses ontology to give a structure to complex medical knowledge and personal medical history so that we can make diagnosis more scientific, and provide better medical services. In this paper, we suggest an ontology that structuralize three knowledge types basic medical data, clinical trial data, and personal health information, which can be used as important information for individually tailored diagnosis. Especially in Oriental medicine diagnosis, both patient's symptoms of illness and physical constitution play a great role; it can lead to distinct diagnosis depending on their combination. Thus, it is much needed to have a diagnostic support system that uses personal health history and physical constitution along with basic medical data and clinical trial data in the field. In this paper, we implemented an Oriental medicine diagnostic support system that provides individualized diagnosis service to each patient by building an ontology on Oriental medicine focused on individual physical constitution and disease information.

• International Journal of Advanced Culture Technology
• /
• 제9권3호
• /
• pp.259-264
• /
• 2021

In modern socity, online communication plays a vital role in social interaction of communicities. It is so common for online users to see display advertisements online while surting the Net. Specifically, most web banners diaplayed on portral sites consist of words, phrase, and sentences. Considering that the primary purpose of adversiting is persuation, the advertisement such as web banners is an examplary case to show the interaction among pragmatics, translation and advertising because the linguistic expressions employed in the banners represent its pragmatic use, leading to persuation and functioning as a communicative tool for the smooth communication between source text producers (adversisers) and target audience (online users). This can be part of the so-called translation process. In particular, we can easily witness the use of English words in web banners. Thus, this paper looks at web banners displayed on major four portal sites-Naver, Daum, Nate, and Zum, giving a special attention to the content contained in the web banners as well as the use of English words. As s result, we found that the frequencies of English words in each portal site were higher when the advertised products were targeting young online users, whereas the frequencies were lower when the users are older group than young people. The finding supports the prgramatic perspective that linguistic expressions are understood in social contexts and shows the so-called translation process which involves a shift from semantic meaning of words to their pragmatic use. Finally, we can conclude that the interaction is possible when we have the framework where translation, pragmatics, and advertising are all communitative components for social interaction within social contexts.

• Journal of Broadcast Engineering
• /
• 제26권4호
• /
• pp.419-428
• /
• 2021

Shadow removal plays a key role for the pre-processing of image processing techniques such as object tracking and detection. With the advances of image recognition based on deep convolution neural networks, researches for shadow removal have been actively conducted. In this paper, we propose a novel method for shadow removal, which utilizes self attention distillation to extract semantic features. The proposed method gradually refines results of shadow detection, which are extracted from each layer of the proposed network, via top-down distillation. Specifically, the training procedure can be efficiently performed by learning the contextual information for shadow removal without shadow masks. Experimental results on various datasets show the effectiveness of the proposed method for shadow removal under real world environments.

• Journal of Korean language education
• /
• 제25권1호
• /
• pp.53-82
• /
• 2014

This study aims to find the method to promote interaction in L2 classrooms. Active interaction between learner-to-learner and learner-to-teacher in L2 classroom plays an important role in language acquisition. In light of this, L2 classroom would benefit with the help of learning tools such as Clickers which helps learners to express their level of understanding during the process of learning itself. This is because the anonymity of Clickers allows learners to express their needs without the social risks associated with speaking up in the class. It allows for an evaluative feedback loop where both learners and teachers understand the level of progress of the learners, better enabling classrooms to adapt to the learners' needs. Eventually this tool promotes participation from learners, This is in turn, believed to be effective in fostering classroom interaction, allowing learning to take place in a more comfortable yet vibrant way. This study is finalized by presenting the result of an experiment conducted to verify the effectiveness of this approach when teaching pragmatic aspect of the Korean expressions with similar semantic functions. As a result of the research, the learning achievement of learners in the experimental group was found higher than the learners' in a control group. Analyzing the data collected from a questionnaire given to the learners, the study presented data suggesting that this approach increased the scope of interactivity in the classroom, thus enhancing more active participation among learners. This active participation in turn led to a marked improvement in their communicative abilities.

• The Journal of the Korea institute of electronic communication sciences
• /
• 제16권5호
• /
• pp.799-806
• /
• 2021

In the IoT environment, event data from sensors is continuously reported over time. Event data obtained in this trend is accumulated indefinitely, so a method for efficient analysis and management of data is required. In this study, a data stream segmentation method was proposed to support the effective selection and utilization of event data from sensors that are continuously reported and received. An identifier for identifying the point at which to start the analysis process was selected. By introducing the role of these identifiers, it is possible to clarify what is being analyzed and to reduce data throughput. The identifier for stream segmentation proposed in this study is a semantic-oriented data stream segmentation method based on the event occurrence of each stream. The existence of identifiers in stream processing can be said to be useful in terms of providing efficiency and reducing its costs in a large-volume continuous data inflow environment.

• Progress in Medical Physics
• /
• 제31권3호
• /
• pp.111-123
• /
• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• 제20권4호
• /
• pp.95-105
• /
• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Journal of Cadastre & Land InformatiX
• /
• 제52권1호
• /
• pp.37-55
• /
• 2022

This study presents the plans to derive quality items for develop the data quality standard for ensuring the quality of 3D building geospatial data in NDT(National Digital Twin). This paper is organized as follows. The first section briefly examines various factors that impact the quality of 3D geospatial data, and proposes the role and necessity of the data quality standard as a means of addressing the data errors properly and also meeting the minimum requirements of stakeholders. The second section analyzes the relationship between the standards - building data model for NDT and ISO 19157: Geospatial data quality - in order to consider directly relevant standards. Finally, we suggest three plans on developing NDT data quality standard: (1) the scope for evaluating data quality, (2) additional quality elements(geometric integrity, geometric fidelity, positional accuracy and semantic classification accuracy), and (3) NDT data quality items model based on ISO 19157. The plans reveled through the study would contribute to establish a way for the national standard on NDT data quality as well as the other standards associated with NDT over the coming years.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.237-250
• /
• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.