Digital Library[ Search Result ]
The Dataset and a Pretrained Language Model for Sentence Classification in Korean Science and Technology Abstracts
Hongbi Ahn, Soyoung Park, Yuchul Jung
http://doi.org/10.5626/JOK.2023.50.6.468
Classifying each sentence according to its role or function is a critical task, particularly in science and technology papers where abstracts contain various types of research-related content. Proper content curation and appropriate meaning tags are necessary but challenging due to the complexity and diversity of the work. For instance, in biomedical-related abstract data (such as PubMed) in foreign languages, the sentences in the abstract typically follow a consistent semantic sequence, such as background-purpose-method-result-conclusion. However, in Korean paper abstracts, the sentences are described in different orders depending on the author. To address this, we have constructed a dataset (PubKorSci-1k) that tags each sentence according to its role in the abstracts of the science and technology domains described in Korean. Additionally, we propose a learning technique for sentence classification based on this dataset.
A Decision Support System for Situation Management based on the Variability of Disaster Situations
Hyesun Lee, Sun-Wha Lim, Eun Joo Kim, Soyoung Park, Kang Bok Lee, Sang Gi Hong
http://doi.org/10.5626/JOK.2022.49.9.755
With increasing frequency and extent of disasters, the importance of prompt and accurate situation management is also increasing. Existing methods to support situation management decision-making can be applied only to specific situation management tasks in limited circumstances, making it difficult to support customized decision-making according to disaster situations. To address this problem, this paper proposed a variability-based situation management decision support method considering characteristics of disaster situations. The proposed method was based on the software product line engineering concept, constructing core information that could be configured by considering variabilities of disaster situation characteristics, thus providing situation management information from the core information according to disaster situations. This method could increase work efficiency by supporting systematic decision-making step by step based on the situation management work process according to the disaster situation. It could increase the speed and accuracy of decision-making by supporting decision-making automation. The feasibility of the method was validated by applying the method to situation management scenarios for different disaster situations.
GPU-Based Real-Time Light Source Estimation for Augmented Reality
Soyoung Park, Sunghun Jo, Sungkil Lee
http://doi.org/10.5626/JOK.2019.46.1.1
To render synthetic objects on static images captured from the real world, estimation of illumination from the images and application for rendering the objects is solicited. In this work, we propose a real-time estimation of light sources, when the 3D-reconstructed geometry of an indoor scene and the corresponding textures are available. To restore the high intensity of the light source, we first convert an LDR image into HDR. The converted image is then used to get 2D positions of the light sources by hierarchical division-based sampling technique. Lastly, 3D positions of the light sources are estimated from texture-geometry mapping. Since the sampling technique stores grid areas generated at each stage in mipmap textures, the division of areas is processed on GPU in a parallel way, which makes it work in real-time. Our approach can be used in rendering where explicit positions of the light sources are asked, such as shadows.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr