• Journal of KSNVE
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• v.2 no.1
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• pp.61-66
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• 1992

In general, structures have various joints such as bonded joint, bolted joint, bearing joint and welded joint. Dynamic modelling of such joints has been the current topic of interest. In this study, the dynamic modelling of plate structures with bonded joint was investigated by using modal testing, sensitivity analysis and condensation-inverse condensation method of FEM. A proper modelling procedure was proposed and the validity was verified.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.141-157
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• 2021

In this study, we aimed to understand research trends of domestic artificial intelligence research. To achieve the goal, we applied network analysis and dynamic topic modeling to domestic research papers on artificial intelligence. Among the papers that have been indexed in KCI (Korean Journal of Citation Index) by 2020, metadata and abstracts of 2,552 papers where the titles or indexed keywords include 'artificial intelligence' both in Korean and English were collected. Keyword, affiliation, subject field, and abstract were extracted and preprocessed for further analyses. We identified main keywords in the field by analyzing keyword co-occurrence networks as well as the degree and characteristics of research collaboration between domestic and foreign institutions and between industry and university by analyzing institutional collaboration networks. Dynamic topic modeling was performed on 1845 abstracts written in Korean, and 13 topics were obtained from the labeling process. This study broadens the understanding of domestic artificial intelligence research by identifying research trends through dynamic topic modeling from abstracts as well as the degree and characteristics of research collaboration through institutional collaboration networks from author affiliation information. In addition, the results of this study can be used by governmental institutions for making policies in accordance with artificial intelligence era.

• Computers and Concrete
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• v.8 no.1
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• pp.23-41
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• 2011

The behaviour of concrete confined by transverse reinforcement is a classical topic. Numerous studies have been conducted to establish the stress-strain relationships for concrete under various confining reinforcement arrangements. Many empirical and semi-empirical formulas exist. Simplified analytical models have also been proposed to evaluate the increase in the strength and ductility of confined concrete. However, relatively few studies have been conducted to utilise advanced computational models for a realistic simulation of the behaviour of concrete confined by transverse reinforcement. As a matter of fact, high fidelity simulations using the latest numerical solvers in conjunction with advanced material constitutive models can be a powerful means to investigating the mechanisms underlying the confining effects of different reinforcement schemes. This paper presents a study on the use of high fidelity finite element models for the investigation of the behaviour of concrete confined by stirrups, as well as the interpretation of the numerical results. The development of the models is described in detail, and the essential modelling considerations are discussed. The models are then validated by simulating representative experimental studies on short columns with different confining reinforcement schemes. The development and distribution of the confining stress and the subsequent increase in the axial strength are examined. The models are shown to be capable of reproducing the behaviour of the confined concrete realistically, paving a way for systematic parametric studies and investigation into complicated confinement, load combination, and dynamic loading situations.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.331-350
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• 2022

This study aims to understand topics of incivility related to COVID-19 from analyzing Twitter posts including COVID-19-related hate speech. To achieve the goal, a total of 63,802 tweets that were created between December 1st, 2019, and August 31st, 2021, covering three targets of hate speech including region and public facilities, groups of people, and religion were analyzed. Frequency analysis, dynamic topic modeling, and keyword co-occurrence network analysis were used to explore topics and keywords. 1) Results of frequency analysis revealed that hate against regions and public facilities showed a relatively increasing trend while hate against specific groups of people and religion showed a relatively decreasing trend. 2) Results of dynamic topic modeling analysis showed keywords of each of the three targets of hate speech. Keywords of the region and public facilities included "Daegu, Gyeongbuk local hate", "interregional hate", and "public facility hate"; groups of people included "China hate", "virus spreaders", and "outdoor activity sanctions"; and religion included "Shincheonji", "Christianity", "religious infection", "refusal of quarantine", and "places visited by confirmed cases". 3) Similarly, results of keyword co-occurrence network analysis revealed keywords of three targets: region and public facilities (Corona, Daegu, confirmed cases, Shincheonji, Gyeongbuk, region); specific groups of people (Coronavirus, Wuhan pneumonia, Wuhan, China, Chinese, People, Entry, Banned); and religion (Corona, Church, Daegu, confirmed cases, infection). This study attempted to grasp the public's anti-citizenship public opinion related to COVID-19 by identifying domestic COVID-19 hate targets and keywords using social media. In particular, it is meaningful to grasp public opinion on incivility topics and hate emotions expressed on social media using data mining techniques for hate-related to COVID-19, which has not been attempted in previous studies. In addition, the results of this study suggest practical implications in that they can be based on basic data for contributing to the establishment of systems and policies for cultural communication measures in preparation for the post-COVID-19 era.