• Journal of the Society of Disaster Information
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• v.7 no.2
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• pp.140-150
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• 2011

Life Environment is rapidly changing and large scale disasters are increasing from the global warming. Although the disaster repair resources are deployed to the disaster fields, the prevention of the disasters is the most effective countermeasures. the disaster sign data is based on the rule of Heinrich. Automatic extraction of the disaster sign data from the web is the focused issues in this paper. We defined the automatic extraction processes and applied information, such as accident nouns, disaster filtering nouns, disaster sign nouns and rules. Using the processes, we implemented the disaster sign data management system. In the future, the applied information must be continuously updated, because the information is only the extracted and analytic result from the some disaster data.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.55-63
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• 2017

One of the purposes of Information Technology (IT) is to support human response to natural and social problems such as natural disasters and spread of disease, and to improve the quality of human life. Recent climate change has happened worldwide, natural disasters threaten the quality of life, and human safety is no longer guaranteed. IT must be able to support tasks related to disaster response, and more importantly, it should be used to predict and minimize future damage. In South Korea, the data related to the damage is checked out by each local government and then federal government aggregates it. This data is included in disaster reports that the federal government discloses by disaster case, but it is difficult to obtain raw data of the damage even for research purposes. In order to obtain data, information extraction may be applied to disaster reports. In the field of information extraction, most of the extraction targets are web documents, commercial reports, SNS text, and so on. There is little research on information extraction for government disaster reports. They are mostly text, but the structure of each sentence is very different from that of news articles and commercial reports. The features of the government disaster report should be carefully considered. In this paper, information extraction method for South Korea government reports in the word format is presented. This method is based on patterns and dictionaries and provides some additional ideas for tokenizing the damage representation of the text. The experiment result is F1 score of 80.2 on the test set. This is close to cutting-edge information extraction performance before applying the recent deep learning algorithms.

• Journal of East-West Nursing Research
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• v.27 no.2
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• pp.153-165
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• 2021

Purpose: The aim of study was to identify ranges of Korean nurses' competency in disaster nursing. Methods: A scoping review was conducted using the Joanna Briggs Institute methodology. The review used information from four databases: RISS, ScienceON, EBSCO Discovery Service, and CINAHL. In this review, key words were 'disaster', 'nurs*', 'competenc*', 'ability' and 'preparedness'. Inclusion and exclusion criteria were identified as strategies to use in this review. The inclusion criteria for this review focused on the following: Korean nurse, articles related to disaster nursing competency, peer-review articles published in the full text in Korean and English. Review articles were excluded. Results: Nineteen studies were eligible for result extraction. A total of 10 categories of disaster nursing competency were identified: Knowledge of disaster nursing, crisis management, disaster preparation, information collection and sharing, nursing record and document management, communication, disaster plan, nursing activities in disaster response, infection management, and chemical, biological, radiation, nuclear, and explosive management. Conclusion: It is necessary to distinguish between Korean nurses' common disaster nursing competency, professional disaster nursing competency, and disaster nursing competency required in nursing practice. Therefore, future research will be needed to explore and describe disaster nursing competency.

• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.179-196
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• 2012

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1297-1304
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• 2013

The quick report and propagate information are increasingly important because nowadays it is hard to predict the damages of flooding by unexpected heavy rain. In addition, there are not many ways to receive disaster information in real time. Accordingly, we designed the system which can earn information from a lot of messages on twitter. Above all, our system can extract and deploy disaster information by comparison with erstwhile social network service mash-up system as only broadcast media. Significant objective of this paper is to design the fastest extract disaster information system of mass media.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.29 no.3
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• pp.88-100
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• 2018

Objectives: In this study, we aimed to conduct a systematic review of studies investigating psychosocial factors affecting children exposed to disasters. Methods: In total, 140 studies were retrieved. The studies were published from 1988 to 2015. A systematic review was performed using the PRISMA guidelines. MEDLINE, EMBASE, Cochrane Central, Web of Science, PsycINFO, PubMed, and Google Scholar were searched. Each database was searched using the following terms: 'Child,' 'Adolescent,' 'Youth,' 'Disaster,' 'Posttraumatic,' 'Psychosocial,' 'Assessment,' 'Evaluation,' and 'Screening.' The identified studies were subjected to data extraction and appraisal. Results: The database search identified 713 articles. Based on the titles and abstracts, the full texts of 118 articles were obtained. The findings of this review can be used as a basis for the design of a psychosocial evaluation tool for disaster preparedness. Conclusion: Given the paramount importance of post-disaster evaluation and the weaknesses of current disaster evaluation tools, the need to develop valid and reliable tools and psychometric evaluations cannot be overstated. Our findings provide current evidence supporting various assessments in children, who are very vulnerable psychologically following disasters.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.90-95
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• 2012

Remotely sensed data provide valuable information on land monitoring due to multi-temporal observation over large areas. Especially, high resolution imagery with 0.6~1.0 m spatial resolutions contain a wealth of information and therefore are very useful for thematic mapping and monitoring change in urban areas. Recently, remote sensing technology has been successfully utilized for natural disaster monitoring such as forest fire, earthquake, and floods. In this paper, an efficient change detection method based on texture differences observed from high resolution multi-temporal data sets is proposed for mapping disaster damage and extracting damage information. It is composed of two parts: feature extraction and detection process. Timely and accurate information on disaster damage can provide an effective decision making and response related to damage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.209-214
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• 2018

In this paper, we recognize the damage caused by a disaster to a facility in the event of a large-scale disaster and present the possible disasters in the form of a matrix. The typhoon was selected as a major disaster and covered mainly the flood damage, a possible damage caused by the typhoon. Flood damage is mainly caused by flooding, and damage is determined by flooding and flow rate, and the results of applying this to low-rise facilities are derived. In addition, the results were derived by applying a method of classification of disaster types in a matrix format to make it easy to see at a glance the connection between disasters caused by damage to a facility. Continuing research in the form presented in this paper will help us identify additional disasters as an occurrence of a disaster.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.661-667
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• 2022

As remote sensing and GIS have been considered to be essential technologies for disasters information production, researches on developing methods for analyzing spatial data, and developing new technologies for such purposes, have been actively conducted. Especially, it is assumed that the use of remote sensing and GIS for disaster management will continue to develop thanks to the launch of recent satellite constellations, the use of various remote sensing platforms, the improvement of acquired data processing and storage capacity, and the advancement of artificial intelligence technology. This spatial issue presents 10 research papers regarding ship detection, building information extraction, ocean environment monitoring, flood monitoring, forest fire detection, and decision making using remote sensing and GIS technologies, which can be applied at the disaster prediction, monitoring and response stages. It is anticipated that the papers published in this special issue could be a valuable reference for developing technologies for disaster management and academic advancement of related fields.

• Smart Structures and Systems
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• v.33 no.4
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• pp.263-279
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• 2024

With the increasing diversity of internet media, available video data have become more convenient and abundant. Related video data-based research has advanced rapidly in recent years owing to advantages such as noncontact, low-cost data acquisition, high spatial resolution, and simultaneity. Additionally, structural nonlinearity extraction has attracted increasing attention as a tool for damage evaluation. This review paper aims to summarize the research experience with the recent developments and applications of video data-based technology for structural nonlinearity extraction and damage evaluation. The most regularly used object detection images and video databases are first summarized, followed by suggestions for obtaining video data on structural nonlinear damage events. Technologies for linear and nonlinear system identification based on video data are then discussed. In addition, common nonlinear damage types in disaster events and prevalent processing algorithms are reviewed in the section on structural damage evaluation using video data uploaded on online platform. Finally, a discussion regarding some potential research directions is proposed to address the weaknesses of the current nonlinear extraction technology based on video data, such as the use of uni-dimensional time-series data as leverage to further achieve nonlinear extraction and the difficulty of real-time detection, including the fields of nonlinear extraction for spatial data, real-time detection, and visualization.