• Fire Science and Engineering
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• v.28 no.2
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• pp.48-53
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• 2014

In this study, we would evaluate fire risk by domestic standard for artificial turf installed on field and roofs. Today domestic regulation for artificial turf only applies to outdoor uses and especially KS M 3888-1 has compulsion but limited to school athletic facilities. Indoor regulation complying with National Emergency Management Agency (No. 2012-35) was enacted as recommendations. Thus this study did combustion test of artificial turf installed on field. Analyzed result by 45 degree flammability test, standard was inadequate to judge the fire risk so we compared and analysed its characteristic through combustion test of flame retardant finishing carpet used as flooring. Test and assessment result of its ignitionability by 45 degree flammability test showed that carpet was satisfied with flame retardant finishing performance standard contrary to artificial turf. For this reason, by conducting cone calorimeter test, the combustion property after ignition would be analyzed and evaluated and then this study will suggest a countermeasure for strengthening standard.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.699-705
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• 2022

Purpose: Sequence control is widely used by being applied to manufacturing, distribution, construction, and automation in the medical industry. With the development of the fourth industry, artificial intelligence convergence technology in the control field is becoming an important factor in the industry. In particular, it is required to evaluate the safety and innovation of facilities where microprocessors and artificial intelligence are fused to existing systems and develop reliable equipment, so it is intended to develop equipment for educational purposes and drive the development of the field. Method: The self-developed all-in-one artificial intelligence controller module is a device that combines artificial intelligence capabilities with existing sequence and PLC control circuits. As the performance evaluation items of this equipment, the recognition ability of motion, voice, text, color, etc. and the stability and reliability of the circuit were evaluated. Conclusion: After designing the sequence and PLC circuit, the performance evaluation items of the integrated integrated artificial intelligence controller module were all satisfied, and there was no problem in the safety and reliability of the circuit.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• International Journal of Concrete Structures and Materials
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• v.5 no.1
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• pp.29-33
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• 2011

This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ($K_{Ic}^s$) and the critical crack tip opening displacement ($CTOD_c$). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of $K_{Ic}^s$ and $CTOD_c$, and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of $K_{Ic}^s$ and $CTOD_c$. The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.

• Journal of the Society of Disaster Information
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• v.6 no.1
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• pp.78-90
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• 2010

Exposure to the outside, the concrete is differential moisture distribution depending on the depth. Such a differential moisture distribution causes the differential drying shrinkage in concrete structures. This thesis is researched to compare the shrinkage of lightweight concrete depending on depth to normal concrete. It is used artificial lightweight aggregate which has 20% of pre-absorb value by lightweight concrete. When water-binder ratio is 30%, average shrinkage of lightweight concrete section decreased than normal concrete, but differential shrinkage of lightweight concrete section increased. However water-binder ratio is 40% and 50% average shrinkage and differential shrinkage of lightweight concrete section decreased than normal concrete.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1998.04a
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• pp.52-57
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• 1998

Becaruse of the continual occurrence of minor and moderate earthquake in Korean peninsula, it is generally considered that Korean is nor located in safe region against probable earthquake and more, even though being recognized as a safe contry in earthquake. It is in particular noted that nowadays there has been much concern about undesirable disaster due to unexpected earthquake since the disaster of 1995 Kobe earthquake. Thus, the objective of this research is to develop appropriate design spectrum which could be practicably used in seismic design of important structures taking into consideration of local physical characteristics. Particularly, we have to keep in mind the lessons from 1985 Mexico earthquake which had disregarded deep research on local ground conditions, being a possible magnification phenomena of ground motions in weak soil layer. Various spectra has been described based on the analysis of historical earthquakes, and appropriate design spectrum has been proposed herein.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.450-450
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• 2023

인공지능 기법의 급격한 발달에 따라 다양한 분야에서 인공지능 기법을 활용하기 위한 노력이 이루어지고 있다. 재난은 발생하기 전에 다양한 전조 현상을 나타내나 수많은 정보 속에서 전조 증상을 정확히 인지하는 것은 매우 어렵다. 따라서 인공지능은 방대한 사전 정보의 해석을 통해 재난 발생의 전조를 신속 정확하게 감지하는데 최적의 기술이다. 최근 OpenAI의 딥러닝 기반의 언어모델인 GPT(Generative Pre-trained Transformer)의 성능이 기대 이상을 나타내면서 많은 분야에서 GPT에 대한 관심과 실험이 시작되고 있다. 본 실험에서는 GPT를 이용하여 재난 검색 및 해석의 특징을 검토하여 보았다. 정확한 재난 기록은 정확한 재난 예측을 위해 반드시 필요한 자료이나 부정확한 재난 기록은 그 기록이 비록 방대하더라도 오히려 예측의 신뢰도를 크게 떨어뜨린 수 있다. 따라서 비지도학습 기반의 대화형 인공지능을 재난 검색에 활용하기 위해서는 인공지능 기법의 인지 특성을 반드시 가늠해 봐야 한다. 향후 보다 많은 연구자가 이에 관심을 가진다면 보다 정확한 인공지능 기반의 재난 탐지 기술의 개발이 가능할 것으로 기대된다.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.357-358
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• 2022

지진, 호우, 태풍, 화재 등 긴급한 재난 알림 전달이 필요한 상황에서 청각장애인은 소리를 통한 알림을 인지할 수 없으며 문자를 통한 알림의 인지율도 비장애인에 비하여 상대적으로 낮은 편으로서 일반적인 수단의 재난알림을 신속하게 인지하기 어려운 경우가 많다. 이와 같은 청각장애인의 재난안전 긴급알림 인지의 취약성 문제를 해결하고자 픽토그램을 통한 재난안전 긴급 알림 시스템이 개발되었다. 본 연구에서는 재난문자 통보문의 문구를 기반으로 인공지능을 통하여 청각장애인이 인지하기 보다 용이한 일련의 픽토그램으로 자동으로 변환하는 기술을 개발하고자 하였다. 이를 위해 재난안전 관련 긴급 통보문과 관련되는 픽토그램 기반의 콘텐츠를 수집하고 문자 기반의 그림 출력에 적합한 인공신경망 구조와 훈련방법을 구성하여 인공신경망 기반으로 재난문자에 대응되는 픽토그램 기반의 청각장애인 재난안전 긴급알림이 생성될 수 있도록 하였다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.123-124
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• 2023

현대 산업현장에서의 생산성과 경쟁력은 안전 및 작업 효율성과 직결되어 있다. 특히, 4차 산업혁명의 중심축인 인공지능(AI) 기술의 발전이 산업현장의 작업 환경과 절차를 혁신하는 데 중요한 역할을 하고 있음이 점차 명확해지고 있다. 이 연구는 인공지능의 기술적 발전과 산업현장의 작업 안전성 및 효율성 간의 관계에 초점을 맞추어, 어떻게 AI 기술의 도입과 활용이 산업현장의 미래를 형성하고 있는지를 탐구하였다.