• Smart Structures and Systems
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• v.13 no.6
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• pp.1065-1094
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• 2014

Civil engineers always face the challenge of uncertainty in planning, building, and maintaining infrastructure. These works rely heavily on a variety of surveying and monitoring techniques. Unmanned aerial vehicles (UAVs) are an effective approach to obtain information from an additional view, and potentially bring significant benefits to civil engineering. This paper gives an overview of the state of UAV developments and their possible applications in civil engineering. The paper begins with an introduction to UAV hardware, software, and control methodologies. It also reviews the latest developments in technologies related to UAVs, such as control theories, navigation methods, and image processing. Finally, the paper concludes with a summary of the potential applications of UAV to seismic risk assessment, transportation, disaster response, construction management, surveying and mapping, and flood monitoring and assessment.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.568-580
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• 2020

Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.

• Smart Structures and Systems
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• v.17 no.3
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• pp.471-489
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• 2016

Modal identification based on ambient vibration data has attracted extensive attention in the past few decades. Since the excitation for ambient vibration tests is mainly from the environmental effects such as wind and traffic loading and no artificial excitation is applied, the signal to noise (s/n) ratio of the data acquired plays an important role in mode identifiability. Under ambient vibration conditions, certain modes may not be identifiable due to a low s/n ratio. This paper presents a study on the mode identifiability of an instrumented cable-stayed bridge with the use of acceleration response data measured by a long-term structural health monitoring system. A recently developed fast Bayesian FFT method is utilized to perform output-only modal identification. In addition to identifying the most probable values (MPVs) of modal parameters, the associated posterior uncertainties can be obtained by this method. Likewise, the power spectral density of modal force can be identified, and thus it is possible to obtain the modal s/n ratio. This provides an efficient way to investigate the mode identifiability. Three groups of data are utilized in this study: the first one is 10 data sets including six collected under normal wind conditions and four collected during typhoons; the second one is three data sets with wind speeds of about 7.5 m/s; and the third one is some blind data. The first two groups of data are used to perform ambient modal identification and help to estimate a critical value of the s/n ratio above which the deficient mode is identifiable, while the third group of data is used to perform verification. A couple of fundamental modes are identified, including the ones in the vertical and transverse directions respectively and coupled in both directions. The uncertainty and s/n ratio of the deficient mode are investigated and discussed. A critical value of the modal s/n ratio is suggested to evaluate the mode identifiability of the deficient mode. The work presented in this paper could provide a base for the vibration-based condition assessment in future.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.31-36
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• 2020

In recent years, increasing the variety of disasters and accidents that accompany large-scale damage. Disasters are accidents with uncertainty and have a direct impact on people's lives, safety and property protection. Therefore, it is necessary to establish and operate safety management systems such as prevention, response, and recovery for various disasters. Therefore, in this paper, a real-time disaster safety management solution in a smart environment was designed to systematically respond to disaster accidents. To this end, 1: 1 or 1: N situation propagation was performed to the situation room, related organizations, and experts through smart devices. Through this, the solution was configured to respond quickly and appropriately through multi-party information sharing and communication. In other words, we designed a solution that applied functions such as real-time and multi-party HD video transmission, mobile-type report management, voice / text situation propagation, location information sharing, recording and history management, and security.

• Journal of Daesoon Thought and the Religions of East Asia
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• v.3 no.2
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• pp.71-92
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• 2024

The world we live in is becoming more convenient thanks to the inventions of science and technology. Still, the world is also becoming more and more unpredictable with the current situation of VUCA (Volatility, Uncertainty, Complexity, Ambiguity). The Covid-19 pandemic brought the biggest global disaster ever with 774,631,444 infected people and 7,031,216 deaths (WHO on February 11, 2024) but it seems that humanity is gradually forgetting this disaster. Meanwhile the economic stimulus packages worth trillions of dollars from governments after the pandemic have further caused the world debt bubble to swell. The bubble burst scenario is something that many economic experts fear. Apparently, in the transitional period of the early decades of the 21st century, the world's economic, cultural, political, social, natural, and environmental aspects have undergone profound transformations: from the real estate and finance crises in the United States since 2008; through the melting of the Arctic ice over the past several decades; to the double disaster of the earthquake and tsunami in Japan in 2011. Especially, in the context of the world economic crisis after the COVID-19 pandemic, the human achievements of the past thousands of years are in jeopardy of being wiped out in an instant. Many people are predicting a bad scenario for a chain collapse. Facing the signals of an imminent economic catastrophe based on the appearance of "the Gray Rhino, Black Swan and White Elephant," many drawn in by Eschatological thought declare that Doomsday will occur shortly. This is the time for many other people to hope for the incoming Messiah. The Messiah is said to appear when people feel despair or suffer a great disaster because faith in the Savior can help them overcome adversity mentally. This research will find out how adherents of Buddhism view and deal with civilizational crises by examining history via symbols associated with Maitreya as based upon the Buddhist Messiah, Maitreya.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.976-983
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• 2023

Purpose: The dynamic behavior of a bridge structure under seismic loading depends on many uncertainties, such as the nature of the seismic waves and the material and geometric properties. However, not all uncertainties have a significant impact on the dynamic behavior of a bridge structure. Since probabilistic seismic performance evaluation considering even low-impact uncertainties is computationally expensive, the uncertainties should be identified by considering their impact on the dynamic behavior of the bridge. Therefore, in this study, a global sensitivity analysis was performed to identify the main parameters affecting the dynamic behavior of bridges with I-curved girders. Method: Considering the uncertainty of the earthquake and the material and geometric uncertainty of the curved bridge, a finite element analysis was performed, and a surrogate model was developed based on the analysis results. The surrogate model was evaluated using performance metrics such as coefficient of determination, and finally, a global sensitivity analysis based on the surrogate model was performed. Result: The uncertainty factors that have the greatest influence on the stress response of the I-curved girder under seismic loading are the peak ground acceleration (PGA), the height of the bridge (h), and the yield stress of the steel (fy). The main effect sensitivity indices of PGA, h, and fy were found to be 0.7096, 0.0839, and 0.0352, respectively, and the total sensitivity indices were found to be 0.9459, 0.1297, and 0.0678, respectively. Conclusion: The stress response of the I-shaped curved girder is dominated by the uncertainty of the input motions and is strongly influenced by the interaction effect between each uncertainty factor. Therefore, additional sensitivity analysis of the uncertainty of the input motions, such as the number of input motions and the intensity measure(IM), and a global sensitivity analysis considering the structural uncertainty, such as the number and curvature of the curved girders, are required.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.899-907
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• 2022

Purpose: Although many studies on seismic fragility analysis of general bridges have been conducted using machine learning methods, studies on curved bridge structures are insignificant. Therefore, the purpose of this study is to analyze the seismic fragility of bridges with I-shaped curved girders based on the machine learning method considering the material property and geometric uncertainties. Method: Material properties and pier height were considered as uncertainty parameters. Parameters were sampled using the Latin hypercube technique and time history analysis was performed considering the seismic uncertainty. Machine learning data was created by applying artificial neural network and response surface analysis method to the original data. Finally, earthquake fragility analysis was performed using original data and learning data. Result: Parameters were sampled using the Latin hypercube technique, and a total of 160 time history analyzes were performed considering the uncertainty of the earthquake. The analysis result and the predicted value obtained through machine learning were compared, and the coefficient of determination was compared to compare the similarity between the two values. The coefficient of determination of the response surface method was 0.737, which was relatively similar to the observed value. The seismic fragility curve also showed that the predicted value through the response surface method was similar to the observed value. Conclusion: In this study, when the observed value through the finite element analysis and the predicted value through the machine learning method were compared, it was found that the response surface method predicted a result similar to the observed value. However, both machine learning methods were found to underestimate the observed values.

• The Korean Journal of Emergency Medical Services
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• v.7 no.1
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• pp.199-212
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• 2003

Fire is a disaster of uncertainty, but since it is an artificial disaster essentially, this study is to emphasize that prevention and suppression of fire should be done more efficiently to develop the national love and trust of fire fighting. And it examines small neighboring living facilities which can be the typical dead ground of fire fighting based on researcher's experiences, intuition and related materials. The main course of the study defines the concept of fire fighting administration, dead ground and small neighboring living facilities. compares and analyzes fire fighting at small neighboring living facilities and the whole fire fighting cases and divides the causes of dead ground of fire fighting into preventive administration and suppressive administration sides. Those causes were resulted from discrepancies of political and social structures, but some of them can be improved depending on firemen's efforts. In addition, the means available for preventing the dead ground of fire fighting based on analyzed causes are suggested. Institutional approaches should be consulted with related agencies and legalized, but most managerial approaches can be introduced easily. Other means are approached for PR.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.369-375
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• 2022

The educational environment of the era of age of uncertainty and human and disaster, where nothing is predictable, so it is bringing about many changes. The purpose of this study is to suggest the implications of Integrative arts therapy in the educational field of unpredictable times as a way to move the minds of people living in various educational changes. As for the research method, the characteristics of the integrative arts therapy shown in the previous studies were examined through the literature method, and then through the cases in the modern education environment practiced we propose three things. First, in the field of university education, students should have an integrative arts therapy approach in terms of teaching methods, and also, second, in order to recover from failure and frustration, continuous gratitude sharing training to discover and acknowledge positivity in the situation by changing perspectives should improve students' self-esteem and self-efficacy. Third, professors should play the role of facilitators and also coaches in knowledge transfer. Therefore the coaching process should be integrated into the process of sustainable education when the integrative arts therapy is approached from the pedagogical curriculum.