• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.688-699
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• 2021

On September 23, 2020, at 1:39 a.m., a bright fireball above Seocheon was observed across the country. Two fireball explosions were identified in the images of the All-Sky Camera (ASC), and the shock waves were recorded at seismic and infrasound stations in the southwestern Korean Peninsula. The location of the explosion was estimated by a Bayesian-based location method using the arrival times of the fireball-associated seismic and infrasound signals at 17 stations. Realistic azimuth- and rang-dependent propagation speeds of sound waves were incorporated into the location method to increase the reliability of the results. The location of the sound source was found to be 36.050°N, 126.855°E at an altitude of 35 km, which was close to the location of the second fireball explosion. The two explosions were identified as sequential infrasound arrivals at local infrasound stations. Simulations of waveforms for long ranges explain the detection results at distant infrasound stations, up to ~266 km from the sound source. The dominant period of the signals recorded at five infrasound stations is about 0.4 s. A period-energy relation suggests the explosion energy was equivalent to ~0.3 ton of TNT.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.519-531
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• 2023

With the aim of preventing safety accidents at construction sites, the company aims to create safe behaviors intended through variables called smart safety and health activities to help reduce industrial accidents. Purpose: It analyzes how smart safety and health activities affect accidents caused by unsafe behavior and changes in worker behavior, which is the root cause, and verifies the hypothesis that it helps prevent safety accidents and protect workers' lives. Method: Smart safety and health activities were selected as independent variables (X), and intended safety and anxiety, which are workers' behavioral intentions, were set as dependent variables (Y), attitude and subjective norms, and planned behavioral control as parameters (M). Exploratory factor analysis, discriminant validity analysis, and intensive validity analysis of safety and health activities were used to analyze the scale's reliability and validity. To verify the hypothesis of behavior change, the study was verified through Bayesian model analysis and MC simulation's probability density distribution. Result: It was found that workers who experienced smart safety and health activities at construction sites had the highest analysis of reducing unstable behavior and performing intended safety behavior. The research hypothesis that this will affect changes in worker behavior has been proven, the correlation between variables has been verified in the structural equation and path analysis of the research analysis, and it has been confirmed that smart safety and health activities can control and reduce worker instability. Conclusion: Smart safety and health activities are a very important item to prevent accidents and change workers' behavior at construction sites.