• International conference on construction engineering and project management
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• 2022.06a
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• pp.945-951
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• 2022

The major objective of this research is to evaluate performance of improved intersections for response time to emergency vehicle preemption. Smart technologies have been introduced to civil infrastructure systems for resilient communities. The technologies need to evaluate their effectiveness and feasibility to confirm their introduction. This research focuses on the performance of emergency vehicle preemption, represented by response time, when smart intersections are introduced in a community. The response time is determined by not only intersections but also a number of factors such as traffic, distance, road conditions, and incident types. However, the evaluation of emergency response has often ignored factors related to emergency vehicle routes. In this respect, this research synthetically analyzes geospatial and incident data using each route of emergency vehicle and conducts before-and-after evaluations. The changes in performance are analyzed by the impact of smart intersections on response time through Bayesian regression models. The result provides measures of the project's performance. This study will contribute to the body of knowledge on modeling the impacts of technology application and integrating heterogeneous data sets. It will provide a way to confirm and prove the effectiveness of introducing smart technologies to our communities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Journal of Korean Biological Nursing Science
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• v.25 no.2
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• pp.95-104
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• 2023

Purpose: The purpose of this study was to analyze the trends and characteristics of infection-related patient safety incident reporting before and during the coronavirus disease 2019 (COVID-19) pandemic in Korea, and to provide basic data for preventing infection-related patient safety incidents and improving their management. Methods: A cross-sectional analysis of secondary national data (Patient Safety Reporting Data) was conducted. In total, 517 infection-related patient safety incidents reported from 2018 to 2021 were analyzed. Changes in the number of reports before and during the COVID-19 pandemic and differences in variables related to infection-related patient safety incidents were analyzed using the chi-square test and independent t-test in SPSS 29.0. Results: This study found that infection-related patient safety incidents decreased during the COVID-19 pandemic compared to before the pandemic. Furthermore, incident-related characteristics, such as the type of healthcare organization, severity of harm, and post-incident actions, changed during the COVID-19 pandemic. Conclusion: The many changes in the infection control system and practices during the COVID-19 pandemic may have contributed to a decrease in the reporting of infection-related patient safety incidents. It is hoped that longitudinal studies on patient safety incidents related to the pandemic and analytical studies on factors influencing patient safety incidents will continue to be conducted to prevent and improve patient safety incidents.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.61-66
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• 2013

The central core of the Traffic Management System is an Incident Management System. Whole approach has been component-orientated, with a secondary emphasis being placed on the traffic characteristics at the sites. The first action taken during the development process was the selection of the required data for each components within the existing infrastructure of Algeria freeway system. After review and analysis of existing incident detection methodologies, Paramics was utilized to test the performance of APID, DELOS algorithms. The existing system of Algeria freeway was tested in a different configuration at different sections of freeway, thereby increasing the validity and scope of the overall findings. The incident detection module has been performed according to predefined system validation specifications. The Paramics simulation was done with the use of synchronous analysis, thereby providing a means for testing the incident detection module.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.3
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• pp.177-185
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• 2012

Wave field measurements were made over a period of 18 days to study the spatial distribution of incident wave on seaweed tarm field around Gumil-up Sea, Wando, Korea. These measured data were compared with data from the Geomun-do ocean weather/wave observation buoy. A numerical simulation model that combined the offshore design wave with the seasonal normal incoming wave was used to study the incident wave distribution surrounding a seaweed farm. The results are summarized as follows. (1) On-site wave measurements showed that the major relationship between maximum and significant wave height was $H_{max}=1.6H_{1/3}$. (2) Offshore incident wave energy reaching the coast was greatly influenced by the wind direction. A north wind reduced the incident wave energy and a south wind increased it. (3) The calculated maximum wave height under the design wave boundany conditions was in the range of 4~5 m and the reduction in the incident wave height ratio ranged from approximately 38.1% to 47.6% at Gumil-up Sea. Under normal wave conditions, the maximum wave heights were 3.6~4.0 m in summer and 2.3~2.7 m in winter while the reduction in the incident wave height ratio was about 41.8% to 49.1%. (4) The sea state in the southern area of Gumil-up was the most affected by ocean waves, whereas the sea state in the northern area was very stable. The significant wave ratio in the south was about six times that in the north.

• Journal of Korean Society of Transportation
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• v.15 no.4
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• pp.143-163
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• 1997

Incident management is significant far the traffic management systems. The management of incidents determines the smoothness of freeway operations. The dynamic nature of incidents and the uncertainty associated with them require solutions based on the incident operator's judgment. Fuzz systems attempt to adapt such human expertise and are designed to replicate the decision making capability of on operator. Fuzzy systems process complex traffic information, and transmit it in a simplified, understandable form to human traffic operators. In this study, fuzzy rules were developed based on data from real incidents on Santa Monica Freeway in LosAngeles. The fuzzy rules ail linguistic based, and hence, user-friendly. A comparison of the results from the linguistic model with the real incident durations indicate that the outputs from the model reliably correspond to real incident durations conditions. The model reliably predicts the freeway incident duration. The modes can thus be used as an effective management tool for freeway incident response systems. The approach could be applied to other problems regarding dispatch systems in transportation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.46-56
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• 2021

Predicting the incident clearance time is important for eliminating the high transportation costs and congestion from non-repetitive congestion caused by incidents. In this study, the factors influencing the clearance time suitable for domestic road conditions were analyzed, using a training dataset for predicting the incident clearance time using artificial neural networks. In a previous study, the under-prediction problem for high incident clearance time was used. In the present study, over-sampling training data applied using the SMOGN technique was obtained and applied to the model as a solution. As a result, the DNN model applying the SMOGN technique could compensate for the limitations of the previously developed prediction model by predicting the clearance time with the highest accuracy among the models developed in the research process with MAE = 18.3 minutes.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.231-237
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• 2017

Threat intelligences collected from cyber incident sharing system and security events collected from Security Information & Event Management system are analyzed and coped with expanding malicious code rapidly with the advent of big data. Analytical classification of the threat intelligence in cyber incidents requires various features of cyber observable. Therefore it is necessary to improve classification accuracy of the similarity by using multi-profile which is classified as the same features of cyber observables. We propose a multi-profile ensemble model performed similarity analysis on cyber incident of threat intelligence based on both attack types and cyber observables that can enhance the accuracy of the classification. We see a potential improvement of the cyber incident analysis system, which enhance the accuracy of the classification. Implementation of our suggested technique in a computer network offers the ability to classify and detect similar cyber incident of those not detected by other mechanisms.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.60-68
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• 2006

High-frequency bistatic scattering measurements from a corrugated surface were made in an acoustic water tank. First the azimuthal scattering pattern was measured from an artificially corrugated surface which has varying impedance. The corrugated surface was installed both transverse to the direction of incident wave and longitudinal to the direction of incident wave. The angle between the corrugated surface and the direction of the incident wave was about $45^{\circ}$. Second, the scattering strengths were measured from the flat sediment and the corrugated sediment. A critical angle of about $37^{\circ}$ was calculated in the acoustic water tank. The measurements were made at three fixed grazing angles: $33^{\circ}$ (lower than critical angle), $37^{\circ}$ (critical angle), and $41^{\circ}$ (higher than critical angle). The scattering angle and the grazing angle are equal in each measurement. Frequencies were from 50 kHz to 100 kHz with an increment of 1 kHz. The corrugated sediment was made transverse to the direction of the incident wave. The first measurement indicates that the scattering patterns depend on the relations between the corrugated surface and the direction of the incident wave. In the second measurement, the data measured from the flat sediment were compared to the APL-UW model and to the NRL model. The NRL model's output shows more favorable comparisons than the APL-UW model. In case of the corrugated sediment, the model and the measured data are different because the models used an isotropic wave spectrum of sediment roughness in the scattering calculations. The isotropic wave spectrum consists of $w_2$ and ${\gamma}_2$. These constants derived from sediment names or bulk size. The model which used the constants didn't consider the effect of a corrugated surface. In order to consider a corrugated surface, the constants were varied in the APL-UW model.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.1
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• pp.1-11
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• 2013

The aim of this study was estimated the characteristics of the wave propagation by the water level conditions using a numerical modeling method at the Wando sea area. For three cases numerical simulation on the condition of incident and incoming of the deepwater design wave and the season normal wave, the spatial distribution of the incident wave at study area were investigated. And the calculated numerical modeling results were compared with measured field wave data. According to on-site wave data measured for 18 days, the range of the significant wave height and period were 0.10~1.14 m, 4.35~8.74 sec, respectively, and the maximum wave height were 0.15~1.66 m. From the results of numerical model for offshore design wave incident, the wave height attacked from Southern-East direction at this study area were over maximum 10.5 m because of rapidly change of water depth. Numerical modeling by three water level conditions of Approxmate Lowest Low Water Level(Approx. L.L.W), Mean Sea Level(M.S.L) and Approximate Highest High Water Level(Approx. H.H.W) were practiced. From the results for the case of Approx. H.W.L, variations of wave height at the back area of islands were about 1.6 m at maximum value for the case of deepwater design wave incoming. The significant wave heights of winter season were bigger than summer under normal wave condition, the incident wave height over 5.5 m decreased by shielding effect of islands. The change of maximum wave height at summer season were distinct than winter and was about 1.2 m and 0.8 m, respectively.