2篇
您的检索式：作者名="Junwon Seo"
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| 1 | Statistical determination of significant curved I-girder bridge seismic response parameters显示文摘Curved steel bridges are commonly used at interchanges in transportation networks and more of these structures continue to be designed and built in the United States. Though the use of these bridges continues to increase in locations that experience high seismicity, the effects of curvature and other parameters on their seismic behaviors have been neglected in current risk assessment tools. These tools can evaluate the seismic vulnerability of a transportation network using fragility curves. One critical component of fragility curve development for curved steel bridges is the completion of sensitivity analyses that help identify influential parameters related to their seismic response. In this study, an accessible inventory of existing curved steel girder bridges located primarily in the Mid-Atlantic United States (MAUS) was used to establish statistical characteristics used as inputs for a seismic sensitivity study. Critical seismic response quantities were captured using 3D nonlinear finite element models. Influential parameters from these quantities were identified using statistical tools that incorporate experimental Plackett-Burman Design (PBD), which included Pareto optimal plots and prediction profiler techniques. The findings revealed that the potential variation in the influential parameters included number of spans, radius of curvature, maximum span length, girder spacing, and cross-frame spacing. These parameters showed varying levels of influence on the critical bridge response. | Junwon Seo | 2013 | Earthquake Engineering and Engineering Vibration2013，12，2： | 1 |
| 2 | Improving Method of Anomaly Detection Performance for Industrial IoT Environment显示文摘Industrial Control System(ICS),which is based on Industrial IoT(IIoT),has an intelligent mobile environment that supports various mobility,but there is a limit to relying only on the physical security of the ICS environment.Due to various threat factors that can disrupt the workflow of the IIoT,machine learning-based anomaly detection technologies are being presented;it is also essential to study for increasing detection performance to minimize model errors for promoting stable ICS operation.In this paper,we established the requirements for improving the anomaly detection performance in the IIoT-based ICS environment by analyzing the related cases.After that,we presented an improving method of the performance of a machine learning model specialized for IIoT-based ICS,which increases the detection rate by applying correlation coefficients and clustering;it provides a mechanism to predict thresholds on a per-sequence.Likewise,we adopted the HAI dataset environment that actively reflected the characteristics of IIoT-based ICS and demonstrated that performance could be improved through comparative experiments with the traditional method and our proposed method.The presented method can further improve the performance of commonly applied error-based detection techniques and includes a primary method that can be enhanced over existing detection techniques by analyzing correlation coefficients between features to consider feedback between ICS components.Those can contribute to improving the performance of several detection models applied in ICS and other areas. | Junwon Kim Jiho Shin Ki-Woong Park Jung Taek Seo | 2022 | Computers, Materials & Continua2022，，9： | 0 |
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