• Journal of the Optical Society of Korea
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• v.14 no.4
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• pp.451-457
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• 2010

Increasing demands on the safety of public train services have led to the development of various types of security monitoring systems. Most of the surveillance systems are focused on the estimation of crowd level in the platform, thereby yielding too many false alarms. In this paper, we present a novel security monitoring system to detect critically dangerous situations such as when a passenger falls from the station platform, or when a passenger walks on the rail tracks. The method is composed of two stages of detecting dangerous situations. Objects falling over to the dangerous zone are detected by motion tracking. 3D depth information retrieved by the stereo vision is used to confirm fallen events. Experimental results show that virtually no error of either false positive or false negative is found while providing highly reliable detection performance. Since stereo matching is performed on a local image only when potentially dangerous situations are found; real-time operation is feasible without using dedicated hardware.

• Atmosphere
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• v.22 no.2
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• pp.245-257
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• 2012

The Asian dust (Hwangsa) forecasting model, Asian Dust Aerosol Model (ADAM) has been modified by using satelliate monitoring of surface vegetation, which enables to simulate dusts occuring not only in springtime but also for all-year-round period. Coupled with the Unified Model (UM), the operational weather forecasting model at KMA, UM-ADAM2 was implemented for operational dust forecasting since 2010, with an aid of development of Meteorology-Chemistry Interface Processor (MCIP) for usage UM. The performance analysis of the ADAM2 forecast was conducted with $PM_{10}$ concentrations observed at monitoring sites in the source regions in China and the downstream regions of Korea from March to December in 2010. It was found that the UM-ADAM2 model was able to simulate quite well Hwangsa events observed in spring and wintertime over Korea. In the downstream region of Korea, the starting and ending times of dust events were well-simulated, although the surface $PM_{10}$ concentration was slightly underestimated for some dust events. The general negative bias less than $35{\mu}g\;m^{3}$ in $PM_{10}$ is found and it is likely to be due to other fine aerosol species which is not considered in ADAM2. It is found that the correlation between observed and forecasted $PM_{10}$ concentration increases as forecasting time approaches, showing stably high correlation about 0.7 within 36 hr in forecasting time. This suggests the possibility that there is potential for the UM-ADAM2 model to be used as an operational Asian dust forecast model.

• Smart Structures and Systems
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• v.5 no.4
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• pp.495-505
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• 2009

Bridges form crucial links in the transportation network especially in high seismic risk regions. This research aims to provide a quantitative methodology for post-earthquake performance evaluation of the bridges. The experimental portion of the research involved shake table tests of a 4-span bridge which was subjected to progressively increasing amplitudes of seismic motions recorded from the Northridge earthquake. As part of this project, a high resolution long gauge fiber optic displacement sensor was developed for post-seismic evaluation of damage in the columns of the bridge. The nonlinear finite element model was developed using Opensees program to simulate the response of the bridge and the abutments to the seismic loads. The model was modified to predict the bent displacements of the bridge commensurate with the measured bent displacements obtained from experimental analysis results. Following seismic events, the tangential stiffness matrix of the whole structure is reduced due to reduction in structural strength. The nonlinear static push over analysis using current damaged stiffness matrix provides the longitudinal and transverse ultimate capacities of the bridge. Capacity loss in the transverse and longitudinal directions following the seismic events was correlated to the maximum displacements of the deck recorded during the events.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.32-39
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• 2016

With the help of a small wearable device, patients reside in an isolated village need constant monitoring which may increase access to care and decrease healthcare delivery cost. As the number of patients' requests increases in simultaneously manner, the web service gateway located in the village hall encounters limitations for performing them successfully and concurrently. The gateway based RESTful technology responsible for handling patients' requests attests an internet latency in case a large number of them submit toward the gateway increases. In this paper, we propose the design tasks of the web service gateway for handling concurrency events. In the procedure of designing tasks, concurrency is best understood by employing multiple levels of abstraction. The way that is eminently to accomplish concurrency is to build an object-oriented environment with support for messages passing between concurrent objects. We also investigate the performance of event-driven architecture for building web service gateway using node.js. The experiments results show that server-side JavaScript with Node.js and MongoDB as database is 40% faster than Apache Sling. With Node.js developers can build a high-performance, asynchronous, event-driven healthcare hub server to handle an increasing number of concurrent connections for Remote Healthcare Monitoring System in an isolated village with no access to local medical care.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1184-1188
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• 2005

The impacts of turbidity flow induced by summer rainfall events on water supply, aquatic ecosystems, and socioeconomics are significant and major concerns in most of reservoirs operations. As a decision support tool, the real-time turbidity flow monitoring and modeling system RTMMS is under development using a laterally integrated two-dimensional (2D) hydrodynamic and water quality model. The objectives of this paper is to present the preliminary field observation results on the characteristics of rainfall-induced turbidity flows and their density flow regimes, and the model performance in replicating the fate and transport of turbidity plume in a reservoir. The rainfall-induced turbidity flows caused significant drop of river water temperature by 5 to $10^{\circ}C$ and resulted in density differences of 1.2 to $2.6kg/m^3$ between inflow water and ambient reservoir water, which consequently led development of density flows such as plunge flow and interflow in the reservoir. The 2D model was set up for the reservoir. and applied to simulate the temperature stratification, density flow regimes, and temporal and spatial turbidity distributions during flood season of 2004 After intensive refinements on grid resolutions , the model showed efficient and satisfactory performance in simulating the observed reservoir thermal stratification and turbidity profiles that all are essentially required to enhance the performance of RTMMS.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.4
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• pp.16-22
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• 2013

This paper presents a method to analyze the voltage sag data obtained from monitoring systems. In order to establish effective countermeasures against voltage sag problems, an assessment of the system performance with respect to voltage sags is needed. Generally, the average annual sag frequency can be estimated by using the recorded voltage sag events for several years. However, the simple average value can not give the information about the errors of estimation. Such an average estimation is not useful for establishing effective solutions for voltage sag problems. Therefore, this paper proposes an effective method based on the Interval Estimation method. The estimation of voltage sag frequency is performed by using the average frequency and Poisson probability model. The proposed method can give the expected annual sag frequency and upper one-sided bound frequency.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.199-206
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• 2023

This study presents a novel monitoring technique for underwater high-voltage direct current (HVDC) cables based on the Distributed Acoustic Sensor (DAS). The proposed technique utilizes vibration and acoustic signals generated on HVDC cables to monitor their condition and detect events such as earthquakes, shipments, tidal currents, and construction activities. To implement the monitoring system, a DAS based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) system was designed, fabricated, and validated for performance. For the HVDC cable monitoring experiments, a testbed was constructed on land, mimicking the cable burial method and protective equipment used underwater. Defined various scenarios that could cause cable damage and conducted experiments accordingly. The developed DAS system achieved a maximum measurement distance of 50 km, a distance measurement interval of 2 m, and a measurement repetition rate of 1 kHz. Extensive experiments conducted on HVDC cables and protective facilities demonstrated the practical potential of the DAS system for monitoring underwater and underground areas.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1263-1271
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• 1999

This paper designs the performance analysis layer for the various performance analysis of parallel programs using event expressions are similar to the normal program language to analyze the events which display a dynamic state exchange of a program. The event expressions suggest operations for overloading and functions which are needed in performance analysis, such as a filtering operation, data format translation functions, performance analysis, static functions, and etc. By using the event expressions, the programmer can modify the event trace data to analyze the performance and analyze more easily and variously than the pre-developed tools.

• Structural Engineering and Mechanics
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• v.82 no.2
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• pp.245-258
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• 2022

Many Structural Health Monitoring (SHM) methods have been proposed for structural damage diagnosis and prognosis. However, SHM for pinched hysteretic structures can be problematic due to the high level of nonlinearity. The model-free hysteresis loop analysis (HLA) has displayed notable robustness and accuracy in identifying damage for full-scaled and scaled test buildings. In this paper, the performance of HLA is compared with seven other SHM methods in identifying lateral elastic stiffness for a six-story numerical building with highly nonlinear pinching behavior. Two successive earthquakes are employed to compare the accuracy and consistency of methods within and between events. Robustness is assessed across sampling rates 50-1000 Hz in noise-free condition and then assessed with 10% root mean square (RMS) noise added to responses at 250 Hz sampling rate. Results confirm HLA is the most robust method to sampling rate and noise. HLA preserves high accuracy even when the sampling rate drops to 50 Hz, where the performance of other methods deteriorates considerably. In noisy conditions, the maximum absolute estimation error is less than 4% for HLA. The overall results show HLA has high robustness and accuracy for an extremely nonlinear, but realistic case compared to a range of leading and recent model-based and model-free methods.