• Journal of Digital Contents Society
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• v.14 no.4
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• pp.589-596
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• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.1139-1146
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• 2009

Collapse of cut slope includes many uncertainties in view of the reason and time. So, in the past, risky cut slopes have been dealt after they've been collapsed through post-management measures. But recently, advanced disaster prevention system is required, and as a part of that RTMS(Real-Time Monitoring System) was developed. In this study, stability of risky cut slope was evaluated by site investigation. To grasp deformation behavior characteristics of slope, numerical analysis based on FEM was performed and using results of that, specific standards for installation of Real-Time Monitoring System were suggested.

• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.21-27
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• 2018

This paper proposed the real-time monitoring methodology of a traffic signal controller. The proposed methodology is based on the simulation technology, and it is necessary to construct a simulation model imitating the behavior of a traffic signal controller. By executing the simulation model, we can obtain the 'nominal system trajectory' of the traffic signal controller. On the other hand, an IoT(Internet of Things)-based monitoring device is implemented in a traffic signal controller. Through the monitoring device, it is possible to obtain the 'actual system trajectory'. By comparing the nominal system trajectory and the actual system trajectory, we can estimate the degree of deterioration of a traffic signal controller.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.568-572
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• 2010

Self-healing is one of the techniques that assure dependability of mission-critical system. Self-healing consists of fault detection and fault recovery and fault detection is important first step that enables fault recovery but it causes overhead. We can detect fault based on model, the detection tasks that notify system's behavior and compare normal behavior model and system's behavior are heavy jobs. In this paper, we propose architecture-based multi-level self-adaptive monitoring method that complements model-based fault detection. The priority of fault detection per component is different in the software architecture. Because the seriousness and the frequency of fault per component are different. If the monitor is adapted to intensive to the component that has high priority of monitoring and loose to the component that has low priority of monitoring, the overhead can be decreased and the efficiency can be maintained. Because the environmental changes of software and the architectural changes bring the changes at the priority of fault detection, the monitor learns the changes of fault frequency and that is adapted to intensive to the component that has high priority of fault detection.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.761-765
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• 2008

This study presents an effective methodology on integrated monitoring system for a maglev guideway using WDM-based FBG sensors. The measuring quantities include both local and global quantities of the guideway response, such as stains, curvatures, and vertical deflections. The strains are directly measured from multiplexed FBG sensors at various locations of the test bridge followed by curvature calculations based on the plane section assumption. Vertical deflections are then estimated using the Bernoulli beam theory and regression analysis. Frequency contents obtained from the proposed method are compared with those from a conventional accelerometer. Verification tests were conducted on the newly-developed Korean Maglev test track. It has been shown that good agreement between the measured deflection and the estimated deflection is achieved. The difference between the two peak displacements was only 3.5% in maximum and the correlations between data from two sensing systems are overall very good. This confirms that the proposed technique is capable of tracing the dynamic behavior of the maglev guideway with an acceptable accuracy. Furthermore, it is expected that the proposed scheme provides an effective tool for monitoring the behavior of the maglev guideway structures without electro magnetic interference.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.38-44
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the real-world driving study, 52 drivers drove approximately 11.0 km of rural road (about 20 min), 7.9 km of urban road (about 25 min), and 20.8 km of highway (about 20 min). The results suggested that the appropriate number of blinks during the last 60 seconds was 4 times, and the head movement interval was 35 seconds. The results from drowsy driving data will be presented in another paper - part 2.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.45-50
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the drowsy driving study, 10 drivers drove approximately 37 km of a monotonous highway (about 22 min) twice. The results suggested that the appropriate duration of eyes continuously closed was 4 seconds. The results from real-world driving data were presented in the other paper - part 1.

• International Journal of High-Rise Buildings
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• v.1 no.1
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• pp.37-51
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• 2012

New generation of tall and complex buildings systems are now introduced that are reflective of the latest development in materials, design, sustainability, construction, and IT technologies. While the complexity in design is being overcome by the availability and advances in structural analysis tools and readily advanced software, the design of these buildings are still reliant on minimum code requirements that yet to be validated in full scale. The involvement of the author in the design and construction planning of Burj Khalifa since its inception until its completion prompted the author to conceptually develop an extensive survey and real-time structural health monitoring program to validate all the fundamental assumptions mad for the design and construction planning of the tower. The Burj Khalifa Project is the tallest structure ever built by man; the tower is 828 meters tall and comprises of 162 floors above grade and 3 basement levels. Early integration of aerodynamic shaping and wind engineering played a major role in the architectural massing and design of this multi-use tower, where mitigating and taming the dynamic wind effects was one of the most important design criteria established at the onset of the project design. Understanding the structural and foundation system behaviors of the tower are the key fundamental drivers for the development and execution of a state-of-the-art survey and structural health monitoring (SHM) programs. Therefore, the focus of this paper is to discuss the execution of the survey and real-time structural health monitoring programs to confirm the structural behavioral response of the tower during construction stage and during its service life; the monitoring programs included 1) monitoring the tower's foundation system, 2) monitoring the foundation settlement, 3) measuring the strains of the tower vertical elements, 4) measuring the wall and column vertical shortening due to elastic, shrinkage and creep effects, 5) measuring the lateral displacement of the tower under its own gravity loads (including asymmetrical effects) resulting from immediate elastic and long term creep effects, 6) measuring the building lateral movements and dynamic characteristic in real time during construction, 7) measuring the building displacements, accelerations, dynamic characteristics, and structural behavior in real time under building permanent conditions, 8) and monitoring the Pinnacle dynamic behavior and fatigue characteristics. This extensive SHM program has resulted in extensive insight into the structural response of the tower, allowed control the construction process, allowed for the evaluation of the structural response in effective and immediate manner and it allowed for immediate correlation between the measured and the predicted behavior. The survey and SHM programs developed for Burj Khalifa will with no doubt pioneer the use of new survey techniques and the execution of new SHM program concepts as part of the fundamental design of building structures. Moreover, this survey and SHM programs will be benchmarked as a model for the development of future generation of SHM programs for all critical and essential facilities, however, but with much improved devices and technologies, which are now being considered by the author for another tall and complex building development, that is presently under construction.

• Journal of the Korean Geotechnical Society
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• v.15 no.4
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• pp.153-166
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• 1999

Since the reliability of results by the existing analyzing method is low, in the case of for excavation performed in urban area whose stability is of great importance, construction control based on field monitoring is always necessary. But the field monitoring reflects only the behavior of construction process that has already been carried out, and it has limitations in predicting the behavior of the expected construction process, which is practically more important for construction control. In this study, construction control system for excavation which can predict the behavior of the expected processes during construction with high degree of accuracy, is developed by adopting inverse analysis. The inverse analied applied field monitoring results to excavation analysis can improve the reliability of predicted results. The developed system uses an elasto-plastic soil spring model for the excavation analysis and the minimization of least squared errors between measured displacements and calculated displacements for the inverse analysis. All the required processes for construction control can be performed as an integrated work within the system reflecting real time application and user's convenience. Their applicabilitis are confirmed by two case studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.22-27
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• 2020

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.