• Proceedings of the Korean Nuclear Society Conference
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• 1996.05d
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• pp.17-22
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• 1996

Individual internal monitoring program by in-vivo measurement technique at the Korea Atomic Energy Research Institute includes the capability for the assessment of uranium and americium lung burdens. This capability is an important part of the health and safety program. This article addresses the lung burden assessment portion of our in vivo measurement capabilities.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.399-418
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• 2015

Automated damage detection through Structural Health Monitoring (SHM) techniques has become an active area of research in the bridge engineering community but widespread implementation on in-service infrastructure still presents some challenges. In the meantime, visual inspection remains as the most common method for condition assessment even though collected information is highly subjective and certain types of damage can be overlooked by the inspector. In this article, a Frequency Response Functions-based model updating algorithm is evaluated using experimentally collected data from the University of Central Florida (UCF)-Benchmark Structure. A protocol for measurement selection and a regularization technique are presented in this work in order to provide the most well-conditioned model updating scenario for the target structure. The proposed technique is composed of two main stages. First, the initial finite element model (FEM) is calibrated through model updating so that it captures the dynamic signature of the UCF Benchmark Structure in its healthy condition. Second, based upon collected data from the damaged condition, the updating process is repeated on the baseline (healthy) FEM. The difference between the updated parameters from subsequent stages revealed both location and extent of damage in a "blind" scenario, without any previous information about type and location of damage.

• Structural Engineering and Mechanics
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• v.71 no.2
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• pp.175-183
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• 2019

This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.3
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• pp.10-21
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• 2015

Structural health monitoring (SHM) is a technique to diagnose an accurate and reliable condition of civil infrastructure by collecting and analyzing responses from distributed sensors. In recent years, aging civil structures have been increasing and they require further developed SHM technology for development of sustainable society. Wireless smart sensor and network technology, which is one of the recently emerging SHM techniques, enables more effective and economic SHM system in comparison to the existing wired systems. Researchers continue on development of the capability and extension of wireless smart sensors, and implement performance validation in various in-laboratory and outdoor full-scale experiments. This paper presents a summary of recent (mostly after 2010) researches on smart sensors, focused on the newly developed hardware, software, and validation examples of the developed smart sensors.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Korean Journal of Ecology and Environment
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• v.49 no.2
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• pp.147-152
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• 2016

This study was conducted to develop a technique of ecosystem health assessment on Korea National Parks. The purpose of natural resource monitoring in national parks is to develop scientific information on the current status and long term trends in the composition, structure, and function of park ecosystems, and to determine how well current management practices are sustaining those ecosystems. The evaluation results will reflect in the park conservation and polices and promote the effect and functions of assessment program to the people. Health assessment steps were performed in order the establishing monitoring goals and objectives, development of the conceptual model, frame establishment, determination of indicators, standard and classification and health assessment. Health Indicators were selected the 13 with common, choice and climate indicators. We developed a pictogram and was separated into five colors to health condition, it was divided into three shape for comparison with the past state. Seoraksan, Odaesan National Park has been rated highly but Bukhansan, Kyeryongsan National Park has been underestimated.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.1-6
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• 2008

The ubiquitous sensor network technique is widely applied to variety of information fields such as home automations, logistics, traffic controls, public administrations, health and environment monitoring and etc. It is particularly useful in the areas where energy consumption is minimal and where continuous monitoring of the surrounding environments, which generates streams of data, are required. In this study, we have designed and implemented a living environment automatic control system which collects the streams of temperature, humidity, light and noise data of a simulated house setting in real-time fashion, then controls the home environment based on the collected data according to the users favorites. In order to differentiate the proposed system from the currently existing similar system, we have demonstrated not only the feasibility of collecting data using sensor network in the controlled environment but also the ability to control the various household equipments through wireless communications.

• Journal of the Korean Geophysical Society
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• v.8 no.2
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• pp.75-80
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• 2005

Optical fibre sensors have shown a potential to serve real time health monitoring of Slope and structure. They can be easily embedded or attached to the structures and are not affected by the electro-magnetic field. Furthermore, they have the flexibility of the sensor size and very highly sensitive. In this study, we conducted several laboratory on slope and field tests using a novel optical sensor based on Brillouin scattering and PVC pipe. One of the advantages of this technique is that the bare fibre itself acts as sensing element without any special fibre processing or preparation. Test results have shown that BOTDR can be a great solution for sensor systems of Slope.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.475-482
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• 2002

Optical fibre sensors have shown a potential to serve real time health monitoring of Slope and structure. They can be easily embedded or attached to the structures and are not affected by the electro-magnetic field. Furthermore, they have the flexibility of the sensor size and very highly sensitive. In this study, we conducted several laboratory on slope and field tests using a novel optical sensor based on Brillouin scattering and PVC pipe. One of the advantages of this technique is that the bare fibre itself acts as sensing element without any special fibre processing or preparation. Test results have shown that BOTDR can be a great solution for sensor systems of Slope.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.