• Proceedings of the Korea Air Pollution Research Association Conference
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• 2009.10a
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• pp.475-476
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• 2009

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.6
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• pp.395-399
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• 2000

Five different freeze-dried recombinant bioluminescent bacteria were used for the detection of cellular stresses caused by endocrine disrupting chemicals. These strains were DPD2794 (recA::luxCDABE), which is sensitive to DNA damage, DPD2540 (fabA::luxCDABE), sensitive to cellular membrane damage, DPD2511 (katG::luxCDABE), sensitive to oxidative damage, and TV1061 (grpE::luxCDABE), sensitive to protein damage. GC2, which emits bioluminescence constitutively, was also used in this study. The toxicity of several chemicals was measured using GC2. Damage caused by known endocrine disrupting chemicals, such as nonyl phenol, bisphenol A, and styrene, was detected and classified according to toxicity mode, while others, such as phathalate and DDT, were not detected with the bacteria. These results suggest that endocrine disrupting chemicals are toxic in bacteria, and do not act via an estrogenic effect, and that toxicity monitoring and classification of some endocrine disrupting chemicals may be possible in the field using these freeze-dried recombinant bioluminescent bacteria.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.233-257
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• 2016

This paper provides an overview on development of long-span bridges monitoring in Japan, with emphasis on monitoring strategies, types of monitoring system, and effective utilization of monitoring data. Because of severe environment condition such as high seismic activity and strong wind, bridge monitoring systems in Japan historically put more emphasis on structural evaluation against extreme events. Monitoring data were used to verify design assumptions, update specifications, and facilitate the efficacy of vibration control system. These were among the first objectives of instrumentation of long-span bridges in a framework of monitoring system in Japan. Later, monitoring systems were also utilized to evaluate structural performance under various environment and loading conditions, and to detect the possible structural deterioration over the age of structures. Monitoring systems are also employed as the basis of investigation and decision making for structural repair and/or retrofit when required. More recent interest has been to further extend application of monitoring to facilitate operation and maintenance, through rationalization of risk and asset management by utilizing monitoring data. The paper describes strategies and several examples of monitoring system and lessons learned from structural monitoring of long-span bridges in Japan.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.717-723
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• 2002

Recently, the interest in safety assessment of civil infrastructures is increasing in Korea. Especially, as bridge structures become large-scale, it is necessary to monitor and maintain the safety state of bridges, which requires the monitoring system that can make a long-term measurement during the service time of bridge. In this paper, advanced fiber optic sensors for long-term measurement, setup techniques of bridge monitoring system and the assessment of measured data are introduced. Attached or embedded optical fiber sensors to structural members of small and big structures including Sung San Bridge are surveyed. For the Sung San Bridge, the responses of the fiber optic sensors by 30 ton weigh truck loads with various speeds ate measured. Monitoring system is also applied to the mock-up of bridges. The monitoring capability of the advanced fiber optic sensor system was confirmed.

• Journal of Sensor Science and Technology
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• v.5 no.5
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• pp.55-62
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• 1996

This paper describes development of Polarized Stereo Monitoring System (KAERI-PSM) and compares the remote handling performance of KAERI-PSM with that of other monitoring systems, electric shutter type stereo monitoring system and general TV monitoring system. Remote handling performance is evaluated by total time and error number on remote operating experiments. Four kinds of remote handling experiments are carried out through 1) directly 2) general TV 3) electric shutter type monitor, and 4) KAERI-PSM. In these experiments six employees are participated and PUMA robot with force-torque reflectional joystic is used. The result of experiments show that camera angle against object is significant factor in monitoring and stereo monitoring system give more performance benifits in terms of accuracy and speed of remote handling operation than general TV monitoring system. In comparision of the polarized and electric shutter type stereo monitoring system, both have similar accuracy and speed for remote handling operation, but the former is superior in image quality and stability of the performance.

• Smart Structures and Systems
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• v.5 no.4
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• pp.469-482
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• 2009

The emerging sensor-based structural health monitoring (SHM) technology has a potential for cost-effective maintenance of aging civil infrastructure systems. The author proposes to integrate continuous and global monitoring using on-structure sensors with targeted local non-destructive evaluation (NDE). Significant technical challenges arise, however, from the lack of cost-effective sensors for monitoring spatially large structures, as well as reliable methods for interpreting sensor data into structural health conditions. This paper reviews recent efforts and advances made in addressing these challenges, with example sensor hardware and health monitoring software developed in the author's research center. The hardware includes a novel fiber optic accelerometer, a vision-based displacement sensor, a distributed strain sensor, and a microwave imaging NDE device. The health monitoring software includes a number of system identification methods such as the neural networks, extended Kalman filter, and nonlinear damping identificaiton based on structural dynamic response measurement. These methods have been experimentally validated through seismic shaking table tests of a realistic bridge model and tested in a number of instrumented bridges and buildings.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

• Nuclear Engineering and Technology
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• v.43 no.4
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• pp.343-354
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• 2011

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.13-25
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• 2020

One of the most important aspects in structural health monitoring is the detection of fatigue damage. Structural components such as heavy-duty bolts work under high dynamic loads, and thus are prone to accumulate fatigue damage and cracks may originate. Those heavy-duty bolts are used, for example, in wind power generation and mining equipment. Therefore, the investigation of new and more effective monitoring technologies attracts a great interest. In this study the acoustic emission (AE) technology was employed to detect incipient damage during fatigue testing of a M36 bolt. Initial results showed that the AE signals have a high level of background noise due to how the load is applied by the fatigue testing machine. Thus, an advanced signal processing method in the time-frequency domain, the Hilbert-Huang Spectrum (HHS), was applied to reveal AE components buried in background noise in form of high-frequency peaks that can be associated with damage progression. Accordingly, the main contribution of the present study is providing insights regarding the detection of incipient damage during fatigue testing using AE signals and providing recommendations for further research.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1555-1562
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• 2017

A loose part monitoring system is used to identify unexpected loose parts in a nuclear reactor vessel or steam generator. It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method. The purpose of this study is to propose a mass estimation method using a Markov decision process and compare its performance with a method using an artificial neural network model proposed in a previous study. First, how to extract feature vectors using discrete cosine transform was explained. Second, Markov chains were designed with codebooks obtained from the feature vector. A 1/8-scaled mockup of the reactor vessel for OPR1000 was employed, and all used signals were obtained by impacting its surface with several solid spherical masses. Next, the performance of mass estimation by the proposed Markov model was compared with that of the artificial neural network model. Finally, it was investigated that the proposed Markov model had matching error below 20% in mass estimation. That was a similar performance to the method using an artificial neural network model and considerably improved in comparison with the conventional methods.