• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.284-286
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• 2012

There has been increasing attention about the prevention and counter-measure of disasters. Particularly, for the case of fire disaster, early detection reduces the damage caused by fire significantly and effective detection method is important. Since most existing detectors need to be located at a close distance to fire, analyzing camera images to find fire becomes active research topic. In this paper, we analyze the color characteristics of fire images in various color spaces and report the experimental detection results. The best result is 77.8% success rate in YIQ space.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Childhood Kidney Diseases
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• v.8 no.2
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• pp.256-261
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• 2004

We report a case of severe(gradeV) unilateral vesicoureteral reflux(VUR) without any renal damage in a 6-month-old boy through the early sil)ling screening test for VUR, whose old brother had reflux nephropathy. The early detection of VUR aroused us to take special precautions to prevent urinary tract infection in this patient. We believe that this approach was helpful to reduce the risk of renal damage in this patient and are reporting the case with a brief review of related literatures. It is our firm suggestion that screening for vesicoureteral reflux should be carried out in every child who has siblings with refluf nephrouathy.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.607-614
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• 2019

It has been an issue among researchers that the tsunamis that occurred on the west coast of Japan in 1983 and 1993 damaged the coastal cities on the east coast of Korea. In order to predict and reduce the damage to the Korean Peninsula effectively, it is necessary to install offshore tsunami observation instruments as part of the system for the early detection of tsunamis. The purpose of this study is to recommend the optimal deployment of tsunami observation instruments in terms of the higher probability of tsunami detection with the minimum equipment and the maximum evacuation and warning time according to the current situation in Korea. In order to propose the optimal location of the tsunami observation equipment, this study will analyze the tsunami propagation phenomena on the east sea by considering the potential tsunami scenario on the west coast of Japan through numerical modeling using the COrnell Multi-grid COupled Tsunami (COMCOT) model. Based on the results of the numerical model, this study suggested the optimal deployment of Korea's offshore tsunami observation instruments on the northeast side of Ulleung Island.

• Smart Structures and Systems
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• v.22 no.1
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• pp.1-11
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• 2018

This study presents early crack detection of steel fiber-reinforced concrete (SFRC) under varying temperature and humidity conditions using an instantaneous electrical impedance acquisition system. SFRC has the self-sensing capability of electrical impedance without sensor installation thanks to the conductivity of embedded steel fibers, making it possible to effectively monitor cracks initiated in SFRC. However, the electrical impedance is often sensitively changed by environmental effects such as temperature and humidity variations. Thus, the extraction of only crack-induced feature from the measured impedance responses is a crucial issue for the purpose of structural health monitoring. In this study, the instantaneous electrical impedance acquisition system incorporated with SFRC is developed. Then, temperature, humidity and crack initiation effects on the impedance responses are experimentally investigated. Based on the impedance signal pattern observation, it is turned out that the temperature effect is more predominant than the crack initiation and humidity effects. Various crack steps are generated through bending tests, and the corresponding impedance damage indices are extracted by compensating the dominant temperature effect. The test results reveal that propagated cracks as well as early cracks are successfully detected under temperature and humidity variations.

• Smart Structures and Systems
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• v.33 no.3
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• pp.227-241
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• 2024

Safety and structural integrity of civil structures, like bridges and buildings, can be substantially enhanced by employing appropriate structural health monitoring (SHM) techniques for timely diagnosis of incipient damages. The information gathered from health monitoring of important infrastructure helps in making informed decisions on their maintenance. This ensures smooth, uninterrupted operation of the civil infrastructure and also cuts down the overall maintenance cost. With an early warning system, SHM can protect human life during major structural failures. A real-time online damage localization technique is proposed using only the vibration measurements in this paper. The concept of the 'Degree of Scatter' (DoS) of the vibration measurements is used to generate a spatial profile, and fractal dimension theory is used for damage detection and localization in the proposed two-phase algorithm. Further, it ensures robustness against environmental and operational variability (EoV). The proposed method works only with output-only responses and does not require correlated finite element models. Investigations are carried out to test the presented algorithm, using the synthetic data generated from a simply supported beam, a 25-storey shear building model, and also experimental data obtained from the lab-level experiments on a steel I-beam and a ten-storey framed structure. The investigations suggest that the proposed damage localization algorithm is capable of isolating the influence of the confounding factors associated with EoV while detecting and localizing damage even with noisy measurements.

• Smart Structures and Systems
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• v.10 no.1
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• pp.47-65
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• 2012

The presence of crack-like defects in mechanical and structural elements produces failures during their service life that in some cases can be catastrophic. So, the early detection of the fatigue cracks is particularly important because they grow rapidly, with a propagation velocity that increases exponentially, and may lead to long out-of-service periods, heavy damages of machines and severe economic consequences. In this work, a non-destructive method for the detection and identification of elliptical cracks in shafts based on stress wave propagation is proposed. The propagation of a stress wave in a cracked shaft has been numerically analyzed and numerical results have been used to detect and identify the crack through the genetic algorithm optimization method. The results obtained in this work allow the development of an on-line method for damage detection and identification for cracked shaft-like components using an easy and portable dynamic testing device.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.59-70
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• 2008

In this paper, we proposed image processing technique for automatic real-time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in variety environments, it is purposeful to propose many studies to minimize and to discover the incident as fast as possible. But we need new specific algorithm because tunnel environment is quite different and it is difficult to apply previous fire detection algorithm to tunnel environment. Therefore, in this paper, we proposed specific algorithm which can be applied in tunnel environment. To minimize false detection in tunnel we used color and motion information. And it is possible to detect exact position in early stage with detection, test, verification procedures. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.