• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.291-302
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• 2004

In this paper, load monitoring technologies using U-shape Magnetoelastic (EM or ME) sensors have been exploited systemically for the first time. The steel rod to be tested is the Japan 7 mm piano steel rod. The load dependence of the magnetic properties of the piano steel rod was manifested. Two experimental designs of U-shape magnetoelastic sensors were introduced, one with double pick-up concentric coils wound on the rod to be tested, the other with pick-up coil on one yoke foot. The former design is used to derive the correlation of the relative permeability with elastic tension, while the latter is aimed to reflect the stress induced magnetic flux variation along the magnetic circuit. Magnetostatic simulations provide interpretations for the yoke foot sensing technology. Tests with double pick-up coils indicate that under proper working points (primary voltages), the relative permeability varies linearly with the axial load for the Japan 7 mm piano steel rod. Tests with pick-up coil on the yoke foot show that the integrated sensing voltage changes quadratically with the load, and error is more acceptable when the working point is high enough.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.68.2-68.2
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• 2014

High Frequency Peakers (HFPs) are radio-loud Active Galactic Nuclei (AGN), which are regarded as being in the earliest evolutionary phase (102-103 years) of radio galaxies. They are expected to be small in size (< ~1 kpc) compared to their host galaxies (~a few 10s kpc), and have convex spectra, which are peaking at high radio frequency (> 5 GHz). Their size and spectral shape are the most obvious supporting evidence of extremely young ages. HFPs are therefore ideal targets to probe the earliest stage of radio sources. To date however, the young radio source classification has been relying mainly on the spectral shape which usually does not cover high enough frequencies where the true peak flux is located. Hence HFPs are often confused with blazars which may show a similar spectral shape and apparent compactness but are a somewhat evolved form of AGNs. Therefore, we have been challenging to identify HFPs among the sample of 19 candidates using the Korean VLBI Network (KVN) which enables us to extend the radio spectrum baseline up to 22 and 43 GHz. These are higher than the frequencies used in most previous studies of HFPs, allowing us to select genuine HFPs. By long-term monitoring of 18 epochs, we have also inspected the variability of the sample to select out blazars which are highly variable yet with a similar radio spectrum. In this work, we present the light curves and spectral properties of the HFP candidates. We discuss the results of our re-identification of HFPs.

• Smart Structures and Systems
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• v.9 no.4
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• pp.335-353
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• 2012

This study suggests a simple two-step method for structural vibration-based health monitoring for beam-like structures which only utilizes mode shape curvature and few natural frequencies of the structures in order to detect and localize cracks. The method is firstly based on the application of wavelet transform to detect crack locations from mode shape curvature. Then particle swarm optimization is applied to evaluate crack depth. As the Rayleigh quotient is introduced to estimate natural frequencies of cracked beams, the relationship of natural frequencies and crack depths can be easily obtained with only a simple formula. The method is demonstrated and validated numerically, using the numerical examples (cantilever beam and simply supported shaft) in the literature, and experimentally for a cantilever beam. Our results show that mode shape curvature and few estimated natural frequencies can be used to detect crack locations and depths precisely even under a certain level of noise. The method can be extended for health monitoring of other more complicated structures.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.68-72
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• 2015

Ultrasonic inspection robot systems have been widely researched and developed for the real-time monitoring of structures such as power plants. However, an inspection robot that is operated in a simple pattern has limitations in its application to various structures in a plant facility because of the diverse and complicated shapes of the inspection objects. Therefore, accurate control of the robot is required to inspect complicated objects with high-precision results. This paper presents the idea that the shape and movement information of an ultrasonic inspector's hand could be profitably utilized for the accurate control of robot. In this study, a polymer flex sensor was applied to monitor the shape of a human hand. This application was designed to intuitively control an ultrasonic inspection robot. The movement and shape of the hand were estimated by applying multiple sensors. Moreover, it was successfully shown that a test robot could be intuitively controlled based on the shape of a human hand estimated using polymer flex sensors.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.7-13
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• 2003

A hot press method was use for the optimal manufacturing condition for a shape memory alloy(SMA) composite. The bonding between the matrix and the reinforcement within the SMA composite by the hot press method was strengthened by cold rolling. In this study, the objective was to develop an on-line monitoring system for the prevention of the crack initiation and propagation by shape memory effort of SMA composite. Shape memory effect was used to prevent the SMA composite from cracking. For the system to be developed, an optimal hE parameter should be determined based on the degree of damage and crack initiation. When the SHA composite was heated by the plate heater attached at the composite, the propagating cracks appeared to be controlled by the compressive force of SMA.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.371-375
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• 2018

Objectives: To successfully introduce an Internet of Things (IoT) system in the hospital environment, this study aimed to identify issues that should be considered while implementing an IoT based on a user demand survey and practical experiences in implementing IoT environment monitoring systems. Methods: In a field test, two types of IoT monitoring systems (on-premises and cloud) were used in Department of Laboratory Medicine and tested for approximately 10 months from June 16, 2016 to April 30, 2017. Information was collected regarding the issues that arose during the implementation process. Results: A total of five issues were identified: sensing and measuring, transmission method, power supply, sensor module shape, and accessibility. Conclusions: It is expected that, with sufficient consideration of the various issues derived from this study, IoT monitoring systems can be applied to other areas, such as device interconnection, remote patient monitoring, and equipment/environmental monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1326-1331
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• 2006

Weld root gap is a important fact of a falling-off weld quality in various kind of weld defect. The welding quality can be controlled by monitoring important parameters, such as, the Arc voltage, welding current and welding speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using neural networks for detecting and monitoring of weld root gap and bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and detect the welding defects.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.16 no.2
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• pp.1-9
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• 2020

Large-scale industrial facility structures continue to deteriorate due to the effects of operating and environmental conditions. The problems of these industrial facilities are potentially causing economic losses, environmental pollution, casualties, and national losses. Accordingly, in order to prevent disaster accidents of large structures in advance, the necessity of diagnosing structures using non-destructive inspection techniques is being highlighted. The defect occurrence, location and defect type of the structure are important parameters for predicting the remaining life of the structure, so continuous defect observation is very important. Recently, many researchers have been actively researching real-time monitoring technology to solve these problems. Structure Health Monitoring Inspection is a technology that can identify and respond to the occurrence of defects in real time, but there is a limit to check the degree of defects and the direction of growth of defects. In order to compensate for the shortcomings of these technologies, the importance of defect imaging techniques is emerging, and in order to find defects in large structures, a method of inspecting a wide range using guided ultrasonic is effective. The work presented here introduces a calculation for the shape factor for evaluation of the damaged area, as well as a variable β parameter technique to correct a damaged shape. Also, we perform research in modeling simulation and an experiment for comparison with a suggested inspection method and verify its validity. The curved structure image obtained by the advanced RAPID algorithm showed a good match between the defect area and the shape.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3122-3127
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• 2007

Water resource can be examined using biological sensors. Algae has been one of the biological sensors used to evaluate and monitor the water pollution. The monitoring system, however, could determine whether the examined water was safe or not. It needs additional expensive chemical test to figure out the cause of the water pollution. In this study, an endeavor is given to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve(FIC) from algae using monitoring system. Fundamental curves are obtained from the experiments with specified amount of toxicant. Baysian method is utilized to determine the unknown toxicant in the water by comparing it with the fundamental curves. The results shows that the proposed method works fairly well.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.1
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• pp.73-78
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• 2008

Water resource can be examined using biological sensors. Algae has been one of the biological sensors used to evaluate and to monitor the water pollution. The monitoring system, however, has not been used to determine what kind of the toxicological substance is in the water. It needs additional expensive chemical test to figure out the cause of the water pollution. In this study, an endeavor is made to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve(FIC) from algae using monitoring system. Fundamental curves are obtained from the experiments with specified amount of toxicant. Baysian method is utilized to determine the unknown toxicant in the water by comparing it with the fundamental curves. The results shows that the proposed method works fairly well.