• Smart Structures and Systems
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• v.6 no.5_6
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• pp.439-459
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• 2010

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.225-231
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• 2015

Improving current pasture productivity by precision management requires practical tools to collect site specific pasture biomass data. Recent developments in unmanned aerial vehicle (UAV) technology provide cost effective and real time applications for site specific data collection. For the mapping of herbage biomass (BM) on a hill pasture, we tested a UAV system with digital cameras (visible and near-infrared (NIR) camera). The field measurements were conducted on the grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17 and June 27, 2014. Plant samples were obtained from 28 sites. A UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number (DN) values of Red and NIR channels were extracted from the aerial photos and a normalized differential vegetation index using DN ($NDVI_{dn}$) was calculated. The results show that the correlation coefficient between BM and $NDVI_{dn}$ was 0.88. For the precision management of hilly grazing pastures, UAV monitoring systems can be a quick and cost effective tool to obtain site-specific herbage BM data.

• Current Optics and Photonics
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• v.8 no.3
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• pp.246-258
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• 2024

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.27 no.2
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• pp.78-83
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• 2016

Laryngopharyngeal reflux (LPR) disease is an extraoesophageal variant of gastro-esophageal reflux disease that can affect the larynx and pharynx. LPR is associated with symptoms of laryngeal irritation such as throat clearing, coughing, and hoarseness. The main diagnostic methods currently used are laryngoscopy and pH monitoring. The most common laryngoscopic signs are redness and swelling of the throat. However, these findings are not specific of LPR and may be related to other causes or can even be found in healthy individuals. Furthermore, the role of pH monitoring in the diagnosis of LPR is controversial. A therapeutic trial with proton pump inhibitors (PPIs) has been suggested to be cost-effective and useful for the diagnosis of LPR. However, the recommendations of PPI therapy for patients with a suspicion of LPR are based on the results of uncontrolled studies, and high placebo response rates suggest a much more complex and multifactorial pathophysiology of LPR than simple acid reflux. Laryngoscopy and pH monitoring have failed as reliable tests for the diagnosis of LPR. Empirical therapy with PPIs is widely accepted as a diagnostic test and for the treatment of LPR. However, further research is needed to develop a definitive diagnostic test for LPR.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.241-244
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• 2007

Cement-grouts are injected into limestone cavities beneath the road in the project area, in order to improve strength and reduce permeability; the extent to which grout has penetrated in cavities need to be monitored in order to determined effectiveness of cement-grout. Geophysical approaches, offer great potential for monitoring the grout injection process in a fast and cost-effective way as well as showing whether the grout has successfully achieved the target. This paper presents the ability of surface electrical resistivity to investigate the verification of the grout placement. In order to image the cement-grout, time-lapse surface electrical resistivity surveys were conducted to compare electrical resistivity images before and after injection. Cement-grout was imaged as anomalies exhibiting low resistivity than the surrounding rocks. In accordance with field monitoring, laboratory study was also designed to monitor the resistivity changes of cement-grout specimens with time-lapse. Time-lapse laboratory measurements indicated that electrical methods are good tool to identify the grouted zone. Pre-and post grouting electrical images showed significant changes in subsurface resistivity at grouted zone. The study showed that electrical resistivity imaging technology can be a useful tool for detecting and evaluating changes in subsurface resistivity due to the injection of the grout.

• Smart Structures and Systems
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• v.15 no.3
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• pp.807-830
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• 2015

When a building structure requires both health monitoring system and vibration control system, integrating the two systems together will be cost-effective and beneficial for creating a smart building structure with its own sensors (nervous system), processors (brain system), and actuators (muscular system). This paper presents a real-time integrated procedure to demonstrate how health monitoring and vibration control can be integrated in real time to accurately identify time-varying structural parameters and unknown excitations on one hand, and to optimally mitigate excessive vibration of the building structure on the other hand. The basic equations for the identification of time-varying structural parameters and unknown excitations of a semi-active damper-controlled building structure are first presented. The basic equations for semi-active vibration control of the building structure with time-varying structural parameters and unknown excitations are then put forward. The numerical algorithm is finally followed to show how the identification and the control can be performed simultaneously. The results from the numerical investigation of an example building demonstrate that the proposed method is feasible and accurate.

• Journal of Power Electronics
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• v.19 no.1
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• pp.307-315
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• 2019

Condition monitoring has been recognized as an effective and low-cost method to enhance the reliability and improve the maintainability of power electronic converters. In power electronic converters, high-frequency oscillation occurs during the switching transients of power transistors, which is known as ringing. The ringing frequency mainly depends on the values of the parasitic capacitance and stray inductance in the oscillation loop. Although circuit stray inductance is an important factor that leads to the ringing, it does not change with transistor aging. A shift in either the inside inductance or junction capacitance is an important failure precursor for power transistors. Therefore, ringing frequency can be used to monitor the health of power transistors. However, the switching actions of power transistors usually result in a dynamic behavior that can generate oscillation signals mixed with background noise, which makes it hard to directly extract the ringing frequency. A frequency extraction method based on empirical mode decomposition (EMD) and Fast Fourier transformation (FFT) is proposed in this paper. The proposed method is simple and has a high precision. Simulation results are given to verify the ringing analysis and experimental results are given to verify the effectiveness of the proposed method.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Smart Structures and Systems
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• v.32 no.5
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• pp.297-308
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• 2023

Dynamic deflection is important for evaluating the performance of a long-span cable-stayed bridge, and its continuous measurement is still cumbersome. This study proposes a dynamic deflection monitoring method for cable-stayed bridge based on Bi-directional Long Short-term Memory (BiLSTM) neural network taking advantages of the characteristics of spatial variation of cable acceleration response (CAR) and main girder deflection response (MGDR). Firstly, the relationship between the spatial and temporal variation of the CAR and the MGDR is described based on the geometric deformation of the bridge. Then a data-driven relational model based on BiLSTM neural network is established using CAR and MGDR data, and it is further used to monitor the MGDR via measuring the CAR. Finally, numerical simulations and field test are conducted to verify the proposed method. The root mean squared error (RMSE) of the numerical simulations are less than 4 while the RMSE of the field test is 1.5782, which indicate that it provides a cost-effective and convenient method for real-time deflection monitoring of cable-stayed bridges.

• Journal of Life Science
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• v.26 no.5
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• pp.503-508
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• 2016

Biosensors have been used as first-step monitoring tools to detect on-site samples in a simple and cost-effective manner. Numerous recombinant microbial biosensors have been exploited for monitoring on-site toxic chemicals and biological signals. Herein, a recombinant microbial biosensor was constructed for monitoring cadmium. The cadmium responding cadC regulatory gene and it’s promoter from Staphylococcus aureus was amplified through PCR, fused with the lacZ gene, and transformed into Escherichia coli BL21 (DE3) cells. In the presence of cadmium, the biosensor cells express β-galactosidase showing red color development with chlorophenol red β-galactopyranoside (CPRG) as the enzymatic substrate. The biosensor cells showed the best β-galactosidase activity after 3 hr induction with cadmium at pH 5 and a detection range from 0.01 μM to 10 mM cadmium with a linearity from 0.01 to 0.1 μM cadmium (y = 0.98 x + 0.142, R² = 0.98). Among the heavy metals, cadmium and lead showed good responses, tin and cobalt showed medium responses, and mercury and copper showed no responses. The biosensor cells showed good responses to several waste waters similar to buffer solution, all spiked with cadmium. The biosensor described herein could be applied for on-site cadmium monitoring in a simple and cost-effective manner without sample pretreatments.