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

• Steel and Composite Structures
• /
• v.50 no.1
• /
• pp.89-105
• /
• 2024

Multichannel analysis of surface waves (MASW) method has exhibited broad application prospects in the nondestructive detection of interfacial debonding in steel-concrete composite structures (SCCS). However, due to the structural diversity of SCCS and the high stealthiness of interfacial debonding defects, the feasibility of MASW method needs to be investigated in depth. In this study, synthetic parametric study on MASW nondestructive debonding detection for SCCSs is performed. The aim is to quantitatively analyze influential factors with respect to structural composition of SCCS and MASW measurement mode. First, stress wave composition and propagation process in SCCS are studied utilizing 2D numerical simulation. For structural composition in SCCS, the thickness variation of steel plate, concrete core, and debonding defects are discussed. To determine the most appropriate sensor arrangement for MASW measurement, the effects of spacing and number of observation points, along with distances between excitation points, nearest boundary, as well as the first observation point, are analyzed individually. The influence of signal type and frequency of transient excitation on dispersion figures from forwarding analysis is studied to determine the most suitable excitation signal. The findings from this study can provide important theoretical guidance for MASW-based interfacial debonding detection for SCCS. Furthermore, they can be instrumental in optimizing both the sensor layout design and signal choice for experimental validation.

• Journal of Korean Society for Geospatial Information Science
• /
• v.25 no.1
• /
• pp.71-78
• /
• 2017

Many studies have been implemented to manage solar plant being supplied widely in recent years. This study analyzed heat emission of solar cell using unmanned aerial vehicle(UAV)-based thermal infrared sensor, and major conclusions are as belows. Firstly, orthomosaic image and digital surface model(DSM) data were acquired using UAV-based RGB sensor, and solar light module layer necessary to analyze the heat emission of solar cell was constructed by these data. Also as a result of horizontal error into validation points using virtual reference service(VRS) survey for evaluating the location accuracy of solar light module layer, higher location accuracy could be acquired like standard error of $dx={\pm}2.4cm$ and $dy={\pm}3.2cm$. And this study installed rubber patch to test the heat emission of solar cell and could analyzed efficiently the location of rubber patch being emitted heat using UAV-based thermal infrared sensor. Also standard error showd as ${\pm}3.5%$ in analysis between calculated cell ratio by rubber patch and analyzed cell ratio by UAV-based thermal infrared sensor. Therefore, it could be efficiently analyzed to heat emission of solar cell using UAV-based thermal infrared sensor. Also efficient maintenance of solar plant could be possible through extracting the code of solar light module being emitted of heat automatically.

• Korean Journal of Remote Sensing
• /
• v.36 no.2_1
• /
• pp.167-178
• /
• 2020

KOMPSAT-3A images have been used in various kinds of applications, since its launch in 2015. However, there were limits to scientific analysis and application extensions of these data, such as vegetation index estimation, because no tool was developed to obtain the surface reflectance required for analysis of the actual land environment. The surface reflectance is a product of performing an absolute atmospheric correction or calibration. The objective of this study is to quantitatively verify the accuracy of top-of-atmosphere reflectance and surface reflectance of KOMPSAT-3A images produced from the OTB open-source extension program, performing the cross-validation with those provided by a site measurement data of RadCalNet, an international Calibration/Validation (Cal/Val) portal. Besides, surface reflectance was obtained from Landsat-8 OLI images in the same site and applied together to the cross-validation process. According to the experiment, it is proven that the top-of-atmosphere reflectance of KOMPSAT-3A images differs by up to ± 0.02 in the range of 0.00 to 1.00 compared to the mean value of the RadCalNet data corresponding to the same spectral band. Surface reflectance in KOMPSAT-3A images also showed a high degree of consistency with RadCalNet data representing the difference of 0.02 to 0.04. These results are expected to be applicable to generate the value-added products of KOMPSAT-3A images as analysisready data (ARD). The tools applied in thisstudy and the research scheme can be extended as the new implementation of each sensor model to new types of multispectral images of compact advanced satellites (CAS) for land, agriculture, and forestry and the verification method, respectively.

• Smart Structures and Systems
• /
• v.10 no.4_5
• /
• pp.411-426
• /
• 2012

The Canton Tower (formerly named Guangzhou New TV Tower) of 610 m high has been instrumented with a long-term structural health monitoring (SHM) system consisting of over 700 sensors of sixteen types. Under the auspices of the Asian-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST), an SHM benchmark problem for high-rise structures has been developed by taking the instrumented Canton Tower as a host structure. This benchmark problem aims to provide an international platform for direct comparison of various SHM-related methodologies and algorithms with the use of real-world monitoring data from a large-scale structure, and to narrow the gap that currently exists between the research and the practice of SHM. This paper first briefs the SHM system deployed on the Canton Tower, and the development of an elaborate three-dimensional (3D) full-scale finite element model (FEM) and the validation of the model using the measured modal data of the structure. In succession comes the formulation of an equivalent reduced-order FEM which is developed specifically for the benchmark study. The reduced-order FEM, which comprises 37 beam elements and a total of 185 degrees-of-freedom (DOFs), has been elaborately tuned to coincide well with the full-scale FEM in terms of both modal frequencies and mode shapes. The field measurement data (including those obtained from 20 accelerometers, one anemometer and one temperature sensor) from the Canton Tower, which are available for the benchmark study, are subsequently presented together with a description of the sensor deployment locations and the sensor specifications.

• Journal of Biosystems Engineering
• /
• v.33 no.1
• /
• pp.30-37
• /
• 2008

Dynamic excitation and response analysis is an acceptable method to determine some of physical properties of agricultural product for quality evaluation. There is a difference in the internal viscoelasticity between sound and defective fruits due to the difference of geometric structures, thereby showing different vibration characteristics. This study was carried out to develop a portable piezoelectric film-based glove sensor system that can separate internally damaged watermelons from sound ones using an acoustic impulse response technique. Two piezoelectric sensors based on polyvinylidene fluoride (PVDF) films to measure an impact force and vibration response were separately mounted on each glove. Various signal parameters including number of peaks, energy ratio, standard deviation of peak to peak distance, zero-crossing rate, and integral value of peaks were examined to develop a regression-estimated model. When using SMLR (Stepwise Multiple Linear Regression) analysis in SAS, three parameters, i.e., zeros value, number of peaks, and standard deviation of peaks were selected as usable factors with a coefficient of determination ($r^2$) of 0.92 and a standard error of calibration (SEC) of 0.15. In the validation tests using twenty watermelon samples (sound 9, defective 11), the developed model provided good capability showing a classification accuracy of 95%.

• Journal of Communications and Networks
• /
• v.7 no.4
• /
• pp.401-407
• /
• 2005

Simulations are currently an essential tool to develop and test wireless sensor networks (WSNs) protocols and to analyze future WSNs applications performance. Researchers often simulate their proposals rather than deploying high-cost test-beds or develop complex mathematical analysis. However, simulation results rely on physical layer assumptions, which are not usually accurate enough to capture the real behavior of a WSN. Such an issue can lead to mistaken or questionable results. Besides, most of the envisioned applications for WSNs consider the nodes to be at the ground level. However, there is a lack of radio propagation characterization and validation by measurements with nodes at ground level for actual sensor hardware. In this paper, we propose to use a low-computational cost, two slope, log-normal path­loss near ground outdoor channel model at 868 MHz in WSN simulations. The model is validated by extensive real hardware measurements obtained in different scenarios. In addition, accurate model parameters are provided. This model is compared with the well-known one slope path-loss model. We demonstrate that the two slope log-normal model provides more accurate WSN simulations at almost the same computational cost as the single slope one. It is also shown that the radio propagation characterization heavily depends on the adjusted model parameters for a target deployment scenario: The model parameters have a considerable impact on the average number of neighbors and on the network connectivity.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.45 no.7
• /
• pp.594-599
• /
• 2017

In this thesis, the development and the verification of the test-aid are described, providing various attitude errors through the electric stimulus to the Sun sensor. This test-aid for 2-axis analogue Sun sensor is used for polarity test in the assembly stage for GK2 satellite. The test-aid used for GK2 satellite is for COMS satellite and, due to the failure risk, manufactured by domestic company. The characteristics of the COMS test-aid used for GK2 satellite and the manufactured test-aid are showed with similar through the several tests. In this thesis, there are conformed the capability for replacing of test-aid because the characteristics of the manufactured test-aid is acquired same as that of the COMS test-aid using the controller tuning functions.

• Korean Journal of Remote Sensing
• /
• v.16 no.1
• /
• pp.37-45
• /
• 2000

Since being launched for ocean observing in 1997, the SeaWiFS sensor has supplied data on ocean chlorophyll distribution and environmental conditions of the atmosphere. Until now, a lot of SeaWiFS data have been archived and utilized for ocean monitoring and land observation. The SeaWiFS sensor has 1km spatial resolution, therefore, it is difficult to obtain data at the coastal zone. Since atmospheric correction algorithms at the coastal area have not been confirmed for chlorophyll algorithm, the ocean color data analysis for coastal zone is not common. In particular, domestic coastal areas have high suspended sediments concentrations and higher absorption influence of colored dissolved organic matter (CDOM), released from in-land, than open-sea. Thus, a useful algorithm for analysis of chlorophyll distribution in domestic coastal areas has not been developed. In this study, empirical algorithms, using data from the ocean color sensor, were developed for monitoring of chlorophyll distribution of coastal areas. In the process of the development of the algorithms, we can find that the red band (665nm) should be used for analyzing of domestic coastal areas near the Yellow Sea.

• Smart Structures and Systems
• /
• v.19 no.1
• /
• pp.1-10
• /
• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.