• Journal of Sensor Science and Technology
• /
• v.31 no.6
• /
• pp.441-447
• /
• 2022

This study describes a novel method for detecting and measuring partial discharge (PD) on an electrical facility such as an insulated power cable or switchgear using fiber optic sensing technology, and a distributed acoustic sensing (DAS) system. This method has distinct advantages over traditional PD sensing techniques based on an electrical method, including immunity to electromagnetic interference (EMI), long range detection, simultaneous detection for multiple points, and exact location. In this study, we present a DAS system for PD detection with performance evaluation and experimental results in a simulated environment. The results show that the system can be applied to PD detection.

• Smart Structures and Systems
• /
• v.33 no.5
• /
• pp.375-383
• /
• 2024

Infrastructure facilities contain various pipe systems, which can be considerably damaged by external loads such as earthquakes. Therefore, structural health monitoring (SHM) and safety assessment of pipes are crucial. Digital twin technology for SHM of pipes is important in the industry. This study proposes a digital twin system that estimates the behavior, stress, and external load of real-scale pipes in real time under simultaneous seismic and external loads using a minimum number of sensors. Vibration tests were performed to construct the digital twin system, and a numerical model was developed that considered the dynamic characteristics of a target pipe. Moreover, a reduced-order modeling technique of a numerical model was applied to enhance its real-time performance. The digital twin system successfully estimated the response of the pipe at all points. Verification of the digital twin system was performed by comparing it with the experimental parameters of a real-scale pipe. The proposed digital twin system can help enhance SHM and system's maintenance.

• Composites Research
• /
• v.16 no.3
• /
• pp.58-67
• /
• 2003

To perform the realtime strain and fracture monitoring of the smart composite structures, two optical fiber sensor systems are proposed. The two types of the coherent sources were used for fracture signal detection - EDFA with FBG and EDFA with Fabry-Perot filter. These sources were coupled to EFPI sensors imbedded in composite specimens. To understand the characteristics of matrix crack signals, at first, we performed tensile tests using surface attached PZT sensors by changing the thickness and width of the specimens. This paper describes the implementation of time-frequency analysis such as short time Fourier transform (STFT) and wavelet transform (WT) for the quantitative evaluation of fracture signals. The experimental result shows the distinctive signal features in frequency domain due to the different specimen shapes. And, from the test of tensile load monitoring using optical fiber sensor systems, measured strain agreed with the value of electric strain gage and the fracture detection system could detect the moment of damage with high sensitivity to recognize the onset of micro-crack fracture signal.

• Smart Structures and Systems
• /
• v.26 no.4
• /
• pp.435-449
• /
• 2020

To effectively extract the vast wind resource, offshore wind turbines are designed with large rotor and slender tower, which makes them vulnerable to external vibration sources such as wind and wave loads. Substantial research efforts have been devoted to mitigate the unwanted vibrations of offshore wind turbines to ensure their serviceability and safety in the normal working condition. However, most previous studies investigated the vibration control of wind turbines in one direction only, i.e., either the out-of-plane or in-plane direction. In reality, wind turbines inevitably vibrate in both directions when they are subjected to the external excitations. The studies on both the in-plane and out-of-plane vibration control of wind turbines are, however, scarce. In the present study, the NREL 5 MW wind turbine is taken as an example, a detailed three-dimensional (3D) Finite Element (FE) model of the wind turbine is developed in ABAQUS. To simultaneously control the in-plane and out-of-plane vibrations induced by the combined wind and wave loads, another carefully designed (i.e., tuned) spring and dashpot are added to the perpendicular direction of each Tuned Mass Damper (TMD) system that is used to control the vibrations of the tower and blades in one particular direction. With this simple modification, a bi-directional TMD system is formed and the vibrations in both the out-of-plane and in-plane directions are simultaneously suppressed. To examine the control effectiveness, the responses of the wind turbine without control, with separate TMD system and the proposed bi-directional TMD system are calculated and compared. Numerical results show that the bi-directional TMD system can simultaneously control the out-of-plane and in-plane vibrations of the wind turbine without changing too much of the conventional design of the control system. The bi-directional control system therefore could be a cost-effective solution to mitigate the bi-directional vibrations of offshore wind turbines.

• Journal of Radiation Protection and Research
• /
• v.31 no.3
• /
• pp.123-128
• /
• 2006

A small radiation detection system is necessary for the direct characterization of alpha/beta-ray contamination inside pipes generated during the decommission of a nuclear facility. In this work, the new type phoswich detector consisting of the ZnS(Ag) and plastic scintillator for ${\alpha}/{\beta}$ simultaneous counting was designed as part of a development of a equipment capable of monitoring radiological contamination inside pipes. The optimum counting conditions in dimensions of scintillator and a detection system were experimentally confirmed and a performance of alpha/beta-ray discrimination was evaluated. As a result, optimum conditions of a detector suitable for monitoring radiological contamination inside pipes and a feasibility of application to pipe-inside were confirmed.

• Biomedical Engineering Letters
• /
• v.8 no.4
• /
• pp.365-371
• /
• 2018

Uninterrupted monitoring of multiple subjects is required for mass causality events, in hospital environment or for sports by medical technicians or physicians. Movement of subjects under monitoring requires such system to be wireless, sometimes demands multiple transmitters and a receiver as a base station and monitored parameter must not be corrupted by any noise before further diagnosis. A Bluetooth Piconet network is visualized, where each subject carries a Bluetooth transmitter module that acquires vital sign continuously and relays to Bluetooth enabled device where, further signal processing is done. In this paper, a wireless network is realized to capture ECG of two subjects performing different activities like cycling, jogging, staircase climbing at 100 Hz frequency using prototyped Bluetooth module. The paper demonstrates removal of baseline drift using Fast Fourier Transform and Inverse Fast Fourier Transform and removal of high frequency noise using moving average and S-Golay algorithm. Experimental results highlight the efficacy of the proposed work to monitor any vital sign parameters of multiple subjects simultaneously. The importance of removing baseline drift before high frequency noise removal is shown using experimental results. It is possible to use Bluetooth Piconet frame work to capture ECG simultaneously for more than two subjects. For the applications where there will be larger body movement, baseline drift removal is a major concern and hence along with wireless transmission issues, baseline drift removal before high frequency noise removal is necessary for further feature extraction.

• Journal of Biomedical Engineering Research
• /
• v.44 no.6
• /
• pp.392-403
• /
• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_3
• /
• pp.1209-1219
• /
• 2019

For consistent vegetation monitoring, it is necessary to generate time-series vegetation index datasets at fine temporal and spatial scales by fusing the complementary characteristics between temporal and spatial scales of multiple satellite data. In this study, we quantitatively and qualitatively analyzed the prediction accuracy of time-series change information extracted from spatio-temporal fusion models of multiple satellite data for vegetation monitoring. As for the spatio-temporal fusion models, we applied two models that have been widely employed to vegetation monitoring, including a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). To quantitatively evaluate the prediction accuracy, we first generated simulated data sets from MODIS data with fine temporal scales and then used them as inputs for the spatio-temporal fusion models. We observed from the comparative experiment that ESTARFM showed better prediction performance than STARFM, but the prediction performance for the two models became degraded as the difference between the prediction date and the simultaneous acquisition date of the input data increased. This result indicates that multiple data acquired close to the prediction date should be used to improve the prediction accuracy. When considering the limited availability of optical images, it is necessary to develop an advanced spatio-temporal model that can reflect the suggestions of this study for vegetation monitoring.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.8 no.4
• /
• pp.293-301
• /
• 2010

Optical fiber cable, as a sensor, was installed on the wall of KAERI(Korea Atomic Energy Research Institute) Underground Research Tunnel(KURT) in order to monitor the physical stability of the tunnel, which was constructed for technical development and demonstration of radioactive waste disposal. This monitoring system has two simultaneous measurements of temperature and strain over time using Brillouin backscatter. According to the results of the monitoring from Jan. 2008 to Nov. 2009, there is no significant displacement or movement at the tunnel wall However, the cumulative volume of total strain increased slightly as time passes with the comparison of the reference observation, which was measured in Jan. 2008. The change in cumulative volume of total strain indicates that the strain level had been affected by saturation and de-saturation phenomena due to groundwater fluctuation at several points at KURT. This system is based on the distributed sensing technique concept, not point sensing. By using this system, a displacement can be detected with the range from $20{\mu}{\varepsilon}$ to $28,000{\mu}{\varepsilon}$ every 1m interval in minimum. A temperature variation can be monitored at every 0.5m interval with the resolution of 0.01 in minimum. Based on the study, this monitoring system is potentially applicable to long term monitoring systems for radioactive waste disposal project as well as other structures and underground openings.

• Journal of Power Electronics
• /
• v.6 no.4
• /
• pp.307-314
• /
• 2006

This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.