• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.112-116
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• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.232-236
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• 2018

A radiation detection sensor was developed and characterized by combining three types of CsI(Tl) scintillators and an array-type SiPM to detect the radioactive contamination of discharged water in real time. The characterization results showed that type 3 exhibited the most desirable characteristics in response linearity (R-square: 0.97889) according to detection sensitivity and incident radiation dose. Furthermore, in terms of spectral characteristics, type 3 exhibited 16.54% at 0.356 MeV (the emission gamma ray energy of $^{133}Ba$), 10.28% at 0.511 MeV (the emission gamma ray energy of $^{22}Na$), 9.68% at 0.356 MeV (the emission gamma ray energy of $^{137}Cs$), and 2.55% and 4.80% at 1.173 MeV and 1.332 MeV (the emission gamma ray energies of $^{60}Co$), respectively. These measurements confirmed the good energy characteristics. The results were used to evaluate the spectral characteristics and energy linearity in a mixed source using type 3 with the best detection characteristics. It was confirmed that the gamma ray peaks of $^{133}Ba$, $^{22}Na$, $^{137}Cs$, and $^{60}Co$ were well resolved. Moreover, it was confirmed that R-square, which is an indicator of energy linearity, was 0.99986. This indicates a good linearity characteristic. Based on this study, further commercialization studies will contribute to measurements in real time and to the management of the contamination caused by radioactive wastewater or radioactive material leakage, which originate from facilities that use radioactive isotopes or care facilities.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.752-759
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• 2013

This paper proposes a method for fault diagnosis and fault-tolerant control of DC-link voltage sensor for two-stage three-phase grid-connected PV inverters. Generally, the front-end DC-DC boost converter tracks the maximum power point (MPP) of PV array and the rear-end DC-AC inverter is used to generate a sinusoidal output current and keep the DC-link voltage constant. In this system, a sensor is essential for power conversion. A sensor fault is detected when there is an error between the sensed and estimated values, which are obtained from a DC-link voltage sensorless algorithm. Fault-tolerant control is achieved by using the estimated values. A deadbeat current controller is used to meet the dynamic characteristic of the proposed algorithm. The proposed algorithm is validated by simulation and experiment results.

• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.246-251
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• 2023

Internet of Things (IoT) systems process signals from various sensors using signal processing algorithms suitable for the signal characteristics. To analyze complex signals, these systems usually use signal processing algorithms in the frequency domain, such as fast Fourier transform (FFT), filtering, and short-time Fourier transform (STFT). In this study, we propose a multi-mode sensor signal processor (SSP) accelerator with an FFT-based hardware design. The FFT processor in the proposed SSP is designed with a radix-2 single-path delay feedback (R2SDF) pipeline architecture for high-speed operation. Moreover, based on this FFT processor, the proposed SSP can perform filtering and STFT operation. The proposed SSP is implemented on a field-programmable gate array (FPGA). By sharing the FFT processor for each algorithm, the required hardware resources are significantly reduced. The proposed SSP is implemented and verified on Xilinxh's Zynq Ultrascale+ MPSoC ZCU104 with 53,591 look-up tables (LUTs), 71,451 flip-flops (FFs), and 44 digital signal processors (DSPs). The FFT, filtering, and STFT algorithm implementations on the proposed SSP achieve 185x average acceleration.

• Journal of Industrial and Engineering Chemistry
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• v.68
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• pp.393-398
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• 2018

This work offers a promising approach for development of a temperature-responsive colorimetric display platform. For this purpose, uniform thermochromic microcapsules consisting of a cholesteric liquid crystal (CLC) core and a thin polyurethane shell layer were fabricated by conducting in-situ condensation polymerization at the interface of monodisperse CLC-in-water emulsion drops. Colloidal packing-driven microcapsule registry led to exact 2-dimensional positioning of CLC microcapsules into a holes-patterned flexible film stencil. Furthermore, we showed that the designated registry of different color types of CLC microcapsules on the stencil enabled development of a microwriting display technology capable of reversible text representation according to temperature change.

• Journal of Sensor Science and Technology
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• v.7 no.2
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• pp.103-110
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• 1998

Pyroelectric infrared detectors based on La-modified $PbTiO_{3}$ (PLT) thin films have been fabricated by RF magnetron sputtering and rnicrornachining technology. The detectors form $Pb_{l-x}La_{x}Ti_{1-x/4}O_{3}$ (x=0.05) thin film ferroelectric capacitors epitaxially grown by RF magnetron sputtering on Pt/MgO (100) substrate. The sputtered PLT thin film exhibits highly c-axis oriented crystal structure that no poling treatment for sensing applications is required. This is an essential factor to increase the yield for realization of an infrared image sensor. Micromachining technology is used to lower the thermal mass of the detector by giving maximum sensor efficiency. Polymide is coated on top of the sensing elements to support the fragile structure and the backside of the MgO substrate is selectively etched to reduce the heat loss. The sensing element exhibited a very high detectivity D* of $8.5{\times}10^{8}cm{\cdot}\sqrt{Hz}/W$ at room temperature and it is about 100 times higher than the case of micromachining technology is not used. A sensing system that detects the position as well as the existence of a human body is realized using the array sensor.

• Aerospace Engineering and Technology
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• v.9 no.2
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• pp.22-30
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• 2010

KARI had integrated and tested GEO satellite in cooperation with Astrium Inc., France. In the middle of integration and test, It was necessary to check GEO satellite SA(solar array) status and require electrical interface verification with bus. SA of GEO satellite have different mechanical characteristics in comparison with those of LEO satellite which was tested in KARI. LEO SA has been deployed by simple mechanical hinge system but GEO SA has been done by more complicated method. so in this paper, we designed the test configuration and analyzed the test result of solar array electrical integration of GEO satellite.

• Journal of Sensor Science and Technology
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• v.27 no.1
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• pp.55-58
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• 2018

Low-cost and large-scale fabrication method of nanohole array, which comprises nanoscale voids separated by a few tens to a few hundreds of nanometers, has opened up new possibilities in biomolecular sensing as well as novel frontier optical devices. One of the key aspects of the nanohole array research is how to control the hole size following each specific needs of the hole structure. Here, we report the extensive study on the fine control of the hole size within the range of 500-2500 nm via surface-catalyzed chemical deposition. The initial hole structures were prepared via conventional photo-lithography, and the hole size was decreased to a designed value through the surface-catalyzed chemical reduction of the gold ion on the predefined hole surfaces, by simple dipping of the hole array device into the aqueous solution of gold chloride and hydroxylamine. The final hole size was controlled by adjusting reaction time, and the optimal experimental condition was obtained by doing a series of characterization experiments. The characterization of size-controlled hole array was systematically examined on the image results of optical microscopy, field emission scanning electron microscopy(FESEM), atomic-force microscopy(AFM), and total internal reflection microscopy.

• Journal of Sensor Science and Technology
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• v.6 no.6
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• pp.476-482
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• 1997

A portable electronic nose system has been fabricated and characterized using an oxide semiconductor gas sensor array and pattern recognition techniques such as principal component analysis and back-propagation artificial neural network. The sensor array consists of six thick-film gas sensors whose sensing layers are Pd-doped $WO_{3}$, Pt-doped $SnO_{2}$, $TiO_{2}-Sb_{2}O_{5}-Pd$-doped $SnO_{2}$, $TiO_{2}-Sb_{2}O_{5}-Pd$-doped $SnO_{2}$ + Pd coated layer, $Al_{2}O_{3}$-doped ZnO and $PdCl_{2}$-doped $SnO_{2}$. The portable electronic nose system consists of an 16bit Intel 80c196kc as CPU, an EPROM for storing system main program, an EEPROM for containing optimized connection weights of artificial neural network, an LCD for displaying gas concentrations. As an application the system has been used to identify 26 carbon monoxide/hydrocarbon (CO/HC) car exhausting gases in the concentration range of CO 0%/HC 0 ppm to CO 7.6%/HC 400 ppm and the identification has been successfully demonstrated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.