• Applied Chemistry for Engineering
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• v.30 no.4
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• pp.399-407
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• 2019

The development of explosive detection technology in a security environment and fear of terrorism at homeland and abroad has been one of the most important issues. Moreover, research works on the explosive detection are highly required to achieve domestic production technology due to the implementation of aviation security performance certification system. Traditionally, explosives are detected by using classical chemical analyses. However, in the view of high sensitivity, rapid analysis, miniaturization and portability electrochemical methods are considered as promising. Most of electrochemical explosive detection technologies are developed in USA, China, Israel, etc. This review highlights the principle and research trend of electrochemical explosive detection technologies carried out overseas in addition to the research direction for future exploration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.5
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• pp.261-268
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• 2019

This paper proposes damage indices efficient on evaluating the seismic safety of cable-stayed bridges, especially dual-plane, cable-stayed bridges with H-type pylons. The research assumes that the location of accelerometers is already defined as given in the 2017 Ministry of the Interior and Safety (MOIS) guideline. In other words, the paper does not attempt to suggest optimal sensor location for the seismic safety evaluation of cable-stayed bridges. The proposed damage indices are based on those for building structures widely applied in the field already. Those include changes in natural frequencies and changes in relative lateral displacements. In addition, the study proposes other efficient damage indices as the rotation changes at the top of pylons and in the midspan of the girder system. Sensitivity analysis for various damage indices is performed through dynamic analysis using selected earthquake ground motions. The paper compares the effectiveness of the damage indices.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.139-145
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• 2019

Precautionary detection of chemical warfare agents (CWAs) has been an important global issue mainly owing to their toxicity. To achieve proper detection, many studies have been conducted to develop sensitive gas sensors for CWAs. In particular, metal-oxide semi-conductors (MOS) have been investigated as promising sensing materials owing to their abundance in nature and excellent sensitivity. In this review, we mainly focus on various MOS-based gas sensors that have been fabricated for the detection of two specific CWA simulants, 2-chloroethyl ethyl sulfide (2-CEES) and dimethyl methyl phosphonate (DMMP), which are simulants of sulfur mustard and sarin, respectively. In the case of 2-CEES, we mainly discuss $CdSnO_3-$ and ZnO-based sensors and their reaction mechanisms. In addition, a method to improve the selectivity of ZnO-based sensors is mentioned. Various sensors and their sensing mechanisms have been introduced for the detection of DMMP. As the reaction with DMMP may directly affect the sensing properties of MOS, this paper includes previous studies on its poisoning effect. Finally, promising sensing materials for both gases are proposed.

• Journal of Sensor Science and Technology
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• v.28 no.4
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• pp.205-215
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• 2019

A flexible polyvinylidene fluoride (PVDF)/polydimethylsiloxane (PDMS) composite prototype with high piezoelectricity and force sensitivity was constructed, and its huge potential for applications such as biomechanical energy harvesting, self-powered health monitoring system, and pressure sensors was proved. The crystallization, piezoelectric, and electrical properties of the composites were characterized using an X-ray diffraction (XRD) experiment and customized experimental setups. The composite can sustain up to 100% strain, which is a huge improvement over monolithic PVDF fibers and other PVDF-based composites in the literature. The Young's modulus is 1.64 MPa, which is closely matched with the flexibility of the human skin, and shows the possibility for integrating PVDF/PDMS composites into wearable devices and implantable medical devices. The $300{\mu}m$ thick composite has a 14% volume fraction of PVDF fibers and produces high piezoelectricity with piezoelectric charge constants $d_{31}=19pC/N$ and $d_{33}=34pC/N$, and piezoelectric voltage constants $g_{31}=33.9mV/N$ and $g_{33}=61.2mV/N$. Under a 10 Hz actuation, the output voltage was measured at 190 mVpp, which is the largest output signal generated from a PVDF fiber-based prototype.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.4
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• pp.298-304
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• 2021

This study aimed to determine the applicability of drones and air quality sensors in environmental monitoring of air pollutant emissions by developing and testing two new methods. The first method used orthoimagery for precise monitoring of pollutant-emitting facilities. The second method used atmospheric sensors for monitoring air pollutants in emissions. Results showed that ground sample distance could be established within 5 cm during the creation of orthoimagery for monitoring emissions, which allowed for detailed examination of facilities with naked eyes. For air quality monitoring, drones were flown on a fixed course and measured the air quality in point units, thus enabling mapping of air quality through spatial analysis. Sensors that could measure various substances were used during this process. Data on particulate matter were compared with data from the National Air Pollution Measurement Network to determine its future potential to leverage. However, technical development and applications for environmental monitoring of pollution-emitting facilities are still in their early stages. They could be limited by meteorological conditions and sensitivity of the sensor technology. This research is expected to provide guidelines for environmental monitoring of pollutant-emitting facilities using drones.

• Current Applied Physics
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• v.18 no.11
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• pp.1255-1260
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• 2018

In this work, a green and simple one-pot route was developed for the synthesis of highly fluorescent aminofunctionalized graphene quantum dots (a-GQDs) via hydrothermal process without any further modification or surface passivation. We synthesized the a-GQDs using glucose as the carbon source and ammonium as a functionalizing agent without the use of a strong acid, oxidant, or other toxic chemical reagent. The as-obtained aGQDs have a uniform size of 3-4 nm, high contents of amino groups, and show a bright green emission with high quantum yield of 32.8%. Furthermore, the a-GQDs show effective fluorescence quenching for $Cu^{2+}$ ions which can serve as effective fluorescent probe for the detection of $Cu^{2+}$. The fluorescent probe using the obtained aGQDs exhibits high sensitivity and selectivity toward $Cu^{2+}$ with the limit of detection as low as 5.6 nM. The mechanism of the $Cu^{2+}$ induced fluorescence quenching of a-GQDs can be attributed to the electron transfer by the formation of metal complex between $Cu^{2+}$ and the amino groups on the surface of a-GQDs. These results suggest great potential for the simple and green synthesis of functionalized GQDs and a practical sensing platform for $Cu^{2+}$ detection in environmental and biological applications.

• Current Optics and Photonics
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• v.5 no.3
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• pp.213-219
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• 2021

We describe a single-channel rubidium (Rb) radio-frequency atomic magnetometer (RFAM) as a receiver that takes magnetic signal resonating with Zeeman splitting of the ground state of Rb. We optimize the performance of the RFAM by recording the response signal and signal-to-noise ratio (SNR) in various parameters and obtain a noise level of 159 $fT{\sqrt{Hz}}$ around 30 kHz. When a resonant radiofrequency magnetic field with a peak amplitude of 8.0 nT is applied, the bandwidth and signal-to-noise ratio are about 650 Hz and 88 dB, respectively. It is a good agreement that RFAM using alkali atoms is suitable for receiving signals in the very low frequency (VLF) carrier band, ranging from 3 kHz to 30 kHz. This study shows the new capabilities of the RFAM in communications applications based on magnetic signals with the VLF carrier band. Such communication can be expected to expand the communication space by overcoming obstacles through the high magnetic sensitive RFAM.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1760-1768
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• 2022

When gamma-camera sensor modules, which are key components of radiation imagers, are derived from the coupling between scintillators and photosensors, the light collection efficiency is an important factor in determining the effectiveness with which the instrument can identify nuclides via their derived gamma-ray spectra. If the pixel area of the scintillator is larger than the pixel area of the photosensor, light loss and cross-talk between pixels of the photosensor can result in information loss, thereby degrading the precision of the energy estimate and the accuracy of the position-of-interaction determination derived from each active pixel in a coded-aperture based gamma camera. Here we present two methods to overcome the information loss associated with the loss of photons created by scintillation pixels that are coupled to an associated silicon photomultiplier pixel. Specifically, we detail the use of either: (1) light guides, or (2) scintillation pixel areas that match the area of the SiPM pixel. Compared with scintillator/SiPM couplings that have slightly mismatched intercept areas, the experimental results show that both methods substantially improve both the energy and spatial resolution by increasing light collection efficiency, but in terms of the image sensitivity and image quality, only slight improvements are accrued.