• Bulletin of the Korean Chemical Society
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• v.35 no.12
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• pp.3547-3552
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• 2014

Laser Raman spectroscopy is one of the most powerful technologies for standoff detection of hazardous materials including explosives. Supported by recent development of laser and sensitive ICCD camera, the technology can identify trace amount of unknown substances in a distance. Using this concept, we built a standoff detection system, in which nanosecond pulse laser and nanosecond gating ICCD technique were delicately devised to avoid the large background noise which suppressed weak Raman signals from the target sample. In standoff detection of explosives which have large kill radius, one of the most important technical issues is the detection distance from the target. Hence, we focused to increase the detection distance up to 54 m by careful optimization of optics and laser settings. The Raman spectra of hazardous materials observed at the distance of 54 m were fully identifiable. We succeeded to detect and identify eleven hazardous materials of liquid or solid particles, which were either explosives or chemical substances used frequently in chemical plants. We also performed experiments to establish the limit of detection (LOD) of HMX at 10 m, which was estimated to be 6 mg.

• Bulletin of the Korean Chemical Society
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• v.34 no.6
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• pp.1668-1672
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• 2013

We developed a standoff Raman detection system for explosive molecules (EMs). Our system was composed of reflective telescope with 310 mm diameter lens, 532 nm pulse laser, and Intensified Charge-Coupled Device (ICCD) camera. In order to remove huge background noise coming from ambient light, laser pulses with nanosecond time width were fired to target sample and ICCD was gated to open only during the time when the scattered Raman signal from the sample arrived at ICCD camera. We performed standoff experiments with military EMs by putting the detector at 10, 20 and 30 m away from the source. The standoff results were compared with the confocal Raman results. Based on our standoff experiments, we were able to observe the peaks in the range of 1200 and $1600cm^{-1}$, where vibrational modes of nitro groups were appeared. The wave numbers and shapes of these peaks may serve as good references in detecting and identifying various EMs.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.612-620
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• 2015

Short-range detection of chemical agents deposited on ground surface using a standoff Raman system employing a pulsed laser at 248 nm is described. Mounted in a vehicle such as an NBC reconnaissance vehicle, the system is protected against toxic chemicals. As most chemicals including chemical warfare agents have unique Raman spectra, the spectra can be used for detecting toxic chemicals contaminated on the ground. This article describes the design of the Raman spectroscopic system and its performance on several chemicals contaminated on asphalt, concrete, sand, etc.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.374-381
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• 2021

Raman spectrometers are studied and developed for the military purposes because of their nondestructive inspection capability to capture unique spectral features induced by molecular structures of colorless and odorless chemical warfare agents(CWAs) in any phase. Raman spectrometers often suffer from random noise caused by their detector inherent noise, background signal, etc. Thus, reducing the random noise in a measured Raman spectrum can help detection algorithms to find spectral features of CWAs and effectively detect them. In this paper, we propose a denoising autoencoder for Raman spectra with a loss function for sample efficient learning using noisy dataset. We conduct experiments to compare its effect on the measured spectra and detection performance with several existing noise reduction algorithms. The experimental results show that the denoising autoencoder is the most effective noise reduction algorithm among existing noise reduction algorithms for Raman spectrum based standoff detection of CWAs.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.1
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• pp.8-14
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• 2014

Brightness temperature spectra acquired from FTIR(Fourier Transform Infrared)-SCADS (Standoff Chemical Agent Detection System) could be available for detection and identification of the chemical agents and pollutants from different background. IR spectrum range of 770 to 1350 $cm^{-1}$ is corresponding to "atmospheric window". A 2-dimensional(2D) brightness temperature spectrum was drawn from combining each data point through automatic continuous scanning of FTIR along with altitude and azimuth. At higher altitude, temperature of background was decreased but scattering effect of atmospheric gases was increased. Increase in temperature difference between background and blackbody in SCADS at higher temperature causes to increases in peak intensity of $SF_6$. This approach shows us a possibility that 2D visual information is acquired from scanning data with a single FTIR-SCADS.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.302-309
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• 2020

Early-detection and monitoring of toxic chemical gas cloud with chemical detector is essential for reducing the number of casualties. Conventional method for chemical detection and reconnaissance has the limitation in approaching to chemically contaminated site and prompt understanding for the situation. Stand-off detector can detect and identify the chemical gas at a long distance but it cannot know exact distance and position. Chemical detection UAV is an emerging platform for its high mobility and operation safety. In this study, we have conducted chemical gas cloud detection with the stand-off chemical detector and the chemical detection UAV. DMMP vapor was generated in the area where the cloud can be detected through the field of view(FOV) of stand-off chemical detector. Monitoring the vapor cloud with standoff detector, the chemical detection UAV moved back and forth at the area DMMP vapor being generated to detect the chemical contamination. The hybrid detection system with standoff cloud detection and point detection by chemical sensors with UAV seems to be very efficient as a new concept of chemical detection.

• Current Optics and Photonics
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• v.1 no.3
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• pp.247-251
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• 2017

In this study, an ultraviolet Raman spectrometer was designed and fabricated to detect chemical contamination on the ground. The region of the Raman spectrum that indicated the characteristics of the chemicals was $350-3800cm^{-1}$. To fabricate a Raman spectrometer operating in this range, the layout and angle of optical components of the spectrometer were designed using a grating equation. Experimental devices were configured to measure the Raman spectra of chemicals based on the fabricated Raman spectrometer. The wavenumber of the spectrometer was calibrated by measuring the Raman spectrum of polytetrafluoroethylene, $O_2$, and $N_2$. The spectral range of the spectrometer was measured to be 23.46 nm ($3442cm^{-1}$) with a resolution of 0.195 nm ($30.3cm^{-1}$) at 253.65 nm. After calibration, the main Raman peaks of cyclohexane, methanol, and acetonitrile were found to be similar to the references within a relative error of 0.55%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.562-569
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• 2019

Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.