• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Archives of Pharmacal Research
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• v.11 no.2
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• pp.134-138
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• 1988

Urine measurements by MNR were made for 25 persons including cancer and non-cancer patients. The aromatic proton signals of NMR wer observed much more often in cancer patients' urine than non-cancer patients' one. To compare the amount of the phenolic compounds excreted in urine between cancer and non-cancer patient, urine analysis by HPLC with UV detector was performed. Total peak area and major peak areas of cancer patients' urine wer emuch greater than those of non-cancer patients' one. To check the phenolic compound excreted in urine, a new jellied reagent named Gift reagent which was based on Millon's reagent, was developed for urine color reaction. When the reagent was tested, the sensitivity and specificity for urine samples of 69 persons including cancer and non-cancer patients were measured by 85.3% and 91.4%, respectively, indicating that the Gift reagent afford a possibility of cancer diagnosis.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.2
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• pp.131-139
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• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.423-428
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• 2022

Very low contents (in the range of 10^-9 g/g) of Ir in mantle-derived rock samples (komatiites) were non-destructively determined by INAA coupled with coincidence gamma-ray spectrometry using 16 Ge detectors. Aliquots of the same samples were analyzed by NiS fire-assay ICP-MS for Ir and other platinum group elements. Because the INAA procedure used in this study is non-destructive and is almost free from spectral interference in gamma-ray spectrometry, the INAA values of Ir contents obtained in this study can be highly reliable. Iridium values obtained by ICP-MS were consistent with the INAA values, implying that the ICP-MS values of Ir obtained in this study are equally reliable. Under the present experimental conditions, detection limits were estimated to be 1 pg/g, which corresponds to 0.1 pg for a sample mass of 0.1 g. These levels can be even lowered by an order of magnitude, if necessary, which cannot be achieved by ICP-MS carried out in this study.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2646-2651
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• 2021

A rectangular-shaped PET system with an adjustable gantry (AGPET) has been developed for imaging small animals. The AGPET system employs a new depth of interaction (DOI) method using a depth dependent reflector patterns and a new digital time pickoff method based on the pulse reconstruction method. To evaluate the performance of the AGPET, timing resolution, intrinsic spatial resolution and point source images were acquired. The timing resolution and intrinsic spatial resolution were measured using two detector modules and Na-22 gamma source. The PET images were acquired in two field of view (FOV) sizes, 30 mm and 90 mm, to demonstrate the characteristic of the AGPET. As a result of in the experiment results, the timing resolution was 0.9 ns using the pulse reconstruction method based on the bi-exponential model. The intrinsic spatial resolution was an average of 1.7 mm and the spatial resolution of PET images after DOI correction was 2.08 mm and 2.25 mm at the centers of 30 mm and 90 mm FOV, respectively. The results show that the proposed AGPET system provided higher sensitivity and resolution for small animal imaging.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1024-1029
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• 2022

Composite scintillation screens on a base of Gd_1.2Y_1.8Ga_2.5Al_2.5O₁₂:Ce (GYAGG) scintillator have been evaluated for neutron detection. Besides the powdered scintillator, the composite includes ⁶LiF particles; both are merged with a binder and deposited onto the light-reflecting aluminum substrate. Results obtained demonstrates that screens are suitable for use with a silicon photomultiplier readout to create a prospective solution for a compact and low-cost thermal neutron sensor. Composite GYAGG/⁶LiF scintillation screen shows a pretty matched sensitivity and γ-background rejection with a widely used ZnS/⁶LiF screens however, possesses forty times faster response.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.7 no.2
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• pp.69-77
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• 2021

No-carrier-added holmium-166 (n.c.a ¹⁶⁶Ho) separation is performed based on the results of separation conditions using stable isotopes dysprosium (Dy) and holmium (Ho) to minimize radioactive waste from separation optimization procedures. Successful separation of two adjacent lanthanides was achieved by cation-exchange chromatography using a sulfonated resin in the H+ form (BP-800) and α-hydroxyisobutyric acid (α-HIBA) as eluent. For the identification process after separation of stable isotopes, the use of chromogenic reagents alternatively enables on-line detection because the lanthanides are hardly absorb light in the UV-vis region or exhibit radioactivity. Four different chromogenic reagents were pre-tested to evaluate suitable coloring reagents, of which 4-(2-Pyridylazo)resorcinol is the most recommendable considering the sensitivity and specificity for lanthanides. Lanthanide radioisotopes (RI) were monitored for separation with an RI detector using a lab-made separation LC system. Under the proper separation conditions, the n.c.a ¹⁶⁶Ho was effectively obtained from a large amount of 100 mg dysprosium target within 2 hrs.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.37.1-37.1
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• 2021

For the first time in Korea, we are developing technology for gravitational wave (GW) detectors as a major R&D program. Our main research target is quantum noise reduction technology which can enhance the sensitivity of a GW detector beyond its limit by classical physics. Technology of generating squeezed vacuum state of light (SQZ) can suppress quantum noise (shot noise at higher frequencies and radiation pressure noise at lower frequencies) of laser interferometer type GW detectors. Squeezing technology has recently started being used for GW detectors and becoming necessary and key components. Our ultimate goal is to participate and make contribution to international collaborations for upgrade of existing GW detectors and construction of next generation GW detectors. This presentation will summarize our results in 2020 and plan for the upcoming years. Technical details will be presented in other family talks.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4628-4636
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• 2023

Cables are indispensable in nuclear power plants for transmitting data measured by various types of detectors, such as self-powered neutron detectors (SPNDs). These cables will generate disturbing signals that must be accurately distinguished and eliminated. Given that the cable current is not very significant, previous research has focused on SPND, with little attention paid to cable evaluation and validation. This paper specifically focuses on the quantitative analysis of cables and proposes a theoretical model to predict cable noise. In this model, the reaction characteristics between irradiated neutrons and cables were discussed thoroughly. Based on the Monte Carlo method, a comprehensive simulation approach of neutron sensitivity was introduced and long-term irradiation experiments in a heavy water reactor (HWR) were designed to verify this model. The theoretical results of this method agree quite well with the experimental measurements, proving that the model is reliable and exhibits excellent accuracy. The experimental data also show that the cable current accounts for approximately 0.2% of the total current at the initial moment, but as the detector gradually depletes, it will contribute more than 2%, making it a non-negligible proportion of the total signal current.