• Progress in Medical Physics
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• v.33 no.2
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• pp.11-24
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• 2022

X-ray detection has widely been applied in medical diagnostics, security screening, nondestructive testing in the industry, etc. Medical X-ray imaging procedures require digital flat detectors operating with low doses to reduce radiation health risks. Recently, metal halide perovskites (MHPs) have shown great potential in high-performance X-ray detection because of their attractive properties, such as strong X-ray absorption, high mobility-lifetime product, tunable bandgap, low-temperature fabrication, near-unity photoluminescence quantum yields, and fast photoresponse. In this paper, we review and introduce the development status of new perovskite X-ray detectors and imaging, which have emerged as a new promising high-sensitivity X-ray detection technology. We discuss the latest progress and future perspective of MHP-based X-ray detection in medical imaging. Finally, we compare the conventional detection methods with quantum-enhanced detection, pointing out the challenges and perspectives for future research directions toward perovskite-based X-ray applications.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.6-6
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• 2018

• Journal of the Korean Institute of Gas
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• v.13 no.1
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• pp.45-51
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• 2009

A safety assessment was performed through the process analysis of hydrogen station. The purpose of this study provides basic information for the standard establishment about hydrogen stations. The processes of hydrogen stations were classified by four steps (process of manufacture, compression, storage, charge). FMEA (Failure Mode and Effect Analysis) method was applied to evaluate safety. Each risk element is following; S (severity), O (occurrence), D (detection). And the priority of order was decided by using RPN (Risk Priority Number) value multiplying three factors. Scenarios were generated based on FMEA results. And consequence analysis was practiced using PHAST program. In the result of C.A, jet fire and explosion were shown as accident types. In case of leakage of feed line in PSA process, concentration of CO gas is considered to prevent CO gas poisoning when the raw material that can product CO gas was used.

• Proceedings of the Korean Magnestics Society Conference
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• 2010.06a
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• pp.96-96
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• 2010

We report the proper resistance-area products in the single crystalline bcc CoFe/MgO tunnel contact on nondegenerate n-Ge desirable for efficient spin injection and detection at room temperature. The electric properties of the crystalline CoFe(5 nm)/MgO(1.5,2.0,2.5 nm)/n-Ge(001) tunnel contacts have been investigated by I-V-T and C-V measurements. Interestingly, the tunnel contact with the 2-nm MgO exhibits the ohmic behavior with low resistance-area products, satisfying the theoretical conditions required for significant spin injection and detection. This result is ascribed to the presence of MgO layer between CoFe and n-Ge, enhancing the Schottky pinning parameter as well as shifting the charge neutrality level.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.144-154
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• 2020

Purpose: This paper is a fundamental study on the applicability of the smart bolt developed for monitoring system to detect the leakage of water supply valve. Method: A leak detection experiments were conducted using the smart bolt having embedded strain sensors and accelerometer. The smart bolt used in study meets the allowable criteria of torque and tensile stress for water supply system, and it can be applied to a joint of the water supply valve by behaving well within the allowable limits. Result: As a result of the simulated leak tests, a leak signal at the valve leak point was detected in a band of 60Hz, and the main pipe leaking point was observed to produce a leak signal having much higher frequency than that of the valve leak point. This seems to result in a total coupled vibration under unconfined conditions of the pipes. Conclusion: The smart bolts appeared applicable to detecting a leaking signal from the water supply valve.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.57-67
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• 2022

CZT detectors, which are compound semiconductors that have been widely used recently for gamma-ray detection purposes, are difficult to detect neutrons because direct interaction with them does not occur unlike gamma-rays. In this paper, a method of detecting and determining energy levels (fast neutrons and thermal neutrons) of neutrons, in addition of identifying energy and nuclide of gamma-rays, and evaluating gamma dose rates using a CZT semiconductor detector is described. Neutrons may be detected by a secondary photoelectric effect or compton scattering process with a characteristic gamma-ray of 558.6 keV generated by a capture reaction (¹¹³Cd + ¹n → ¹¹⁴Cd + 𝛾) with cadmium (Cd) in the CZT detector. However, in the case of fast neutrons, the probability of capture reaction with cadmium (Cd) is very low, so it must be moderated to thermal neutrons using a moderator and the material and thickness of moderator should be determined in consideration of the portability and detection efficiency of the equipment. Conversely, in the case of thermal neutrons, the detection efficiency decreases due to shielding effect of moderator itself, so additional CZT detector that do not contain moderator must be configured. The CZT detector that does not contain moderator can be used to evaluate energy, nuclide, and gamma dose-rate for gamma-rays. The technology proposed in this paper provides a method for detecting both neutrons and gamma-rays using a CZT detector.

• YAKHAK HOEJI
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• v.42 no.2
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• pp.149-152
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• 1998

A high performance liquid chromatographic method was developed for the determination of DA-6034 in biological fluids using internal standard. Plasma containing DA-6034 and inter nal standard was extracted by liquid-liquid extraction at an acidic pH. After evaporation of the organic layer, the drug and internal standard were reconstituted with mobile phase and injected into the column. They were separated by high performance liquid chromatography on inertsil ODS II column at 334 nm. The detection limit of DA-6034 in plasma was 0.02 ${\mu}$g/ml. In this method, the range of recovery and coefficients of variation were 96-110% and 0.40-3.78%, respectively. There was no interference from endogenous substances. Urine and bile were analysed using the deproteinization method and the detection limit of DA-6034 was 1${\mu}$g/l.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.893-904
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• 2006

To improve sensitivity of biosensor as yeast two-hybrid detection system for estrogenic activity of suspected chemicals, we tested effects of several combinations of the bait and fish components in the two-hybrid system on Saccharomyces cerevisiae inducted a chromosome-integrated lacZ reporter gene that was under the control of CYC1 promoter and the upstream Gal4p-binding element $UAS_{GAL}$. The bait components that were fused with the Gal4p DNA binding domain are full-length human estrogen receptor ${\alpha}$ and its ligand-binding domain. The fish components that were fused with the Gal4p transcriptional activation domain were nuclear receptor-binding domains of co-activators SRC1 and TIF2. We found that the combination of the full-length human estrogen receptor ${\alpha}$ with the nuclear receptor-binding domain of co-activator SRC1 was most effective for the estrogen-dependent induction of reporter activity among the two-hybrid systems so far reported. The relative strength of transcriptional activation by representative natural and xenobiotic chemicals was well correlated with their estrogenic potency that had been reported with other assay systems.

• International Journal of Oral Biology
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• v.46 no.1
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• pp.1-6
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• 2021

Since the outbreak of coronavirus disease 2019 (COVID-2019), the infection has spread worldwide due to the highly contagious nature of severe acute syndrome coronavirus (SARS-CoV-2). To manage SARS-CoV-2, the development of diagnostic assays that can quickly and accurately identify the disease in patients is necessary. Currently, nucleic acid-based testing and serology-based testing are two widely used approaches. Of these, nucleic acid-based testing with quantitative reverse transcription-PCR (RT-qPCR) using nasopharyngeal (NP) and/or oropharyngeal (OP) swabs is considered to be the gold standard. Recently, the use of saliva samples has been considered as an alternative method of sample collection. Compared to the NP and OP swab methods, saliva specimens have several advantages. Saliva specimens are easier to collect. Self-collection of saliva specimens can reduce the risk of infection to healthcare providers and reduce sample collection time and cost. Until recently, the sensitivity and accuracy of the data obtained using saliva specimens for SARS-CoV-2 detection was controversial. However, recent clinical research has found that sensitive and reliable data can be obtained from saliva specimens using RT-qPCR, with approximately 81% to 95% correspondence with the data obtained from NP and OP swabs. These data suggest that self-collected saliva is an alternative option for the diagnosis of COVID-19.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.