• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3604-3613
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• 2022

The Physical Protection System (PPS) plays an important role and must effectively deal with various adversary attacks in nuclear security. In specific single adversary path scenarios, we can calculate the PPS effectiveness by EASI (Estimated Adversary Sequence Interruption) through Probability of Interruption (P_I) calculation. EASI uses a single value of the probability of detection (P_D) and the probability of alarm communications (P_C) in the PPS. In this study, we develop a multi-path analysis code based on EASI to evaluate the effectiveness of PPS. Our quantification method for P_I considers the variability and uncertainty of P_D and P_C value by Monte Carlo simulation. We converted the 2-D scheme of the nuclear facility into an Adversary Sequence Diagram (ASD). We used ASD to find the adversary path with the lowest probability of interruption as the most vulnerable paths (MVP). We examined a hypothetical facility (Hypothetical National Nuclear Research Facility - HNNRF) to confirm our code compared with EASI. The results show that implementing the variability extension can estimate the P_I value and its associated uncertainty. The multi-path analysis code allows the analyst to make it easier to assess PPS with more extensive facilities with more complex adversary paths. However, the variability of the P_D value in each protection element allows a significant decrease in the PI value. The possibility of this decrease needs to be an important concern for PPS designers to determine the P_D value correctly or set a higher standard for PPS performance that remains reliable.

• Journal of People, Plants, and Environment
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• 제24권6호
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• pp.585-593
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• 2021

Background and objective: Illegal open-air incineration, which is criticized as a leading source of air pollutants among agricultural activities, currently requires constant effort and attention. Countries around the world have been undertaking studies on the emission of heavy metal substances in fine dust discharged during the incineration process. A precise analytical method is required to examine the harmful effects of particulate pollutants on the human body. Methods: In order to simulate open-air incineration, the infrastructure needed for incineration tests complying with the United States Environmental Protection Agency (EPA) Method 5G was built, and a large-area analysis was conducted on particulate pollutants through automated scanning electron microscopy (SEM)-energy-dispersive X-ray spectroscopy (EDS). For the test specimen, high-density polyethylene (HDPE) waste collected by the DangJin Office located in Choongcheongnam-do was used. To increase the identifiability of the analyzed particles, the incineration experiment was conducted in an incinerator three times after dividing the film waste into 200 g specimens. Results: Among the metal particulate matters detected in the HDPE waste incineration test, transition metals included C (20.8-37.1 wt%) and O (33.7-37.9 wt%). As for other chemical matters, the analysis showed that metal particulate matters such as metalloids, alkali metals, alkaline earth metals, and transition metals reacted to C and C-O. Si, a representative metalloid, was detected at 14.8-20.8 wt%, showing the highest weight ratio except for C and O. Conclusion: In this study, the detection of metal chemicals in incinerated particulate matters was effectively confirmed through SEM-EDS. The results of this study verified that HDPE waste adsorbs metal chemicals originating from soil due to its own properties and deterioration, and that when incinerated, it emits particulate matters containing transition metals and other metals that contribute to the excessive production and reduction of reactive oxygen species.

• Journal of the Korean Society of Radiology
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• 제17권1호
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• pp.17-23
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• 2023

In this study, the thermoluminescence kinetics of electron trap in Li₆Y_0.5Gd_0.5(BO₃)₃ (LY_0.5G_0.5BO) scintillator for neutron detection composed of Li, Gd, and B with a high neutron response cross-section were investigated. The thermoluminescence glow curve of the LY_0.5G_0.5BO scintillation single crystal was measured and analyzed using the peak shape method, the initial rise method, and the machine learning algorithm to evaluate the physical parameters of the electron trap. The glow curve of the LY_0.5G_0.5BO scintillation single crystal consisted of a single peak. As a result of analyzing this peak, the activation energy, emission order, and frequency factor of the electron trap were 0.61 eV, 1.1, and 1.7×10⁷ s^-1, respectively. In addition, the possibility of thermoluminescence analysis of scintillators using machine learning was confirmed.

• Journal of Sensor Science and Technology
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• 제31권6호
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• pp.455-460
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• 2022

Brush-like ZnO hierarchical nanostructures decorated with MgxZn1-xO (x = 0.1, 0.2, 0.3, 0.4, and 0.5) were fabricated and examined for application to a gas sensor. They were synthesized using vapor phase growth (VPG) on indium tin oxide (ITO) substrates. To generate electronic accumulation at ZnO surface, MgZnO nanoparticles were prepared by sol-gel method, and the ratio of Mg and Zn was adjusted to optimize the device for NO₂ gas detection. As the electrons in the accumulation layer generated by the heterojunction reacted faster and more frequently with the gas, the sensitivity and speed improved. When tested as sensing materials for gas sensors at 100 ppm NO₂ at 300℃, these MgZnO decorated ZnO nanostructures exhibited an improvement from 165 to 514 times compared to pristine ZnO. The response and recovery time of the MgZnO decorated ZnO samples were shorter than those of the pristine ZnO. Various analyzing techniques, including field-emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray powder diffraction (XRD) were employed to confirm the growth morphology, atomic composition, and crystalline information of the samples, respectively.

• The Journal of the Korea institute of electronic communication sciences
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• 제18권1호
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• Smart Structures and Systems
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• 제31권2호
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.

• Composites Research
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• 제36권3호
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• pp.217-223
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• 2023

In this work, the preparation and its oil adsorption behavior of polyacrylonitrile-based carbon nanofiber sponge were investigated. The prepared carbon sponges showed excellent selective oil adsorption in the mixture of water and oil, and the adsorption capacity of reused carbon nanofiber sponge was also investigated. Further, carbon nanofiber sponge adsorbent with internally structured channel showed fast oil adsorption behavior due to a capillary phenomenon. After use, sponge adsorbent was heat-treated at 800℃ under N² and studied the possibility of a sensor for electrochemical detection of 4-aminophenol.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.199-201
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• 2021

The Internet of Things (IoT) is a technology that can organically connect people and things without time and space constraints by using communication network technology and sensors, and transmit and receive data in real time. The IoT used in all industrial fields has limitations in terms of storage allocation, such as device size, memory capacity, and data transmission performance, so it is important to manage power consumption to effectively utilize the limited battery capacity. In the prior research, there is a problem in that security is deteriorated instead of improving power efficiency by lightening the security algorithm of the encryption module. In this study, we proposes a low-power security architecture that can utilize high-performance security algorithms in the IoT environment. This can provide high security and power efficiency by using relatively complex security modules in low-power environments by executing security modules only when threat detection is required based on inspection results.

• Nuclear Engineering and Technology
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• 제55권7호
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• pp.2349-2355
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• 2023

In the TRITIUM project, an on-site monitoring system is being developed to measure tritium (³H) levels in water near nuclear power plants. The quite low-energy betas emitted by ³H have a very short average path in water (5 ㎛ as shown by simulations for 18 keV electrons). This path would be further reduced by impurities present in the water, resulting in a significant reduction of the detection efficiency. Therefore, one of the essential requirements of the project is the elimination of these impurities through a filtration process and the removal of salts in solution. This paper describes a water treatment system developed for the project that meets the following requirements: the water produced should be of near-pure water quality according to ISO 3696 grade 3 standard (conductivity < 10 µS/cm); the system should operate autonomously and be remotely monitored.

• Journal of the Korea Society of Computer and Information
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• 제28권4호
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.