• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.104-115
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• 2019

Since the Fukushima Daiichi accident, there has been an emphasis on the risk resulting from multi-unit accidents. Human reliability analysis (HRA) is one of the important issues in multi-unit probabilistic safety assessment (MUPSA). Hence, there is a need to properly identify all the human and organizational factors relevant to a multi-unit incident scenario in a nuclear power plant (NPP). This study identifies and categorizes the human and organizational factors relevant to a multi-unit incident scenario of NPPs based on a review of relevant literature. These factors are then analyzed to ascertain all possible unit-to-unit interactions that need to be considered in the multi-unit HRA and the pattern of interactions. The human and organizational factors are classified into five categories: organization, work device, task, performance shaping factors, and environmental factors. The identification and classification of these factors will significantly contribute to the development of adequate strategies and guidelines for managing multi-unit accidents. This study is a necessary initial step in developing an effective HRA method for multiple NPP units in a site.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.618-625
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• 2021

In this paper, the single-photon avalanche diodes (SPADs) featuring three different p-well implantation doses (∅_p-well) of 5.0 × 10¹², 4.0 × 10¹², and 3.0 × 10¹² atoms/cm² under the identical device layouts were fabricated and characterized to evaluate the effects of field enhanced mechanisms on primary dark pulses due to the maximum electric field. From the I-V curves, the breakdown voltages were found as 23.2 V, 40.5 V, and 63.1 V with decreasing ∅_p-well, respectively. By measuring DCRs as a function of temperature, we found a reduction of approximately 8% in the maximum electric field lead to a nearly 72% decrease in the DCR at V_ex = 5 V and T = 25 ℃. Also, the activation energy increased from 0.43 eV to 0.50 eV, as decreasing the maximum electric field. Finally, we discuss the importance of electric field engineering in reducing the field-enhanced mechanisms contributing to the DCR in SPADs and the benefits on the SPADs related to different types of radiation detection applications.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2640-2645
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• 2021

We report the development of a gamma-ray imaging device, named Large-Area Hybrid Gamma Imager (LAHGI), featuring high imaging sensitivity and good imaging resolution over a broad energy range. A hybrid collimation method, which combines mechanical and electronic collimation, is employed for a stable imaging performance based on large-area scintillation detectors for high imaging sensitivity. The system comprises two monolithic position-sensitive NaI(Tl) scintillation detectors with a crystal area of 27 × 27 cm² and a tungsten coded aperture mask with a modified uniformly redundant array (MURA) pattern. The performance of the system was evaluated under several source conditions. The system showed good imaging resolution (i.e., 6.0-8.9° FWHM) for the entire energy range of 59.5-1330 keV considered in the present study. It also showed very high imaging sensitivity, successfully imaging a 253 µCi ¹³⁷Cs source located 15 m away in 1 min; this performance is notable considering that the dose rate at the front surface of the system, due to the existence of the ¹³⁷Cs source, was only 0.003 µSv/h, which corresponds to ~3% of the background level.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1483-1490
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• 2021

Many studies are undertaken into nuclear power plants (NPPs) in preparation for accidents exceeding design standards. In this paper, we analyze the applicability of various wireless communication technologies as accident countermeasures in different NPP environments. In particular, a commercial wireless communication module (WCM) is investigated by measuring leakage current and packet error rate (PER), which vary depending on the intensity of incident radiation on the module, by testing at a Co-60 gamma-ray irradiation facility. The experimental results show that the WCMs continued to operate after total doses of 940 and 1097 Gy, with PERs of 3.6% and 0.8%, when exposed to irradiation dose rates of 185 and 486 Gy/h, respectively. In short, the lower irradiation dose rate decreased the performance of WCMs more than the higher dose rate. In experiments comparing the two communication protocols of request/response and one-way, the WCMs survived up to 997 and 1177 Gy, with PERs of 2% and 0%, respectively. Since the request/response protocol uses both the transmitter and the receiver, while the one-way protocol uses only the transmitter, then the electronic system on the side of the receiver is more vulnerable to radiation effects. From our experiments, the tested module is expected to be used for design-based accidents (DBAs) of "Category A" type, and has confirmed the possibility of using wireless communication systems in NPPs.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1167-1175
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• 2021

The process of automation and monitoring in industrial control system involves the use of many types of sensors. A programmable logic controller plays an important role in the automation of the different processes in the power plant system. The major control units are boiler for temperature and pressure, turbine for speed of motor, generator for voltage, conveyer belt for fuel. The power plant units are controlled using microcontrollers and PLCs, but FPGA can be the feasible solution. The paper focused on the design and simulation of hardware chip to monitor boiler, turbine, generator and conveyer belt. The hardware chip of the plant is designed in Xilinx Vivado Simulator 17.4 software using VHDL programming. The methodology includes VHDL code design, simulation, verification and testing on Virtex-5 FPGA hardware. The system has four independent buzzers used to indicate the status of the boiler, generator, turbine motor and conveyer belt in on/off conditions respectively. The GSM is used to display corresponding message on the mobile to know the status of the device in on/off condition. The system is very much helpful for the industries working on plant automation with FPGA hardware integration.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.22-29
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• 2022

In this paper, for the purpose of designing an real-time unmanned monitoring system, the YOLOv5s (small) object detection model was applied on the NVIDIA TX2^TM AI (Artificial Intelligence) edge computing platform in order to design the fundamental function of an unmanned monitoring system that can detect objects in real time. YOLOv5s was applied to the our real-time unmanned monitoring system based on the performance evaluation of object detection algorithms (for example, R-CNN, SSD, RetinaNet, and YOLOv5). In addition, the performance of the four YOLOv5 models (small, medium, large, and xlarge) was compared and evaluated. Furthermore, based on these results, the YOLOv5s model suitable for the design purpose of this paper was ported to the NVIDIA TX2^TM AI edge computing system and it was confirmed that it operates normally. The real-time unmanned monitoring system designed as a result of the research can be applied to various application fields such as an security or monitoring system. Future research is to apply NMS (Non-Maximum Suppression) modification, model reconstruction, and parallel processing programming techniques using CUDA (Compute Unified Device Architecture) for the improvement of object detection speed and performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2873-2878
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• 2023

The purpose of this study is to perform radiation monitoring by acquiring gamma images and real-time optical images for ^99mTc vial source using charge couple device (CCD) cameras equipped with the proposed compact gamma camera. The compact gamma camera measures 86×65×78.5 mm³ and weighs 934 g. It is equipped with a metal 3D printed diverging collimator manufactured in a 45 field of view (FOV) to detect the location of the source. The circuit's system uses system-on-chip (SoC) and field-programmable-gate-array (FPGA) to establish a good connection between hardware and software. In detection modules, the photodetector (multi-pixel photon counters) is tiled at 8×8 to expand the activation area and improve sensitivity. The gadolinium aluminium gallium garnet (GAGG) measuring 0.5×0.5×3.5 mm³ was arranged in 38×38 arrays. Intrinsic and extrinsic performance tests such as energy spectrum, uniformity, and system sensitivity for other radioisotopes, and sensitivity evaluation at edges within FOV were conducted. The compact gamma camera can be mounted on unmanned equipment such as drones and robots that require miniaturization and light weight, so a wide range of applications in various fields are possible.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.52-62
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• 2023

Various decontamination technologies have been developed for removing contaminated areas in industries. Although it is important to consider parameters such as safety, cost, and time when selecting the decontamination technology, till date their comparative study is missing. Furthermore, different decontamination technologies influence the decontamination effects in different ways. Therefore, this study compares different decontamination techniques for the steam generator using a multicriteria decision-making method. A steam generator is a large device comprising both low- and very low-level waste (LLW, VLLW) and reflects the difference in weights of the standards according to the classification of the waste. For LLW and VLLW decontaminations, chemical oxidizing reduction decontamination (CORD) and decontamination grit blasting were used as the preferred techniques, respectively, considering the purpose of decontamination differs based on the initial state of waste. An expert survey revealed that safety in LLW and waste minimization in VLLW exhibited high preference. This evaluation method can be applied not only to the comparison between each process, but also to the creation of process scenarios. Therefore, determining the decontamination approach using logical decision-making methods may improve the safety and economic feasibility of each step in the decommissioning process and ensure a public acceptance.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.