• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.2
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• pp.245-261
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• 2019

Radioactive contaminant from a nuclear facility moves to the ecosystem by run-off or groundwater flow. Among the two mechanisms, contaminant plume through a river can be easily detected through a surface water monitoring system, but radioactive contaminant transport in groundwater is difficult to monitor because of lack of information on flow path. To understand the contaminant flow in groundwater, understanding of the geo-environment is needed. We suggest a method to decide on monitoring location and points around an imaginary nuclear facility by using the results of site characterization in the study area. To decide the location of a monitoring well, groundwater flow modeling around the study area was conducted. The results show that, taking account of groundwater flow direction, the monitoring well should be located at the downstream area. Also, monitoring sections in the monitoring well were selected, points at which groundwater moves fast through the flow path. The method suggested in the study will be widely used to detect potential groundwater contamination in the field of oil storage caverns, pollution by agricultural use, as well as nuclear use facilities including nuclear power plants.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.3
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• pp.279-298
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• 2019

This study comprehensively addresses recent progress at KAERI in waste treatment technology to cope with waste produced by pyroprocessing, which is used to effectively manage spent fuel. The goal of pyroprocessing waste treatment is to reduce final waste volume, fabricate durable waste forms suitable for disposal, and ensure safe packaging and storage. KAERI employs grouping of fission products recovered from process streams and immobilizes them in separate waste forms, resulting in product recycling and waste volume minimization. Novel aspects of KAERI approach include high temperature treatment of spent oxide fuel for the fabrication of feed materials for the oxide reduction process, and fission product concentration or separation from LiCl or LiCl-KCl salt streams for salt recycling and higher fission-product loading in the final waste form. Based on laboratory-scale tests, an engineering-scale process test is in progress to obtain information on the performance of scale-up processes at KAERI.

• Tunnel and Underground Space
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• v.31 no.4
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• pp.243-269
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• 2021

Since the early 2010s, the social importance of research and practical projects targeting deep geological disposal of high-level nuclear waste, underground CO₂ storage and characterization of deep subsurface by borehole investigation has been increasing. In this regard, there is also a significant increase in the need for in situ test technology to obtain quantitative and reliable information on the hydraulic characteristics of deep rock mass. Through years of research and development, we have independently set up Deep borehole Hydraulic Test System (DHTS) based on the key apparatuses designed and made with our own technology. Using this system, high precision constant pressure injection tests were successfully completed at the two 1 km boreholes located in Mesozoic granite and sedimentary rock regions, Gyeongju. During the field tests, it was possible to measure very low flow rate below 0.01 l/min with micro flow rate injection/control module. In this paper, the major characteristics of DHTS are introduced and also some results obtained from the high precision field tests under the deep and low permeable rock mass environment are briefly discussed.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Tunnel and Underground Space
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• v.32 no.2
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• pp.107-119
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• 2022

Excavation-Disturbed Zone (EDZ) is an important design factor in constructing final disposal facilities for spent nuclear fuel, since EDZ affects mechanical stability including a spacing between disposal holes, and the hydraulic properties within EDZ plays a significant role in estimating in-flow rate of groundwater as well as a subsequent corrosion rate of a canister. Thus, it is highly required to characterize in-situ EDZ with precision and control the EDZ occurrence while excavating disposal facilities and constructing relevant underground research facilities. In this report, we not only reviewed EDZ-related researches carried out in the ONKALO facility of Finland but also examined appropriate methods for field inspection and quality control of EDZ occurrence. From the review, GPR can be the most efficient method for in-situ characterization of EDZ since it does not demand drilling a borehole that may disturb a surrounding environment of caverns. And the EDZ occurrence was dominant at a cavern floor and it ranged from 0 to 70 cm. These can provide useful information in developing necessary EDZ-related regulations for domestic disposal facilities.

• Analytical Science and Technology
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• v.35 no.3
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• pp.93-115
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• 2022

Potential impurities in pharmaceuticals could be produced during manufacture, distribution, and storage and affect quality and safety of pharmaceuticals. In particular, highly reactive impurities could result in carcinogenic (mutagenic) effects on human body. International Conference on Harmonisation (ICH) has provided M7(R1) guideline for "Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk" and recommended an adoption of this guideline to the authorities. ICH M7(R1) guideline provides classification, accepted intakes, and controls of potential impurities in pharmaceuticals. However, since appropriate and unified analytical methods for impurities in pharmaceuticals have not been provided in this guideline, most potential impurities in pharmaceuticals are still difficult to manage and supervise by pharmaceutical companies and regulatory authorities, respectively. In this review, we briefly described definition of unintended mutagenic impurities, basic information in ICH M7(R1) guideline, and analytical methods to determine potential impurities. This review would be helpful to manage and supervise potential impurities in pharmaceuticals by pharmaceutical companies and regulatory authorities.

• Smart Media Journal
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• v.11 no.3
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• pp.39-53
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• 2022

Cloud of Things (CoT) can provide IoT applications with unlimited storage functions and processing power supported by cloud services. However, in a centralized cloud of things, it can create a single point of failure that can lead to bottleneck problems, outages of the CoT network. In this paper, to solve the problem of centralized cloud of things and interoperate between different service domains, we propose an IoT service interoperability system using distributed fog computing and blockchain technology. Distributed fog is used to provide real-time data processing and services in fog systems located at a geographically close distance to IoT devices, and to enable service interoperability between each fog using smart contracts and distributed ledgers of the blockchain. The proposed system provides services within a region close to the distributed fog entrusted with the service from the cloud, and it is possible to access the services of other fogs without going through the cloud even between fogs. In addition, by sharing a service right token issuance information between the cloud and fog nodes using a blockchain network, the integrity of the token can be guaranteed and reliable service interoperability between fog nodes can be performed.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.36-41
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• 2021

Although the technology for prediction or analysis using artificial intelligence is constantly developing, a black-box problem does not interpret the decision-making process. Therefore, the decision process of the AI model can not be interpreted from the user's point of view, which leads to unreliable results. We investigated the problems of artificial intelligence and explainable artificial intelligence using Blockchain to solve them. Data from the decision-making process of artificial intelligence models, which can be explained with Blockchain, are stored in Blockchain with time stamps, among other things. Blockchain provides anti-counterfeiting of the stored data, and due to the nature of Blockchain, it allows free access to data such as decision processes stored in blocks. The difficulty of creating explainable artificial intelligence models is a large part of the complexity of existing models. Therefore, using the point cloud to increase the efficiency of 3D data processing and the processing procedures will shorten the decision-making process to facilitate an explainable artificial intelligence model. To solve the oracle problem, which may lead to data falsification or corruption when storing data in the Blockchain, a blockchain artificial intelligence problem was solved by proposing a blockchain-based explainable artificial intelligence model that passes through an intermediary in the storage process.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.84-91
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• 2021

In the 21st century, a number of storm and flood disasters caused by rapidly changing climate change is increasing, and the number of flood accidents at construction sites is also increasing. However, no specific reduction measures have been presented and thereby safety management to prevent flood accident need to be improved. Therefore, in this study, the inundation pattern by downpour at the excavation site was interpreted and the inundation risk quantification method was used to classify the risk magnitude. Finally, using the fish-bone diagram, we derived the major reasons of inundation accident at construction site systematically. The simulation results showed that the inundation depths of small-scale excavation sites and excavation sites exceeded 3 m due to the fluid flowing through the excavation surface. In addition, depending on the excavation site, a high velocity temporarily observed and decreased due to the storage effect, or high velocity surpassing 10 m/s continued. Since this type of flooding can pose a risk to most or all workers, if proper management measures are insufficient, fatal damage to life and property could occur. Consideration of the roots of these disasters is judged to be helpful in understanding the causes of inundation accidents that result in casualties and presenting accident reduction measures.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.495-507
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• 2021

With the recent industrial development, accidental pollution in riverine environments has frequently occurred. It is thus necessary to simulate pollutant transport and dispersion using water quality models for predicting pollutant residence times. In this study, we conducted a field experiment in a meandering reach of the Sum River, South Korea, to validate the field applicability and prediction accuracy of RAMS+ (River Analysis and Modeling System+), which is a two-dimensional (2D) stream flow/water quality analysis program. As a result of the simulation, the flow analysis model HDM-2Di and the water quality analysis model CTM-2D-TX accurately simulated the 2D flow characteristics, and transport and mixing behaviors of the pollutant tracer, respectively. In particular, CTM-2D-TX adequately reproduced the elongation of the pollutant cloud, caused by the storage effect associated with local low-velocity zones. Furthermore, the transport model effectively simulated the secondary flow-driven lateral mixing at the meander bend via 2D dispersion coefficients. We calculated the residence time for the critical concentration, and it was elucidated that the calculated residence times are spatially heterogeneous, even in the channel-width direction. The findings of this study suggest that the 2D water quality model could be the accidental pollution analysis tool more efficient and accurate than one-dimensional models, which cannot produce the 2D information such as the 2D residence time distribution.