• Journal of Radiation Protection and Research
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• v.45 no.2
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• pp.69-75
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• 2020

Background: Dose conversion coefficients (DCCs) have been commonly used to estimate radiation-dose absorption by human organs based on physical measurements of fluence or kerma. The International Commission on Radiological Protection (ICRP) has reported a library of DCCs, but few studies have been conducted on their applicability to non-Caucasian populations. In the present study, we collected a total of 8 Korean pediatric and adult voxel phantoms to calculate the organ DCCs for idealized external photon-irradiation geometries. Materials and Methods: We adopted one pediatric female phantom (ETRI Child), two adult female phantoms (KORWOMAN and HDRK Female), and five adult male phantoms (KORMAN, ETRI Man, KTMAN1, KTMAN2, and HDRK Man). A general-purpose Monte Carlo radiation transport code, MCNPX2.7 (Monte Carlo N-Particle Transport extended version 2.7), was employed to calculate the DCCs for 13 major radiosensitive organs in six irradiation geometries (anteroposterior, posteroanterior, right lateral, left lateral, rotational, and isotropic) and 33 photon energy bins (0.01-20 MeV). Results and Discussion: The DCCs for major radiosensitive organs (e.g., lungs and colon) in anteroposterior geometry agreed reasonably well across the 8 Korean phantoms, whereas those for deep-seated organs (e.g., gonads) varied significantly. The DCCs of the child phantom were greater than those of the adult phantoms. A comparison with the ICRP Publication 116 data showed reasonable agreements with the Korean phantom-based data. The variations in organ DCCs were well explained using the distribution of organ depths from the phantom surface. Conclusion: A library of dose conversion coefficients for major radiosensitive organs in a series of pediatric and adult Korean voxel phantoms was established and compared with the reference data from the ICRP. This comparison showed that our Korean phantom-based data agrees reasonably with the ICRP reference data.

• Nuclear Engineering and Technology
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• v.47 no.4
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• pp.479-487
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• 2015

The thallium-doped sodium iodide [NaI(Tl)] scintillation detector is preferred as a gamma spectrometer in many fields because of its general advantages. A silicon photomultiplier (SiPM) has recently been developed and its application area has been expanded as an alternative to photomultiplier tubes (PMTs). It has merits such as a low operating voltage, compact size, cheap production cost, and magnetic resonance compatibility. In this study, an array of SiPMs is used to develop an NaI(Tl) gamma spectrometer. To maintain detection efficiency, a commercial NaI(Tl) $2^{\prime}{\times}2^{\prime}$ scintillator is used, and a light guide is used for the transport and collection of generated photons from the scintillator to the SiPMs without loss. The test light guides were fabricated with polymethyl methacrylate and reflective materials. The gamma spectrometer systems were set up and included light guides. Through a series of measurements, the characteristics of the light guides and the proposed gamma spectrometer were evaluated. Simulation of the light collection was accomplished using the DETECT 97 code (A. Levin, E. Hoskinson, and C. Moison, University of Michigan, USA) to analyze the measurement results. The system, which included SiPMs and the light guide, achieved 14.11% full width at half maximum energy resolution at 662 keV.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.1
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• pp.31-36
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• 2021

This study is for the collection and analysis of historical records on forest culture. It aims to satisfy the people's needs for forest culture by collecting and analyzing forest-related materials described in the law codes of the Joseon Dynasty to grasp the general matters of the forest culture at that time. From the provisions related to forests in the law codes of the Joseon Dynasty (Gyeongguk Daejeon, Sokdaejeon, Daejeon Tongpyeon and Daejeonhoetong), it was possible to gain insights into the process of changes in forest administration and policies, and changes in forest landscape, etc. Analysis of the Gyeongguk Daejeon revealed the following facts; at that time, special tree species or trees produced only in specific regions were managed separately; and there was a lot of attention paid to manage the landscape of specific areas; for reserved forest management regulations, beyond the level of logging bans, attention was paid not only to planting and cultivation, but also to landscape creation. In addition, it was found that private ownership of forests was generally forbidden, however, the ownership of a certain range of forests around the tombs was recognized for funeral rites.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.136-143
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• 2018

Cellular automata have been applied to simulations in many fields such as astrophysics, social phenomena, fire spread, and evacuation. Using cellular automata, this study develops a model for consequence analysis of the dispersion of hazardous chemicals, which is required for risk assessments of and emergency responses for frequent chemical accidents. Unlike in cases of detailed plant safety design, real-time accident responses require fast and iterative calculations to reduce the uncertainty of the distribution of damage within the affected area. EPA ALOHA and KORA of National Institute of Chemical Safety have been popular choices for these analyses. However, this study proposes an initiative to supplement the model and code continuously and is different in its development of free software, specialized for small and medium enterprises. Compared to the full-scale computational fluid dynamics (CFD), which requires large amounts of computation time, the relative accuracy loss is compromised, and the convenience of the general user is improved. Using Python open-source libraries as well as meteorological information linkage, it is made possible to expand and update the functions continuously. Users can easily obtain the results by simply inputting the layout of the plant and the materials used. Accuracy is verified against full-scale CFD simulations, and it will be distributed as open source software, supporting GPU-accelerated computing for fast computation.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.1
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• pp.1-10
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• 2020

The application of multiphase flows is increasingly being applied to analyze phenomena such as single phase flows where the fluid boundary changes continuously over time or the problem of mixing a liquid phase and a gas phase. In particular, multiphase flow models that take into account incompressible Newtonian fluids for liquid and gas are often applied to solve the problems of the free water surface such as wave fields. In general, multi-phase flow models require time-based the surface tracking of each fluid's phase boundary, which determines the accuracy of the final calculation of the model. This study evaluates the advection performance of representative VOF-type boundary tracking techniques applied to various CFD numerical codes. The effectiveness of the FCT method to control the numerical flux to minimize the numerical diffusion in the conventional VOF-type boundary tracking method and advection calculation was mainly evaluated. In addition, the possibility of tracking performance of free surface using CIP method (Yabe and Aoki, 1991) was also investigated. Numerical results show that the FCT-VOF method introducing an anti-diffusive flux to precent excessive diffusion is superior to other methods under the confined conditions in this study. The results from this study are expected to be used as an important basic data in selecting free surface tracking techniques applied to various numerical codes.

• Journal of Radiation Protection and Research
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• v.31 no.2
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• pp.97-104
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• 2006

In recent years, the MOSFET dosimeter has been widely used in various medical applications such as dose verification in radiation therapeutic and diagnostic applications. The MOSFET dosimeter is, however, mainly made of silicon and shows some energy dependence for low energy Photons. Therefore, the MOSFET dosimeter tends to overestimate the dose for low energy scattered photons in a phantom. This study determines the correction factors to compensate these dependences of the MOSFET dosimeter in ATOM phantom. For this, we first constructed a computational model of the ATOM phantom based on the 3D CT image data of the phantom. The voxel phantom was then implemented in a Monte Carlo simulation code and used to calculate the energy spectrum of the photon field at each of the MOSFET dosimeter locations in the phantom. Finally, the correction factors were calculated based on the energy spectrum of the photon field at the dosimeter locations and the pre-determined energy and directional dependence of the MOSFET dosimeter. Our result for $^{60}Co$ and $^{137}Cs$ photon fields shows that the correction factors are distributed within the range of 0.89 and 0.97 considering all the MOSFET dosimeter locations in the phantom.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.189-194
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• 2018

Unmanned technology is a representative technology that integrates various technologies like electric, electronic, mechanical, artificial intelligence, ICT technology, ect. In special emphasize, ground technology has been developing exponentially in the military field and expanding its utilization area. The patent information analysis method presented in this study, proposes a new patent analysis methodology for patent information analysis and patent information on unmanned ground technology. The patent information analysis processor has 6 levels to extract core patents and technologies. The process consists of: selection of technology to be analyzed, classification of detailed technology / key keyword selection, patent information collection / noise reduction, selection of patent information analysis method, patent information analysis, finally, core patents and key technologies that are extracted. Patent information on unmanned ground technology is also analyzed in this study. First, the technical classification of ground unmanned technology is carried out in detail. The core technology and core patents of ground unmanned technology were extracted through CPP and IPC code connectivity analysis. The results of patent information analysis using proposed patent information analysis method that can be applied to various fields of technology and analysis. These can be used as a material to forecast the direction of future research and development on the technology to be analyzed.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.