• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.395-408
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• 2018

Even after the Fukushima nuclear accident in 2011, the rate of production of electric energy using nuclear energy is increasing, but there is a great danger such as the radioactive waste produced when using nuclear power, the catastrophic accident of nuclear power plant, and connection with nuclear weapons. In particular, Cs present in the ionic form of alkaline elements has a long half-life (30.17 years) because it is readily absorbed by the organism and emits intense gamma rays, thus presenting a serious radiation hazard. Therefore, it must be completely removed before it can be released into the natural ecosystem, because it can adversely affect not only humans but also natural ecosystems. Many adsorbents and ion exchangers which have high Cs removal efficiency have been used in recent years to completely separate and remove by self separation in water. Many adsorbents and ion exchangers which have high Cs removal efficiency have been used in recent years to completely separate and remove by self separation in water. In addition, researches have been doing to synthesize magnetic materials with adsorbents such as HCF and PB, and it shows a great effect in the removal rate of Cs present in wastewater or the maximum Cs adsorption amount. In particular, when a magnetic material was applied, excellent results were obtained in which only Cs was selectively removed from other cations. However, new problems such as applicability in the sea where Cs is directly released, applicability in various pH ranges, and failure to preserve the magnetizing force possessed by the magnetic body have been found. However, researches using ferromagnetic field with stronger magnetic properties than those of magnetic bodies is considered to be insufficient. Therefore, it is considered that if the researches combining the ferromagnetic field with the magnetization ability and functional adsorbents more actively, the radioactive material Cs which adversely affects the natural ecosystem can be effectively removed.

• Advances in Computational Design
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• v.4 no.2
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• pp.119-139
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• 2019

Planning and management building projects should tackle the coordination of works and the management of limited spaces, traffic and supplies. Activities cannot be performed without the resources available and resources cannot be used beyond the capacity of workplaces. Otherwise, workspace congestion will negatively affect the flow of works. Better on-site management allows for substantial productivity improvements and cost savings. The procurement system should be able to manage a wider variety of materials and products of the required quality in order to have less stock, in less time, using less space, with less investment and avoiding multiple storage stations. The objective of this paper is to demonstrate the advantages of using the Chronographic modeling, by combining spatiotemporal technical scheduling with the 4D simulations, the Last Planner System and the Takt-time when modeling the construction of building projects. This paper work toward the aforementioned goal by examining the impact that material flow has on site occupancy. The proposed spatiotemporal model promotes efficient site use, defines optimal site-occupancy and workforce-rotation rates, minimizes intermediate stocks, and ensures a suitable procurement process. This paper study the material flow on the site and consider horizontal and vertical paths, traffic flows and appropriate means of transportation to ensure fluidity and safety. This paper contributes to the existing body of knowledge by linking execution and supply to the spatial and temporal aspects. The methodology compare the performance and procurement processes for the proposed Chronographic model with the Gantt-Precedence diagram. Two examples are presented to demonstrate the benefits of the proposed model and to validate the related concepts. This validation is designed to test the model's graphical ability to simulate construction and procurement.

• The Research Journal of the Costume Culture
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• v.29 no.1
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• pp.103-120
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• 2021

This study interprets Siberian shaman costumes from the perspective of Siberian shamanism's spiritual culture by combining theoretical and empirical studies. According to the natural environment and language families, the Siberian people are classified into the Altai, Tungus, Ural, and Paleo-Siberian groups. Se Yin's research classifies the spiritual culture of Siberian shamanism as cosmic, spiritual, and nature view. Eliade's research has divided Siberian shaman costumes into form, headdress, and ornament. According to the present study, shaman costume form and decoration reflect the Siberian three-tiered cosmic view, such that the shaman's head, body and feet correspond to the upperworld, middleworld and underworld. In addition, animism, totemism and ancestral worship appear in the shamanism's spiritual view. For example, the costume's form shows the totem of each tribe, while the costume accessories reflect animal worship, plant worship and ancestral worship. Finally, shamanism's nature view mainly manifests through three processes: personification, deification, and ethics. As an intermediary between man and the spirits, shaman use their clothing to reproduce the image of half man and half spirit. The shaman's costumes are deified and considered to have divine power. For example, the animals represented on the costume help the shaman travel through space. Generally, good animals help a shaman enter the upperworld, while animals that help a shaman enter the underworld are considered evil. Also, the number of hanging accessories represents the shaman's ability.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.2
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• pp.105-113
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• 2022

Purpose: The purpose of this study is to investigate the effect of non-invasive transcranial direct current stimulation (tDCS) on muscle activity, including 10 m WT, TUG, and BBS, in hemiplegic stroke patients. Methods: This study was conducted on 42 inpatients diagnosed with hemiplegia due to stroke at hospital B in Daejeon for more than 6 months. Walking training was conducted for six weeks, five times a week for 30 minutes, with a general walking group (14 people), tDCS walking group (14 people), and tDCS (sham) walking group (14 people). Results: As a result of the study, the change in the muscle activity before and after tDCS intervention was significantly increased in the tibialis anterior muscle in the CG group. In the EG group, the erector spine (lumbar), rectus femoris, and tibialis anterior muscles significantly increased. In the SEG group, significant increases were observed in the rectus femoris and tibialis anterior muscles. Significant differences were found in the rectus femoris and tibialis anterior muscles in the comparison between groups after intervention according to tDCS application. Also, 10 m WT, TUG, and BBS were significantly increased in the CG, EG, and SEG groups after intervention, and there were significant differences in 10 m WT, TUG, and BBS in comparison between groups after intervention according to tDCS application. Conclusion: As a result, tDCS is an effective in improving the walking ability of stroke patients, and in particular, it effectively increases the muscle activity of the rectus femoris and tibialis anterior muscles, which act directly on walking, and also improves the speed and stability of walking. It is considered being an effective method to increase the gait of stroke patients by combining it with the existing gait training.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.668-674
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• 2022

Machine learning techniques using visual data have high usability in fields of industry and service such as scene recognition, fault detection, security and user analysis. Among these, user analysis through the videos from CCTV is one of the practical way of using vision data. Also, many studies about lightweight artificial neural network have been published to increase high usability for mobile and embedded environment so far. In this study, we propose the network combining the object detection and classification for mobile graphic processing unit. This network detects pedestrian and face, classifies age and gender from detected face. Proposed network is constructed based on MobileNet, YOLOv2 and skip connection. Both detection and classification models are trained individually and combined as 2-stage structure. Also, attention mechanism is used to improve detection and classification ability. Nvidia Jetson Nano is used to run and evaluate the proposed system.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.385-391
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• 2022

The relevance of the published study lies in the fact that since the introduction of the first Global Distribution System, new information and communication technologies have constantly been changing the tourism industry. In the context of a current digital environment, travel agencies can't avoid participating in digital transformation processes aimed at rethinking operational models, skills, and organisational structures in the regions. This publication aims to present and provide a critical overview of digitalisation processes in tourism development in the regions of the Russian Federation, as well as to reflect on the challenges to the widespread digitalisation processes in the regional tourism sector. The subject of research is digitalisation processes, as they radically transform the modern tourism industry, in the regions as well. The pragmatic research paradigm was considered the most appropriate for the study of tourism digitalisation processes in the regions, as it does not require the selection of a specific theoretical basis for data collection. The pragmatic approach forms an alternative to classical theoretical approaches and serves as a particular type of grounded theory, combining both inductive and deductive methods. No software was used for the inductive part of the analysis. The deductive part was conducted using the qualitative data analysis software Nvivo 11. Given the wide diversity of interested parties in the regional tourism digital area, a stratified purposive sampling method was preferred due to its ability to adequately represent the full picture of the phenomenon under study. The selection and stratum criteria were chosen to maximise the representation of different perspectives in the regional tourism digital area. The novelty of the study is due to the digitalisation processes, with an implication of new needs, while opening up promising opportunities for more productive tourism business in the regions of the Russian Federation. Currently, e-tourism in the Russian Federation has become a subject of lively debate among scholars and practitioners. However, the involvement of advanced digitalisation technologies in the field of information processes in the regions of the Russian Federation is of a very sporadic character.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.273-284
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• 2022

The goal of this paper is to propose the use of machine learning platforms in education to train learners to recognize data biases. Learners can cultivate the ability to recognize when learners deal with AI data and systems when they want to prevent damage caused by data bias. Specifically, this paper presents a method of data bias education using MachineLearningforKids, focusing on the case of AI baseball referee. Learners take the steps of selecting a specific topic, reviewing prior research, inputting biased/unbiased data on a machine learning platform, composing test data, comparing the results of machine learning, and present implications. Learners can learn that AI data bias should be minimized and the impact of data collection and selection on society. This learning method has the significance of promoting the ease of problem-based self-directed learning, the possibility of combining with coding education, and the combination of humanities and social topics with artificial intelligence literacy.

• Fashion & Textile Research Journal
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• v.24 no.6
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• pp.719-730
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• 2022

This study explored the impact of beauty influencers' characteristics and product characteristics on new product acceptance intentions and studied the mediating effects of consumer trust in this process. A survey was conducted from February 22, 2021, to February 28, 2021, with Gen Y and Gen Z women in China, and 379 questionnaires were analyzed. The conclusions are as follows: First, the characteristics of beauty influencers are authenticity and expertise, similarity, attractiveness, interactivity, familiarity, and trustworthiness; product characteristics are cost, image, product quality, product perception, sales promotion, and sustainability. Second, partial beauty influencers' characteristics and partial product characteristics have a positive impact on consumer confidence and acceptance intention of the new product. Third, the mediating effect of consumer trust in the process by which beauty influencers' characteristics and product characteristics influence the intention of new product acceptance was determined. Therefore, when beauty companies use influencers in marketing, it is necessary to understand their characteristics, consider their professionality and authenticity, examine their reliability, and assess their ability to form connections with images and viewers that match their products. Additionally, to increase the acceptance intention of new products, companies should present the price of high-quality products, product sensibilities, and corporate images of products and establish measures that can positively affect consumers' acceptance intention of new products by combining them with the characteristics of beauty influencers.