• 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.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.41-56
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• 2021

New drug discovery and development research enable clinical treatment that saves human life and improves the quality of life, but the possibility of success with new drugs is significantly low despite a long time of 14 to 16 years and a large investment of 2 to 3 trillion won in traditional methods. As artificial intelligence is expected to radically change the new drug development paradigm, artificial intelligence new drug discovery and development projects are underway in various forms of collaboration, such as joint research between global pharmaceutical companies and IT companies, and government-private consortiums. This study uses the TOE framework and the Value-based Adoption Model, and the technical, organizational, and environmental factors that should be considered for the acceptance of AI technology at the level of the new drug research organization are the value of artificial intelligence technology. By analyzing the explanatory power of the relationship between perception and intention to use, it is intended to derive practical implications. Therefore, in this work, we present a research model in which technical, organizational, and environmental factors affecting the introduction of artificial intelligence technologies are mediated by strategic value recognition that takes into account all factors of benefit and sacrifice. Empirical analysis shows that usefulness, technicality, and innovativeness have significantly affected the perceived value of AI drug development systems, and that social influence and technology support infrastructure have significant impact on AI Drug Discovery and Development systems.

• Geomechanics and Engineering
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• v.24 no.6
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• pp.519-529
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• 2021

In coal mining activities, the abutment stress of the coal has to undergo cyclic loading and unloading, affecting the strength and seepage characteristics of coal; additionally, it can cause dynamic disasters, posing a major challenge for the safety of coal mine production. To improve the understanding of the dynamic disaster mechanism of gas outburst and rock burst coupling, triaxial devices are applied to axial pressure cyclic loading-unloading tests under different axial stress peaks and different pore pressures. The existing empirical formula is use to perform a non-linear regression fitting on the relationship between stress and permeability, and the damage rate of permeability is introduced to analyze the change in permeability. The results show that the permeability curve obtained had "memory", and the peak stress was lower than the conventional loading path. The permeability curve and the volume strain curve show a clear symmetrical relationship, being the former in the form of a negative power function. Owing to the influence of irreversible deformation, the permeability difference and the damage of permeability mainly occur in the initial stage of loading-unloading, and both decrease as the number of cycles of loading-unloading increase. At the end of the first cycle and the second cycle, the permeability decreased in the range of 5.777 - 8.421 % and 4.311-8.713 %, respectively. The permeability decreases with an increase in the axial stress peak, and the damage rate shows the opposite trend. Under the same conditions, the permeability of methane is always lower than that of helium, and it shows a V-shape change trend with increasing methane pressures, and the permeability of the specimen was 3 MPa > 1 MPa > 2 MPa.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

• Analyses & Alternatives
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• v.4 no.2
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• pp.103-132
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• 2020

The purpose of this study is to observe whether intergenerational differences exist in support among major Korean political parties and, if so, how they exist, based on the results of the survey conducted nationwide. To achieve the purpose of the study, a questionnaire was prepared based on conjoint analysis, and the collected data was analyzed by applying a random parameter logit model. The main results of model analysis are summarized as follows. First, among the policy variables, statistically significant results were observed in the generation of 20s and 30s for the education variable. It was found that both 20s and 30s aimed for equal education at a higher level than other generations. Especially, the highest intensity aim for equal education culture was observed in the 20s. Second, the coefficients of major political parties were observed with a high level of statistical significance. This appears to be a result suggesting that voters decide on their voting behavior through thorough policy comparisons in addition to comprehensive consideration on various current issues. Third, a clear support for conservative parties was observed in the generation of 20s. A clear and intense distribution of preference for political parties classified as conservatives was observed in the 20s generation, which can be said to be mainly college students. This seems to be a profound founding related to the issue of "conservatization of the 20s," which has recently become a hot topic in Korean society. Fourth, a high level of support for progressive parties was observed in the 30s and 40s. The Justice Party can be classified as a minority party in the National Assembly House as of January 2019. Nevertheless, it was maintained at a relatively high level in national recognition, and it is presumed that the background was high level of support from the 30s and 40s. Fifth, a large level of standard deviation was observed in the preference for conservative parties in the 50s. This means that some respondents who are in their 50s or older strongly support the Liberty Korea Party, and some respondents in the same generation strongly disapprove it. Due to this countervailing power, it seems that the average support level for the Liberal Korean Party is low in the generations of 50s and older.?

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.19-36
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• 2022

Purpose: The purpose of this study is to analyze the effects of COVID 19. The effects of COVID 19 are grouped into 5; economic stress, mental stress, health stress, task concern, self-confidence. We introduce the concept of personal ambidexterity that is necessary power for cabin crews to provide appropriate and efficient service to passengers. Ambidexterity consists of exploiting existing resources to sustain and exploring the new including method of performing task, customer, market etc. The former is necessary to maintain present condition while the latter is necessary to prepare for the future. Also motive is considered as a stimulating factor for task. Previous studies show that motive affects ambidexterity and we try to analyze whether COVID 19 effects influence this relationship. Research design, data, and methodology: Considering the relationship between the variables, we designed to measure the influence of the effects of COVID 19 by analyzing the moderating effects of them. For empirical analysis we distributed survey questionnaire and collected. Total of 361 samples are used fo the analysis. For analysis program, SPSS version 23 was used. Regression analysis and moderating effect analysis were conducted. Results: Study results show that first, the variables of economic stress, mental stress, health stress, task concern, self confidence affects personal ambidexterity and service provision. Also ambidexterity affects service provision significantly. Among COVID 19 effects, economic stress, task concern, and self confidence has moderating effects. On the other hand, new work environment does not have moderating effect. Conclusions: In conclusion, the effects of COVID 19 are wide and various. Among them the most serious effect is that COVID 19 is depriving workers of self confidence and passion toward the work. To remedy stresses and restore self confidence and passion, each worker should make his/her own efforts, such as, learning more to become more competitive, also firms should do make efforts to protect employees and to rebuild trust between firm and employees in every respect. Especially firms should realize that economic stress can be treated by economic compensation as the situation turns to normal but trust as well as self confidence and passion is not easy to restore.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3194-3210
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• 2022

Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.863-873
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• 2021

The mechanical dispersion which dominates solute transport in porous media is caused by the difference in flow velocity within pores. Longitudinal dispersion coefficient and longitudinal dispersivity that are hydro-dispersive parameters of advection-dispersion equation can only be obtained by experiment. Hydraulic mean radius that represents the amount and intensity of flowing water within pores can be obtained by the formula using the factors for physical properties. A slug injection test was conducted and a power type empirical formula for obtaining a longitudinal dispersivity using a hydraulic mean radius in rockfill porous media was derived. It is possible to obtain the longitudinal dispersivity depending on transport distance because it contains a formula for a scale constant, and expected to be applicable to waterways filled with homogeneous gravel and small flow rate.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.