• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.183-200
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• 2022

A variety of simulation approaches based on automated driving technologies have been proposed to develop traffic operations strategies to prevent traffic crashes and alleviate congestion. The maneuver of simulated autonomous vehicles (AVs) needs to be realistic and be effectively differentiated from the behavior of manually driven vehicles (MVs). However, the verification of simulated AV maneuvers is limited due to the difficulty in collecting actual AVs trajectory and interaction data with MVs. The purpose of this study is to develop a methodology to evaluate the suitability of AV maneuvers based on both driving and traffic simulation experiments. The proposed evaluation framework includes the requirements for the behavior of individual AVs and the traffic stream performance resulting from the interactions with surrounding vehicles. A driving simulation approach is adopted to evaluate the feasibility of maneuvering of individual AVs. Meanwhile, traffic simulations are used to evaluate whether the impact of AVs on the performance of traffic stream is reasonable. The outcome of this study is expected to be used as a fundamental for the design and evaluation of transportation systems using automated driving technologies.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.1-10
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• 2021

The purpose of this study is to explore a significant HPWS(High Performance Work System) model for the entertainment industry. HPWS is one of the most studied themes for managing human resources as well as a set of practices to elicit employees' commitment to an organization. Recently, the entertainment industry is growing rapidly, but it is difficult for entertainment firms to retain a stable profit unlike the manufacturing industry. This is because the performance of entertainment business tends to rely heavily on the capabilities and synergy of human resources. In order to suggest a systematic way to manage these, this research identified an effective HPWS model for entertainment business and provides a competitive advantage to entertainment firms, using ANP(Analytic Network Process). ANP is a multicriteria decision making technique that allows dependences and feedbacks among decision elements in the hierarchical or network structures in a holistic manner. The pairwise comparison data that prioritized the criteria of HPWS was collected from 28 team leaders in entertainment firms. According to our results, the most critical factor for HPWS in entertainment business is "employee involvement in decision-making." The sub-factors such as "open communication," "distributive decision-making," and "performance-driven reward" have a greater effect. These findings could provide implications for entertainment firms to determine which practices should be taken into account to accomplish HPWS.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.132-142
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• 2022

The purpose of this study is to interpret the MCU's universal worldview from the perspective of geometry and to storytell narrative elements with mathematical imagination. For storytelling, data from the Phase 3 series aired from 2016 to 2019 was used. The Phase 3 series stimulates the imagination of the public with the sense of reality shown in the narrative and images based on geometrical theory and various predictions about future technology. Imagination is the driving force for diverse and original thinking about the unexperienced, and the ability to find order in chaos and create new perceptions of matter. The power of imagination is very necessary not only in artistic activities, but also in the scientific field where logic and rationality are important. Bachelard's imagination aims for art, the primitive realm of human beings, and contains sincerity and passion for the wonders of nature and all things. By exploring the MCU's worldview and superhero narrative through geometrical logic and imagination-driven imagery, you can understand the cosmic messages and laws in the film. From a convergence point of view of art and science, various and original techniques based on mathematics and scientific imagination used in MCU video production will help to improve the quality of video analysis.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Korean Association of Arts Management
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• no.55
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• pp.75-113
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• 2020

This research aims to identify current issues of arts education programs which have designed for North Korean Refugee Youth in South Korea and then navigates further steps to better serve the group. The purpose of study also pertains to the '2010 Seoul Agenda: Goals for the Development of Arts Education' announced at the Second World Conference on Arts Education, particularly, to the third Goal indicating how arts education practices can contribute to resolving social and cultural issues and promoting social cohesion and intercultural dialogues (UNESCO, 2010). This research engages with critical theory approach and adopts qualitative content analysis for collected documents and interview data. Based on the findings of this research, interview participants found its need of current arts education program for North Korean refugee youth become more value-driven and participant-centered. Currently, those programs available seem to incline to helping their adjustment to the South Korean society through arts-related activities, such as enhancing Korean language skills and learning Korean culture. Rather, it has been addressed that providing emotional and psychological supports and opportunities to search their own 'voice(s)' should be core objectives of the arts education programs. Moreover, when it is offered, arts educators and administrators need to make sure that participants can feel safe and secure as being present at the space for programs in order to express and encounter their true inner voice(s).

• Journal of Korea Entertainment Industry Association
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• v.15 no.6
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• pp.55-70
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• 2021

This study investigates the following important financial questions using entertainment enterprises: 1) how does cash reserve affect a firm's financial value? 2) what factors influence the level of cash retention of a firm? For empirical tests, we use accounting and financial data of entertainment companies listed in the KOSPI and KOSDAQ markets for a long-term time period covering from 2000 to 2018. The main findings of this paper are as follows: First, entertainment companies maintain higher level of cash holdings compared to non-entertainment companies. Second, the cash holdings of entertainment enterprises have positive influence on firms' financial value. Third, among various firm characteristics known for affecting the cash holdings level, leverage and profitability exhibit strong relationships in entertainment enterprises. Entertainment firms with lower leverage and higher profitability tend to reserve more cash inside them. These findings suggest that entertainment companies are highly valued by stock market participants as having prospective opportunities, thus, firms with sufficient cash holdings tend to have higher firm value. In addition, these findings imply that cash in entertainment enterprises functions as a substitute for debts and the cash holdings are less likely driven by agency problems.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.331-338
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• 2022

Successor representation (SR) is a model of human reinforcement learning (RL) mimicking the underlying mechanism of hippocampal cells constructing cognitive maps. SR utilizes these learned features to adaptively respond to the frequent reward changes. In this paper, we evaluated the performance of SR under the context where changes in latent variables of environments trigger the reward structure changes. For a benchmark test, we adopted SR-Dyna, an integration of SR into goal-driven Dyna RL algorithm in the 2-stage Markov Decision Task (MDT) in which we can intentionally manipulate the latent variables - state transition uncertainty and goal-condition. To precisely investigate the characteristics of SR, we conducted the experiments while controlling each latent variable that affects the changes in reward structure. Evaluation results showed that SR-Dyna could learn to respond to the reward changes in relation to the changes in latent variables, but could not learn rapidly in that situation. This brings about the necessity to build more robust RL models that can rapidly learn to respond to the frequent changes in the environment in which latent variables and reward structure change at the same time.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.383-392
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established. Therefore, uncertainty analysis are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an unexpected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.