• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.213-221
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• 2018

South Korea has been reluctant to open its geospatial information overseas to ensure security as a divided country. However, this cannot continue as the domestic and international environments related to geospatial information and the industrial ecosystem of information and communication technologies have been changing dramatically. Within this context, this study aims to analyze the causal relations among relevant variables and how they change and interact with time using a systems thinking process. First, causal maps were created for the domains of national security, map-based convergence service, and corporate competition. Then, the causal maps for each domain were integrated, based on which the points for policy intervention and dominant feedback loops were identified. The analysis results showed that securing the self-sufficiency of domestic geospatial businesses is a key element to determine the whole causal map, and the variable that changes the dominant feedback loop from a vicious circle to a virtuous one is the decision to open geospatial information overseas. In this study, I found the policy leverage that is a policy intervention point that can produce a great effect with little input by building a causal map of the interactions between major variables. This study is significant in that it identified and analyzed the dominant feedback loop as to which causal structure would dominate the system in the long term. The results of this study can be used to discuss not only the impacts of map data overseas opening on the national security and geospatial information industry, but also the interactions in the future when Google or other global companies request to release the geospatial information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.503-514
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• 2019

This paper proposes a blockchain-based urban service-operation model that can enhance usability by integrating several data-driven services operated in a city. In the proposed model, in order to encourage the participation of service users, the providers of data and values that can be consumed and utilized by each service acquire incentives, and consumers can use various services by paying the incentives. In this way, the proposed service model provides a structure in which various services can be interworked within the incentive system. The characteristics of blockchain technology can also guarantee service operation and management transparency. In addition, in this paper, by establishing and operating a prototype, the efficiency and operability of the proposed model are verified. As a result, three implemented data-driven urban management services are organically inter-compatible based on the concept of the proposed integrated incentive system. In the future, the proposed service model can be applied as an elemental technology of urban operational and management architectures based on citizen participation using local currency, and by cooperating with local economic revitalization projects of interest to many local governments. It is expected that the expansion of the blockchain technology area will also be possible through convergence with smart city services.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.197-202
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• 2019

In the era of the fourth revolution, interest in content production using proven engines in the broadcasting sector, such as Vizrt, is growing. The new visual effects required in the 5G era are critical to content production training. Vizrt has a good production time utility and affordability for broadcast and media content. In this paper, we are going to use this to present a practical case of the theorem and application of the basic training course in the production of virtual content, and to present the basic training direction. In the introduction, the graphic algorithm analyzed and studied the characteristics and environmental factors of the Vizrt engine. In this paper, the production process was studied separately, and the work carried out through engine implementation was presented. The VS Studio Foundation was provided as a practical production case at each stage. The Vizrt engine operator process is important in graphic approach and application, and through the results of the lecture, the method of understanding and implementing algorithms for virtual reality perspective suitable for basic learning was studied. Based on practice, the research method of main theory was to create Vizrt contents specialized in 5G contents work in each sector and to implement graphic production in new areas from contents image. Through this study, we came to the conclusion of the basic training method through virtual reality content work based on Vizrt by practicing content creation according to the subject. It also proposes the effect of creating Vizrt content and the direction of building Vizrt basic training courses.

• The Mathematical Education
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• v.60 no.4
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• pp.467-491
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• 2021

This study is a study on the coding mathematics curriculum that converges elementary and middle school mathematics and information subjects and a minimum coding game-based education method for this. For the past 3 years, the coding mathematics curriculum and educational methods to effectively operate the curriculum were studied by applying them to 6th graders of elementary school and 1st graders of middle school. As a result of the first year of research, the coding mathematics curriculum was modified to a coding environment including the mathematical concept of a three-dimensional coordinate space, and the three-dimensional object was improved to be output as a real 3D print. As a result of the 2nd year study, it was improved so that even low-level students can build buildings by introducing different level commands for each component of the building so that self-directed learning is possible. As a result of the 3rd year study, a teaching-learning strategy based on a minimal coding game was designed to induce an increase in the level of computational thinking, and evaluation and feedback for diagnosing computational thinking were developed. Educational methods to promote self-directed learning and computing thinking ability, and researched coding mathematics curriculum are meaningful for the research and practice of the convergence education of school mathematics and informatics.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1244-1251
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• 2022

Marine accidents due to a loss of stability, have been gradually increasing over the last decade. Measures must be taken on the roll reduction of a ship. Amongst the measures, building an anti-roll tank in a ship is recognized as the most simple and effective way to reduce the roll motion. Therefore, this study aims to develop a computational model for a U-tube type anti-roll tank and to validate it by experiment. In particular, to validate the developed computational model, the height of the free surface in the tank was measured in the experiment. To develop a computational model, the mesh dependency test was carried out. Further, the effects of a turbulence model, time step size, and the number of iterations on the numerical solution were analyzed. In summary, a U-tube type anti-roll tank simulation had to be performed accurately with conditions of a realizable k-𝜖 turbulence model, 10^-2s time step size, and 15 iterations. In validation, the two cases of measured data from the experiment were compared with the numerical results. In the present study, STAR-CCM+ (ver. 17.02), a RANS-based commercial solver was used.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.27-33
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• 2023

In this study, it was analyzed with the dyeing pattern of Diospyros kaki Thunb (persimmon) and was tried to numerically evaluate how the dyeing pattern in silk fabrics and cells was changed by different mordants. When the dyed silk fabrics were sufficiently dried, the silk fabrics were found to have a pale yellow color. Interestingly, iron II sulfate mordant changed the color change the most, silk fabrics were dyed with a color close to brown or dark purple. For numerical analysis, 19% and 62.5% color changes could be induced by sodium tartrate plus citric acid and copper acetate, respectively. Iron II sulfate induced the greatest difference than that of untreated mordants at 88%. About 5% and 10% of Chinese hamster ovary (CHO) cells were stained by sodium tartrate plus citric acid and copper acetate, respectively. The staining effect induced by iron II sulfate was about 2.4 times higher than the staining effect by sodium tartrate plus citric acid. In previous studies, staining results have been visually confirmed. However, this results not only visually confirmed the dyeing, but also quantified the color change. In particular, if numerical results are continuously integrated into big data, any researcher will be able to easily obtain similar results even if the method, time, volume, etc. are changed. In addition, the numerical data of this study is considered to be an important basis for building a database for IoT construction and computer analysis.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.413-420
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• 2024

After Friedrich Nietzsche's advocacy of nihilism, many literary works, dramas, and films have depicted aspects of human psychology associated with nihilism. Animation, too, has been used to convey nihilism, with narratives infused with nihilistic themes produced as both TV series and theatrical animations. Particularly, animation, as a visual medium capable of realizing any imaginative image unlike other media, possesses distinctive characteristics from live-action cinematography and differs from comics in its temporal properties. Hence, this study aims to analyze how Nietzsche's defined three stages of nihilism are represented within animation characters and how they construct various scenarios, using the anime "Attack on Titan" as a case study. The research unfolds by first examining Nietzsche's types of nihilism and the three stages through a review of literature, while also investigating the portrayal of nihilism in mass media and considering the unique attributes of animation. Secondly, building upon the literature review, the analysis interprets the narrative and constructed world of the chosen case study from a nihilistic perspective, examining four major characters through the stages of passive nihilism, active nihilism, and eternal recurrence. The findings demonstrate that the anime conveys two messages regarding negation and affirmation of one's life and existence, thereby offering viewers an opportunity to deeply contemplate human existence. This study is considered significant as it examines how Nietzschean nihilism is portrayed within the popular entertainment medium of animation.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.