• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.179-192
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• 2019

Additional frequencies are allocated in maritime digital data exchange to alleviate overload of the VHF data link. The shipborne ad-hoc network (SANET) for this frequencies was subsequently proposed, which provides various IP-based services to ships on behalf of satellite communications. In SANET, a ship should determine its own IP address to achieve IP connectivity to the shore. Accordingly, this paper proposes a SANET configuration (SANETconf) protocol as an IP configuration protocol. SANETconf propagates non-overlapping IP addresses across the network from the shore to ships. A ship obtains its IP address by exchanging Request and Response messages with its neighbors. Therefore, SANETconf eliminates the process of DAD and managing the movement of ships. Extensive simulations were performed to verify the applicability of SANETconf. Based on results, 85% of the ships can determine their own IP address within one frame. Also, SANETconf has a high resource efficiency by using 0.024 percent of resources for IP configuration.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• International Area Studies Review
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• v.20 no.1
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• pp.105-125
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• 2016

The expansionary monetary policy was practiced after 2001 in Japan to treat the deflation spiral, and reduced only the nominal interest rates and domestic household demand. One of the most serious factors for this failure was the change of private sector's expectancy. This paper has studied the effect of Japanese monetary policy in 21c., with empirical research based on a renewed macroeconomic model and the VAR. The empirical analysis shows that the effect of monetary policy on the national income during 2001.01-2015.03 is weaker than that of 1985.01-1994.04. Money volume has a diminutive effect on the growth of GDP within a short term after 2001. The change in the expectations of the private sectors might have been the cause of ineffectiveness of the expansive monetary policy. Economic agents learned from the past Japanese financial crisis that an expansive monetary policy increased the inflation rate and caused the 'bubbles to burst' afterwards. The VAR analysis says that the effectiveness of monetary policy on the economic depression declined over the past 20 years and the expansion of money volume has no influence on exchange rate and net export. This means that the expansive monetary policy lost its effect on net export and national income steadily. Monetary policy makers have to recognize this fact, and to consider another anti-cycle political instrument, i.e. the fiscal policy with government debt.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.183-193
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• 2020

This study attempts to interpret the causes of offshoring and reshoring, find out facilitating factors and the areas where these happen mainly. In viewpoint of self-organization phenomena, the theory of prospect, quantitative analysis is performed by utilizing actual data of American Reshoring Association. This study shows that offshoring to the U.S. is positively correlated with market power in the U.S. and innovation of investment countries, while reshoring to U.S. is positively correlated with market power in the U.S. not with technology innovation. The reshoring of U.S. companies is influencing offshoring to U.S, emerging countries such as Asia tends to focus offshoring in short catch up cycle area like IT. This study is expected to contribute to investment support policy and decision for optimal production site. Further study will complete the economic benefit assessment model by reinforcing the impact factors of reshoring and offshoring.

• Smart Media Journal
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• v.9 no.4
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• pp.66-72
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• 2020

As the online education market expands, educational contents with various presentation methods are being developed and released. In addition, it is imperative to develop content that reflects the usability and user environment of users who use this educational content. However, for qualitative growth of contents that will support quantitative expansion of markets, existing model analysis methods are urgently needed at a time when development direction of newly developed contents is secured. In this process of content development, a typical model for setting development goals is needed, as the rules of the prototype affect the entire development process and the final development outcome. It can also provide a positive benefit that screens the issue of performance dualization between processes due to the absence of communication between a single entity or between a number of entities. In the case of AR-based educational content which is effective to secure data necessary for development by securing samples of similar categories because there are not enough ready-made samples released. Therefore, a big data statistical analysis service is needed that can easily collect data and make decisions using big data. In this paper, we would like to design analysis services that enable the selection and detection of intuitive multidimensional factors and attributes, and propose big data-based statistical analysis services that can assist cooperative activities within an organization or among many companies.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.113-126
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• 2021

The future energy consumption pattern will show a very different pattern from the present due to the increase of distributed power sources such as renewable energy and the birth of the concept of prosumers, etc. Accordingly, it can be predicted that the direction of establishment of an appropriate production and supply plan considering the stability and consumption efficiency of the entire power grid will also be different from now. This paper proposes a simulation model that can test a new operational strategy when faced with a number of possible future environments. Through the proposed model, it is possible to simulate and analyze power consumed and supplied in a future Smart Grid environment, in which a large amount of new concepts including energy storage service (ESS) and distributed energy resources (DER) will be added. In particular, it is possible to model complex systems structurally by using DEVS formalism among the ABM (Agent-Based Model) methodologies that can model decision-making for each agent existing in the grid, and several factors can be easily added to the grid. The simulation model was verified using given dataset in the current situation, and scenario analysis was performed by simply adding an ESS, one of the main elements of the smart grid, to the model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.117-129
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• 2021

This study intends to verify the effectiveness of incentive schemes at Busan Port' aimed at promoting transshipment cargo. The current incentive schemes of Busan port intended to increase the T/S cargo volume have been disputed constantly. It is imperative, therefore, to restructure the system in accordance with the planned strategy. In this study, in order to ensure objectivity of the incentive schemes, all the parties (carriers, terminal operators and Port Authority) with a direct interest are answered and analyzed using the AHP methodology. Effectiveness is the top priority in the analysis of beneficiary validity of incentive schemes, when incentives are provided to Global overseas carriers compared with Global national carriers, IntraAsia carriers and Terminal operators. In the analysis of incentive schemes, direct cash support corresponding to the quantity of the T/S cargo had the highest effectiveness compared with exemption of port dues, cost compensation, port infrastructure support and subsidy for the service opening. The study results, therefore, reference the Port Authority when restructuring the schemes. This study has been conducted only focusing on the Busan port; however, the findings may have significant implications for overseas Port Authorities intending to implement incentive systems to promote cargo volumes similar to those at Busan port.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.585-596
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• 2022

The purpose of this study is to propose an assessment method to analyze clay collectively referred to as fine particles of clay components contained in fine aggregates, and to propose quality standards for clay use through correlation with the performance of concrete to verify the properties of clay measured according to the method. As a result, it is analyzed that it will be suitably utilized as a method to assess the fine particles of the clay component of fine aggregates through the component analysis of XRF. Regarding the related quality standards, considering the error rate of about 10% of KCS 14 20 10, the related quality standards were analyzed to be safe when Al₂O₃+Fe₂O₃+MgO is 23.5% or less and SiO₂+K₂OSiO₂+K2₂O is 66.5% or more. To build on this study, it is expected that a comprehensive review will be conducted through additional follow-up studies such as on clay of coarse aggregates and durability analysis to establish a system for quality control of the soil fraction of aggregates.