• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.237-245
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• 2018

The purpose of this study is to suggest the improvement plan for an effective benefit sharing support policy through empirical analysis on the benefit sharing operation of government as well as the benefit sharing support policy of government affecting firm performance. Therefore, this study analyzed current problems on benefit sharing operation of government as well as performed a survey for 225 participating and cooperation firms regarding firm performance. This study found some improvements such as insufficient diverse incentive measures leading to the difficulty of participating firm's increase and spread, insufficient substantial benefit sharing such a cash allocation between consignment firms, the increase of biz management system model failing to induce innovation of new technology or product, and difficult spread into 2nd, 3rd cooperative firms besides 1st ones, meanwhile, verifying the positive influence of government benefit sharing support policy on both participating and cooperation firms, especially on the performance of cooperation firms. As a further study, it is necessary to increase the objectiveness and accuracy of a research through verification of the interrelationships between firm statue and performance on the basis of more objective and quantitative data such a sales increase or R&D capability of cooperation firms.

• Convergence Security Journal
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• v.21 no.2
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• pp.11-18
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• 2021

To protect tangible and intangible assets, most of the companies are conducting information protection monitoring by using various security equipment in the IT service network. As the security equipment that needs to be protected increases in the process of upgrading and expanding the service network, it is difficult to monitor the possible exposure to the attack for the entire service network. As a countermeasure to this, various studies have been conducted to detect external attacks and illegal communication of equipment, but studies on effective monitoring of the open service ports and construction of illegal communication monitoring system for large-scale service networks are insufficient. In this study, we propose a framework that can monitor information leakage and illegal communication attempts in a wide range of service networks without large-scale investment by analyzing 'Netflow statistical information' of backbone network equipment, which is the gateway to the entire data flow of the IT service network. By using machine learning algorithms to the Netfllow data, we could obtain the high classification accuracy of 94% in identifying whether the Telnet service port of operating equipment is open or not, and we could track the illegal communication of the damaged equipment by using the illegal communication history of the damaged equipment.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.591-598
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• 2021

The digital phase-locked loops(DPLL) is a circuit used for phase synchronization and has been generally used in various fields such as communication and circuit fields. State estimators are used to design digital phase-locked loops, and infinite impulse response state estimators such as the well-known Kalman filter have been used. In general, the performance of the infinite impulse response state estimator-based digital phase-locked loop is excellent, but a sudden performance degradation may occur in unexpected situations such as inaccuracy of initial value, model error, and disturbance. In this paper, we propose a minimum variance finite impulse response filter with optimal horizon for designing a new digital phase-locked loop. A numerical method is introduced to obtain the measured value interval length, which is an important parameter of the proposed finite impulse response filter, and to obtain a gain, the covariance matrix of the error is set as a cost function, and a linear matrix inequality is used to minimize it. In order to verify the superiority and robustness of the proposed digital phase-locked loop, a simulation was performed for comparison and analysis with the existing method in a situation where noise information was inaccurate.

• Korea Science and Art Forum
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• v.37 no.2
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• pp.101-112
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• 2019

Regardless of the size, the role and meaning of the space required for human daily life are the same. Especially, as the spatial design is small and the size is small, the careful design and problem solving are needed to enable comfortable and convenient life even in a narrow space. The purpose of this study is to design a convergence model that utilizes the advantages of BIM, which can simulate actual design based on a new one-person housing space design plan optimized for one person. This study applied the BIM design technology to one-person housing 2D design, and the suitability examination and the space optimization design of the interior design were carried out. As a result of the study, utility of space improvement, consideration of housing environment, interference check, application of eco-friendly housing system, and MEP design item were derived. Therefore, BIM space design in interior space has been confirmed as a way to overcome limit and inefficiency of 2D design which is applied to actual space by various space design elements. Based on the results of this study, the One-person housing space model, which is applied to the study, is a pure creation designed based on various one-person housing of social and cultural peculiarities derived from previous research. This design example was applied to BIM technology to confirm the detailed and practical design possibility.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.191-198
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• 2021

Since the size of training dataset become large and the model is getting deeper to achieve high accuracy in deep learning, the deep neural network training requires a lot of computation and it takes too much time with a single node. Therefore, distributed deep learning is proposed to reduce the training time by distributing computation across multiple nodes. In this study, we propose hybrid allreduce strategy that considers the characteristics of each layer and communication and computational overlapping technique for synchronization of distributed deep learning. Since the convolution layer has fewer parameters than the fully-connected layer as well as it is located at the upper, only short overlapping time is allowed. Thus, butterfly allreduce is used to synchronize the convolution layer. On the other hand, fully-connecter layer is synchronized using ring all-reduce. The empirical experiment results on PyTorch with our proposed scheme shows that the proposed method reduced the training time by up to 33% compared to the baseline PyTorch.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.18-23
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• 2018

FPSO consists of topside modularized plants for production of crude oil, and hullside structures that serve as support for the topside and storage of produced crude oil. The structural behavior of the FPSO topside module and its supporting hull depends on the interface structure that connects them, and the interface structure consists of a combination of individual unit support structures called Module Support Seat (MSS). Types of interface structures are various and, accordingly, the basic design of the FPSO topside module structure is greatly influenced, so various design methods should be considered from the initial design phase. Structural design of FPSO topside module requires consideration of the number of MSSs, connection type, and structural analysis options such as the range of finite element models, load conditions, and boundary conditions for verification of structural strength. In this study, the comparison combination cases for the above considerations were derived and the strength evaluation was performed, and the structural behavior characteristics of the topside module were compared and analyzed through a detailed review of the analysis results. The results of this study are considered to be a good reference for designing a more reliable topside module structure.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.237-243
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• 2019

Floating platforms made of PE (PolyEthylene) are often located in shallows of seas, rivers or lakes. They are widely used for marine pensions, marine pontoons, marine bridges, etc. These products are characterized by good flexibility, recyclability, chemical resistance and weatherability with corrosion resistance. Existing PE floating platforms have a simple structure in which one pipe is fastened to one bracket, but this has limited application, even if a user modifies the arrangement. Therefore, we developed a structure that allows buoyancy pipes of various sizes to be fastened to one bracket and verified the structural safety of the product using the finite element method. From the results of structural analysis for buoyancy pipes of different diameters, the maximum stress ratio was 0.78 compared with allowable criteria of 1.0, which represented sufficient safety for a model with 500 mm diameter pipes. Based on the results of this study, further research to evaluate the structural safety of various floating platforms can be carried out in the further; it will also be necessary to establish related evaluation criteria.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.553-560
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• 2020

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

• Knowledge Management Research
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• v.23 no.3
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• pp.23-44
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• 2022

With the development of blockchain technology, play-to-earn (P2E) games, one of the decentralized applications (dApps), are receiving great social attention. P2E games are positively evaluated as areas with high growth potential based on blockchain technology, and at the same time, they are negatively evaluated as speculative as people can cash P2E game items in the form of cryptocurrency. In this situation, the purpose of this study is to investigate factors affecting the intention to use P2E games. Along with the discussion of hedonic system adoption, we consider the factors with perceived enjoyment, economic incentive, and social influence. In order to verify our research model, data were collected from 350 adults with P2E game experience or recognition, and a structural equation model was carried out. The analysis results find that perceived enjoyment and subjective norm have a significant positive effect on the intention to use P2E games, and economic incentive does not have a significant effect. In addition, peer influence and external influence have a significant positive effect on subjective norm. Drawing on these findings, we present several academic and practical implications for future research.

• Journal of the Korean Society of Radiology
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• v.16 no.6
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• pp.697-702
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• 2022

For imaging in positron emission tomography(PET), it is necessary to measure the position of the scintillation pixel interacting with the gamma rays incident on the detector. To this end, in the conventional system, a flood image of the scintillation pixel is obtained, the imaged area of each scintillation pixel is separated, and the position of the scintillation pixel is specified and acquired as a digital signal. In this study, a deep learning method was applied based on the signal formed by the photosensor of the detector, and a method was developed to directly acquire a digital signal without going through various procedures. DETECT2000 simulation was performed to verify this and evaluate the accuracy of position measurement. A detector was constructed using a 6 × 6 scintillation pixel array and a 4 × 4 photosensor, and a gamma ray event was generated at the center of the scintillation pixel and summed into four channels of signals through the Anger equation. After training the deep learning model using the acquired signal, the positions of gamma-ray events that occurred in different depth directions of the scintillation pixel were measured. The results showed accurate results at every scintillation pixel and position. When the method developed in this study is applied to the PET detector, it will be possible to measure the position of the scintillation pixel with a digital signal more conveniently.