• Fisheries and Aquatic Sciences
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• v.21 no.9
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• pp.23.1-23.8
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• 2018

Background: Russian sturgeon (Acipenser gueldenstaedtii) is an emerging candidate species in the Korean aquaculture domain owing to its highly valued caviar. Although the embryonic development of this species was previously described, the complete image data on the morphological differentiation of developing embryos have not been yet fully available. Further, with the viewpoint of larval production in hatchery, the effects of temperature on embryonic viability and the temporal window of hatching event have not been extensively studied. Hence, the objective of this study was to provide a complete set of photographic image data on the embryogenesis and also to examine the effects of incubation temperatures on embryonic viability and hatching event in farm-bred Russian sturgeon. Results: Typical characteristics of embryonic development including uneven, holoblastic cleavages with unequal blastomeres, followed by the formation of germ layer, neurulation, and organogenesis until hatching, were documented. Under different temperature conditions (12, 16, or $20^{\circ}C$), viability of embryos incubated at $12^{\circ}C$ was significantly lower as relative to those of 16 and $20^{\circ}C$ incubated embryos. Hatchability of embryos was higher, and the timing of hatching event was more synchronized at $20^{\circ}C$ than at 12 and $16^{\circ}C$. Conclusion: Data from this study suggest that the incubation of Russian sturgeon embryos at $20^{\circ}C$ would be desirable in the hatchery practice with respect to the good hatchability of embryos and the synchronization of hatching events. Additionally, the updated image data for complete embryonic development could be a useful reference guide for not only developmental researches but also artificial propagation of Russian sturgeon in farms.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.610-622
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• 2003

Metadata Registry was developed to dynamically manage metadata and to increase interoperability between various and heterogeneous metadata. The built metadata registry can be used as a standard guideline for creation of new databases and it provides a radical data integration mechanism. However, in the situation that an enormous databases must be integrated progressively, there is a limit to the existing metadata-based approach. In case that each database has no statistical information for its use rate and the restricted cost is given to us for a unit time, existing metadata-based approaches do not provide how to select some databases to be preferentially integrated and to build a metadata registry progressively, In this paper, we propose a methodology that can create progressively metadata registries in the case. The proposed methodology is based on data visibility and hierarchical metadata registry. We also describe the system that have been developed for applying the methodology to a real domain, and then described its results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6145-6158
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• 2019

It is a challenge for the current security industry to respond to a large number of malicious codes distributed indiscriminately as well as intelligent APT attacks. As a result, studies using machine learning algorithms are being conducted as proactive prevention rather than post processing. The k-NN algorithm is widely used because it is intuitive and suitable for handling malicious code as unstructured data. In addition, in the malicious code analysis domain, the k-NN algorithm is easy to classify malicious codes based on previously analyzed malicious codes. For example, it is possible to classify malicious code families or analyze malicious code variants through similarity analysis with existing malicious codes. However, the main disadvantage of the k-NN algorithm is that the search time increases as the learning data increases. We propose a fast k-NN algorithm which improves the computation speed problem while taking the value of the k-NN algorithm. In the test environment, the k-NN algorithm was able to perform with only the comparison of the average of similarity of 19.71 times for 6.25 million malicious codes. Considering the way the algorithm works, Fast k-NN algorithm can also be used to search all data that can be vectorized as well as malware and SSDEEP. In the future, it is expected that if the k-NN approach is needed, and the central node can be effectively selected for clustering of large amount of data in various environments, it will be possible to design a sophisticated machine learning based system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.2
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• pp.113-120
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• 2009

To numerically simulate aerodynamics of rotor-airframe interaction in a rigorous manner, we need to solve the Navier-Stokes system for a rotor-airframe combination in a single computational domain. This imposes a computational burden since rotating blades and a stationary body have to be simultaneously dealt with. An efficient alternative is a momentum source method in which the action of rotor is approximated as momentum source in a stationary mesh system built around the airframe. This makes the simulation much easier. The magnitude of the momentum source is usually evaluated by the blade element theory, which often results in a poor accuracy. In the present work, we evaluate the momentum source from the simulation data by using the Navier-Stokes equations only for a rotor system. Using this data, we simulated the time-averaged steady rotor-airfame interaction and developed the unsteady rotor-airframe interaction. Computations were carried out for the simplified rotor-airframe model (the Georgia Tech configuration) and the results were compared with experimental data. The results were in good agreement with experimental data, suggesting that the present approach is a usefull method for rotor-airframe interaction analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.284-287
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• 2022

In order to respond to the complexity and uncertainty of water management due to the climate crisis, K-water established a digital twin water management platform based on our experience in operating ICT infrastructure such as hydrological data sensing and high-quality data management and water management capabilities. In this platform, data from related organizations and real-time observation data in the basin are displayed on 3D topographic domain. Also it is configured to support optimal decision-making through simulation for various situations, displaying and analyzing results, and feedback on them. It is completed to establish the platform for Sunjim river basin. Based on this technologies and experience, K-water is planning to expand this digital twin to 5 major rivers in Korea. Through this, it plans to build comprehensive decision-making system for efficient water management considering various conditions in entire basin. Also it aims to create a new water industrial ecosystem and contribute to secure technological competitiveness cooperating with private companies.

• Clinical and Experimental Vaccine Research
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• v.11 no.1
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• pp.96-103
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• 2022

Purpose: Studies on the immune responses to severe acute respiratory syndrome coronavirus 2 vaccines are necessary to evaluate the ongoing vaccination programs by correlating serological response data and clinical effectiveness data. We performed a longitudinal immunological profiling of health care workers vaccinated with mRNA-1273 (Moderna, Cambridge, MA, USA). Half of these vaccinees had experienced a mild coronavirus disease 2019 (COVID-19) infection in the spring of 2020 ("COVID-recovered" cohort), whereas the other half of the vaccinees had no previous COVID-19 infection ("COVID-naive" cohort). Materials and Methods: Serum was drawn at multiple time points and subjected to assays measuring anti-Spike immunoglobulin G (IgG), avidity of anti-Spike IgG, avidity of anti-receptor binding domain (RBD) IgG, virus neutralizing activity, and interferon-γ release from stimulated lymphocytes. Results: Between both cohorts and within each cohort, we found remarkable inter-individual differences regarding cellular and humoral immune responses to the Moderna mRNA-1273 vaccine. Conclusion: First, our study indicates that the success of mRNA-1273 vaccinations should be verified by serological assays in order to identify "low-responders" to vaccination. Second, the kinetics of anti-S IgG and neutralizing activity correlate well with clinical effectiveness data, thus explaining incipient protection against infection 2 weeks after the first dose of mRNA-1273 in COVID-naive vaccinees. Third, our IgG-avidity data indicate that this incipient protection is mediated by low-avidity anti-RBD IgG and low-avidity anti-S IgG.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.

• Convergence Security Journal
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• v.17 no.5
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• pp.63-69
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• 2017

In the early 1990s, IETF(Internet Engineering TaskForce) had started the discussion on new address protocol that can modify and supplement various drawbacks of existing IPv4 address protocol with the introduction of CIDR(Classless Inter-Domain Routing) which is a temporary solution for IPv4 address depletion, NAT, private IP address. While various standards related to new address protocol has been proposed, the SIPP(Simple Internet Protocol Plus) was adopted among them because it is regarded as the most promising solution. And this protocol has been developed into current IPv6. The new concepts are introduced with modifying a lot of deficiencies in the exisitng IPv4 such as real-time data processing, performance on QoS, security and the efficiency of routing. Since many security threats in IPv6 environment still exist, the necessity of stable data communication environment has been brought up continuously. This paper deveopled one-way communication algorithm in IPv6 based on the high possibility of protecting the system from uncertain and potential risk factors if the data is transmitted in one way. After the analysis of existing IPv6 and ICMPv6, this paper suggests one-way communication algorithm as a solution for existing IPv6 and ICMPv6 environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.547-553
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• 2020

As aging infrastructures increase along with time, the efficient maintenance becomes more significant and accurate responses from the sensors are pre-requisite. Among various responses, strain is commonly used to detect damage such as crack and fatigue. Optical fiber sensor is one of the promising sensing techniques to measure strains with high-durability, immunity for electrical noise, long transmission distance. Fiber Bragg Grating (FBG) is a point sensor to measure the strain based on reflected signals from the grating, while Brillouin Optic Correlation Domain Analysis (BOCDA) is a distributed sensor to measure the strain along with the optical fiber based on scattering signals. Although the FBG provides the signal with high accuracy and reproducibility, the number of sensing points is limited. On the other hand, the BOCDA can measure a quasi-continuous strain along with the optical fiber. However, the measured signals from BOCDA have low accuracy and reproducibility. This paper proposed a multi-fidelity data-fusion method based on Gaussian Process Regression to improve the fidelity of the strain distribution by fusing the advantages of both systems. The proposed method was evaluated by laboratory test. The result shows that the proposed method is promising to improve the fidelity of the strain.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.109-119
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• 2022

The computational efficiency of reverse time migration (RTM) based on numerical modeling is not secured due to the high-frequency band of several hundred Hz or higher for data acquired through a three-dimensional (3D) ultra-high-resolution (UHR) seismic survey. Therefore, this study develops an RTM program to derive high-quality 3D geological structures using UHR seismic data. In the traditional 3D RTM program, an excitation amplitude technique that stores only the maximum amplitude of the source wavefield and a domain-limiting technique that minimizes the modeling area where the source and receivers are located were used to significantly reduce memory usage and calculation time. The program developed through this study successfully derived a 3D migration image with a horizontal grid size of 1 m for the 3D UHR seismic survey data obtained from the Korea Institute of Geoscience and Mineral Resources in 2019, and geological analysis was conducted.