• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.53.4-54
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• 2021

Although the era of very large telescopes has come, small telescopes still have advantages for fast follow-up and long-term monitoring observation. Intensive monitoring survey of nearby galaxies (IMSNG) aims to understand the nature of the supernovae (SNe) by catching the early light curve from them with the network of small telescopes from 0.4-m to 1.0-m all around the world. To achieve the scientific goals with heterogeneous facilities, three factors are important. First, automatic processes as soon as data is uploaded will increase efficiency and shorten the time. Second, searching for transients is necessary to deal with newly emerged transients for fast follow-up observation. Finally, the Integrated process for different telescopes gives a homogeneous output, which will eventually make connections with the database easy. Here, we introduce the integrated pipeline, 'gppy' based on Python, for more than 10 facilities having various configurations and its performance. Processes consist of image pre-process, photometry, image align, image combine, photometry, and transient search. In the connected database, homogeneous output is summarized and analyzed additionally to filter transient candidates with light curves. This talk will suggest the future work to improve the performance and usability on the other projects, gravitational wave electromagnetic wave counterpart in Korea Observatory (GECKO), and small telescope network of Korea (SOMANGNET).

• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.377-393
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• 2016

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.107.1-107.1
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• 2014

We apply a machine learning algorithm, artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.248-252
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• 1998

The ACE Engineering, Inc. (ACE) of Seoul, Korea and The Energy Systems Institute (SEI) of Irkutsk, Russia has extensively studied the formation of an interconnected electric power systems throughout the North Eastern Asia Region(NEAR). The region encompasses East Siberia (ESR), Far East of Russia(FER), North East China(NEC), Mongolia(MON), North Korea(NKOR), South Korea(SKOR). Although geographically adjacent to each other, these countries and territories have different levels and rates of economic development, possess different reserves of energy resources which complement each other and hence, can interact to their mutual benefits. This Project is called Peace Network Project (PNP) because it seems to contribute for development of power economy and clean environment. In a word, the PEACE Network is expected to serve as "Power Economy And Clean Environment Network" and to promote the international cooperation. to expedite the peaceful reunification of North & South Korea and to revive the Korean culture in the North, and eventually contribute to the human prosperity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1652-1659
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• 2008

With the increase of huge amount of data in network systems, ultimate high-speed network has become an essential requirement. In such systems, the encryption and decryption process for security becomes a bottle-neck. For this reason, the need of hardware implementation is strongly emphasized. In this study, a mixed inner and outer round pipelining architecture is introduced to achieve high speed performance of ARIA hardware. Multiplexers are used to control the lengths of rounds for 3 types of keys. Merging of encryption module and key initialization module increases the area efficiency. The proposed hardware architecture is implemented on reconfigurable hardware, Xilinx Virtex2-pro. The hardware architecture in this study shows that the area occupied 6437 slices and 128 BRAMs, and it is translated to throughput of 24.6Gbit/s with a maximum clock frequency of 192.9MHz.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.120-123
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• 2016

최근 국내 기관이나 금융권을 넘어선 사기업에 대한 정통망 법의 망분리에 대한 적용이 이슈화되고 있다. 그러나 현실적인 망분리 적용과 운영에는 비용적인 문제부터 기술적인 문제까지 다양한 어려움이 산재해있다. 이에 본 논문에서는 SDN기반의 'Openflow를 적용한 경로 기반 망분리 구축 방안'을 제안한다. OpenFlow Switch의 Flow Table의 Processing 과정인 Pipeline을 이중화시켜 Packet 통신을 경로기반의 In/External Network로 운영하는 방안이다. 이를 통해 기존 망분리 환경 대비 비용과 자원 운영의 효율성, 보안성 향상의 다각적인 효과를 기대한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.265-269
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• 2017

Because of abrupt changes for velocity in water, transient flow is occurred in practical life. To reduce and avoid the high or low pressure of pipe network system, various surge protection facilities are used to prevent the risk in pipe network system. Especially, we focused on study not only preventing positive and negative pressure but also selecting adequate equipment for high pressurized pipelines. Several critical cases were considered by undertaking a steady state hydraulic study and transient dynamic simulation and we suggested that the surge vessel of various surge protection system was recommended to control high and low pressures on pipeline system in perspective.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of Korean Society of Water and Wastewater
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• v.13 no.3
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• pp.73-82
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• 1999

In this study, the optimal analysis for pipe network is performed for the combined ideal pipe network system(CASE 1, CASE 2 and CASE 3) which is composed of 25 nodes, 41 elements, and 1 fixed nodal head with evaluating pressure variation distribution of main and branch in grid composed drainage pipe network. The linear analysis technique used as the analysis method in this study, the KYPIPE being used extensively as the linear technique to design and analysis of pipe network is applied. Firstly, in the analysis of pipe network, the CASE 2 and CASE 3 supply same thing(value) in the result of considering the total flow provided each pipeline, but in the general intension in the case of CASE 2, relative width of supply is more large than CASE 1 and CASE 3. Secondly, in the analysis technique of pipe network, CASE 3 is analysed largest as a result of comparing with same heads, and in the order of their size CASE 2 and CASE 1 were determined but the difference doesn't appear to be obvious. Thirdly, as the result of determining main factor, pressure in the design and analysis of net work. CASE 3 is from Node 3 to 25 than CASE 1 and CASE 2 and it is determined in the order of their size, CASE 2 and CASE 1. Finally, in this study, discharge flow distribution is evaluated in the same condition with 3-type CASE in the case of branch position for designing optimal composed drainage pipe network. As the result of that, branch pipe perform. Therefore, it is thought that the efficient and reasonable management of water supply and sewerage design will be possible if it give all our energies to study at the pipe system design in and out of country in the future.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.41.1-41.1
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• 2018

We introduce a current status and future plans of Korea Microlensing Telescope Network (KMTNet) microlensing experiments, which include an observational strategy, pipeline, event-finder, and collaborations with Spitzer. The KMTNet experiments were initiated in 2015. From 2016, KMTNet observes 27 fields including 6 main fields and 21 subfields. In 2017, we have finished the DIA photometry for all 2016 and 2017 data. Thus, it is possible to do a real-time DIA photometry from 2018. The DIA photometric data is used for finding events from the KMTNet event-finder. The KMTNet event-finder has been improved relative to the previous version, which already found 857 events in 4 main fields of 2015. We have applied the improved version to all 2016 data. As a result, we find that 2597 events are found, and out of them, 265 are found in KMTNet-K2C9 overlapping fields. For increasing the detection efficiency of event-finder, we are working on filtering false events out by machine-learning method. In 2018, we plan to measure event detection efficiency of KMTNet by injecting fake events into the pipeline near the image level. Thanks to high-cadence observations, KMTNet found fruitful interesting events including exoplanets and brown dwarfs, which were not found by other groups. Masses of such exoplanets and brown dwarfs are measured from collaborations with Spitzer and other groups. Especially, KMTNet has been closely cooperating with Spitzer from 2015. Thus, KMTNet observes Spitzer fields. As a result, we could measure the microlens parallaxes for many events. Also, the automated KMTNet PySIS pipeline was developed before the 2017 Spitzer season and it played a very important role in selecting the Spitzer target. For the 2018 Spitzer season, we will improve the PySIS pipeline to obtain better photometric results.