• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.6-7
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• 2020

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data.

• Archives of Plastic Surgery
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• v.35 no.1
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• pp.28-35
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• 2008

Purpose: The pedicle of transverse rectus abdominis myocutaneous(TRAM) flap and deep inferior epigastric arterial perforator flap is deep inferior epigastic artery (DIEA) and accurate anatomic knowledge about perforator of DIEA is very important for the elevation of these flap. The authors investigated a detailed vascular network of perforator of DIEA in Koreans. Methods: 24 fresh cadavers were studied. Among them, 15 were examined based on the plain X-ray examination for the distribution and location of perforator of DIEA. And 9 fresh cadavers were examined based on the 3-dimensional computed tomography(CT) study for the distance between ending point of perforator of DIEA and mother artery, the distance between most medial mother artery and midline, the distance between most lateral mother artery and midline, and the running type of perforators of DIEA. Results: Based on the plain X-ray examination, suitable(external diameter$${\geq_-}0.5mm$$) perforators of DIEA are located between the level of umbilicus and 8 cm below it. Based on the 3D-CT study, average distance between the ending point of perforator of DIEA and the mother artery is 30.26 mm on the left, 28.62 mm on the right, respectively. The average distance between most medial mother artery and midline is 17.13 mm on the left, 15.76 mm on the right, respectively. The average distance between most lateral mother artery and midline is 56.31 mm on the left, 50.90 mm on the right, respectively. The main running course of suitable perforators of DIEA is type a, which is a direct musculocutaneous perforator vessel from main vascular axis passing outward to join the subdermal plexus, directly. Conclusion: 3-dimensional computed tomography study as well as plain X-ray examination provided more accurate and detail informations about perforators of DIEA in Koreans. These informations will help us understand the detailed vascular anatomy and operation with ease and safe in the lower abdomen of Koreans.

• Annual Conference of KIPS
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• 2019.10a
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• pp.115-117
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• 2019

누리온 시스템은 Intel Xeon Phi 아키텍쳐를 기반한 8305개의 노드로 구성되었고, 이론 성능 25.7페타플롭스를 갖춘 시스템으로 2018년도에 도입되었다. 누리온 시스템은 그동안 KISTI가 지속적으로 수행해 온 국내 계산과학자를 지원하는 한편, 빅데이터를 기반으로 하는 거대 규모의 딥러닝 등의 새로운 AI 분야에서도 슈퍼컴퓨팅을 활용할 수 있도록 전략적으로 지원하고 있다. 본 논문에서는 이러한 거대 규모 딥러닝을 수행하는데 있어 발생하는 주요 이슈들과 이러한 이슈들을 누리온 시스템에서는 어떻게 해결하고 있는지에 대해 소개한다.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.37.2-37.2
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• 2010

The IMS (Infrared or Intermediate-wide, Medium-deep Survey) program for the search of z~7 quasars has been running since last year. In order to discover enough number of quasars at z~7, a strategy sufficing both survey area (~150 square deg.) and image depth (23 AB mag in J filter), together with using existing multi-wavelength data is chosen. We have been carrying imaging observations with the UKIRT 4m telescope, now covering ~50 square deg. (including UKIDSS survey area) of J-band data. We then used selection in color-color space to choose high-z quasar candidates having the rest-frame Ly-alpha break, and to exclude contamination from stars and galaxies at low-z. We show quasar candidates of redshift z~7 and z~6, out of 25 square deg. data analyzed, and note implications and future plans.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.169-170
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• 2019

Lately, as artificial intelligence becomes a source of global competitiveness, the government is strategically fostering artificial intelligence that is the base technology of future new industries such as autonomous vehicles, drones, and robots. Domestic artificial intelligence research and services have been launched mainly in Naver and Kakao, but their size and level are weak compared to overseas. Recently, deep learning has been conducted in recent years while recording innovative performance in various pattern recognition fields including speech recognition and image recognition. In addition, deep running has attracted great interest from industry since its inception, and global information technology companies such as Google, Microsoft, and Samsung have successfully applied deep learning technology to commercial products and are continuing research and development. Therefore, we will look at artificial intelligence which is attracting attention based on previous research.

• Steel and Composite Structures
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• v.44 no.2
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• pp.199-210
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• 2022

This study build finite element analysis (FEA) models describing the bending events of coiled tubing (CT) at the wellhead and trips into the hole, accurately provide the state of stress and strain while the CT is in service. The bending moment and axial force history curves are used as loads and boundary conditions in the diametrical growth models to ensure consistency with the actual working conditions in field operations. The simulation diametrical growth results in this study are more accurate and reasonable. Analysis the factors influencing fatigue and diametrical growth shows that the internal pressure has a first-order influence on fatigue, followed by the radius of the guide arch, reel and the CT diameter. As the number of trip cycles increase, fatigue damage, residual stress and strain cumulatively increase, until CT failure occurs. Significant residual stresses remain in the CT cross-section, and the CT exhibits a residual curvature, the initial residual bending configuration of CT under wellbore constraints, after running into the hole, is sinusoidal. The residual stresses and residual bending configuration significantly decrease the buckling load, making the buckling and buckling release of CT in the downhole an elastic-plastic process, exacerbating the helical lockup. The conclusions drawn in this study will improve CT models and contribute to the operational and economic success of CT services.

• Journal of Electrical Engineering and Technology
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• v.1 no.3
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• pp.396-405
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• 2006

The visual analysis of buried channel (Be) devices such as buried channel MOSFETs and CCDs (Charge Coupled Devices) is investigated to give better understanding and insight for their electrical behaviours using a 3-dimensional (3-D) numerical simulation. This paper clearly demonstrates the capability of the numerical simulation of 'EVEREST' for characterising the analysis of a depletion mode MOSFET and BC CCD, which is a simulation software package of the semiconductor device. The inverse threshold and punch-through voltages obtained from the simulations showed an excellent agreement with those from the measurement involving errors of within approximately 1.8% and 6%, respectively, leading to the channel implanted doping profile of only approximately $4{\sim}5%$ error. For simulation of a buried channel CCD an advanced adaptive discretising technique was used to provide more accurate analysis for the potential barrier height between two channels and depletion depth of a deep depletion CCD, thereby reducing the CPU running time and computer storage requirements. The simulated result for the depletion depth also showed good agreement with the measurement. Thus, the results obtained from this simulation can be employed as the input data of a circuit simulator.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.63-70
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• 2018

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.235-242
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• 2019

Recently, the use of artificial intelligence technology including deep learning has become active in various fields. In particular, several algorithms showing superior performance in object recognition and detection based on deep learning technology have been presented. In this paper, we propose the proper direction for the implementation of mobile healthcare application that user's convenience is effectively reflected. By effectively analyzing the current state of use satisfaction research for the existing fitness applications and the current status of mobile healthcare applications, we attempt to secure survival and superiority in the fitness application market, and, at the same time, to maintain and expand the existing user base.