• Structural Monitoring and Maintenance
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• 제4권2호
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• pp.115-131
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• 2017

Classical flutter of wind turbine blades indicates a type of aeroelastic instability with fully attached boundary layer where a torsional blade mode couples to a flapwise bending mode, resulting in a mutual rapid growth of the amplitudes. In this paper the monitoring problem of onset of flutter is investigated from a detection point of view. The criterion is stated in terms of the exceeding of a defined envelope process of a specific maximum torsional vibration threshold. At a certain instant of time, a limited part of the previously measured torsional vibration signal at the tip of blade is decomposed through the Empirical Mode Decomposition (EMD) method, and the 1st Intrinsic Mode Function (IMF) is assumed to represent the response in the flutter mode. Next, an envelope time series of the indicated modal response is obtained in terms of a Hilbert transform. Finally, a flutter onset criterion is proposed, based on the indicated envelope process. The proposed online flutter monitoring method provided a practical and direct way to detect onset of flutter during operation. The algorithm has been illustrated by a 907-DOFs aeroelastic model for wind turbines, where the tower and the drive train is modelled by 7 DOFs, and each blade by means of 50 3-D Bernoulli-Euler beam elements.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.3081-3083
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• 2005

한국형 고속전철차량의 자동제어 구현을 위해서 우선 다양한 종류의 장치들로부터 상태정보(Line Voltage-열차가선전압, Bogie Hunting, Preset Speed, PWM, Train Velocity, Brake Pressure, Reservoir Pressure)를 취득해야하며, Main Process Unit(MPU)에서의 고속 Data 처리를 위해서 취득한 Analog Data를 신속하게 Digital Data로 변환해야 한다. 또한 열차내의 특수한 조건(Noise, Vibration)에서도 안정적인 데이터의 취득을 만족시켜야한다. 이와 같은 상황을 고려한 독자적이 통합형 데이터 취득 장치 -Integrated Data Acquisition Board(IDAB)-의 설계방안을 제시하였다.

• 천문학회보
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• 제42권2호
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• pp.60.2-61
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• 2017

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.77 for flare classification and 0.83 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

• Journal of Communications and Networks
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• 제7권2호
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• pp.157-170
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• 2005

The combination of multiple antennas and multi-carrier code division multiple-access (MC-CDMA) is a strong candidate for the downlink of the next generation mobile communications. The study of such systems in scenarios that model real-life trans-missions is an additional step towards an optimized achievement. We consider a realistic MIMO channel with two or four transmit antennas and up to two receive antennas, and channel state information (CSI) mismatches. Depending on the mobile terminal (MT) class, its number of antennas or complexity allowed, different data-rates are proposed with turbo-coding and asymptotic spectral efficiencies from 1 to 4.5 bit/s/Hz, using three algorithms developed within the European IST-MATRICE project. These algorithms can be classified according to the degree of CSI at base-station (BS): i) Transmit space-frequency prefiltering based on constrained zero-forcing algorithm with complete CSI at BS; ii) transmit beamforming based on spatial correlation matrix estimation from partial CSI at BS; iii) orthogonal space-time block coding based on Alamouti scheme without CSI at BS. All presented schemes require a reasonable complexity at MT, and are compatible with a single-antenna receiver. A choice between these algorithms is proposed in order to significantly improve the performance of MC-CDMA and to cover the different environments considered for the next generation cellular systems. For beyond-3G, we propose prefiltering for indoor and pedestrian microcell environments, beamforming for suburban macrocells including high-speed train, and space-time coding for urban conditions with moderate to high speeds.

• Asian Journal of Atmospheric Environment
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• 제7권1호
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• pp.38-47
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• 2013

$PM_{10}$ concentrations were measured at four monitoring sites at the Daechaung station of the Seoul subway. The four locations included two tunnels, a platform, and a waiting room. The outside site of the subway was also monitored for comparison purposes. In addition, the effect of the platform screen doors (PSDs) recently installed to isolate the $PM_{10}$ in a platform from a tunnel were evaluated, and a comparison between $PM_{10}$ levels during rush and non-rush hours was performed. It was observed that $PM_{10}$ levels in the tunnels were generally higher than those in the other locations. This might be associated with the generation of $PM_{10}$ within the tunnel due to the train braking and wear of the subway lines with the motion of the trains, which promotes the mixing and suspension of particulate matter. During this tunnel study, it was observed that the particle size of $PM_{10}$ ranged from 1.8 to 5.6 ${\mu}m$. It was revealed that the $PM_{10}$ levels in the tunnels were significantly increased by the PSDs, while those in the platform and waiting room decreased. As a result, in order to estimate the effect of ventilation system on $PM_{10}$ levels in the tunnels, fans with inverters were operated. It was found that the concentration of $PM_{10}$ was below 150 ${\mu}g/m^3$ when the air flow rate into a tunnel was approximately 210,000-216,000 CMH.

• 한국환경보건학회지
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• 제34권1호
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• pp.34-41
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• 2008

The objective of this study is to provide the research data on the actual concentrations of $PM_{10},\;PM_{2.5},\;PM_1\;and\;CO_2$ in Seoul subway carriages. Mean concentrations of $PM_{10},\;PM_{2.5}\;and\;PM_1,\;and\;CO_2$ in subway carriages were investigated at levels of $215.1{\pm}101.4{\mu}g/m^3,\;86.9{\pm}38.6{\mu}g/m^3,\;27.0{\pm}11.4{\mu}g/m^3,\;and\;1,588{\pm}714ppm$, respectively. The mean concentrations in subway carriages were higher when the train ran on an underground track rather than on an above ground track. The measured concentration of particulate matter varied with the time of day and was highest in the morning, followed by noon and evening while the $CO_2$ concentration was highest in the morning, followed by evening and noon. In relation to correlation among the pollutants: the correlation between $PM_{10}\;and\;PM_{2.5}$ was 0.92, and that between $PM_{2.5}\;and\;PM_1$ was 0.94. The inclusion rate of $PM_{2.5}\;to\;PM_{10}$ was $41{\pm}7%$ and that of $PM_1\;to\;PM_{2.5}\;was\;32{\pm}4%$. In addition, the $CO_2$ concentration had a positive relation with the number of people in a carriage, whereas the concentration of $PM_{10}$ had negative correlation to the number of people. In relation to these two pollutants we calculated using a regression equation (34.06+0.04$CO_2$(ppm)-0.09 PM10$({\mu}g/m^3)$($R^2$=0.30, p<0.01, n=707), that a maximum number of 61 persons would ensure that each pollutant is maintained below the criteria level, applicable to subway stations.

• 한국대기환경학회지
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• 제32권2호
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• pp.216-232
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• 2016

In present, the Seoul City is undergoing traffic congestion problems caused by rapid urbanization and population growth. Thus the City government has reorganized the mass transportation system since 2004 and the subway has become a very important means for public transit. Since the subway system is typically a closed environment, the indoor air quality issues have often raised by the public. Especially since a huge amount of PM (particulate matter) is emitted from ground tunnels passing through the subway train, it is now necessary to assess the characteristics and behaviors of fine PM inside the tunnel. In this study, the concentration patterns of $PM_1$, $PM_{2.5}$, and $PM_{10}$ in the Seoul subway line-2 were analyzed by real-time measurement during winter (Jan 13, 2015) and summer (Aug 7, 2015). The line-2 consisting of 51 stations is the most busy circular line in Seoul having the railway of 60.2 km length. The the one-day average $PM_{10}$ concentrations were $148{\mu}g/m^3$ in winter and $66.3{\mu}g/m^3$ in summer and $PM_{2.5}$ concentrations were $118{\mu}g/m^3$ and $58.5{\mu}g/m^3$, respectively. The $PM_{2.5}/PM_{10}$ ratio in the underground tunnel was lower than the outdoor ratio and also the ratio in summer is higher than in winter. Further the study examined structural types of underground subsections to explain the patterns of elevated PM concentrations in the line-2. The subsections showing high PM concentration have longer track, shorter curvature radius, and farther from the outdoor stations. We also estimated the outdoor PM concentrations near each station by a spatial statistical analysis using the $PM_{10}$ data obtained from the 40 Seoul Monitoring Sites, and further we calculated $PM_{2.5}/PM_{10}$ and $PM_1/PM_{10}$ mass ratios near the outdoor subway stations by using our observed outdoor $PM_1$, $PM_{2.5}$, and $PM_{10}$ data. Finally, we could develop pollution maps for outdoor $PM_1$ and $PM_{2.5}$ near the line-2 by using the kriging method in spatial analysis. This methodology may help to utilize existing $PM_{10}$ database when managing and control fine particle problems in Korea.

• 한국융합학회논문지
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• 제9권7호
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• pp.77-86
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• 2018

본 연구는 대학생 핵심역량을 기초하여 학생들에게 다양한 교육기회를 제공하고 산업변화에 따른 산업인력 수요에 적합한 대학인력 양성이 가능하도록 대학생의 핵심역량을 설정하고자 시도되었다. 연구절차는 핵심역량에 대한 문헌고찰과 학생, 교수, 산업체, 학부모 대상의 핵심역량의 중요도에 대한 조사 및 전문가의 타당도를 통해 대학생 핵심역량을 설정하였다. 연구결과 문헌고찰을 통해 14개의 역량을 도출하였으며, 재학생, 졸업생, 교수 대상으로 중요도와 전문가 타당도를 통해 창의력, 문제해결능력, 자기주도능력의 종합적 문제해결역량, 전문지식보유능력, 정보처리능력, 기술활용능력의 산업체맞춤전문역량, 배려 섬김인성, 사회봉사의 실천 나눔역량, 의사소통능력의 의사소통역량, 자기관리능력, 대인관계능력, 리더십, 외국어 능력, 글로벌 역량의 글로컬 리더십의 최종 5대 핵심역량과 세부 14개 하위 역량이 설정되었다. 본 연구결과에 기초하여 교육과정 개발과 운영에 적용하여 대학생들의 역량을 증진시키고, 향후 역량측정도구 개발을 통해 역량증진 정도를 파악하는 것이 필요하다.

• 스마트미디어저널
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• 제9권2호
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• pp.22-32
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• 2020

The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer's disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.129-134
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• 1996

1차 년도 G-7 개발 과제로 수행된 자기 공명 진단 장치 (Magnetic Resonance Imaging System)의 개발 내용을 간략히 소개하였다. 성공적인 IT Compact 자기 공명 진단 장치의 완성을 위해 일차적으로 (1)RF (고주파), Gradient(경사 자계), Spectrometer 등의 Hard-ware 관련 MRI 핵심부분, (2) RF, Gradient, Spectrometer, Magnet 등의 각 Sub-system을 연결, 조합, 조정하여 하나의 체계적인 시스템으로 통합하고 운영하는 과정(System Integration), (3)사용자와 시스템을 연결하는 User Interface, Data Base Management, Real time 운영 SW 등과 (4)임상에 적용하여 구체적인 성능과 효용성을 확인하는 기술 등에 대하여 집중 연구하였다. 개발 방법은 (1)지난 16년간 국내에 축적 된 연구 개발 인력들을 최대한 활용하고 (2)연구 개발을 국제화 시켜 필요한 경우 부분별로 개발 인력을 해외에서 보완하고 (3)소수 정예 전문 인력 주의와 요소 기술 또는 중요 부품을 경쟁성 검토 후 필요 시 Out-sourcing 활용으로 최저의 비용으로 개발 기간을 최소화 하는 데 두었다. 개발된 1.0Tesla자기 공명 영상 장치는 미국 물리 학회에서 규격화한 Phantom및 임상 적용을 통하여 서울대 의대 연구 팀과 지속적으로 성능을 평가해 왔다. 개발된 시스템의 해상도는 $256{\times}256$ head 영상에서 1mm 이 하의 해상도를 가짐을 resolution phantom 을 통하여 확인할 수 있었고, $512{\times}512$ 영상에서 는 약 0.5 mm 의 물체를 분리 해냄으로써 외제 시스템들 보다 우수하게 평가 되었다. 차폐 경사코일의 Eddy current영향은2%이내로 촬영 시 영향은 거의 무시할 수 있었다. 또한, 개발된 영상 기법들, 즉 Multislice/Multi Echo, Oblique angle imaging, 64 Echo train을 갖는 고속 촬영 기술들이 자기 공명 장치에 장착되어 임상 적용에 문제가 없도록 하였다. 또한 20mT/m/Amp의 강력한 능동 차폐 경사 자계 코일(Active Shield Gradient Coil)을 기본 사양으로 하고, 수신단을 최대 6개로 확장토록 하여 2차년도의 초고속 촬영 기법(EPI) 및 Phased Array 코일 촬영이 가능토록 하였다. 1차 년도 개발 과제 수행 결과와 향후 개발 과제를 바탕으로 최종 목표인 국제 경쟁력이 있는 자기 공명 진단 장치 즉 기능과 영상의 질은 선진국 제품과 동일하거나 우수하되, 저가격을 구현한 상용화 제품이 완성되어, 첨단 의료기기로서 산업 구조 고도화에 기여하고 수입대체 뿐만 아니 라 수출을 통한 국익 창출과 국가의 기술을 통한 위상 제고에 기여되길 기대한다.