• 한국음향학회지
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• 제19권6호
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• pp.17-22
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• 2000

항공기나 선박과 같은 복잡한 구조물의 광대역 진동, 소음 예측을 위해 통계에너지해석법(SEA)이 널리 이용되고 있다. SEA를 이용해 접수 구조물의 진동, 소음을 정확히 해석하기 위해서는 접수에 의한 각 파라메터의 변화를 알아야만 한다. 본 연구에서는 기본 결합 요소인 'L'형과 'T'형 선결합 구조물에서 접수를 고려한 연성손실계수를 해석하고 공기중 진동시의 해석 결과와 비교하였다. 또한 'L'형, 'T'형 선결합을 가지는 단순한 형상의 steel box가 수중에서 진동하는 경우에, 접수에 의한 연성손실계수 변화가 세부시스템의 진동에 미치는 영향을 살펴보았다. 이를 통해, 구조물이 접수될 때 발생하는 연성손실계수의 변화를 확인하였으며, SEA를 이용한 접수 구조물의 진동 및 소음 해석시 결과의 신뢰성을 높이기 위해서는 접수에 의한 모드밀도, 내부손실계수 변화와 더불어 접수에 의한 연성손실계수 변화를 반드시 고려하여야 함을 확인하였다.

• 한국실내디자인학회논문집
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• 제25권2호
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• pp.92-100
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• 2016

Recently, new detached houses on a small plot in low-rise residential urban area have attracted more public interests than ever before. This study aims to categorize basic types of interior plans in small urban detached houses by analyzing the layouts of the public space(LDK) within them which are popular in Japan. The results of this study may be summarized as follows. Unit floor plans were basically classified as 16 types according to the layout of LDK. Among these, the LDK type in a straight line shows the most at 28.9%. Again, these plans were sub-classified into 38 types by plan composition. The new 'LDKL' (a new term the authors propose to be used) type was observed in cases where K was directly connected to L. This type appears at 9 out of 38 types. In the connection of LDK, the LDK type shows the most at 60.8%; secondly the L-DK type at 29.9%; thereafter the LD-K type at 6.2%; and lastly L-D-K type, the least frequently at 1.0%. The cases of inter-level connection between LDK and the surrounding space were observed in various cases such as the open workplace of the upstairs hallway visually connected with LDK through the void space and as the attic space of the pitched roof visually linked to it.

• 천문학회보
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• 제42권1호
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• pp.51.3-51.3
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• 2017

To investigate the connection between radio activity and AGN outflows, we present a study of ionized gas kinematics by using [O III] ${\lambda}5007$ emission line along the radio jet for six radio AGNs. These AGNs are selected based on the radioactivity (L1.4GHz ${\geq}$ 1039.8 erg s-1) as well as optical properties as type 2 AGNs. By using the high spatial resolution of the Red Channel Cross Dispersed Echellette Spectrograph at the Multiple Mirror Telescope, we investigate in detail the [O III] and stellar kinematics. We spatially resolve and probe the central AGN-photoionization sizes, which is important in understanding the structures and evolutions of galaxies. We find that the typical central AGN-photoionization sizes of our targets are in range of 1.8-3.8 kpc. We study the [O III] kinematics along the radio jets to test whether there is a link between gas outflows in the narrow-line region and radio jet emissions. Contrary to our expectation, we find no evidence that the gas outflows are directly connected to radio jet emission.

• 천문학회보
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• 제35권2호
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• pp.33.1-33.1
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• 2010

We investigate the relation between the presence of bars in galaxies and AGN activities. Bars are believed to play an important role in fueling of AGN. Although there have been many previous studies on this topic, "the AGN-Bar Connection" is still an open question. To better understand the connection, we use a volume-limited sample of 9,726 late-type galaxies brighter than $M_r$=-19.5+5logh at $0.02{\leqq}z{\leqq}0.05489$, drawn from SDSS DR7. Among galaxies in the sample, 1,963 galaxies are classified as AGN-host galaxies based on the emission-line ratios while barred galaxies are identified by visual inspection. The bar fraction in AGN host galaxies (22.5%) is 3-times higher than in star-forming galaxies (8.6%). However, this trend is simply caused by the fact that the bar fraction increases with galaxy mass or luminosity and that AGN host galaxies are on average more massive than star-forming galaxies. Nevertheless, we find that among AGN host galaxies, the bar fraction increases with the Eddington ratio $(L_{[OIII]}/M_{[BH]})$, and this trend remains intact even at fixed galaxy luminosity and stellar velocity dispersion. These results imply that bars play a role in triggering AGNs.

• Smart Structures and Systems
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• 제29권4호
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.