• 제목/요약/키워드: temporal fluctuation

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여수해만 수온의 시공간적 변동특성 (Temporal and spatial fluctuation characteristics of sea surface temperature in Yeosu Bay, Korea)

  • 추효상
    • 수산해양기술연구
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    • 제56권4호
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    • pp.322-339
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    • 2020
  • Temporal and spatial fluctuations of surface water temperature in Yeosu Bay for the period from 2010 to 2011 were studied using the data from temperature monitoring buoys deployed at 32 stations in the south coast of Korea. Temperatures in the northern part of the bay are higher in summer and lower in winter than in the southern part of the bay. The lowest and highest temperature of the annual mean are found at the eastern coast of POSCO and at the west of Dae Island, respectively. Cold water masses appear at estuarine area when the discharge of Sumjin river is affluent. Amplitude of temperature fluctuation whose period is less than semi-diurnal is largest at Hadong coast and around Dae Island. Spectral analysis of surface water temperature shows a significant peak at a periodic fluctuation of 0.5 to 24 days and about 15-day period of predominant fluctuation is most frequent in Yeosu Bay. From the cross-correlation analysis of temperature fluctuations, Yeosu Bay can be classified into six areas; the south area affected by South Sea of Korea, the mixed area in the center of the bay, the estuarine area affected by river discharge at the north of the bay, the hot waste water area near Hadong coast, the area around Dae Island and the area near Noryang Channel affected by the water in Jinju Bay, respectively.

EOF와 CSEOF를 이용한 한반도 강수의 변동성 분석 (Investigation of Korean Precipitation Variability using EOFs and Cyclostationary EOFs)

  • 김광섭;순밍동
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2009년도 학술발표회 초록집
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    • pp.1260-1264
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    • 2009
  • Precipitation time series is a mixture of complicate fluctuation and changes. The monthly precipitation data of 61 stations during 36 years (1973-2008) in Korea are comprehensively analyzed using the EOFs technique and CSEOFs technique respectively. The main motivation for employing this technique in the present study is to investigate the physical processes associated with the evolution of the precipitation from observation data. The twenty-five leading EOF modes account for 98.05% of the total monthly variance, and the first two modes account for 83.68% of total variation. The first mode exhibits traditional spatial pattern with annual cycle of corresponding PC time series and second mode shows strong North South gradient. In CSEOF analysis, the twenty-five leading CSEOF modes account for 98.58% of the total monthly variance, and the first two modes account for 78.69% of total variation, these first two patterns' spatial distribution show monthly spatial variation. The corresponding mode's PC time series reveals the annual cycle on a monthly time scale and long-term fluctuation and first mode's PC time series shows increasing linear trend which represents that spatial and temporal variability of first mode pattern has strengthened. Compared with the EOFs analysis, the CSEOFs analysis preferably exhibits the spatial distribution and temporal evolution characteristics and variability of Korean historical precipitation.

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Super-Resolution Optical Fluctuation Imaging Using Speckle Illumination

  • Kim, Min-Kwan;Park, Chung-Hyun;Park, YongKeun;Cho, Yong-Hoon
    • 한국진공학회:학술대회논문집
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    • 한국진공학회 2014년도 제46회 동계 정기학술대회 초록집
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    • pp.403.1-403.1
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    • 2014
  • In conventional far-field microscopy, two objects separated closer than approximately half of an emission wavelength cannot be resolved, because of the fundamental limitation known as Abbe's diffraction limit. During the last decade, several super-resolution methods have been developed to overcome the diffraction limit in optical imaging. Among them, super-resolution optical fluctuation imaging (SOFI) developed by Dertinger et al [1], employs the statistical analysis of temporal fluorescence fluctuations induced by blinking phenomena in fluorophores. SOFI is a simple and versatile method for super-resolution imaging. However, due to the uncontrollable blinking of fluorophores, there are some limitations to using SOFI for several applications, including the limitations of available blinking fluorophores for SOFI, a requirement of using a high-speed camera, and a low signal-to-noise ratio. To solve these limitations, we present a new approach combining SOFI with speckle pattern illumination to create illumination-induced optical fluctuation instead of blinking fluctuation of fluorophore.. This technique effectively overcome the limitations of the conventional SOFI since illumination-induced optical fluctuation is possible to control unlike blinking phenomena of fluorophore. And we present the sub-diffraction resolution image using SOFI with speckle illumination.

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정상인 수면 뇌파 탈경향변동분석 (Detrended Fluctuation Analysis on Sleep EEG of Healthy Subjects)

  • 신홍범;정도언;김의중
    • 수면정신생리
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    • 제14권1호
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    • pp.42-48
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    • 2007
  • 목 적:뇌파의 비선형적 특성을 연구하는 방법으로 탈경향 변동분석이 사용되고 있다. 본 연구에서는 정상인 수면 뇌파에 탈경향변동분석을 적용하여 수면뇌파의 비선형적 특성, 채널 별 차이, 수면단계별 차이를 규명하고자 하였다. 방 법:정상인 12명($23.8{\pm}2.5$세, 남:여=7:5)를 대상으로 야간수면다원검사를 시행하였다. 수면다원검사를 통해 얻어진 뇌파를 채널 별, 수면단계별로 나누어 탈경향변동분석 시행 후 여기서 얻어진 축척지수(scaling exponent)를 선형혼합모형 분석을 통해 비교하였다. 결 과:정상인 수면다원검사에서 얻어진 뇌파의 축척지수는 1 내외의 값을 보여 장기-시간적연관성, 자기유사성을 보였다. C3 채널의 축적지수가 O1채널의 축적지수보다 높은 값을 보였다. 수면단계가 진행함에 따라 축적지수는 증가하였으며, 1단계 수면과 렘수면은 축적지수는 통계적 차이를 보이지 않았다. 결 론:정상인 수면 뇌파는 탈경향변동분석에서 무축척요동(scale-free fluctuation), 장기-시간적 관련성(long-range temporal correlation), 자기유사성(self-similarity) 및 스스로 짜여진 고비성(self-organized criticality) 등의 비선형적 특성을 보였다. 탈경향변동분석에서 얻어진 축적지수는 뇌파 채널 별, 수면단계별 차이를 보여 수면 뇌파를 연구하는 중요한 도구로 사용될 수 있다.

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Assessing the Nano-Dynamics of the Cell Surface

  • Bae, Chil-Man;Park, Ik-Keun;Butler, Peter J.
    • 비파괴검사학회지
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    • 제32권3호
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    • pp.263-268
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    • 2012
  • It is important to know the mechanism of cell membrane fluctuation because it can be readout for the nanomechanical interaction between cytoskeleton and plasma membrane. Traditional techniques, however, have drawbacks such as probe contact with the cell surface, complicate analysis, and limit spatial and temporal resolution. In this study, we developed a new system for non-contact measurement of nano-scale localized-cell surface dynamics using modified-scanning ion-conductance microscopy. With 2 nm resolution, we determined that endothelial cells have local membrane fluctuations of ~20 nm, actin depolymerization causes increase in fluctuation amplitude, and ATP depletion abolishes all membrane fluctuations.

진주만 해역 수온의 시공간적 변동 특성 (Temporal and Spatial Variations of Sea Surface Temperature in Jinju Bay in the South Coast of Korea)

  • 추효상;윤은찬
    • 해양환경안전학회지
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    • 제21권4호
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    • pp.315-326
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    • 2015
  • 진주만해역 수온의 시공간적 변동특성을 장기 연속수온관측 자료를 이용하여 분석하였다. 수온은 1월 말 최저, 8월 초 최대이고 만 북쪽이 중앙과 남쪽보다 계절변동이 작다. 하계 최고수온의 최저와 최고는 지족수로 주변에 출현한다. 노량수로와 대방수로는 조류 유 출입에 따른 수층 간 연직혼합으로 수온변동이 작다. 외해수 영향이 작은 만 남쪽은 동계 해면냉각과 하계 가열에 의한 변동이 현저하다. 바람은 대방수로 주변이 강해 조류와 함께 이 해역 표층의 혼합정도에 큰 영향을 준다. 만내수온이 균일하게 낮고 소조기 서풍이 강해져 노량수로에 동쪽방향 항류가 출현할 때 만 북쪽 해역에 난수가 유입되는 양상을 보여준다. 노량수로 해역은 7~20일의 장주기, 창선도 서쪽과 지족수로는 장주기와 반일주기, 만 중앙은 장주기와 일일주기 수온변동이 우세하다. Coherence 분석결과, 노량수로의 수온변동은 만 내 정점과 상관성이 크고 위상이 앞서나 대방수로보다는 느리다. 대방수로의 수온변동은 만 서쪽과 중앙 일부에 영향을 준다. 상호상관계수분석으로 진주만은 노량수로역, 만 북쪽 수렴발산역, 대방수로역, 창선도연안수역, 만 중앙혼합수역, 만 중앙내 만수역으로 분류되었다.

Active Exchange of Water and Nutrients between Seawater and Shallow Pore Water in Intertidal Sandflats

  • Hwang, Dong-Woon;Kim, Gue-Buem;Yang, Han-Soeb
    • Ocean Science Journal
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    • 제43권4호
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    • pp.223-232
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    • 2008
  • In order to determine the temporal and spatial variations of nutrient profiles in the shallow pore water columns (upper 30 cm depth) of intertidal sandflats, we measured the salinity and nutrient concentrations in pore water and seawater at various coastal environments along the southern coast of Korea. In the intertidal zone, salinity and nutrient concentrations in pore water showed marked vertical changes with depth, owing to the active exchange between the pore water and overlying seawater, while they are temporally more stable and vertically constant in the sublittoral zone. In some cases, the advective flow of fresh groundwater caused strong vertical gradients of salinity and nutrients in the upper 10 cm depth of surface sediments, indicating the active mixing of the fresher groundwater with overlying seawater. Such upper pore water column profiles clearly signified the temporal fluctuation of lower-salinity and higher-Si seawater intrusion into pore water in an intertidal sandflat near the mouth of an estuary. We also observed a semimonthly fluctuation of pore water nutrients due to spring-neap tide associated recirculation of seawater through the upper sediments. Our study shows that the exchange of water and nutrients between shallow pore water and overlying seawater is most active in the upper 20 cm layer of intertidal sandflats, due to physical forces such as tides, wave set-up, and density-thermal gradient.

Phase Diagram에 의한 밀폐캐비티의 비정상 유동특성 (Unsteady Flow Characteristics of Closed Cavity by Phase Diagram)

  • 조대환
    • Journal of Advanced Marine Engineering and Technology
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    • 제23권6호
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    • pp.770-777
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    • 1999
  • In this study a phase diagram has been used to investigate the unsteadiness of two-dimensional lid-driven closed flows within a square cavity for twelve Reynolds numbers; $7.5{\times}10^3,\; 8{\times}10^3,\; 8.5{\times}10^3,\; 9{\times}10^3,\; 9.5{\times}10^3,\; 10^4,\;1.5{\times}10^4,\;2{\times}10^4,\; 3{\times}10^4,\; 7.5{\times}10^4$ and $10^5$. The results indicate that the first critical Reynolds number at which the flow unsteadiness of sinusoidal fluctuation appears from the temporal variation of total kinetic energy curves is assumed of sinusoidal fluctuation appears form the temporal variation of total kinetic energy curves is assumed to be in the neigh-bourhood of $Re=8.5{\times}10^3$ The second critical Reynolds number where the periodic amplitude and frequency collapse to random disturbance being existed around $Re=1.5{\times}10^4$ The exponentially decreasing vortices formed at the lower two corners are found commonly at the time-mean flow pattern of $Re=3{\times}10^4$.

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