• 천문학논총
• /
• 제7권1호
• /
• pp.231-253
• /
• 1992

A model for the distribution of stars in the disk and the spheroid of our Galaxy is reexamined from an edge-on view of the Galaxy obtained by selecting infrared sources from the IRAS Point Source Catalog. The sources are counted as a function of galactic latitude. longitude and $12{\mu}m$ apparent magnitude. The source counts are reasonably separated into the disk component and the spheroid component contributions and each of the contributions is further interpreted as a convolution of a spatial density distribution and a luminosity function based on the least-square fit method. The spatial density of the disk component has an exponential radial scale length of $h_R{\sim}2.6\;kpc$ and the vertical distribution follows a canonical $sech^2$ law with a scale height $h_z{\sim}240\;pc$. The distribution of the spheroid component can be represented by an oblate spheriod with an axis ratio $k{\sim}0.61$ and a de Vaucouleurs' $r^{1/4}$ law with an effective radius of $R_e{\sim}120\;pc$. The steep density gradient of the spheroid component is consistent with that of late M giants in the central bulge. The luminosity functions of the disk and the spheroid component stars resemble respectively those of the K luminosity function of disk M giants (Garwood and Jones 1986) and the bolometric luminosity function of M giants in bulge fields (Frogel et al, 1990).

• 대한의용생체공학회:의공학회지
• /
• 제30권5호
• /
• pp.373-380
• /
• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• 한국공간정보시스템학회:학술대회논문집
• /
• 한국공간정보시스템학회 1999년도 워크샵 2권1호
• /
• pp.69-89
• /
• 1999

• 한국공간정보시스템학회:학술대회논문집
• /
• 한국공간정보시스템학회 1999년도 워크샵 2권1호
• /
• pp.91-100
• /
• 1999

• Journal of Biosystems Engineering
• /
• 제39권4호
• /
• pp.377-388
• /
• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• ETRI Journal
• /
• 제45권1호
• /
• pp.131-137
• /
• 2023

The phase reconstruction process in digital holographic microscopy involves a trade-off between the phase error and the high-spatial-frequency components. In this reconstruction process, if the narrow region of the sideband is windowed in the Fourier domain, the phase error from the DC component will be reduced, but the high-spatial-frequency components will be lost. However, if the wide region is windowed, the 3D profile will include the high-spatial-frequency components, but the phase error will increase. To solve this trade-off, we propose the high-variance pixel averaging method, which uses the variance map of the reconstructed depth profiles of the windowed sidebands of different sizes in the Fourier domain to classify the phase error and the high-spatial-frequency components. Our proposed method calculates the average of the high-variance pixels because they include the noise from the DC component. In addition, for the nonaveraged pixels, the reconstructed phase data created by the spatial frequency components of the widest window are used to include the high-spatialfrequency components. We explain the mathematical algorithm of our proposed method and compare it with conventional methods to verify its advantages.

• 대한기계학회논문집B
• /
• 제33권5호
• /
• pp.373-380
• /
• 2009

The evaporation process of multi-component fuel is different from one of a single component, because the properties of each component affects among the components. In actual engine, the spatial distribution of fuel vapor concentration dominates auto-ignition and initial combustion, and depends on the volatility and diffusivity of each component fuel contained in the multi-component fuel. Then, this study proposes a simplified numerical scheme for analysis of evaporation process of multi-component fuel sprays. Evaporation process is calculated by KIVA-II code based on the simple two-phases region that is approximated by modified saturated liquid-vapor line, which was obtained by connecting the 50% distillation temperature for each component under several pressure fields. Consequently, it can be quantitatively simulated that vapor of low boiling fuel component mostly exists around nozzle and spray tip region, the high boiling duel component, on the other hand, mostly appears near the spray tip.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
• /
• pp.97-99
• /
• 1993

A direct vector control for induction machine based on determination of the spatial position of the airgap flux from the third harmonic component of the stator phase voltage is presented in this Paper. The relationship between the airgap fundamental flux component and the third harmonic flux component obtained from the stator third harmonic voltage is presented at the end of this paper.

• Structural Engineering and Mechanics
• /
• 제65권5호
• /
• pp.633-644
• /
• 2018

This paper presents the results of an assessment of the seismic fragility of a long, curved multi-frame bridge under multi-support earthquake excitations. To achieve this aim, the numerical model of columns retrofitted with elliptical steel jackets was developed and validated using existing experimental results. A detailed nonlinear numerical model of the bridge that can capture the inelastic response of various components was then created. Using nonlinear time-history analyses for a set of stochastically generated spatially variable ground motions, component demands were derived and then convolved with new capacity-based limit state models to obtain seismic fragility curves. The comparison of failure probabilities obtained from uniform and multi-support excitation analyses revealed that the consideration of spatial variability significantly reduced the median value of fragility curves for most components except for the abutments. This observation indicates that the assumption of uniform motions may considerably underestimate seismic demands. Moreover, the spatial correlation of ground motions resulted in reduced dispersion of demand models that consequently decreased the dispersion of fragility curves for all components. Therefore, the spatial variability of ground motions needs to be considered for reliable assessment of the seismic performance of long multi-frame bridge structures.

• 대한의용생체공학회:의공학회지
• /
• 제24권4호
• /
• pp.287-292
• /
• 2003

다채널 고감도 자기 센서에 의해 획득되는 심자도 신호는 심전도 신호에 비하여 심장 질환의 진단에 여러 장점을 가지고 있지만 외부 자기 잡음에 의해 쉽게 영향을 받는다는 단점이 있다 외부 자기 잡음은 획득된 신호의 시간적 추이를 분석하여 제거할 수도 있지만, 다채널 시스템에서는 다채널 신호 사이의 공간적 상관 관계에 근거하여 제거하는 방법이 보다 효과 적이다. 된 연구에서는 이러한 방법의 하나로 PCA(Prinicipal component analysis)를 이용하여 구성한 공간 필터로 심자도 신호에서 외부 자기 잡음 성분을 효과적으로 분리해 내는 방법을 제안하였다. 제안한 방법을 61채널 심자도 시스템을 이용해 얻은 정상인 심자도 신호에 적용했을 때 자기 잡음 제거가 효율적으로 이루어짐을 실험적으로 보였다.