• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.894-900
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• 2005

A new approach to the straightforward implementation of the unscented filter in a unit quaternion space is proposed for spacecraft attitude estimation. Since the unscented filter is formulated in a vector space and the unit quaternions do not belong to a vector space but lie on a nonlinear manifold, the weighted sum of quaternion samples does not produce a unit quaternion estimate. To overcome this difficulty, a method of weighted mean computation for quaternions is derived in rotational space, leading to a quaternion with unit norm. A quaternion multiplication is used for predicted covariance computation and quaternion update, which makes a quaternion in a filter lie in the unit quaternion space. Since the quaternion process noise increases the uncertainty in attitude orientation, modeling it either as the vector part of a quaternion or as a rotation vector is considered. Simulation results illustrate that the proposed approach successfully estimates spacecraft attitude for large initial errors and high tip-off rates, and modeling the quaternion process noise as a rotation vector is more optimal than handling it as the vector part of a quaternion.

• 대한수학회보
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• 제46권4호
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• pp.713-720
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• 2009

We find all left-invariant minimal unit vector fields and strongly normal unit vector fields on a Lie group which is isometric to the hyperbolic space.

• IEIE Transactions on Smart Processing and Computing
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• 제4권1호
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• pp.16-21
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• 2015

Most video services are transmitted in wireless networks. In a network environment, a packet of video is likely to be lost during transmission. For this reason, numerous error concealment (EC) algorithms have been proposed to combat channel errors. On the other hand, most existing algorithms cannot conceal the whole missing frame effectively. To resolve this problem, this paper proposes a new Adaptive Prediction Unit-based Motion Vector Extrapolation (APMVE) algorithm to restore the entire missing frame encoded by High Efficiency Video Coding (HEVC). In each missing HEVC frame, it uses the prediction unit (PU) information of the previous frame to adaptively decide the size of a basic unit for error concealment and to provide a more accurate estimation for the motion vector in that basic unit than can be achieved by any other conventional method. The simulation results showed that it is highly effective and significantly outperforms other existing frame recovery methods in terms of both objective and subjective quality.

• 대한수학회보
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• 제55권5호
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• pp.1339-1350
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• 2018

We will find a necessary and sufficient condition for unit Killing vector fields to be minimal and provide an application of the obtained result.

• 대한수학회보
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• 제47권5호
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• pp.951-960
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• 2010

We provide the set of left-invariant minimal unit vector fields on the semi-direct product $\mathbb{R}^n\;{\rtimes}_p\mathbb{R}$, where P is a nonsingular diagonal matrix and on the 7 classes of 4-dimensional solvable Lie groups of the form $\mathbb{R}^3\;{\rtimes}_p\mathbb{R}$ which are unimodular and of type (R).

• Korean Journal of Mathematics
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• 제6권2호
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• pp.303-311
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• 1998

We study the conformally flat unit vector bundle $E_1$ of constant scalar curvature for the bundle ${\pi}:E^{n+2}{\rightarrow}M^n$ over an Einstein manifold M.

• The Journal of the Acoustical Society of Korea
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• 제20권4E호
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• pp.31-38
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• 2001

A large-scale synthesis database for a unit selection based synthesis method usually retains redundant synthesis unit instances, which are useless to the synthetic speech quality. In this paper, to eliminate those instances from the synthesis database, we proposed a new pruning method called weighted vector quantization (WVQ). The WVQ reflects relative importance of each synthesis unit instance when clustering the similar instances using vector quantization (VQ) technique. The proposed method was compared with two conventional pruning methods through the objective and subjective evaluations of the synthetic speech quality: one to simply limit maximum number of instance, and the other based on normal VQ-based clustering. The proposed method showed the best performance under 50% reduction rates. Over 50% of reduction rates, the synthetic speech quality is not seriously but perceptibly degraded. Using the proposed method, the synthesis database can be efficiently reduced without serious degradation of the synthetic speech quality.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.134-137
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• 2005

Unsupervised spectral angle classification (USAC) is the algorithm that can extract ground object information with the minimum 'Spectral Angle' operation on behalf of 'Spectral Euclidian Distance' in the clustering process. In this study, our algorithm uses the unit vector instead of the spectral distance to compute the mean of cluster in the unsupervised classification. The proposed algorithm (MUSAC) is applied to the Hyperion and ETM+ data and the results are compared with K-Meails and former USAC algorithm (FUSAC). USAC is capable of clearly classifying water and dark forest area and produces more accurate results than K-Means. Atmospheric correction for more accurate results was adapted on the Hyperion data (Hyperion-FLAASH) but the results did not have any effect on the accuracy. Thus we anticipate that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but also hyperspectral images. Furthermore the cluster unit vector can be an efficient technique for determination of each cluster mean in the USAC.

• Korean Journal of Geomatics
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• 제5권1호
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• 한국조경학회지
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• 제29권1호
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• pp.22-31
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• 2001

The purpose of this study is to compare calculated pollutant loadings using pollutant load unit factors and vector type coverage, and expected mean concentration(EMC) and raster type of digital elevation model(DEM). This study is also focusing on comparison of the advantages and the disadvantages of the two methods, and seeking for a method of calculation of pollutant loadings using DEM. Estimation of pollutant inputs using pollutant load unit factors has limitations in identifying seasonal variations of pollutant loadings. Seasonal changes of runoffs should be considered in the calculation of pollutant loadings from catchments into reservoirs. Evaluation of pollutant inputs using runoff-coefficient and EMC can overcome these drawbacks. Proper EMC and runoff-coefficient values for the Koeup stream catchments of the Koheung estuarine lake were drawn from review of related papers. Arc/Info was employed to establish database of spatial and attribute data of point and non-point pollutant sources and characteristics of the catchments. ArcView was used to calculate point and non-point pollutant loadings. Pollutant loads estimated with either unit factors-coverages, i.e., pollutant load unit factors and vector coverages f point sources and land use, or EMC and digital elevation mode(DEM) were compared with stream monitoring loads. We have found that some differences were shown between monitoring results and estimated loads by Unit Factors-Coverage and EMC-DEM. Monthly variations of pollutant loads evaluated with EMC-DEM were similar to those with monitoring result. The method using EMC-DEM can calculate accumulated flows and pollutant loads and can be utilized to identify stream networks. A future research on correcting the difference between vector type stream using flow direction grid and digitalizing vector type should be conducted in order to obtain more exact calculation of pollutant loadings.