• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.732-734
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• 2003

In the middle -resolution remote sensing, the Ground Sampled Distance(GSD) sensed and sampled by the detector is generally larger than the size of objects(or materials) of interest, in which case several objects are embedded in a single pixel and cannot be detected spatially. This study is intended to solve this problem of a hyperspectral data with high spectral resolution. We examined the detection algorithm, Linear Spectral Mixing Model, and also made a test on the Hyperion data. To find class Endmembers, we applied two methods, Spectral Library and Geometric Model, and compared them with each other.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.493-498
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• 2004

The objective of the present study is to compare various conventional and recently evolved satellite image-processing techniques and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwang coastal area. Several conventional techniques performed to extract such information fiom the Landsat-TM imagery do not seem to yield better information about the aquaculture farms, and lead to misclassification. The large errors between the actual and extracted aquaculture farm information are due to existence of spectral confusion and inadequate spatial resolution of the sensor. This leads to possible occurrence of mixture pixels or 'mixels' of the source of errors in the classification techniques. Understanding the confusing and mixture pixel problems requires the development of efficient methods that can enable more reliable extraction of aquaculture farm information. Thus, the more recently evolved methods such as the step-by-step partial spectral end-member extraction and linear spectral unmixing methods are introduced. The farmer one assumes that an end-member, which is often referred to as 'spectrally pure signature' of a target feature, does not appear to be a spectrally pure form, but always mix with the other features at certain proportions. The assumption of the linear spectral unmxing is that the measured reflectance of a pixel is the linear sum of the reflectance of the mixture components that make up that pixel. The classification accuracy of the step-by-step partial end-member extraction improved significantly compared to that obtained from the traditional supervised classifiers. However, this method did not distinguish the aquaculture ponds and non-aquaculture ponds within the region of the aquaculture farming areas. In contrast, the linear spectral unmixing model produced a set of fraction images for the aquaculture, water and soil. Of these, the aquaculture fraction yields good estimates about the proportion of the aquaculture farm in each pixel. The acquired proportion was compared with the values of NDVI and both are positively correlated (R$^2$ =0.91), indicating the reliability of the sub-pixel classification.ixel classification.

• Journal of Biomedical Engineering Research
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• v.30 no.1
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• pp.94-102
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• 2009

In this paper, we propose a hearing impairment simulator considering reduced frequency selectivity of the hearing impaired, and verify it's performance through experiments. The reduced frequency selectivity was embodied by spectral smearing using linear prediction coding(LPC). The experiments are composed of 4 kinds of tests; pure tone test, speech reception threshold(SRT) test, and word recognition score(WRS) test without spectral smearing and with spectral smearing. The experiments of the hearing impairment simulator were performed with 9 subjects who have normal hearing. The amount of spectral smearing was controlled by LPC order. The percentile score of WRS test without smearing is $89.78{\pm}2.420%$. The scores of WRS with 24th LPC order and with 8th LPC order are $88.00{\pm}3.556%$ and $83.78{\pm}2.123%$ respectively. It is verified that WRS score is lowered by decreasing LPC order. This is a reasonable result considering that spectral smearing is getting heavier according to decreasing LPC order. It is confirmed that spectral smearing using LPC simulates the reduced frequency selectivity of the hearing impaired and affects the clearness of speech reception.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.1
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• pp.43-52
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• 2021

According to several seismic design standards, a ground motion time history should be selected similar to the design response spectrum, or a ground motion time history should be modified by matching procedure to the design response spectrum through the time-domain method. For the response spectrum matching procedure, appropriate seed ground motions need to be selected to maintain recorded earthquake accelerogram characteristics. However, there are no specific criteria for selecting the seed ground motions for applying this methodology. In this study, the characteristics of ground motion time histories between seed motions and spectral matched motions were compared. Intensity measures used in the design were compared, and their change by spectral matching procedure was quantified. In addition, the seed ground motion sets were determined according to the response spectrum shape, and these sets analyzed the response of nonlinear and equivalent linear single degrees of freedom systems to present the seed motion selection conditions for spectral matching. As a result, several considerations for applying the time domain spectral matching method were presented.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.11
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• pp.840-845
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• 2014

Vibration profiles consist of two kinds of pattern, random and harmonic, at general engineering problems and the detailed vibration test mode of a target system is decided by the spectral condition that is exposed under operation. In moving mobility, random responses come generally from road source; whereas the harmonic responses are triggered from rotating machinery parts, such as combustion engine or drive shaft. Different spectral input may accumulate different damage in frequency domain since the accumulated fatigue damage dependent on the pattern of input spectrum in high cyclic loading condition. To evaluate the sensitivity of spectral damage according to different loading conditions, a linear elastic system is introduced to conduct a uniaxial vibration testing. Measured data, acceleration and strain, is analyzed using energy isocline function and then, the calculated fatigue damage is compared by different loading cases, random and harmonic.

• Korean Journal of Geomatics
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• v.4 no.1
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• pp.1-8
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• 2004

In the middle-resolution remote sensing, the Ground Sampled Distance (GSD) that the detector senses and samples is generally larger than the actual size of the objects (or materials) of interest, and so several objects are embedded in a single pixel. In this case, as it is impossible to detect these objects by the conventional spatial-based image processing techniques, it has to be carried out at sub-pixel level through spectral properties. In this paper, we explain the sub-pixel analysis algorithm, also known as the Linear Spectral Mixing (LSM) model, which has been experimented using the Hyperion data. To find Endmembers used as the prior knowledge for LSM model, we applied the concept of the convex geometry on the two-dimensional scatter plot. The Atmospheric Correction and Minimum Noise Fraction techniques are presented for the pre-processing of Hyperion data. As LSM model is the simplest approach in sub-pixel analysis, the results of our experiment is not good. But we intend to say that the sub-pixel analysis shows much more information in comparison with the image classification.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

• Communications of the Korean Mathematical Society
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• v.9 no.3
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• pp.617-627
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• 1994

Algebraic spectral subspaces and admissible operators were introduced by K. B. Laursen and M. M. Neumann in 1988 [L88], [N]. These concepts are useful in automatic continuity problems of intertwining linear operators on Banach spaces. In this paper we characterize the algebraic spectral subspaces of generalized scalar operators. From this characterization we show that generalized scalar operators are admissible. Also we show that doubly power bounded operators are generalized scalar. And using the spectral capacity we show that a generalized scalar operator is decomposable. Then we give an example of an operator which is not admissible but decomposable.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.2
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• pp.247-257
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• 2008

In this paper, we show that for a semi-shift the analytic spectral subspace coincides with the algebraic spectral subspace. Using this result, we have the following result. Let T be a decomposable operator on a Banach space ${\mathcal{X}}$ and let S be a semi-shift on a Banach space ${\mathcal{Y}}$. Then every linear operator ${\theta}:{\mathcal{X}}{\rightarrow}{\mathcal{Y}}$ with $S{\theta}={\theta}T$ is necessarily continuous.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.