• Proceedings of the Korean Printing Society Conference
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• 2000.04a
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• pp.35-44
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• 2000

Because Image on the CRT change under different illuminants, human is difficult to see original color of object. If what is information of used illuminant on capturing object know, image can be transformed according to viewing condition using the linear matrix method. To know information of used illuminant at an image, the spectral radiance of illuminant can be estimated using the linear model of Maloney and Wandell form an image. And then image can be properly transformed it using color appearance model. In this paper, we predict the spectral radiance of illuminant using spectral power distribution of specular light and using surface spectral reflectance at maximum gray area. and then we perform visual experiments for the corresponding color reproduction according to viewing condition. In results, we ensure that the spectral radiance of illuminant at an image can be well estimated using above algorithms and that human visual system is 70% adapted to the monitor's white point and 30% to ambient light when viewing softcopy images.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.925-928
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• 2005

In this paper, a spectral element model is developed for the uniform straight pipelines conveying internal unsteady fluid. The spectral element matrix is formulated by using the exact frequency-domain solutions of the pipe-dynamics equations. The spectral element dynamic analyses are then conducted to evaluate the accuracy of the present spectral element model and to investigate the vibration characteristics and internal fluid transients of an example pipeline system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.738-741
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• 2009

This research is concerned for improving the result of speech recognition under the noisy speech. We knew that spectral subtraction and recovery of valleys in spectral envelope obtained from noisy speech are more effective for the improvement of the recognition. In this research, the averaged spectral envelope obtained from vowel spectrums are used for the emphasis of valleys. The vocalic spectral information at lower frequency range is emphasized and the spectrum obtained from consonants is not changed. In simulation, the emphasis coefficients are varied on cepstral domain. This method is used for the recognition of noisy digits and is improved.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1287-1300
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• 2017

Based on the observation that ignoring the angle dependency of multigroup resonance cross sections within a fuel pellet would result in nontrivial underestimation of the spatial self-shielding of flux, a parametrized spectral superhomogenization (SPH) factor library (PSSL) method is developed as a practical means of resolving the problem. Region-wise spectral SPH factors are calculated by the normal and transport corrected SPH iterations after ultrafine group slowing down calculations over various light water reactor pin-cell configurations. The parametrization is done with fuel temperature, U-238 number density, fuel radius, moderator source represented by ${\Sigma}_{mod}V_{mod}$, and the number density ratio of resonance nuclides to that of U-238 in a form of resonance interference correction factors. The parametrization is successful in that the root mean square errors of the interpolated SPH factors over the fuel regions of various pin-cells are within 0.1%. The improvement in reactivity error of the PSSL method is shown to be superior to that by the original SPH method in that the reactivity bias of -200 pcm to -300 pcm vanishes almost completely. It is demonstrated that the environment effect takes only about 4% in the reactivity improvement so that the pin-cell based PSSL method is effective in the assembly problems.

• Geomechanics and Engineering
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• v.13 no.3
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• pp.411-429
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• 2017

A Spectral Element Method for 3D seismic wave propagation simulation is derived based on the three-dimensional fluctuating elastic dynamic equation. Considering the 3D real terrain and the attenuation characteristics of the medium, the topographic effect of Wenchuan earthquake is simulated by using the Spectral Element Method (SEM) algorithm and the ASTER DEM model. Results show that the high PGA (peak ground acceleration) region was distributed along the peak and the slope side away from the epicenter in the epicenter area. The overall distribution direction of high PGA and high PGV (peak ground velocity) region is parallel to the direction of the seismogenic fault. In the epicenter of the earthquake, the ground motion is to some extent amplified under the influence of the terrain. The amplification effect of the terrain on PGA is complicated. It does not exactly lead to amplification of PGA at the ridge and the summit or attenuation of PGA in the valley.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.69-72
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• 2000

This study proposes a new calculation method for generating real nighttime lamp-lit images. In order to improve the color appearance in the prediction of a nighttime lamp-lighted scene, the lamp-lit image is synthesized based on spectral distribution using the estimated local spectral distribution of the headlamps and the surface reflectance of every object. The principal component analysis method is introduced to estimate the surface color of an object, and the local spectral distribution of the headlamps is calculated based on the illuminance data and spectral distribution of the illuminating headlamps. HID and halogen lamps are utilized to create beam patterns and captured road scenes are used as background images to simulate actual headlamp-lit images on a monitor. As a result, the reproduced images presented a color appearance that was very close to a real nighttime road image illuminated by single and multiple headlamps.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.748-750
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• 1999

In this study, a speech enhancement is presented based on the utilization of well-known auditory mechanism, noise masking. The speech enhancement approach adopted here is to derive an modifier that achieves audible noise suppression. This modification selectively affects the perceptually significant spectral values, and is therefore less prone to introduction of unwanted distortions than methods that affect the complete STSA and produces more enhanced results at low SNR as well as at high SNR. The speech enhancement method adopted here needs exact estimation of the minimum specteal value per critical band because it uses only the minimum spectral value per critical band. For this, the method adopted here uses the modified spectral subtraction that is more flexible than power spectral subtraction. So, the result in experiment represented better SNR than before.

• ETRI Journal
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• v.31 no.4
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• pp.451-453
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• 2009

This letter proposes the use of vowel sound detection for voice activity detection. Vowels have distinctive spectral peaks. These are likely to remain higher than their surroundings even after severe corruption. Therefore, by developing a method of detecting the spectral peaks of vowel sounds in corrupted signals, voice activity can be detected as well even in low signal-to-noise ratio (SNR) conditions. Experimental results indicate that the proposed algorithm performs reliably under various noise and low SNR conditions. This method is suitable for mobile environments where the characteristics of noise may not be known in advance.