• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.110-112
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• 2003

This paper presents an alleviant technique for solving ill-conditioned linear systems using spectral adaptive mapping, which is based on spectral mapping theorem. The conventional approaches to solve the ill-conditioned linear systems are divided into reformulation and alleviant technique. So far, the alleviant technique is evaluated the most effective one. In this paper, an adaptive mapping of spectrum is adopted to alleviate the condition number of ill-conditioned linear systems. A shift constant, which is a dominant factor of the spectral adaptive mapping that are proposed, is assessed by the system spectrum. The proposed spectral adaptive mapping technique is tested to demonstrated the validation on several size Hilbert matrices and small scale power systems, which are provide the promising results.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.425-427
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• 2004

Motivation of this study is based on these two aspects: geologic uses of ASTER and application scheme of Spectral Mixture Analysis. This study aims at geologic mapping for mineral exploration using ASTER and LANDSAT 7 ETM+ at Mongolian plateau region by SMA. After basic pre-processing such as the normalization, geometric corrections and calibration of reflectance, related to endmembers selection and spectral signature deviation, both methods using spectral library and using PPI(Pixel Purity Index) are performed and compared on a given task. Based on these schemes, SMA is performed using LANDSAT 7 ETM+ and ASTER image. As the results, fraction map showing geologic rock types are enough to meet purposes such as geologic mapping and mineral potential mapping in the case of both uses of these different types of remotely sensed images. It concluded that this approach based on SMA with LANDSAT and ASTER is regarded as one of effective schemes for geologic remote sensing.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.39-45
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• 1995

This paper presents noisy speech recognition method based on spectral mapping techniques of speaker adaptation method. In the presented method, the spectral mapping training makes the spectral distortion of noisy speech reduced, and for the more correctively spectral mapping, let the adjustment window;s slope be adaptive to several word lengths. As a result of recognition experiment, the recognition rate is higher than that of the conventional method using VQ and DTW without noise processing. Even when SNR level is 0 dB, the recognition rate is 10 times more than that using the conventional method. It is confirmed that the speacker adaptation technique using the spectral mapping training has an ability to improve the recognition performance for noisy speech.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1015-1020
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• 1994

This paper presents the Korean digit recognition method under noise environment using the spectral mapping training based on static supervised adaptation algorithm. In the presented recognition method, as a result of spectral mapping from one space of noisy speech spectrum to another space of speech spectrum without noise, spectral distortion of noisy speech is improved, and the recognition rate is higher than that of the conventional method using VQ and DTW without noise processing, and even when SNR level is 0 dB, the recognition rate is 10 times of that using the conventional method. It has been confirmed that the spectral mapping training has an ability to improve the recognition performance for speech in noise environment.

• Kyungpook Mathematical Journal
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• v.47 no.1
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• pp.77-79
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• 2007

In this note we point out how a theorem of Gamelin and Garnett from function theory can be used to establish a spectral mapping theorem for an arbitrary contraction and an associated class of $H^{\infty}$-functions.

• The Journal of the Acoustical Society of Korea
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• v.13 no.3
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• pp.25-32
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• 1994

This paper presents the Korean digit recognition method under noise environment using the spectral mapping training based on static supervised adaptation algorithm. In the presented recognition method, as a result of spectral mapping from one space of noisy speech spectrum to another space of speech spectrum without noise, spectral distortion of noisy speech is improved, and the recognition rate is higher than that of the conventional method using VQ (vector quatization) and DTW(dynamic time warping) without noise processing, and even when SNR level is 0dB, the recognition rate is 10 times of that using the conventional method. It has been confirmed that the spectral mapping training has an ability to improve the recognition performance for speech in noise environment.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.21-32
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• 2007

Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential to reduce the uncertainty in mapping fuels and offers the best approach for improving our abilities. Especially, Hyperspectral sensor have a great potential for mapping vegetation properties because of their high spectral resolution. The objective of this paper is to evaluate the potential of mapping fuel properties using Hyperion hyperspectral remote sensing data acquired in April, 2002. Fuel properties are divided into four broad categories: 1) fuel moisture, 2) fuel green live biomass, 3) fuel condition and 4) fuel types. Fuel moisture and fuel green biomass were assessed using canopy moisture, derived from the expression of liquid water in the reflectance spectrum of plants. Fuel condition was assessed using endmember fractions from spectral mixture analysis (SMA). Fuel types were classified by fuel models based on the results of SMA. Although Hyperion imagery included a lot of sensor noise and poor performance in liquid water band, the overall results showed that Hyperion imagery have good potential for wildfire fuel mapping.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.68-86
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• 2015

Future wireless network aims to integrate different radio access networks (RANs) to provide a seamless access and service continuity. In this paper, a new resource denotation method is proposed in the WLAN and LTE heterogeneous networks based on a concept of spectral bandwidth mapping. This method simplifies the denotation of system resources and makes it possible to calculate system residual capacity, upon which an economic model-based network selection algorithm is designed in both under-loaded and over-loaded scenarios in the heterogeneous networks. The simulation results show that this algorithm achieves better performance than the utility function-based access selection (UFAS) method proposed in [12] in increasing system capacity and system revenue, achieving load balancing and reducing the new call blocking probability in the heterogeneous networks.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.239-251
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• 2016

Hyperspectral image is widely used for land cover classification because it has a number of narrow bands and allow each pixel to include much more information in comparison with previous multi-spectral image. However, Higher spectral resolution of hyperspectral image results in an increase in data volumes and a decrease in noise efficiency. SAM(Spectral Angle Mapping), a method based on vector inner product to compare spectrum distribution, is a highly valuable and popular way to analyze continuous spectrum of hyperspectral image. SAM is shown to be less accurate when it is used to analyze hyperspectral image for land cover classification using spectral library. this inaccuracy is due to the effects of atmosphere. We suggest a decision tree based method to compensate the defect and show that the method improved accuracy of land cover classification.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.397-400
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• 2007

For mapping the distribution of heavy metals in the mining area, field spectroscopy and hyperspectral remote sensing were used in this study. Although heavy metals are spectrally featureless from the visible to the short wave infrared range, possible variations in spectral signal due to heavy metals bound onto minerals can be explained with the metal binding reaction onto the mineral surface. Variations in the spectral absorption shapes of lattice OH and oxygen on the mineral surface due to the combination of heavy metals were surveyed over the range from 420 to 2400 nm. Spectral parameters such as peak ratio and peak area were derived and statistically linked to metal concentration levels in the streambed samples collected from the dry stream channels. The spatial relationships between spectral parameters and concentrations of heavy metals were yielded as well. Based on the observation at a ground level for the relationship between spectral signal and metal concentration levels, the spectral parameters were classified in a hyperspectral image and the spatial distribution patterns of classified pixels were compared with the product of analysis at the ground level. The degree of similarity between ground dataset and image dataset was statistically validated. These techniques are expected to support assessment of dispersion of heavy metal contamination and decision on optimal sampling point.