• Journal of Photoscience
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• v.9 no.2
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• pp.457-459
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• 2002

Sunlight causes various types of adverse skin changes on the sun-exposed areas of the skin, in which the most hazardous one is the induction of malignant skin tumours. FT -IR spectra were obtained from specimens excised from normal skin, BCCs, SCCs, MMs, nevi, lesions of solar keratosis and Bowen's disease. Tissue samples from freshly frozen specimens were cut into 2 sections in strictly sequential order to be stained with H & E for histopathological analysis, and then to be air-dried on CaF$_2$ slide glasses for further spectral data acquisition from defined area of interest. Intra- and inter-sample variations were estimated within grouped lesion categories according to each skin component. Mean spectra for each type of tissue pathology in the 800-1800 $cm^{-1}$ / region was interpreted using the classical group frequency approach that showed the most visible differences in spectra of benign, premalignant and malignant changes directly related to protein conformation and nucleic acid bases. The relative intensity of the nucleic acid peak was increased with progression to malignancy. In addition, PCA was able to evaluate and maximise the differences in the spectra by reducing the number of variables characterizing each patient and pathology category. This type of approach to non-destructively estimate the complexity of IR-spectra of inhomogeneous samples such as skin demonstrates the advantage of FT -IR microspectroscopy to be able to observe diseased states (benign, premalignant, malignant) and distinguish them from normal against a huge background of inter- and intra-subject variability.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.1 s.41
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• pp.1-7
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• 2005

The optimal seismic retrofitting of NPP(Nuclear Power Plant) cabinet structures that contain seismic category 1 relays was studied in this paper. During earthquake event, the failure modes of relays are not appeared in form of structural failure, but are appeared in form of contact chatter of relay. Therefore, the retrofitting of cabinet has to be aimed at the reducing of the structural response, such as acceleration. In this study, the optimal characteristic values of dampers were searched by ${\mu}$-GA (micro-Genetic Algorithm) scheme for several installation patterns. To keep accuracy and efficiency of analysis, the structural models of cabinet were considered as a frame structure. The responses of structure were obtained inform of acceleration response spectra derived from the results of nonlinear time history analysis including damping nonlinearity. The objective function of the optimum procedure was constructed based on the maximum ratio of maximum spectral value and target GERS (General Equipment Ruggedness Spectra). The results show the good improvements of fitness for adequate retrofitting pattern. Especially, the improvements of fitness were remarkable when the values of damping exponents are low.

• Atmosphere
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• v.26 no.1
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.270-277
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• 2010

In this paper, we proposed an algorithm that improves tone quality of noisy audio signals by using ICA(Independent Component Analysis) algorithm and perceptual filters. Many algorithms have been proposed to eliminate the noise from the audio signals, such as spectral subtraction method, perceptual filter, etc. The perceptual filter uses a noise that is acquired from silent ranges in the input signal. In this case, the improvement rate of tone quality decreases if the noise energy is changed by the environmental variation in a signal frame. But the proposed method estimates a noise that is changed at each frame using ICA algorithm. The estimated noise is applied to perceptual filter. To show the performance of the proposed algorithm, several tests are performed to various input signals. With the proposed algorithm, we could confirm the enhancement of tone quality in terms of segmental SNR (SSNR), noise-to-mask ratio (NMR) and Degradation Category Rating (DCR) test.