• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.55-65
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• 2020

Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.9-14
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• 2017

In this paper, we propose that the contrast features of octave spectrum can be used to show spectral contrast features of some music clips. It shows the relative spectral distribution rather than average spectrum. From the experiment, it can be seen the method of spectral contrast features has a good performance in classification of music styles. Another comparative experiment shows that the method of spectral contrast features can better distinguish different music styles than the method of MFCC features that commonly used previously in the classification system of music styles.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.771-774
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• 2013

This study investigates the relationship between voice quality measurements and emotional states, in addition to conventional prosodic and cepstral features. Open quotient, harmonics-to-noise ratio, spectral tilt, spectral sharpness, and band energy were analyzed as voice quality features, and prosodic features related to fundamental frequency and energy are also examined. ANOVA tests and Sequential Forward Selection are used to evaluate significance and verify performance. Classification experiments show that using the proposed features increases overall accuracy, and in particular, errors between happy and angry decrease. Results also show that adding voice quality features to conventional cepstral features leads to increase in performance.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.62.1-62.1
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• 2013

The Cassini/Visual Infrared Mapping Spectrometer (VIMS) observed the sun through the atmosphere of Titan, and provided vertically-resolved 63 spectra from 49 km to 987 km for the 1 - 5 micron range (Bellucci, 2008). Bellucci et al. (2009) analyzed selected spectral ranges where the band absorptions of $CH_4$ and CO are strong by constructing synthetic spectra including $CH_4$ and CO lines, but without including haze absorptions in their synthetic spectra. Kim et al. (2011) and Sim et al. (2013) were able to extract detailed spectral features of fundamental (Dv = 1) and overtone (Dv = 2) bands of the haze from the VIMS spectra by excluding the adjacent influences of strong $CH_4$ absorptions using a radiative transfer program, which includes effects of absorption and emission of lines of these molecules, and absorption and scattering of haze particles. In this presentation, we extend our detailed analyses to other remaining wavelengths in order to provide the spectral characteristics of the Titanian haze for the entire 1 - 5 micron range and to identify any additional haze spectral features and an unidentified feature near 4.3 microns reported by Bellucci et al. (2009).

• Animal Bioscience
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• v.36 no.2
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• pp.256-263
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• 2023

Objective: Feed molecular structures can affect its availability to gastrointestinal enzymes which impact its digestibility and absorption. The molecular spectroscopy-attenuated total reflectance Fourier transform infrared vibrational spectroscopy (ATR-FTIR) is an advanced technique that measures the absorbance of chemical functional groups on the infrared region so that we can identify and quantify molecules and functional groups in a feed. The program aimed to reveal the association of intrinsic molecular structure with nutrient supply to animals from canola feedstocks and co-products from bio-oil processing. The objective of this study was to characterize special intrinsic carbohydrate and protein-related molecular structure spectral profiles of feedstock and co-products (meal and pellets) from bio-oil processing from two source origins: Canada (CA) and China (CH). Methods: The samples of feedstock and co-products were obtained from five different companies in each country arranged by the Canola Council of Canada (CCC). The molecular structure spectral features were analyzed using advanced vibrational molecular spectroscopy-ATR-FTIR. The spectral features that accessed included: i) protein-related spectral features (Amide I, Amide II, α-helix, β-sheet, and their spectral intensity ratios), ii) carbohydrate-related spectral features (TC1, TC2, TC3, TC4, CEC, STC1, STC2, STC3, STC4, TC, and their spectral intensity ratios). Results: The results showed that significant differences were observed on all vibrationally spectral features related to total carbohydrates, structural carbohydrates, and cellulosic compounds (p<0.05), except spectral features of TC2 and STC1 (p>0.05) of co-products, where CH meals presented higher peaks of these structures than CA. Similarly, it was for the carbohydrate-related molecular structure of canola seeds where the difference between CA and CH occurred except for STC3 height, CEC and STC areas (p>0.05). The protein-related molecular structures were similar for the canola seeds from both countries. However, CH meals presented higher peaks of amide I, α-helix, and β-sheet heights, α-helix:β-sheet ratio, total amide and amide I areas (p<0.05). Conclusion: The principal component analysis was able to explain over 90% of the variabilities in the carbohydrate and protein structures although it was not able to separate the samples from the two countries, indicating feedstock and coproducts interrelationship between CH and CA.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.361-364
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• 2008

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. High spatial resolution classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, extracting the spatial information is one of the most important steps in high resolution satellite image classification. In this paper, we propose a new spatial feature extraction method. The extracted features are integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a Support Vector Machines classifier. In order to evaluate the proposed feature extraction method, we applied our approach to KOMPSAT-2 data and compared the result with the other methods.

• ETRI Journal
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• v.39 no.6
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• pp.832-840
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• 2017

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception-based spatial and spectral-domain noise-reduced harmonic features are extracted from multichannel audio and used as high-resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short-term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.592-600
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• 2006

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.648-650
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• 2003

Efficient multi-resolution image fusion aims to take advantage of the high spectral resolution of Landsat TM images and high spatial resolution of SPOT panchromatic images simultaneously. This paper presents a multi-resolution data fusion scheme, based on multirate image representation. Motivated by analytical results obtained from high-resolution multispectral image data analysis: the energy packing the spectral features are distributed in the lower frequency bands, and the spatial features, edges, are distributed in the higher frequency bands. This allows to spatially enhancing the multispectral images, by adding the high-resolution spatial features to them, by a multirate filtering procedure. The proposed method is compared with some conventional methods. Results show it preserves more spectral features with less spatial distortion.