• 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.

• Progress in Superconductivity and Cryogenics
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• v.26 no.2
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• pp.1-4
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• 2024

We investigated Pb_10-xCu_x(PO₄)₆ (0.9 < x < 1.1) (LK-99) and Cu₂S, presumed to be contained as an impurity in LK-99, in a wide spectral range from far infrared to ultraviolet using optical spectroscopy. The optical conductivity spectra of both samples were obtained from measured reflectance spectra at various temperatures from 80 to 434 K. Both samples showed several infrared-active phonons in the far and mid-infrared regions. LK-99 showed typical insulating features with a band gap of ~1 eV. Cu₂S showed a nonmonotonic temperature-dependent trend and two energy gaps: one energy gap of ~93 meV and a band gap of 2.42 eV. Our results indicate that LK-99 cannot be a superconductor because it is an insulator with a large band gap.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.598-603
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• 2003

This paper presents a sub-band feature extraction approach in which the feature extraction method in the individual frequency sub-bands is determined in terms of speech recognition accuracy. As in the multi-band paradigm, features are extracted independently in frequency sub-regions of the speech signal. Since the spectral shape is well structured in the low frequency region, the all pole model is effective for feature extraction. But, in the high frequency region, the nonparametric transform, discrete cosine transform is effective for the extraction of cepstrum. Using the sub-band specific feature extraction method, the linguistic information in the individual frequency sub-bands can be extracted effectively for automatic speech recognition. The validity of the proposed method is shown by comparing the results of speech recognition experiments for our method with those obtained using a full-band feature extraction method.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.91-97
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• 2012

This paper first proposes the speech recognition algorithm by detection of the speech and noise sections at each frame, then proposes the reduction algorithm of environmental noise by multi-band filter which removes the background noises at each frame according to detection of the speech and noise sections. The proposed algorithm reduces the background noises using filter bank sub-band domain after extracting the features from the speech data. In this experiment, experimental results of the proposed noise reduction algorithm by the multi-band filter demonstrate using the speech and noise data, at each frame. Based on measuring the spectral distortion, experiments confirm that the proposed algorithm is effective for the speech by corrupted the noise.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.49.2-49.2
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• 2019

We present results from our long-term observing campaign, using the NASA IRTF at Maunakea, to obtain 2 - 4 ㎛ spectra of 118 red point sources in the line of sight to the Galactic Center (GC). Our sample is largely composed of point sources selected from near- and mid-infrared photometry, but also includes a number of massive young stellar objects. Many of these sources show high foreground extinction as shown by deep 3.4 ㎛ aliphatic hydrocarbon absorption feature, which is a characteristic of the diffuse ISM and comes from the long line of sight through the diffuse medium toward the Central Molecular Zone (CMZ), the central 300 pc region of the GC. The deep 3.1 ㎛ H₂O ice absorption band coming from the local, dense material in the GC CMZ suggests that most sources are likely located in the GC CMZ. A few of these sources show weak CCH₃OH ice absorption at 3.535 ㎛, which can provide a strong constraint on the CCH₃OH ice formation in the unique environment of the CMZ. From the best-fitting models, the optical depths of these features are determined and used to generate a well-rounded view of the ice composition across the GC CMZ and the spectral characteristics of massive YSOs in the GC.

• Proceedings of the KSRS Conference
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• v.1
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• pp.25-28
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• 2006

We propose a new method of masking cloud-affected pixels in satellite ocean color imageries such as of GLI. Those pixels, mostly found around cloud pixels or in scattered cloud area, have anomalous features in either in chlorophyll-a estimate or in water reflectance. This artifact is most likely caused by residual error of inter-band registration correction. Our method is to check the pixel-wise 'soundness' of the spectral water reflectance Rw retrieved after the atmospheric correction. First, we define two spectral ratio between water reflectance, IRR1 and IRR2, each defined as RW(B1)/RW (B3) RW (B3) and as RW (B2)/RW(B4) respectively, where $B1{\sim}B4$ stand for 4 consecutive visible bands. We show that an almost linear relation holds over log-scaled IRR1 and IRR2 for shipmeasured RW data of SeaBAM in situ data set and for GLI cloud-free Level 2 sub-scenes. The method we propose is to utilize this nature, identifying those pixels that show significant discrepancy from that relationship. We apply this method to ADEOS-II/GLI ocean color data to evaluate the performance over Level-2 data, which includes different water types such as case 1, turbid case 2 and coccolithophore bloom waters.

• International Journal of Oral Biology
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• v.46 no.3
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• pp.105-110
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• 2021

The physicochemical properties of crystalline titanium dioxide nanoparticles (TiO₂ NPs) were investigated by comparing amorphous (amTiO₂), anatase (aTiO₂), metaphase of anatase-rutile (arTiO₂), and rutile (rTiO₂) NPs, which were prepared at various calcination temperatures (100℃, 400℃, 600℃, and 900℃). X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses confirmed that the phase-transformed TiO₂ had the characteristic features of crystallinity and average size. The surface chemical properties of the crystalline phases were different in the spectral analysis. As anatase transformed to the rutile phase, the band of the hydroxyl group at 3,600-3,100 cm^-1 decreased gradually, as assessed using Fourier transform infrared spectroscopy (FT-IR). For ultraviolet-visible (UV-Vis) spectra, the maximum absorbance of anatase TiO₂ NPs at 309 nm was blue-shifted to 290 nm at the rutile phase with reduced absorbance. Under the electric field of capillary electrophoresis (CE), TiO₂ NPs in anatase migrated and detected as a broaden peak, whereas the rutile NPs did not. In addition, anatase showed the highest photocatalytic activity in an UV-irradiated dye degradation assay in the following order: aTiO₂ > arTiO₂ > rTiO₂. Overall, the phases of TiO₂ NPs showed characteristic physicochemical properties regarding size, surface chemical properties, UV absorbance, CE migration, and photocatalytic activity.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.