• Korean Journal of Radiology
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• v.19 no.6
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• pp.1077-1088
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• 2018

Objective: To correlate CT parameters on detector-based dual-energy CT enterography (DECTE) with Crohn's disease activity index (CDAI) and externally validate quantitative CT parameters. Materials and Methods: Thirty-nine patients with CD were retrospectively enrolled. Two radiologists reviewed DECTE images by consensus for qualitative and quantitative CT features. CT attenuation and iodine concentration for the diseased bowel were also measured. Univariate statistical tests were used to evaluate whether there was a significant difference in CTE features between remission and active groups, on the basis of the CDAI score. Pearson's correlation test and multiple linear regression analyses were used to assess the correlation between quantitative CT parameters and CDAI. For external validation, an additional 33 consecutive patients were recruited. The correlation and concordance rate were calculated between real and estimated CDAI. Results: There were significant differences between remission and active groups in the bowel enhancement pattern, subjective degree of enhancement, mesenteric fat infiltration, comb sign, and obstruction (p < 0.05). Significant correlations were found between CDAI and quantitative CT parameters, including number of lesions (correlation coefficient, r = 0.573), bowel wall thickness (r = 0.477), iodine concentration (r = 0.744), and relative degree of enhancement (r = 0.541; p < 0.05). Iodine concentration remained the sole independent variable associated with CDAI in multivariate analysis (p = 0.001). The linear regression equation for CDAI (y) and iodine concentration (x) was y = 53.549x + 55.111. For validation patients, a significant correlation (r = 0.925; p < 0.001) and high concordance rate (87.9%, 29/33) were observed between real and estimated CDAIs. Conclusion: Iodine concentration, measured on detector-based DECTE, represents a convenient and reproducible biomarker to monitor disease activity in CD.

• Journal of IKEEE
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• v.22 no.4
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• pp.999-1005
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• 2018

This paper proposed a search-based high resolution frequency estimation method in power systme. The proposed frequency estimation method adopts a slope-based adaptive search as a base of adaptive estimation structure. The architectural and operational parameters in this adaptive algorithm are changed using the information from context layer analysis of the signals including a localized full-search of spectral peak. The convergence rate of the proposed algorithm becomes much faster than those of other conventional slope-based adaptive algorithms by effectively reducing search range with the application of the localized full-search of spectrum peak. The improvements in accuracy and convergence rate of the proposed algorithm are confirmed through the performance comparison with other representative frequency estimation methods, such as, DFT(discrete Fourier transform) method, ECKF(extended complex Kalman filter), and MV(minimum variable) method.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.573-587
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• 2019

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis.An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multi-periodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.2
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• pp.88-99
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• 2019

This study presents the results of analysis for the wave data that were consecutively collected from February 2013 to November 2018 at the location of 1.6 km offshore from Namhangjin beach. The water depth at the location is 30.5 m and waves were measured by AWAC (Acoustic Wave And Current meter). By using wave-by-wave analysis and spectral analysis, wave heights and periods were evaluated and then the relationships between the quantities obtained by the two methods were proposed based on linear regression analysis. In addition, monthly and yearly variations of the significant wave height and period, and the peak wave direction were analyzed. Moreover, the relationship between the significant wave height and period was newly suggested. Variability and probability distribution of the significant wave period with respect to the significant wave height were also examined.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.107-115
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• 2021

Purpose: This study aimed to demonstrate the presence of dental caries through a photoacoustic imaging system with visible and near-infrared wavelengths, highlighting the differences between the 2 spectral regions. The depth at which carious tissue could be detected was also verified. Materials and Methods: Fifteen permanent molars were selected and classified as being sound or having incipient or advanced caries by visual inspection, radiography, and optical coherence tomography analysis prior to photoacoustic scanning. A photoacoustic imaging system operating with a nanosecond pulsed laser as the light excitation source at either 532 nm or 1064 nm and an acoustic transducer at 5 MHz was developed, characterized, and used. En-face and lateral(depth) photoacoustic signals were detected. Results: The results confirmed the potential of the photoacoustic method to detect caries. At both wavelengths, photoacoustic imaging effectively detected incipient and advanced caries. The reconstructed photoacoustic images confirmed that a higher intensity of the photoacoustic signal could be observed in regions with lesions, while sound surfaces showed much less photoacoustic signal. Photoacoustic signals at depths up to 4 mm at both 532 nm and 1064 nm were measured. Conclusion: The results presented here are promising and corroborate that photoacoustic imaging can be applied as a diagnostic tool in caries research. New studies should focus on developing a clinical model of photoacoustic imaging applications in dentistry, including soft tissues. The use of inexpensive light-emitting diodes together with a miniaturized detector will make photoacoustic imaging systems more flexible, user-friendly, and technologically viable.

• 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.

• Animal Bioscience
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• v.35 no.7
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• pp.1048-1058
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• 2022

Objective: The present study was to investigate the extraction conditions of dietary fiber from dried cassava pulp (DCP) and cassava distiller's dried grains (CDG) under different NaOH concentrations, and the Fourier transform infrared (FTIR) was used to determine the dietary fiber components. Methods: The dried samples (DCP and CDG) were treated with various concentrations of NaOH at levels of 2%, 4%, 6%, and 8% using a completely randomized design with 4 replications of each. After extraction, the residual DCP and CDG dietary fiber were dried in a hot air oven at 55℃ to 60℃. Finally, the oven dried extracted dietary fiber was powdered to a particle size of 1 mm. Both extracted dietary fibers were analyzed for their chemical composition and determined by FTIR. Results: The DCP and CDG treated with NaOH linearly or quadratically or cubically (p<0.05) increased the total dietary fiber (TDF) and insoluble fiber (IDF). The optimal conditions for extracting dietary fiber from DCP and CDG were under treatment with 6% and 4% NaOH, respectively, as these conditions yielded the highest TDF and IDF contents. These results were associated with the FTIR spectra integration for a semi-quantitative analysis, which obtained the highest cellulose content in dietary fiber extracted from DCP and CDG with 6% and 4% NaOH solution, respectively. The principal component analysis illustrated clear separation of spectral distribution in cassava pulp extracted dietary fiber (DFCP) and cassava distiller's dried grains extracted dietary fiber (DFCDG) when treated with 6% and 4% NaOH, respectively. Conclusion: The optimal conditions for the extraction of dietary fiber from DCP and CDG were treatment with 6% and 4% NaOH solution, respectively. In addition, FTIR spectroscopy proved itself to be a powerful tool for fiber identification.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.193-202
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• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Korean Journal of Audiology
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• v.24 no.2
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• pp.71-78
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• 2020

Background and Objectives: Currently limited information is available on speech stimuli processing at the subcortical level in the recipients of cochlear implant (CI). Speech processing in the brainstem level is measured using speech-auditory brainstem response (S-ABR). The purpose of the present study was to measure the S-ABR components in the sound-field presentation in CI recipients, and compare with normal hearing (NH) children. Subjects and Methods: In this descriptive-analytical study, participants were divided in two groups: patients with CIs; and NH group. The CI group consisted of 20 prelingual hearing impairment children (mean age=8.90±0.79 years), with ipsilateral CIs (right side). The control group consisted of 20 healthy NH children, with comparable age and sex distribution. The S-ABR was evoked by the 40-ms synthesized /da/ syllable stimulus that was indicated in the sound-field presentation. Results: Sound-field S-ABR measured in the CI recipients indicated statistically significant delayed latencies, than in the NH group. In addition, these results demonstrated that the frequency following response peak amplitude was significantly higher in CI recipients, than in the NH counterparts (p<0.05). Finally, the neural phase locking were significantly lower in CI recipients (p<0.05). Conclusions: The findings of sound-field S-ABR demonstrated that CI recipients have neural encoding deficits in temporal and spectral domains at the brainstem level; therefore, the sound-field S-ABR can be considered an efficient clinical procedure to assess the speech process in CI recipients.

• Journal of Audiology & Otology
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• v.24 no.2
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• pp.71-78
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• 2020

Background and Objectives: Currently limited information is available on speech stimuli processing at the subcortical level in the recipients of cochlear implant (CI). Speech processing in the brainstem level is measured using speech-auditory brainstem response (S-ABR). The purpose of the present study was to measure the S-ABR components in the sound-field presentation in CI recipients, and compare with normal hearing (NH) children. Subjects and Methods: In this descriptive-analytical study, participants were divided in two groups: patients with CIs; and NH group. The CI group consisted of 20 prelingual hearing impairment children (mean age=8.90±0.79 years), with ipsilateral CIs (right side). The control group consisted of 20 healthy NH children, with comparable age and sex distribution. The S-ABR was evoked by the 40-ms synthesized /da/ syllable stimulus that was indicated in the sound-field presentation. Results: Sound-field S-ABR measured in the CI recipients indicated statistically significant delayed latencies, than in the NH group. In addition, these results demonstrated that the frequency following response peak amplitude was significantly higher in CI recipients, than in the NH counterparts (p<0.05). Finally, the neural phase locking were significantly lower in CI recipients (p<0.05). Conclusions: The findings of sound-field S-ABR demonstrated that CI recipients have neural encoding deficits in temporal and spectral domains at the brainstem level; therefore, the sound-field S-ABR can be considered an efficient clinical procedure to assess the speech process in CI recipients.