• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.118-128
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• 2021

Purpose: To investigate the value of MR textural analysis, including use of diffusion-weighted imaging (DWI) to differentiate malignant from benign soft-tissue tumors on 3T MRI. Materials and Methods: We enrolled 69 patients (25 men, 44 women, ages 18 to 84 years) with pathologically confirmed soft-tissue tumors (29 benign, 40 malignant) who underwent pre-treatment 3T-MRI. We calculated MR texture, including mean, standard deviation (SD), skewness, kurtosis, mean of positive pixels (MPP), and entropy, according to different spatial-scale factors (SSF, 0, 2, 4, 6) on axial T1- and T2-weighted images (T1WI, T2WI), contrast-enhanced T1WI (CE-T1WI), high b-value DWI (800 sec/mm²), and apparent diffusion coefficient (ADC) map. We used the Mann-Whitney U test, logistic regression, and area under the receiver operating characteristic curve (AUC) for statistical analysis. Results: Malignant soft-tissue tumors had significantly lower mean values of DWI, ADC, T2WI and CE-T1WI, MPP of ADC, and CE-T1WI, but significantly higher kurtosis of DWI, T1WI, and CE-T1WI, and entropy of DWI, ADC, and T2WI than did benign tumors (P < 0.050). In multivariate logistic regression, the mean ADC value (SSF, 6) and kurtosis of CE-T1WI (SSF, 4) were independently associated with malignancy (P ≤ 0.009). A multivariate model of MR features worked well for diagnosis of malignant soft-tissue tumors (AUC, 0.909). Conclusion: Accurate diagnosis could be obtained using MR textural analysis with DWI and CE-T1WI in differentiating benign from malignant soft-tissue tumors.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.801-811
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• 2019

Objective: To determine whether diffusion kurtosis imaging (DKI) is effective in monitoring tumor response to neoadjuvant chemotherapy in patients with osteosarcoma. Materials and Methods: Twenty-nine osteosarcoma patients (20 men and 9 women; mean age, 17.6 ± 7.8 years) who had undergone magnetic resonance imaging (MRI) and DKI before and after neoadjuvant chemotherapy were included. Tumor volume, apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), and change ratio (ΔX) between pre-and post-treatment were calculated. Based on histologic response, the patients were divided into those with good response (≥ 90% necrosis, n = 12) and those with poor response (< 90% necrosis, n = 17). Several MRI parameters between the groups were compared using Student's t test. The correlation between image indexes and tumor necrosis was determined using Pearson's correlation, and diagnostic performance was compared using receiver operating characteristic curves. Results: In good responders, MD_post, ADC_post, and MK_post values were significantly higher than in poor responders (p < 0.001, p < 0.001, and p = 0.042, respectively). The ΔMD and ΔADC were also significantly higher in good responders than in poor responders (p < 0.001 and p = 0.01, respectively). However, no significant difference was observed in ΔMK (p = 0.092). MD_post and ΔMD showed high correlations with tumor necrosis rate (r = 0.669 and r = 0.622, respectively), and MD_post had higher diagnostic performance than ADC_post (p = 0.037) and MK_post (p = 0.011). Similarly, ΔMD also showed higher diagnostic performance than ΔADC (p = 0.033) and ΔMK (p = 0.037). Conclusion: MD is a promising biomarker for monitoring tumor response to preoperative chemotherapy in patients with osteosarcoma.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2792-2799
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• 2015

In this paper, we utilize source mel-frequency cepstral coefficients (SMFCCs), skewness, and kurtosis extracted in glottal flow signals to improve speaker recognition performance. Generally, because the high band magnitude response of glottal flow signals is somewhat flat, the SMFCCs are extracted using the response below the predefined cutoff frequency. The extracted SMFCC, skewness, and kurtosis are concatenated with conventional feature parameters. Then, dimensional reduction by the principal component analysis (PCA) and the linear discriminat analysis (LDA) is followed to compare performances with conventional systems under equivalent conditions. The proposed recognition system outperformed the conventional system for large scale speaker recognition experiments. Especially, the performance improvement was more noticeable for small Gaussan mixtures.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05c
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• pp.170-176
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• 2002

In this papers, we proposed the feature extraction method due to partial discharge type of transformers. For wavelet transform, Daubechie's filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion, skewness, kurtosis) about acoustic emission signal generated from each partial discharge type. The defects which could occur in a transformer were simulated by using needle-plane electrode, IEC electrode and Void electrode. Also, these coefficients are used to identify signal of partial discharge type electrode fault in transformer. As a result, from compare of acoustic emission amplitude and acoustic average value, we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. otherwise, In case of skewness and kurtosis, we are obtained results of Needle-Plane electrode electrode> Void electrode> IEC electrode.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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• pp.894-899
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• 2002

In this papers, we proposed the new method in order to diagnosis partial discharge type of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion, skewness, kurtosis) about high frequency current signal per 3-electrode type (needle-plane electrode, IEC electrode and Void electrode.). Also. these coefficients are used to identify Signal of internal partial discharge in transformer. As a result. from compare of high frequency current signal amplitude and average value. we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. otherwise. In case of skewness and kurtosis, we are obtained results of Void electrode> IEC electrode > Needle-Plane electrode. As Improved method in order to diagnosis partial discharge type of transformers, we use neural network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1294-1301
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• 2014

In this paper, separate probabilistic distribution models for voiced and unvoiced speech are estimated and utilized to improve speaker recognition performance. Also, in addition to the conventional mel-frequency cepstral coefficient, skewness, kurtosis, and harmonic-to-noise ratio are extracted and used for voiced speech intervals. Two kinds of scores for voiced and unvoiced speech are linearly fused with the optimal weight found by exhaustive search. The performance of the proposed speaker recognizer is compared with that of the conventional recognizer which uses mel-frequency cepstral coefficient and a unified probabilistic distribution function based on the Gassian mixture model. Experimental results show that the lower the number of Gaussian mixture, the greater the performance improvement by the proposed algorithm.

• The Mathematical Education
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• v.41 no.1
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• pp.109-126
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• 2002

In this thesis. we estimated the good reliability coefficient ${\beta}$$\sub$k/ that is unbiased, consistent and more efficient than Cronbach's ${\alpha}$$\sub$k/ in splitting of a test into several formats of subtests and several properties of ${\beta}$$\sub$k/ are also represented. The tables of coefficients of skewness and kurtosis are represented to test the significance of departures from normality. We got the cumulative normal plots of z'from the distribution which is departed from normality using the Bock's approximation procedure and we finally enumerated the transformed standardized scores z'and a new raw score X' which enable us to proceed further evaluation procedures depending on our assessment policy.

• Tribology and Lubricants
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• v.30 no.6
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• pp.356-363
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• 2014

Marine diesel engines operate in environments in which damage easily occurs from corrosion. Recently, damage to cylinder liners has increased from corrosion wear caused by increased engine power. This damage can cause serious problems in the economy. Thus, many researchers have treated and studied damaged cylinder liners. However, a method is necessary for real-time monitoring of damage to cylinder liners during operation of the engine, before serious damage can occur. This study carries out reciprocating friction and wear tests on a cast iron specimen under various corrosion atmospheres and verifies the variations of friction coefficient and friction surface. Additionally, the friction coefficient and friction status are predicted by using a neural network that learns the vibration and frequency spectrum data from an acceleration sensor. According to our conclusions, amplitude is distributed highly at high frequencies, and values of standard deviation and kurtosis are high when damage to the friction surface is serious. The accuracy rate of the friction coefficient predicted by the neural network is over 80% of the real measured value without NaCl, and application of the neural network is very effective for diagnosing the friction condition and damage to the cylinder liner.

• Geophysics and Geophysical Exploration
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• v.19 no.4
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• pp.220-227
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• 2016

Marine seismic data have not only primary signals from subsurface but also ghost signals reflected from the sea surface. The ghost decreases temporal resolution of seismic data because it attenuates specific frequency components. For eliminating the ghost signals effectively, the exact ghost delaytimes and reflection coefficients are required. Because of undulation of the sea surface and vertical movements of airguns and streamers, the ghost delaytime varies spatially and randomly while acquiring seismic data. The reflection coefficient is a function of frequency, incidence angle of plane-wave and the sea state. In order to estimate the proper ghost delaytimes considering these characteristics, we compared the ghost delaytimes estimated with L-1 norm, L-2 norm and kurtosis of the deghosted trace and its autocorrelation on synthetic data. L-1 norm of autocorrelation showed a minimal error and the reflection coefficient was calculated using Kirchhoff approximation equation which can handle the effect of wave height. We applied the estimated ghost delaytimes and the calculated reflection coefficients to remove the source and receiver ghost effects. By removing ghost signals, we reconstructed the frequency components attenuated near the notch frequency and produced the migrated stack section with enhanced temporal resolution.

• Bulletin of the Korean Chemical Society
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• v.30 no.2
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• pp.343-347
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• 2009

In this study, the surface of inorganic fine powders is hybridized with nanovesicles containing tocopheryl acetate prepared with hydrogenated lecithin via a coating process. From AFM and SEM analyses it is found that the surface of the nanovesicle-coated fine powders lost their traditional topology and improved in terms of their roughness, skewness, and kurtosis. In addition, TEM observations revealed the formation of a 5 nm thick coating layer on the surface of the fine powders. These hybridized powders, in which bioactive materials such as tocopheryl acetate can be embedded, can be employed as a part of a drug delivery system due to their special ability to control release rate and temperature selectivity. Physical properties of the powders, i.e., the different angle and friction coefficient, were excellent.