• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.1
• /
• pp.321-340
• /
• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Korean Journal of Radiology
• /
• v.22 no.9
• /
• pp.1481-1489
• /
• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

• Journal of Korean Society of Forest Science
• /
• v.103 no.2
• /
• pp.233-239
• /
• 2014

This study was carried out to propose the criteria for the prediction of landslide occurrence through analysis the influence of each factor by using the quantification theory. The results obtained from this study are summarized as follows. From a stepwise regression analysis between the landslide area($m^2$) and environmental factors, the factors strongly affecting the landslide sediment($m^2$) were the Parents rock (igneous), cross slope(complex), coniferous forests (forest type) and slope gradient ($21{\sim}30^{\circ}$). According to the range, it was shown in order of Cross slope (0.2922), Parents rock (0.2691), Forest type (0.2631) and Slope gradient (0.2312). The range of prediction score of landslide occurrence has been distributed between score 0 and score 1.0556, the median value was score 0.5278. The prediction for class I was over 0.7818, for class II was 0.5279 to 0.7917, for class III 0.2694 to 0.5278 and for class IV was below 0.2693. The prediction on landslide occurrence appeared relatively high accuracy rate as 72% for class I and II. Therefore, this score table for landslide will be very useful for judgement of dangerous slope.

• Korean Journal of Radiology
• /
• v.22 no.3
• /
• pp.334-343
• /
• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• Proceedings of the KSME Conference
• /
• 2003.04a
• /
• pp.201-206
• /
• 2003

This study is test for STS304 specimen using bending and torsion state. Rounded specimen and notched specimen including fracture surface investigation was comparatively experimented, fatigue life according to degree of surface finishing was examined. Fatigue fracture probability of notched canilever specimens were predicted by P-S-N curve, median rank and Weibull distribution. And at the relation with the rotational speed and stress, the fatigue life of the test specimen was higher at high speed than low speed If summarize STS304 torsion result of fatigue test, is as following. Fatigue life prediction was available by Weibull statistics distribution, and 50% breakdown probability correlation equation was appeared as following.

• Journal of Family Resource Management and Policy Review
• /
• v.23 no.2
• /
• pp.99-115
• /
• 2019

This study examined the insolvency likelihood of young debtors from the 2018 Household Financial and Welfare Survey. This study used the Household Default Risk Index (HDRI), which considers the ratio of total debt to total assets (DTA), and a total debt service ratio (DSR) to examine the insolvency level of debtors. The descriptive analyses showed no difference in frequency of households with a high probability of insolvency between those less than 35 years of age and those over 35 years of age. However, the median HDRI value for those less than 35 years of age was higher than those over 35 years of age. The multivariate analyses indicated that educational expenses for young Korean debtors was a factor that increased their probability of insolvency, while income was the only variable that decreased their insolvency likelihood.

• The Journal of the Petrological Society of Korea
• /
• v.25 no.4
• /
• pp.311-324
• /
• 2016

The characteristics of the rock cleavage in Jurassic granite from Geochang were analysed. The evaluation for three quarrying planes and three rock cleavages was performed using the parameters such as (1) reduction ratio between the value of spacing and the value of length, (2) microcrack spacing frequency(N), (3) total spacing($1mm{\geq}$), (4) exponential constant(a), (5) magnitude of exponent(${\lambda}$), (6) mean spacing($S_{mean}$), (7) difference value($S_{mean}-S_{median}$) between mean spacing and median spacing($S_{median}$) and (8) density of spacing. Especially the close dependence between the above spacing parameters and the parameters from the spacing-cumulative frequency diagrams was derived. The discrimination factors representing three quarrying planes and three rock cleavages were acquired through these mutual contrast. The analysis results of the research are summarized as follows. First, the reduction ratios of frequency(N), mean value, median value, the above difference value($S_{mean}-S_{median}$) and density for three rock cleavages are in orders of G(grain, (G1 + G2)/2) < H(hardway, (H1 + H2)/2) < R(rift, (R1 + R2)/2), H < G $\ll$ R, H < G $\ll$ R, H < G < R and H < G $\ll$ R. The values of the above five parameters for three planes show the various orders of R'(rift plane) $\ll$ H'(hardway plane) < G'(grain plane), R' $\ll$ G' < H', R' < H' < G', R' < G' < H' and R' $\ll$ H' < G', respectively. Second, the values of (I) parameters(2, 3, 4 and 5) and (II) parameters(6, 7 and 8) are in orders of (I) H < G < R and (II) R < G < H. On the contrary, the values of the above two groups(I~II) of parameters for three planes show reverse orders. Third, to review the overall characteristics of the arrangement among the six diagrams, these diagrams show an order of R2 < R1 < G2 < G1 < H2 < H1 from the related chart. In other words, above six diagrams can be summarized in order of rift(R1 + R2) < grain(G1 + G2) < hardway(H1 + H2). These results indicate a relative magnitude of rock cleavage related to microcrack spacing. Especially, two parameters for each diagram, the above difference value($S_{mean}-S_{median}$) and mean spacing, could provide advanced information for prediction the order of arrangement among the diagrams. Finally, the general chart for three planes and three rock cleavages were made. From the related chart, three exponential straight lines for three rock cleavages show an order of R(R1 + R2) < G(G1 + G2) < H(H1 + H2). On the contrary, three lines for three planes show an order of H'(R2 + G2) < G'(R1 + H2) < R'(G1 + H1). Consequently, correlation of the mutually reverse order between three planes and three rock cleavages can be drawn from the related chart.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.1
• /
• pp.191-194
• /
• 2012

We conducted a case-control study in China to clarify the association between XRCC1-Arg399Gln polymorphism and HCC risk. A total of 150 cases and 158 controls were selected from the the Affiliated Hospital of Qingdao University from May 2008 to May 2010. XRCC1-Arg399Gln polymorphism was based upon duplex polymerase-chain-reaction with the confronting-two-pairprimer (PCR-CTPP) method. All analyses were performed using the STATA statistical package. A significantly increased risk was associated with the Arg/Gln genotype (adjusted OR 1.78, 95%CI=1.13-2.79) compared with genotype Arg/Arg. In contrast, the Gln/Gln genotype had non-significant increased risk of HCC with adjusted OR (95%CI) of 1.69 (0.93-2.66). A significant association was found between positive HBsAg and Arg/Gln, with an OR of 3.43 (95% CI=1.45-8.13). Patients carrying Gln/Gln genotypes showed significantly lower median survival than Arg/Arg genotypes (HR=1.38, 95% CI=1.04-1.84). Further Kaplan-Meier analysis showed decreased median survival in Arg/Gln+Gln/Gln genotype carriers in comparison to Arg/Arg carriers (HR=1.33, 95% CI=1.02-1.76). In conclusion, we observed that XRCC1-Arg399Cln polymorphism is associated with susceptibility to HCC, and XRCC1 Gln allele genotype showed significant prognostic associations.

• Journal of Korean Society of Forest Science
• /
• v.110 no.1
• /
• pp.64-71
• /
• 2021

Recently, climate change has gradually accelerated the occurrence of landslides. Among the various effects caused by landslides,debris flow is recognized as particularly threatening because of its high speed and propagating distance. In this study, the impacts of various factors were analyzed using quantification theory(I) for the prediction of debris flow hazard soil volume in Seoraksan National Park, Korea. According to the range using the stepwise regression analysis, the order of impact factors was as follows: vertical slope (0.9676), cross slope (0.6876), altitude (0.2356), slope gradient (0.1590), and aspect (0.1364). The extent of the normalized score using the five-factor categories was 0 to 2.1864, with the median score being 1.0932. The prediction criteria for debris flow occurrence based on the normalized score were divided into four grades: class I, >1.6399; class II, 1.0932-1.6398; class III, 0.5466-1.0931; and class IV, <0.5465. Predictions of debris flow occurrence appeared to be relatively accurate (86.3%) for classes I and II. Therefore, the prediction criteria for debris flow will be useful for judging the dangerousness of slopes.

• Korean Journal of Radiology
• /
• v.21 no.6
• /
• pp.746-755
• /
• 2020

Objective: To identify predictors of pulmonary fibrosis development by combining follow-up thin-section CT findings and clinical features in patients discharged after treatment for COVID-19. Materials and Methods: This retrospective study involved 32 confirmed COVID-19 patients who were divided into two groups according to the evidence of fibrosis on their latest follow-up CT imaging. Clinical data and CT imaging features of all the patients in different stages were collected and analyzed for comparison. Results: The latest follow-up CT imaging showed fibrosis in 14 patients (male, 12; female, 2) and no fibrosis in 18 patients (male, 10; female, 8). Compared with the non-fibrosis group, the fibrosis group was older (median age: 54.0 years vs. 37.0 years, p = 0.008), and the median levels of C-reactive protein (53.4 mg/L vs. 10.0 mg/L, p = 0.002) and interleukin-6 (79.7 pg/L vs. 11.2 pg/L, p = 0.04) were also higher. The fibrosis group had a longer-term of hospitalization (19.5 days vs. 10.0 days, p = 0.001), pulsed steroid therapy (11.0 days vs. 5.0 days, p < 0.001), and antiviral therapy (12.0 days vs. 6.5 days, p = 0.012). More patients on the worst-state CT scan had an irregular interface (59.4% vs. 34.4%, p = 0.045) and a parenchymal band (71.9% vs. 28.1%, p < 0.001). On initial CT imaging, the irregular interface (57.1%) and parenchymal band (50.0%) were more common in the fibrosis group. On the worst-state CT imaging, interstitial thickening (78.6%), air bronchogram (57.1%), irregular interface (85.7%), coarse reticular pattern (28.6%), parenchymal band (92.9%), and pleural effusion (42.9%) were more common in the fibrosis group. Conclusion: Fibrosis was more likely to develop in patients with severe clinical conditions, especially in patients with high inflammatory indicators. Interstitial thickening, irregular interface, coarse reticular pattern, and parenchymal band manifested in the process of the disease may be predictors of pulmonary fibrosis. Irregular interface and parenchymal band could predict the formation of pulmonary fibrosis early.