• Journal of Climate Change Research
• /
• v.7 no.3
• /
• pp.325-334
• /
• 2016

We projected the distribution of Hedera rhombea, an evergreen broad-leaved climbing plant, under current climate conditions and predicted its future distributions under global warming. Inaddition, weexplained model uncertainty by employing 9 single Species Distribution model (SDM)s to model the distribution of Hedera rhombea. 9 single SDMs were constructed with 736 presence/absence data and 3 temperature and 3 precipitation data. Uncertainty of each SDM was assessed with TSS (Ture Skill Statistics) and AUC (the Area under the curve) value of ROC (receiver operating characteristic) analyses. To reduce model uncertainty, we combined 9 single SDMs weighted by TSS and resulted in an ensemble forecast, a TSS weighted ensemble. We predicted future distributions of Hedera rhombea under future climate conditions for the period of 2050 (2040~2060), which were estimated with HadGEM2-AO. RF (Random Forest), GBM (Generalized Boosted Model) and TSS weighted ensemble model showed higher prediction accuracies (AUC > 0.95, TSS > 0.80) than other SDMs. Based on the projections of TSS weighted ensemble, potential habitats under current climate conditions showed a discrepancy with actual habitats, especially in the northern distribution limit. The observed northern boundary of Hedera rhombea is Ulsan in the eastern Korean Peninsula, but the projected limit was eastern coast of Gangwon province. Geomorphological conditions and the dispersal limitations mediated by birds, the lack of bird habitats at eastern coast of Gangwon Province, account for such discrepancy. In general, potential habitats of Hedera rhombea expanded under future climate conditions, but the extent of expansions depend on RCP scenarios. Potential Habitat of Hedera rhombea expanded into Jeolla-inland area under RCP 4.5, and into Chungnam and Wonsan under RCP 8.5. Our results would be fundamental information for understanding the potential effects of climate change on the distribution of Hedera rhombea.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.20 no.6
• /
• pp.1116-1124
• /
• 1996

The factors to presume the shapes of bending and draping were examined in this study, by applying the similar phenomenon and theory of analysis. The findings were as followings: 1. The value of deflection angle (f) of deflection curve were almost consistent with those of K number and the shapes of deflection curve were congruent, under the condition of that the values of EI/w are almost similar and the lengths of samples are consistent. 2. The values of drape area, drape coefficient, mean of deflection angle, and $\pi$ number were consistently estimated and the shapes of drape were almost the same, under the condition of that the values of EI/w are similar and the diameter of samples are consistent. 3. In using the samples with different values of EI/w, scale factor, kl, was obtained from the formula, the shapes of bending of the referent samples and compsactive smaples was geometrically similar, which the lengths of samples were 1,1'and were satisfied with the formula, hi: L'11, and their $\pi$ number were also consistent. 4. In applying the samples with different values of Rllw, scale factor (kl) was obtained and then, when semidiameter of samples was adjusted to be satisfied with the formula, k1=L/L, the shapes of draping of referent samples and comparative samples were geometrically similar. Furthermore, their $\pi$ number was also consistent. 5. The shares of bending and draping could be changed in terms of three factors such as the lengths of samples, bending ridigity, and weigths per unit area. $\pi$ number was obtained from theory of similar phenomenon, which was index to presume shapes of bending and the shapes of draping getting from the three factors.

• Physical Therapy Rehabilitation Science
• /
• v.6 no.4
• /
• pp.176-181
• /
• 2017

Objective: The timed up and go (TUG) test is method used to determine the functional mobility of persons with stroke. Its reliability, validity, reaction rate, fall prediction, and psychological characteristics concerning ambulation ability have been validated. However, the relationship between TUG performance and community ambulation ability is unclear. The purpose of this study was to investigate whether the TUG performance time could indicate community ambulation levels (CAL) differentially in persons with chronic stroke. Design: Cross-sectional study. Methods: Eighty-seven stroke patients had participated in this study. Based on the self-reporting survey results on the difficulties experienced when walking outdoors, the subjects were divided into the independent community ambulation (ICA) group (n=35) and the dependent community ambulation group (n=52). Based on the area under the curve (AUC), the discrimination validity of the TUG performance time was calculated for classifying CAL. The Binomial Logistic Regression Model was utilized to produce the likelihood ratio of selected TUG cut-off values for the distinguishing of community ambulation ability. Results: The selected TUG cut-off values and the area under the curve were <14.87 seconds (AUC=0.871, 95% confidence interval=0.797-0.945), representing a mid-level accuracy. Concerning the likelihood ratio of the selected TUG cut-off value, it was found that the group with TUG performance times shorter than 14.87 seconds showed a 2.889 times higher probability of ICA than those with a TUG score of 14.87 seconds or longer (p<0.05). Conclusions: The TUG can be viewed as an assessment tool that is capable of classifying CAL.

• Journal of Trauma and Injury
• /
• v.35 no.3
• /
• pp.181-188
• /
• 2022

Purpose: Inferior vena cava (IVC) collapse is related to hypovolemia. Sonography has been used to measure the IVC diameter, but there is variation depending on the skill of the operator and it is difficult to obtain accurate measurements in patients who have a large amount of intestinal gas or are obese. As a modality to obtain accurate measurements, we measured the diameters of the IVC and aorta on trauma computed tomography scans and investigated the correlation between the IVC to aorta ratio and the shock index in blunt trauma patients. Methods: We retrospectively analyzed the medical records of 588 trauma patients who were transferred to the regional trauma center (level 1) of Wonkang University Hospital from March 2020 to February 2021. We included trauma patients 18 years or older who met the trauma activation criteria and underwent trauma computed tomography scans with intravenous contrast within 40 minutes of admission. The shock index was calculated from vital signs before trauma computed tomography scan, and measurements of the anteroposterior diameter of the IVC (AP), the transverse diameter of the IVC (T), and aorta were made 10 mm above the right renal vein in the venous phase. Results: Overall, 271 patients were included in this study, of whom 150 had a shock index ≤0.7 and 121 had a shock index >0.7. The T to AP ratio and AP to aorta ratio were significantly different between groups. Cutoffs were identified for the T to AP ratio and AP to aorta ratio (2.37 and 0.62, respectively) that produced clinically useful sensitivity and specificity for predicting a shock index >0.7, demonstrating moderate accuracy (T to AP ratio: area under the curve, 0.71; sensitivity, 59%; specificity, 87% and AP to aorta ratio: area under the curve, 0.70; sensitivity, 55%; specificity, 91%). Conclusions: The T to AP ratio and AP to aorta ratio are useful for predicting hemorrhagic shock in trauma patients.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.2
• /
• pp.47-52
• /
• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• The Journal of the Acoustical Society of Korea
• /
• v.41 no.2
• /
• pp.199-209
• /
• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• Korean Journal of Radiology
• /
• v.22 no.3
• /
• pp.297-307
• /
• 2021

Objective: To compare the performance of simulated abbreviated breast MRI (AB-MRI) and full diagnostic (FD)-MRI in distinguishing between benign and malignant lesions detected by MRI and investigate the features of discrepant lesions of the two protocols. Materials and Methods: An AB-MRI set with single first postcontrast images was retrospectively obtained from an FD-MRI cohort of 111 lesions (34 malignant, 77 benign) detected by contralateral breast MRI in 111 women (mean age, 49.8. ± 9.8; range, 28-75 years) with recently diagnosed breast cancer. Five blinded readers independently classified the likelihood of malignancy using Breast Imaging Reporting and Data System assessments. McNemar tests and area under the receiver operating characteristic curve (AUC) analyses were performed. The imaging and pathologic features of the discrepant lesions of the two protocols were analyzed. Results: The sensitivity of AB-MRI for lesion characterization tended to be lower than that of FD-MRI for all readers (58.8-82.4% vs. 79.4-100%), although the findings of only two readers were significantly different (p < 0.05). The specificity of AB-MRI for lesion characterization was higher than that of FD-MRI for 80% of readers (39.0-74.0% vs. 19.5-45.5%, p ≤ 0.001). The AUC of AB-MRI was comparable to that of FD-MRI for all readers (p > 0.05). Fifteen percent (5/34) of the cancers were false-negatives on AB-MRI. More suspicious margins or internal enhancement on the delayed phase images were related to the discrepancies. Conclusion: The overall performance of AB-MRI was similar to that of FD-MRI in distinguishing between benign and malignant lesions. AB-MRI showed lower sensitivity and higher specificity than FD-MRI, as 15% of the cancers were misclassified compared to FD-MRI.

• The Journal of the Convergence on Culture Technology
• /
• v.5 no.4
• /
• pp.413-420
• /
• 2019

The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.26 no.4
• /
• pp.193-200
• /
• 2023

Purpose: Crohn's disease (CD) is a chronic, idiopathic bowel disorder that can progress to partial or complete bowel obstruction. At present, there are no reliable diagnostic tests that can readily distinguish between acute inflammatory, purely fibrotic and mixed inflammatory and fibrotic. Our aim is to study the utility of contrast enhanced ultrasound (CEUS) in combination with shear wave elastography (SWE) to differentiate fibrotic from inflammatory strictures in children with obstructive CD of the terminal ileum. Methods: Twenty-five (19 male) children between 2016-2021 with CD of the terminal ileum were recruited into the study. Among these patients, 22 had CEUS kinetic measurements of tissue perfusion, including wash-in slope (dB/sec), peak intensity (dB), time to peak intensity (sec), area under the curve (AUC) (dB sec), and SWE. In total, 11 patients required surgery due to bowel obstruction. Histopathologic analysis was performed by a pathologist who was blinded to the CEUS and SWE test results. Results: Patients that underwent surgical resection had significantly higher mean area under the curve on CEUS compared to patients responsive to medical therapy (p=0.03). The AUC also correlated with the degree of hypertrophy and the percent fibrosis of the muscularis propria, as determined by histopathologic grading (p<0.01). There was no difference in the mean elastography measurements between these two patient groups. Conclusion: CEUS is a useful radiological technique that can help identify pediatric patients with medically refractory obstructive fibrotic strictures of the terminal ileum that should be considered for early surgical resection.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.1
• /
• pp.22-29
• /
• 2016

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target detection technique has been considered as a difficult technique. In this paper, we describe the basic concept of Fractional Fourier transform and optimal transform order. Then we analyze the relationship between time-frequency characteristics of an LFM signal and its spectrum using Fractional Fourier transform. Based on the analysis results, we present active sonar target detection method. To verify the performance of proposed methods, we compared the results with conventional FFT-based matched filter. The experimental results demonstrate the superiority of the proposed method compared to the conventional method in the aspect of AUC(Area Under the ROC Curve).