• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.1
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• pp.41-46
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• 2014

In this paper, we propose a novel voice activity detection (VAD) algorithm using feature vectors based on TE (teager energy). Specifically, power spectral deviation (PSD), which is used as the feature for the VAD in the IS-127 noise suppression algorithm, is obtained after the input signal is transfomed by Teager energy operator. In addition, the TE-based likelihhod ratio are derived in each frame to modifiy the PSD for further VAD. The performance of our proposed VAD algorithm are evaluated by objective testing (total error rate, receiver operating characteristics, perceptual evaluation of speech quality) under various environments, and it is found that the proposed method yields better results than conventional VAD algorithms in the non-stationary noise environments under 5 dB SNR (total error rate = 2.6% decrease, PESQ score = 0.053 improvement).

• Atmosphere
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• v.33 no.5
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• pp.519-530
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• 2023

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACI_YrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill than the no optimized one (ACI_NoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACI_YrOpt were higher than those of ACI_NoOpt, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.

• Journal of Korean Academy of Nursing
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• v.46 no.1
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• pp.149-158
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• 2016

Purpose: This study was done to develop of the Korean intensive care delirium screening tool (KICDST). Methods: The KICDST was developed in 5 steps: Configuration of conceptual frame, development of preliminary tool, pilot study, reliability and validity test, development of final KICDST. Reliability tests were done using degree of agreement between evaluators and internal consistency. For validity tests, CVI (Content Validity Index), ROC (Receiver Operating Characteristics) analysis, known group technique and factor analysis were used. Results: In the reliability test, the degree of agreement between evaluators showed .80~1.00 and the internal consistency was KR-20=.84. The CVI was .83~1.00. In ROC analysis, the AUC (Area Under the ROC Curve) was .98. Assessment score was 4 points. The values for sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value were found to be 95.0%, 93.7%, 94.4%, 95.0% and 93.7%, respectively. In the known group technique, the average delirium screening tool score of the non-delirium group was $1.25{\pm}0.99$ while that of delirium group was $5.07{\pm}1.89$ (t= - 16.33, p <.001). The factors were classified into 3 factors (cognitive change, symptom fluctuation, psychomotor retardation), which explained 67.4% of total variance. Conclusion: Findings show that the KICDST has high sensitivity and specificity. Therefore, this screening tool is recommended for early identification of delirium in intensive care patients.

• Korean Journal of Clinical Oncology
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• v.14 no.2
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• pp.135-141
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• 2018

Purpose: Axillary lymph node dissection (ALND) and sentinel lymph node biopsy (SLNB) are important for staging of patients with node-positive breast cancer. However, these can be avoided in select micrometastatic diseases, preventing postoperative complications. The present study evaluated the ability of axillary lymph node maximum standardized uptake value (SUVmax) on positron emission tomography-computed tomography (PET-CT) to predict axillary metastasis of breast cancer. Methods: The records of invasive breast cancer patients who underwent pretreatment (surgery and/or chemotherapy) PET-CT between January 2006 and December 2014 were reviewed. ALNs were preoperatively evaluated by PET-CT. Lymph nodes were dissected by SLNB or ALND. SUVmax was measured in both the axillary lymph node and primary tumor. Student t-test and chi-square test were used to analyze sensitivity and specificity. Receiver operating characteristic (ROC) and area under the ROC curve (AUC) analyses were performed. Results: SUV-tumor (SUV-T) and SUV-lymph node (SUV-LN) were significantly higher in the triple-negative breast cancer (TNBC) group than in other groups (SUV-T: 5.99, P<0.01; SUV-LN: 1.29, P=0.014). The sensitivity (0.881) and accuracy (0.804) for initial ALN staging were higher in fine needle aspiration+PET-CT than in other methods. For PET-CT alone, the subtype with the highest sensitivity (0.870) and negative predictive value (0.917) was TNBC. The AUC for SUV-LN was greatest in TNBC (0.797). Conclusion: The characteristics of SUV-T and SUV-LN differed according to immunohistochemistry subtype. Compared to other subtypes, the true positivity of axillary metastasis on PET-CT was highest in TNBC. These findings could help tailor management for therapeutic and diagnostic purposes.

• Hip & pelvis
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• v.30 no.4
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• pp.219-225
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• 2018

Purpose: Ischiofemoral impingement (IFI)-primarily diagnosed by magnetic resonance imaging (MRI)-is an easily overlooked disease due to its low incidence. The purpose of this study was to evaluate the usefulness of false profile view as a screening test for IFI. Materials and Methods: Fifty-eight patients diagnosed with IFI between June 2013 and July 2017 were enrolled in this retrospective study. A control group (n=58) with matching propensity scores (age, gender, and body mass index) were also included. Ischiofemoral space (IFS) was measured as the shortest distance between the lateral cortex of the ischium and the medial cortex of lesser trochanter in weight bearing hip anteroposterior (AP) view and false profile view. MRI was used to measure IFS and quadratus femoris space (QFS). The receiver operating characteristics (ROC), area under the ROC curve (AUC) and cutoff point of the IFS were measured by false profile images, and the correlation between the IFS and QFS was analyzed using the MRI scans. Results: In the false profile view and hip AP view, patients with IFI had significantly decreased IFS (P<0.01). In the false profile view, ROC AUC (0.967) was higher than in the hip AP view (0.841). Cutoff value for differential diagnosis of IFI in the false profile view was 10.3 mm (sensitivity, 88.2%; specificity, 88.4%). IFS correlated with IFS (r=0.744) QFS (0.740) in MRI and IFS (0.621) in hip AP view (P<0.01). Conclusion: IFS on false profile view can be used as a screening tool for potential IFI.

• Journal of Chest Surgery
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• v.35 no.1
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• pp.27-35
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• 2002

Background: A perioperative myocardial infarction(PMI) is one of the major complications after CABG. Among diagnostic methods of PMI, CK-MB activity assays have been increasingly replaced by CK-MB mass assays, which have more sensitive, simple measurement. Also, new cardiac-specific and -sensitive marker, cardiac troponin I(cTnl), has been shown to be a marker of myocardial infarction. We report our evaluation of clinical significance of CK-MB mass and cTnl as a marker of PMI after CABG. Material and Method: We studied 32 patients who underwent CABG at Kangdong Sacred Hospital between April 2000 and April 2001. Postoperative serum CK-MB activity level, serum CK-MB mass, cTnl, electrocardiogram, echocardiogram, and clinical data were recorded prospectively The diagnosis of PMI was defined as positive 2 among 3 or all of the following , by a new Q wave on the electrocardiogram, by serum CK-MB activity higher than 200 lU/L within 72 hours after operation, and by new regional wall motion abnormality on the echocardiogram. Result: After CABG, 3 patients had sustained a PMI according to current diagnostic criteria. As serum CK-MB activity time course, a level of CK-MB activity 12 hours after CABG had very linear correlated significance with serum CK-MB mass 24hours(R=0.946) and cTnl 48 hours(R=0.933) after CABG(p=0.000). As we used a receiver operating characteristics curve(ROC curve) for a diagnostic cutoff value in patients with PMI, serum CK-MB mass levels higher than 30.05 ug/L 24 hours after CABG detected the presence of PMI with an area under the ROC curve of 1.0, a sensitivity of 100%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 100%. Also serum cTnl levels higher than 17.15 ug/L 48 hours after CABG detected the presence of PMI with an area under the ROC curve of 0.98, a sensitivity of 100%, a specificity of 96.6%, a positive preclictive value of 75%, and a negative predictive value of 100% Conclusion: We concluded that both the measurement of CK-MB mass and cTnl are the easier, accurate methods as a diagnostic marker of PMT after CABG, also as a proposal of diagnostic cutoff value enables to an early detection of PMI. However, a 1arger number of patient will be needed because of statistic limitation that a small number of participating patients, a small number of PMI.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.839-852
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• 2018

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.11
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• pp.30-41
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• 2010

In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition. The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image. The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM. The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.55-63
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• 2011

This paper describes new moving target detection technique combining two algorithms to detect targets and reject clutters in video frame images for surveillance system: One obtains the region of moving target using phase correlation method using $N{\times}M$ sub-block images in frequency domain. The other uses adaptive threshold using learning weight for extracting target candidates in subtracted image. The block region with moving target can be obtained using the characteristics that the highest value of phase correlation depends on the movement of largest image in block. This technique can be used in camera motion environment calculating and compensating camera movement using FFT phase correlation between input video frame images. The experimental results show that the proposed algorithm accurately detects target(s) with a low false alarm rate in variety environment using the receiver operating characteristics (ROC) curve.