• Korean Journal of Radiology
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• v.24 no.5
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• pp.395-405
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• 2023

Objective: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1251-1259
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• 2022

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.250-258
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• 2007

Current speech recognition technology s achieved high performance with the development of hardware devices, however it is insufficient for some applications where high reliability is required, such as voice control of powered wheelchairs for disabled persons. For the system which aims to operate powered wheelchairs safely by voice in real environment, we need to consider that non-voice commands such as user s coughing, breathing, and spark-like mechanical noise should be rejected and the wheelchair system need to recognize the speech commands affected by disability, which contains specific pronunciation speed and frequency. In this paper, we propose non-voice rejection method to perform voice/non-voice classification using both YIN based fundamental frequency(F0) extraction and reliability in preprocessing. We adopted a multi-template dictionary and acoustic modeling based speaker adaptation to cope with the pronunciation variation of inarticulately uttered speech. From the recognition tests conducted with the data collected in real environment, proposed YIN based fundamental extraction showed recall-precision rate of 95.1% better than that of 62% by cepstrum based method. Recognition test by a new system applied with multi-template dictionary and MAP adaptation also showed much higher accuracy of 99.5% than that of 78.6% by baseline system.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2
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• pp.61-68
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• 2007

The frequency response of a microphone, which indicates the frequency range that a microphone can output within the approved level, is one of the most significant standards used to measure the characteristics of a microphone. At present, conventional methods of measuring the frequency response are complicated and involve the use of expensive equipment. To complement the disadvantages, this paper suggests a new algorithm that can measure the frequency response of a microphone in a simple manner. The algorithm suggested in this paper generates the Optimized Aoshima's Time Stretched Pulse(OATSP) signal from a computer via a standard speaker and measures the impulse response of a microphone by convolution the inverse OATSP signal and the received by the microphone to be measured. Then, the frequency response of the microphone to be measured is calculated using the signals. The performance test for the algorithm suggested in the study was conducted through a comparative analysis of the frequency response data and the measures of frequency response of the microphone measured by the algorithm. It proved that the algorithm is suitable for measuring the frequency response of a microphone, and that despite a few errors they are all within the error tolerance.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.155-163
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• 2009

In text-to-speech systems, the conversion of text into prosodic parameters is necessarily composed of three steps. These are the placement of prosodic boundaries. the determination of segmental durations, and the specification of fundamental frequency contours. Prosodic boundaries. as the most important and basic parameter. affect the estimation of durations and fundamental frequency. Break prediction is an important step in text-to-speech systems as break indices (BIs) have a great influence on how to correctly represent prosodic phrase boundaries, However. an accurate prediction is difficult since BIs are often chosen according to the meaning of a sentence or the reading style of the speaker. In Japanese, the prediction of an accentual phrase boundary (APB) and major phrase boundary (MPB) is particularly difficult. Thus, this paper presents a method to complement the prediction errors of an APB and MPB. First, we define a subtle BI in which it is difficult to decide between an APB and MPB clearly as a variable break (VB), and an explicit BI as a fixed break (FB). The VB is chosen using the classification and regression tree, and multiple prosodic targets in relation to the pith and duration are then generated. Finally. unit-selection is conducted using multiple prosodic targets. In the MOS test result. the original speech scored a 4,99. while proposed method scored a 4.25 and conventional method scored a 4.01. The experimental results show that the proposed method improves the naturalness of synthesized speech.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.392-400
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• 2009

In this paper, we propose an effective speech reinforcement technique base on ITU-T G.729A CS-ACELP codec under the near-end background noise environments. In general, since the intelligibility of the far-end speech for the near-end listener is significantly reduced under near-end noise environments, we require a far-end speech reinforcement approach to avoid this phenomena. In contrast to the conventional speech reinforcement algorithm, we reinforce the excitation signal of the codec's parameters received from the far-end speech signal based on the G.729A speech codec under various background noise environments. Specifically, we first estimate the excitation signal of ambient noise at the near-end through the encoder of the G.729A speech codec, reinforcing the excitation signal of the far-end speech transmitted from the far-end. we specially propose a novel approach to directly reinforce the excitation signal of far-end speech signal based on the decoder of the G.729A. The performance of the proposed algorithm is evaluated by the CCR (Comparison Category Rating) test of the method for subjective determination of transmission quality in ITU-T P.800 under various noise environments and shows better performances compared with conventional SNR Recovery methods.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.572-577
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• 2009

Large corpus-based concatenating Text-to-Speech (TTS) systems can generate natural synthetic speech without additional signal processing. To prune the redundant speech segments in a large speech segment DB, we can utilize a decision-tree based triphone clustering algorithm widely used in speech recognition area. But, the conventional methods have problems in representing the acoustic transitional characteristics of the phones and in applying context questions with hierarchic priority. In this paper, we propose a new clustering algorithm to downsize the speech DB. Firstly, three 13th order MFCC vectors from first, medial, and final frame of a phone are combined into a 39 dimensional vector to represent the transitional characteristics of a phone. And then the hierarchically grouped three question sets are used to construct the triphone trees. For the performance test, we used DTW algorithm to calculate the acoustic similarity between the target triphone and the triphone from the tree search result. Experimental results show that the proposed method can reduce the size of speech DB by 23% and select better phones with higher acoustic similarity. Therefore the proposed method can be applied to make a small sized TTS.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.289-295
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• 2024

This study analyzes the correlation between the accracy of rifle and the result of engagement. And estimates the improvement cost of the rifle accordingly. For this experiment, an engagement class simulation model(AWAM: Army Weapon Effectiveness Analysis Model) was used. We also selected the rifle, which is a portable weapon for the experiment. Prior to this experiment, we conducted a reliability test(VV&A: Verification, Validation and Accreditation) on the model. The VV&A process is mainly done during the development of the DM&S model, which is also necessary for the operation of the M&S. We confirmed the need for VV&A during the experiment and obtained reliable experimental results using the corrected values. In the Accuracy Experiment we found that the 20% improvement is the most effective. And we were able to estimate the cost of acquiring a rifle with a 20% higher accuracy. The cost was estimated by simple regression analysis based on the price of the current rifle. Through this study, we could know the impact of the accuracy of rifle on the experimental results and estimate the cost of improved rifle.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.585-590
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• 2024

In this study, rail surface defects are increasing due to the aging of urban railway rails, but in the detailed guidelines for track performance evaluation established by the country, rail surface damage is inspected with the naked eye of engineers and simple measuring tools. With the recent enactment of the Track Diagnosis Act, a large budget has been invested and the volume of rail diagnosis is rapidly increasing, but it is difficult to secure the reliability of diagnosis results using labor-intensive visual inspection techniques. It is very important to discover defects in the rail surface through periodic track tours and visual inspection. However, evaluating the severity of defects on the rail surface based on the subjective judgment of the inspector has significant limitations in predicting damage inside the rail. In this study, the rail internal crack characteristics due to rail surface damage were studied. In field measurements, rail surface damage locations were selected, samples of various damage types were collected, and the rail surface damage status was evaluated. In indoor testing, we intend to analyze the correlation between rail surface defects and internal defects using a electron scanning microscope (SEM). To determine the crack growth rate of urban railway rails currently in use, the Gaussian probability density function was applied and analyzed.