• Korean Journal of Pharmacognosy
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• v.43 no.3
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• pp.198-200
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• 2012

Korean Folk Medicine 'DdaeJukNaMu' has been used mainly to cure toothache and neuralgia. With regard to the botanical origin of 'DdaeJukNaMu', it has been considered to be Styrax species of Styracaceae, but there was no pharmacognostical confirmation on it. To clarify the botanical origin of 'DdaeJukNaMu', the anatomical characteristics of the branch of Styrax species growing wild in Korea, Styrax japonica and Styrax obassia were studied. As a result, it was clarified that 'DdaeJukNaMu' was the branch of Styrax japonica.

• Speech Sciences
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• v.12 no.3
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• pp.7-17
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• 2005

This study aimed at investigating the validity and reliability of the Daegu Diagnostic Aphasia Examination (DDAE). The DDAE has been developed to assess aphasics' receptive and expressive language ability. One hundred and forty eight aphasics (96 males and 52 females) diagnosed as aphasics participated in this study. Reliability coefficients showed that the DDAE was highly consistent and accurate (Cronbach's a = .76$\sim$.82). For its' content-validity, a 5-point scale was administered. Four speech and language pathologists served as evaluator. The receptive language mean score was 4.29, the expressive language mean score was 4.09, and the right-hemisphere function mean score was 4.00. For construct validity, the correlation total scores were calculated. The results showed a significant correlation.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2220-2238
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• 2024

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.670-677
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• 2019

As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and MultiTask AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.