• 생약학회지
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• 제43권3호
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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.

• 음성과학
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• 제12권3호
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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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• 제56권6호
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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.

• 한국음향학회지
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• 제38권6호
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• pp.670-677
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• 2019

음성 또는 음향 이벤트 신호에서 발생하는 배경 잡음은 인식기의 성능을 저하시키는 원인이 되며, 잡음에 강인한 특징을 찾는데 많은 노력을 필요로 한다. 본 논문에서는 딥러닝을 기반으로 다중작업 오토인코더(Multi-Task AutoEncoder, MTAE) 와 와설스타인식 생성적 적대 신경망(Wasserstein GAN, WGAN)의 장점을 결합하여, 잡음이 섞인 음향신호에서 잡음과 음성신호를 추정하는 네트워크를 제안한다. 본 논문에서 제안하는 MTAE-WGAN는 구조는 구배 페널티(Gradient Penalty) 및 누설 Leaky Rectified Linear Unit (LReLU) 모수 Parametric ReLU (PReLU)를 활용한 변수 초기화 작업을 통해 음성과 잡음 성분을 추정한다. 직교 구배 페널티와 파라미터 초기화 방법이 적용된 MTAE-WGAN 구조를 통해 잡음에 강인한 음성특징 생성 및 기존 방법 대비 음소 오인식률(Phoneme Error Rate, PER)이 크게 감소하는 성능을 보여준다.