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인버터의 전류측정 오차에 기인하는 교류전동기의 토크리플 저감
윤덕용,홍순찬 전력전자학회 1998 전력전자학회 논문지 Vol.3 No.4
본 논문에서는 벡터제어방식의 인버터에 의하여 구동되는 교류전동기 제어 시스템에서 전류특정회로에서의 측정오차에 기인하는 전동기의 토크리플을 저감하는 방법을 제안한다. 2상의 전류를 측정하는 회로에서의 오프셋 전압과 전압증폭률이 서로 다를 때 전동기 출력토크에 발생되는 리플을 각각 정량적으로 분석하고, 이로부터 온라인 상태에서 실시간으로 토크리플을 제거할 수 있는 알고리즘을 제시하였다. 제안된 방식의 유용성을 확인하기 위하여 이를 영구자석형 동기전동기에 적용하였을 경우에 대하여 출력토크의 리플을 계산하고 이를 제거하는 알고리즘을 컴퓨터로 시뮬레이션하였다. This paper proposes a novel method to reduce the torque ripple due to the non-ideality of the current sensing parts in vector-controlled inverter-fed AC motor drive systems. For PMSM(Permanent Magnet Synchronous Motor), motor output torque equations are derived in terms of their offset voltages and different voltage transducing gains. And the effects of phase current errors on motor torque are analyzed for both salient PMSM and non-salient PMSM. The proposed method can eliminate the torque ripple by nulling the offset voltages and setting the voltage transducing gains to the same value. To verify the proposed method, digital simulations are carried out for non-salient PMSM.
Fe-20Cu 계 소결체의 성질에 미치는 성형밀도 및 소결시간의 영향에 관한 연구
윤덕용,이채우 대한금속재료학회(대한금속학회) 1975 대한금속·재료학회지 Vol.13 No.3
The effects of green density and sintering time on various properties of sintered Fe-20% Cu with and without graphite addition were investigated. Sintering was carried out at 1140℃ in hydrogen atmosphere. The specimens were cylindrical ring shape and dimensional changes in O. D. and I. D. after compacting and sintering were carefully measured and compared. Fe-20% Cu compacts showed rapid growth during first 10 minutes of sintering with subsequent densification. The samples with higher green density showed larger dimensional growth and smaller shrinkage during the initial and subsequent stages of sintering. Observations of the microstructures indicate that the results can be best explained by the penetration of the iron particle boundaries by liquid copper at the beginning of the sintering process. Addition of graphitereduces the growth but has little effect on the shinkage. The effects of green density and sintering time on and degree of oil impregnation and radial crushing strength wera also investigated. The results closely follow general expectations. With increased sintering time the amount of open porosity decreased while the closed porosity did not show much change.
Pharmacogenomic information from CPIC and DPWG guidelines and its application on drug labels
윤덕용,Lee Soyoung,반무성,장인진,이승환 대한임상약리학회 2020 Translational and Clinical Pharmacology Vol.28 No.4
There are several hurdles to overcome before implementing pharmacogenomics (PGx) in precision medicine. One of the hurdles is unawareness of PGx by clinicians due to insufficient pharmacogenomic information on drug labels. Therefore, it might be important to implement PGx that reflects pharmacogenomic information on drug labels, standard of prescription for clinicians. This study aimed to evaluate the level at which PGx was being used in clinical practice by comparing the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines and drug labels of the US Food and Drug Administration (FDA) and the Korea Ministry of Food and Drug Safety (MFDS). Two PGx guidelines and drugs labels were scrutinized, and the concordance of the pharmacogenomic information between guidelines and drug labels was confirmed. The concordance of the label between FDA and MFDS was analyzed. In FDA labels, the number of concordant drug with guidelines was 24, while 13 drugs were concordant with MFDS labels. The number of drugs categorized as contraindication, change dose, and biomarker testing required was 7, 12 and 12 for the FDA and 8, 5 and 4 for the MFDS, respectively. The pharmacogenomic information of 9 drugs approved by both FDA and MFDS was identical. In conclusion, pharmacogenomic information on clinical implementation guidelines was limited on both FDA and MFDS labels because of various reasons including the characteristics of the guidelines and the drug labels. Therefore, more effort from pharmaceutical companies, academia and regulatory affairs needs to be made to implement pharmacogenomic information on drug labels.
Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model
윤덕용,임홍석,정경원,김태영,이석훈 대한의료정보학회 2019 Healthcare Informatics Research Vol.25 No.3
Objectives: Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert. Methods: To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset. Results: When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96). Conclusions: The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.