• 대한한의학회지
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• 제30권3호
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• pp.98-105
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• 2009

Objective: Increased aortic and carotid arterial augmentation index (AI) is associated with the risk of cardiovascular disease. The most widely used approach for determining central arterial AI is by calculating the aortic pressure waveform from radial arterial waveforms using a transfer function. But how the change of waveform by applied pressure and the pattern of the change rely on subject's characteristics has not been recognized. In this study, we use a new method for measuring radial waveform and observe the change of waveform and the deviation of radial AI in the same position by applied pressure. Method: Forty-six non-patient volunteers (31 men and 15 women, age range 21-58 years) were enrolled for this study. Informed consent in a form approved by the institutional review board was obtained in all subjects. Blood pressure was measured on the left upper arm using an oscillometric method, radial pressure waves were recorded with the use of an improved automated tonometry device. DMP-3000(DAEYOMEDI Co., Ltd. Ansan, Korea) has robotics mechanism to scan and trace automatically. For each subject, we performed the procedure 5 times for each applied pressure level. We could thus obtain 5 different radial pulse waveforms for the same person's same position at different applied pressures. All these processes were repeated twice for test reproducibility. Result: Aortic AI, peripheral AI and radial AI were higher in women than in men (P<0.01), radial AI strongly correlated with aortic AI, and radial AI was consistently approximately 39% higher than aortic AI. Relationship between representative radial AI of DMP-3000 and peripheral AI of SphygmoCor had strongly correlation. And there were three patterns in change of pulse waveform. Conclusion: In this study, it is revealed the new device was sufficient to measure how radial AI and radial waveform from the same person at the same time change under applied pressure and it had inverse-proportion to applied pressure.

• 대한한의학회지
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• 제35권3호
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• pp.155-165
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• 2014

Objectives: The pulse diagnosis to identify the symptoms has been considered important in Korean medicine. The position and character of disease would be confirmed by pulse diagnosis of left and right radial artery. This paper is to analyze the characteristics and differences of left and right blood vessels. Methods: In this study, left and right radial artery and dorsalis pedis artery was measured and analyzed by using condenser typed pulse analyzer. Commercially available pulse analyzer was used to measure the radial artery. The pulse wave was measured in 20 laboratory healthy men and women. The blood vessel aging degree and index of augmentation of blood vessel was obtained from the measured pulse wave graph and the characteristics and differences of the left and right blood vessel was analyzed. Results: The significant difference of pulse transit time between the right handed and non-right handed was not found. The correlation of radial artery and dorsalis pedis artery had no significant difference. By obtaining the blood vessel aging index (AGI) and augmentation index (AI) of blood vessel at the left and right radial artery, the significant difference between right handed and non-right handed was not found. Conclusions: The result of this study would help to explain the characteristic of blood vessel with respect to the left and right handed. We suggest that research of pulse wave of the left and right blood vessel using pulse analyzer should be needed in further study.

• 한국재료학회지
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• 제7권2호
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• pp.152-156
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• 1997

본 논문은 분쇄한 dickite의 메카노케미칼 효과에 관해서 연구한 것이다. 유성밀을 사용하여 분쇄한 dickite의 메카노케미칼 효과를 알아보기 위하여, 분쇄시간을 달리하여 얻어진 dickite에 대하여 시차열분속과 X선 동경분포함수를 이용하여 정밀구조분석을 하였다. 분쇄한 dickite에 대하여 동경분포함수 해석 결과, $SiO_{4}$ 사면체는 분쇄한 dickite내에서 국소단위구조로 그대로 잔류하고 있었다. 한편 AI-O의 배위수 및 원자간 거리는 분쇄시간의 증가에 따라 점점 감소하였고, 이에 대한 정량화가 가능했다. 이는 시차열분석 결과의 AI주위의 환경변화와 일치하였다. 따라서, 분쇄함에 따른 dickite의 메카노케미칼 현상은 AI주위의 환경변화에 기인된 것으로 볼 수 있다.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3150-3163
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• 2023

To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multiphysics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.

• 대한치과교정학회지
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• 제51권2호
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• pp.77-85
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• 2021

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.

• 감성과학
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• 제12권3호
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• pp.299-306
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• 2009

본 연구에서는 비침습적으로 압력 맥파를 검출하는 토노메트리 방식의 맥파 측정 장비인 SphygmoCor(AtCor, Australia)를 사용하여 측정자세에 따라 맥파를 측정하였다. 측정 결과를 통해 선 자세, 앉은 자세, 누운 자세에 따른 맥파의 분석 지표들의 변화를 분석하였다. 또한 측정된 데이터를 저혈압군, 정상혈압군, 고혈압군으로 구분하여 혈압군에 따른 맥파의 비교를 수행하였다. 그 결과 자세에 따른 요골동맥의 파형에서 유의차를 보인 분석지표는 맥파의 진폭에 해당되는 압력 보다 주요 피크가 발생되는 시간에서 나타났다. 맥압의 경우 맥파증대계수(AI)나 중심동맥압과 요골동맥압의 비율과 같이 맥압 간의 비율이 자세에 따른 변별력이 있었다. 사후검정에 따라 각 자세별로 상호 간에 모두 유의한 차이를 보인 분석지표는 주파(P_$T_1$)와 반사파(P_$T_2$)의 시간, 심박출지속시간(ED), 심박수(HR)로 나타났다. 혈압군에 대한 비교에서 대부분 맥파의 시간에 해당되는 지표보다 주요 피크의 진폭에 해당되는 맥파의 압력을 나타내는 지표가 혈압군에 따른 유의차를 보였다.

• 융합정보논문지
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• 제11권10호
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• pp.20-26
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• 2021

본 논문에서는 영상 인식 환경에서 영상 정렬에 필요한 회전각 추정 방법 중 낮은 사양의 임베디드 기반 환경에 적용 가능한 방법을 제안하고 기존의 복소 모멘트를 이용하는 방법과 비교하였다. 제안된 방법은 영상을 극좌표로 변환한 후 거리축 방향으로 투영된 1차원 프로파일의 유사도 매칭을 통하여 회전각을 추정한다. 추가로 연산을 더 단순화시킨 투영 프로파일의 벡터합을 이용하는 방법을 선택할 수도 있다. 이진 패턴 영상과 흑백 명암영상을 대상으로 진행한 실험을 통하여 제안된 방법의 추정 오차가 기존의 복소 모멘트를 이용하는 방법과 큰 차이가 없으며 보다 적은 연산과 낮은 시스템 자원이 요구됨을 보였다. 추후 확장을 위하여 흑백 명암영상에서 회전 중심을 일치시키는 방법에 관한 연구가 필요할 것이다.

• Geomechanics and Engineering
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• 제12권3호
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.350-353
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• 1991

This study develops an expert system which solves the problems of the MTr/feeder overloads and the feeder constraints in automated radial distribution systems. Then, the objective is to perform the network reconfiguration by switching the tie and sectionalizing switches which eliminates the system violation, while achieving the load balance of the MTrs/feeders. To reduce the search space, an expert system based on heuristic rules is presented, and implemented in AI language Prolog. This system adopts the best-first tree search technique. The computational results are also prepared to show the performance of the heuristic algorithm developed.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.1049-1050
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• 2008

It is difficult to measure the pulse wave in a short time because radial artery position and located depth are different depending on the person. In this paper, the pulse wave measurement system was developed using 3 channel piezoresistive sensor array to detect the most significant pulse wave. Augmentation Index(AI) and Heart Rate(HR) analysis are also available for predicting cardiovascular risks. The developed system is small and easy to use. And it is promising to be used as home healthcare device.