• The Journal of Korean Medicine
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• v.30 no.3
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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.

• The Journal of Korean Medicine
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• v.35 no.3
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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.

• Korean Journal of Materials Research
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• v.7 no.2
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• pp.152-156
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• 1997

In this paper, I meritioned ,I study on thc rnechanochernic;~l effec ts of grountl iljckite To investigate the mechanochemical effects of ground tlickite 1,y planet,~r\- mill. a structul-ill i~nalvsis of dickite prepared with different grinding time has been made 11y X-ray diffraction and ilifierenti,~l tl~i.~-ni;~i analysis. 'l'he $SiO_{4}$ hecira was unchanged bl- milling and remained ;is the local ordering unit s t t - ~ c ~ u ~ - c ~ s in the ground dickitc samples I ) v analyzing the radial distribution function. On the other hand, with an increi~se of the grinding time a decrease of coordination number and atomic distance bet\vccn aluminum and oxygen hi1c.r l~een octurreil, and Li~~antitativcl\. coniirrned them This result corresponded to the local c,hange around aliimi~iuni I>V tliftercntii~l t1ii:rm;il ani~lysis. Therefore, the mechanochemical phenomina of ground tiickite werc3 h e to the local c~l~mge around aluminum \IT. the prolonged grinding.

• Nuclear Engineering and Technology
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• v.55 no.9
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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.

• The korean journal of orthodontics
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• v.51 no.2
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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.

• Science of Emotion and Sensibility
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• v.12 no.3
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• pp.299-306
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• 2009

In this study, pulse waves were measured at radial artery using non-invasive tonometric pulse pressure measurement system, SphygmoCor(AtCor, Australia), according to subject's posture. Then it was analysed whether the pulse wave parameters, which contain heart activities, change among three different postures (upright stand, sit, and supine). And it was also verified that the pulse wave parameters change among blood pressure level groups(hypotensive, normotensive, and hypertensive). As a results, posture effects were verified in time information of pulse wave rather than amplitude. But some parameters calculated by ratio of two amplitude, such as augmented index(AI) and ratio of central aortic pulse and radial artery pulse, showed significant difference according to postures. In post hoc test, time to the $1^{st}$ and $2^{nd}$ pulse peak(P_$T_1$, and P_$T_2$), ED(ejection duration), and HR(heart rate) showed significant difference among posture groups with each other. In comparison of blood pressure groups, it was verified that the parameters related to amplitude of pulse wave showed significant difference rather than time information.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.20-26
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• 2021

In this paper, we studied the rotation angle estimation methods required for image alignment in an image recognition environment. In particular, a rotation angle estimation method applicable to a low specification embedded-based environment was proposed and compared with the existing method using complex moment. The proposed method estimates the rotation angle through similarity mathcing of the 1D projection profile along the radial axis after converting an image into polar coordinates. In addition, it is also possible to select a method of using vector sum of the projection profile, which more simplifies the calculation. Through experiments conducted on binary pattern images and gray-scale images, it was shown that the estimation error of the proposed method is not significantly different from that of complex moment-based method and requires less computation and system resources. For future expansion, a study on how to match the rotation center in gray-scale images will be needed.

• Geomechanics and Engineering
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• v.12 no.3
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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.

• Proceedings of the KIEE Conference
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• 1991.07a
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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.

• Proceedings of the IEEK Conference
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• 2008.06a
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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.