http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Yazdanpanah Mehrdad,Cameron Jessie,Chappel Chandra,Yuan Libin 대한진단검사의학회 2024 Annals of Laboratory Medicine Vol.44 No.3
Background: Plasma oxalate measurements can be used for the screening and therapeutic monitoring of primary hyperoxaluria. We developed a gas chromatography-mass spectrometry (GC-MS) assay for plasma oxalate measurements with high sensitivity and suitable testing volumes for pediatric populations. Methods: Plasma oxalate was extracted, derivatized, and analyzed by GC-MS. We measured the ion at m/z 261.10 to quantify oxalate and the 13C2-oxalate ion (m/z: 263.15) as the internal standard. Method validation included determination of the linear range, limit of blank, limit of detection, lower limit of quantification, precision, recovery, carryover, interference, and dilution effect. The cut-off value between primary and non-primary hyperoxaluria in a pediatric population was analyzed. Results: The detection limit was 0.78 μmol/L, and the linear range was up to 80.0 μmol/L. The between-day precision was 5.7% at 41.3 μmol/L and 13.1% at 1.6 μmol/L. The carryover was <0.2%. The recovery rate ranged from 90% to 110%. Interference analysis showed that Hb did not interfere with plasma oxalate quantification, whereas intralipids and bilirubin caused false elevation of oxalate concentrations. A cut-off of 13.9 μmol/L showed 63% specificity and 77% sensitivity, whereas a cut-off of 4.15 μmol/L showed 100% specificity and 20% sensitivity. The minimum required sample volume was 250 μL. The detected oxalate concentrations showed interference from instrument conditioning, sample preparation procedures, medications, and various clinical conditions. Conclusions: GC-MS is a sensitive assay for quantifying plasma oxalate and is suitable for pediatric patients. Plasma oxalate concentrations should be interpreted in a clinical context.
A new damage detection indicator for beams based on mode shape data
Yazdanpanah, O.,Seyedpoor, S.M.,Bengar, H. Akbarzadeh Techno-Press 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.53 No.4
In this paper, a new damage indicator based on mode shape data is introduced to identify damage in beam structures. In order to construct the indicator proposed, the mode shape, mode shape slope and mode shape curvature of a beam before and after damage are utilized. Mode shape data of the beam are first obtained here using a finite element modeling and then the slope and curvature of mode shape are evaluated via the central finite difference method. In order to assess the robustness of the proposed indicator, two test examples including a simply supported beam and a two-span beam are considered. Numerical results demonstrate that using the proposed indicator, the location of single and multiple damage cases having different characteristics can be accurately determined. Moreover, the indicator shows a better performance when compared with a well-known indicator found in the literature.
Robot Control Using Intelligent Gain Sliding Mode
Azita Yazdanpanah,Dr. Abbas ali Rezaee,Dr. Ahmad Faraahi 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.1
In this research, intelligent sliding mode controls are presented as robust controls for robot manipulators. The objective of the study is to design controls for robot manipulators without the knowledge of the boundary of the uncertainties by using an intelligent sliding mode control (SMC) while elucidating the robustness of the fuzzy SMC. A sliding mode control provides for unlimited accuracy in presence of bounded disturbance, although the sliding mode controller also causes chattering. Chattering is undesirable for use with actual component, since it might causes damage to them with a subsequent loss of accuracy. Such chatter is caused by overestimation of the controller gain. An intelligent sliding mode is proposed as a solution to the problems created by chattering; to illustrate, a continuum robot manipulator is simulated with an intelligent sliding mode control. The performance of intelligent gain sliding mode controller is demonstrated through the simulation results. The results of the simulations show the effectiveness for chattering mitigation by means of avoiding overestimation, and the robustness of an intelligent sliding mode control.
A new damage detection indicator for beams based on mode shape data
O. Yazdanpanah,S.M. Seyedpoor,H. Akbarzadeh Bengar 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.53 No.4
In this paper, a new damage indicator based on mode shape data is introduced to identify damage in beam structures. In order to construct the indicator proposed, the mode shape, mode shape slope and mode shape curvature of a beam before and after damage are utilized. Mode shape data of the beam are first obtained here using a finite element modeling and then the slope and curvature of mode shape are evaluated via the central finite difference method. In order to assess the robustness of the proposed indicator, two test examples including a simply supported beam and a two-span beam are considered. Numerical results demonstrate that using the proposed indicator, the location of single and multiple damage cases having different characteristics can be accurately determined. Moreover, the indicator shows a better performance when compared with a well-known indicator found in the literature.
Omid Yazdanpanah,Minwoo Chang,Minseok Park,채윤병 국제구조공학회 2023 Structural Engineering and Mechanics, An Int'l Jou Vol.85 No.4
A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.
LETTER TO THE EDITOR : High Levels of Anti-Heat Shock Protein 27 Antibody in Pemphigus Vulgaris
Mohammad Javad Yazdanpanah,Ali Reza Taji,Zari Javidi,Fakhrozaman Pezeshkpoor,Amir Ali Rahsepar,Shima Tavallaie,Akram Momenzadeh,Saber Shojaie Noori,Mohsen Khoddami,Sara Rahsepar,Majid Ghayour M 대한피부과학회 2013 Annals of Dermatology Vol.25 No.2
지붕층 가속도를 활용한 비모델 기반 최대층간변위비 추정
오미드야즈단파나 ( Omid Yazdanpanah ),장민우 ( Minwoo Chang ) 한국구조물진단유지관리공학회 2022 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.26 No.1
In this paper, a nonmodel-based procedure incorporating machine learning techniques is introduced to estimate the peak story drift ratios (SDR) of buildings with eccentrically braced frames. The database includes average spectral acceleration intensity measure, wavelet-based refined damage-sensitive feature (rDSF), assembled only by the roof absolute acceleration response, geometric information, as predictor variables, and peak story drift ratios for the prototype models, as the target. Random forest machine learning regression is employed to predict the peak SDR. To compute the improved wavelet-based rDSF and promote a nonmodel-based approach, the first mode frequency, estimated numerically using Auto-Regressive model with exogenous input, is employed.