Search

Your search keyword '"Wen,Fang"' showing total 380 results
Start Over Author "Wen,Fang" Publisher elsevier b.v.
380 results on '"Wen,Fang"'

45. PET/MRI in Endometrial Cancer: Imaging Biomarkers are Associated with Disease Progression and Overall Survival.

Author

Shih, I-Lun, Yen, Ruoh-Fang, Chen, Chi-An, Cheng, Wen-Fang, Chen, Bang-Bin, Zheng, Quan-Yin, Cheng, Mei-Fang, Chen, Jenny Ling-Yu, and Shih, Tiffany Ting-Fang

Abstract

To evaluate the association between positron emission tomography (PET)/magnetic resonance imaging (MRI) biomarkers and survival outcomes in patients with endometrial cancer. Between April 2014 and April 2016, 88 patients with newly diagnosed endometrial cancer participated this prospective study and underwent [18F] fluorodeoxyglucose PET/MRI. Sixty-nine patients with measurable tumors on PET/MRI were included in the image analysis. Imaging biomarkers included the minimum and mean apparent diffusion coefficients (ADC min and ADC mean), maximum standardized uptake value (SUV max), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumors. The log-rank test and Cox proportional hazards model were used to assess the relationship between imaging biomarkers and survival. After a median follow-up of 80 months, 15 (22%) patients had tumor progression and six (9%) patients died. The results of ADC min , ADC mean , and SUV max did not show a significant association with progression-free survival (PFS) and overall survival (OS). Significantly shorter PFS was noted in patients with primary tumors with higher MTV (P < 0.001) and TLG (P < 0.001). Significantly shorter OS was also noted in patients with primary tumors with higher MTV (P = 0.048) and TLG (P = 0.034). In the multivariate analysis, MTV was an independent predictor of PFS (hazard ratio = 10.84, P = 0.033). PET/MRI biomarkers, particularly MTV and TLG, are associated with PFS and OS in patients with endometrial cancer. MTV was an independent predictor of PFS. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

50. Position-based visual servoing of a 6-RSS parallel robot using adaptive sliding mode control.

Author

Zhu, Ningyu, Xie, Wen-Fang, and Shen, Henghua

Subjects
SLIDING mode control, PARALLEL robots, RADIAL basis functions, ROBOT kinematics, KALMAN filtering, ADAPTIVE filters
Abstract

The trajectory tracking control of parallel robots is challenging due to their complicated dynamics and kinematics. This paper proposes a position-based visual servoing (PBVS) approach for a 6-Revolute-Spherical-Spherical (6-RSS) parallel robot using adaptive sliding mode control in Cartesian space. A photogrammetry sensor C-Track 780 in the eye-to-hand configuration is adopted to measure the real-time pose of the robot end-effector, which can avoid the calculation of robot forward kinematics and provide more flexibility for controller design. An adaptive Kalman filter is utilized to deal with uncertain noises in visual measurements to increase the pose estimation accuracy. A sliding mode controller with strong robustness is designed to cope with system uncertainties, and a radial basis function (RBF) neural network is incorporated to realize the auto-tuning of the control gains, which make the robot effectively track different trajectories with time-varying conditions in real applications. Based on Lyapunov theorem, the stability analysis of the controller has been done. Experiments have been conducted to validate the effectiveness of the proposed strategy and illustrate the superiority of the designed controller. • A position-based visual servoing scheme is proposed for trajectory tracking control of a 6-RSS parallel robot. • An adaptive sliding mode controller based on RBF neural network is developed. • A photogrammetry sensor C-Track 780 with an adaptive Kalman filter is adopted for pose estimation. • The stability of the controller is proved using Lyapunov theorem. • Experimental results demonstrate the effectiveness and superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results