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6 results on '"Zheng Zi-Jun"'

5. A Modified Dynamic Window Approach to Obstacle Avoidance Combined with Fuzzy Logic

Author

Sun Chun-Long, Wu Jingjing, Zhang Hong, Zheng Zi-Jun, Zhou Deqiang, and An Wei

Subjects
Linear programming, business.industry, Face (geometry), Obstacle avoidance, Dynamic window approach, Robot, Point (geometry), Artificial intelligence, business, Algorithm, Fuzzy logic, Collision avoidance, Mathematics
Abstract

As a classic reactive method for obstacles avoidance, the dynamic window approach (DWA) uses an objective function to choose the optimal velocity commands. The objective function consists of three weighted terms, respectively urging the robot to face to the target point, avoid crash and move fast. The environment where robot works is usually complex and changeable, and different situations need different weights. However, the original DWA and its extensions use constant weights, and there is no existing algorithm for changing them. In this paper, we propose a modified DWA combined with fuzzy logic. This can be done by analyzing the information about goal and obstacles, and then using fuzzy logic to decide suitable weights in real time. In contrast with the original DWA, the proposed method makes robot move more safely and smoothly.

Published
2015

6. [Total generalized variation minimization based on projection data for low?dose CT reconstruction].

Author

Niu SZ, Wu H, Yu ZF, Zheng ZJ, and Yu GH

Subjects
Phantoms, Imaging, Radiation Dosage, Signal-To-Noise Ratio, Algorithms, Image Processing, Computer-Assisted, Radiographic Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed
Abstract

Objective: To obtain high?quality low?dose CT images using total generalized variation regularization based on the projection data for low?dose CT reconstruction., Methods: The projection data of the CT images were transformed from Poisson distribution to Gaussian distribution using the linear Anscombe transform. The transformed data were then restored by an efficient total generalized variation minimization algorithm. Reconstruction was finally achieved by inverse Anscombe transform and filtered back projection (FBP) method., Results: The image quality of low?dose CT was greatly improved by the proposed algorithm in both Clock and Shepp?Logan phantoms. The signal?to?noise ratios (SNRs) of the Clock and Shepp-Logan images reconstructed by FBP algorithm were 17.752 dB and 19.379 dB, which were increased by the proposed algorithm to 24.0352 and 23.4181 dB, respectively. The NMSE of the Clock and Shepp?Logan images reconstructed by FBP algorithm was 0.86% and 0.58%, which was reduced by the proposed algorithm to 0.2% and 0.23%, respectively., Conclusion: The proposed method can effectively suppress noise and strip artifacts in low?dose CT images when piecewise constant assumption is not possible.

Published
2017

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