28 research outputs found
Semantic translation with convolutional encoder-decoder networks for viewpoint estimation
No full textResidual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
No full textIndustrial parts are increasingly being designed to be more lightweight in modern manufacturing for energy saving and material cost reduction. However, the high-speed motion of flexible systems tends to excite severe residual vibrations that result in positioning accuracy degradation and loss of productivity. This study proposes a closed-form trajectory optimization method for vibration suppression based on trapezoidal velocity profiles, which are most widely used in industrial robots and machines. First, the formulation and minimum time solution under actuator limits of the motion profile are defined. Then, the relationship between the trajectory parameters and the vibration response is investigated. It is shown that residual vibration can be eliminated by properly tuning the acceleration/deceleration switching times according to the natural frequency. Based on the derived vibration suppression conditions, a tuning procedure for time parameters compliant with actuator limits is established to generate fast and precise movement. A main advantage of the proposed method is easy implementation for general machines without requiring extra computational resources or modification to the control system. The effectiveness and practicality of the proposed approach are verified through experiments conducted on a robot. The experimental results show that the optimized trajectory achieves superior residual vibration reduction performance
Classification of Chromosome Karyotype Based on Faster-RCNN with the Segmatation and Enhancement Preprocessing Model
No full text
Multi-Focus Images Fusion for Fluorescence Imaging Based on Local Maximum Luminosity and Intensity Variance
No full textDue to the limitations on the depth of field of high-resolution fluorescence microscope, it is difficult to obtain an image with all objects in focus. The existing image fusion methods suffer from blocking effects or out-of-focus fluorescence. The proposed multi-focus image fusion method based on local maximum luminosity, intensity variance and the information filling method can reconstruct the all-in-focus image. Moreover, the depth of tissue’s surface can be estimated to reconstruct the 3D surface model
SNc Neuron Detection Method Based on Deep Learning for Efficacy Evaluation of Anti-PD Drugs
No full textPredictive Active Disturbance Rejection Control of Pan-tilt Visual Tracking System with Parameter Optimization
No full textEnlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement
No full textImage restoration is a low-level visual task, and most CNN methods are
designed as black boxes, lacking transparency and intrinsic aesthetics. Many
unsupervised approaches ignore the degradation of visible information in
low-light scenes, which will seriously affect the aggregation of complementary
information and also make the fusion algorithm unable to produce satisfactory
fusion results under extreme conditions. In this paper, we propose
Enlighten-anything, which is able to enhance and fuse the semantic intent of
SAM segmentation with low-light images to obtain fused images with good visual
perception. The generalization ability of unsupervised learning is greatly
improved, and experiments on LOL dataset are conducted to show that our method
improves 3db in PSNR over baseline and 8 in SSIM. Zero-shot learning of SAM
introduces a powerful aid for unsupervised low-light enhancement. The source
code of Enlighten Anything can be obtained from
https://github.com/zhangbaijin/enlighten-anythingComment: it will be revise
Nasolabial Folds Extraction based on Neural Network for the Quantitative Analysis of Facial Paralysis
No full textDesign Procedure for Motion Profiles with Sinusoidal Jerk for Vibration Reduction
No full textHigh-speed motions performed by industrial machines can induce severe vibrations that degrade the positioning accuracy and efficiency. To address this issue, this paper proposes a novel motion profile design method utilizing a sinusoidal jerk model to generate fast and smooth motions with low vibrations. The expressions for the acceleration, velocity, and displacement were obtained through successive integrations of the continuous jerk profile. A minimum-time solution with actuator limits was formulated based on an analysis of the critical constraint conditions. Differing from previous studies, the current study introduces an analytical optimization procedure for the profile parameters to minimize both the motion duration and excitation frequency contents corresponding to the system pole. By examining the correlation between the input motion profiles and system responses, the conditions for vibration elimination were identified, highlighting the significance of specific time intervals in controlling the vibration amplitude. Numerical and experimental studies were conducted to validate the effectiveness of the proposed method. The comparative results illustrate that this method outperforms existing baseline techniques in terms of smoothness and vibration attenuation. The residual-vibration level and settling time are significantly reduced with the optimized sinusoidal jerk profile, even in the presence of modeling errors, contributing to higher productivity
