A5102796716- Advanced Neural Network Applications
- Industrial Vision Systems and Defect Detection
- Tactile and Sensory Interactions
- Inertial Sensor and Navigation
- Space Satellite Systems and Control
- Robotic Mechanisms and Dynamics
- Advanced X-ray and CT Imaging
- Advanced Sensor and Energy Harvesting Materials
- Non-Destructive Testing Techniques
- Dielectric materials and actuators
Sun Yat-sen University
2024-2025
Guangxi University
2021
In the testing of chips, defect diagnostics in X-ray images packaging chips is mainly performed by humans, which time-consuming and inefficient. To overcome abovementioned problems, a novel intelligent system based on hybrid deep learning for chip was proposed. The consists four successive stages: image segmentation normalization, reconstruction detection, contour matching, qualification diagnosis. first stage used to localize external contours target remove extraneous backgrounds through...
As a non-destructive detection method, X-rays are widely used in the field of electronic component inspection. However, subsequent defect needs to be completed manually, which leads poor efficiency and low reliability due large number components. To solve above problems, we propose X-ray image method based on deep learning. On one hand, have designed an algorithm for segmentation correction images. other case fewer samples variable forms, only use defect-free training. We unsupervised...
Abstract Hydrogels with intrinsic high stretchability and flexibility are extremely attractive for soft electronics. However, the existing complicated laborious methods (such as mold curing) to fabricate microstructured hydrogel (MH) still limit development of hydrogel-based sensors flexible devices. Herein, we use digital light processing 3D printing technology rapidly construct double-network (DN) ionic conductive hydrogel, then design print fingerprint-like MH film manufacture an...