A dataset for deep learning based detection of printed circuit board surface defect.

Printed circuit board (PCB) may display diverse surface defects in manufacturing. These defects not only influence aesthetics but can also affect the performance of the PCB and potentially damage the entire board. Thus, achieving efficient and highly accurate detection of PCB surface defects is fundamental for quality control in fabrication.

Detection of breakage and impurity ratios for raw sugarcane based on estimation model and MDSC-DeepLabv3.

Broken cane and impurities such as top, leaf in harvested raw sugarcane significantly influence the yield of the sugar manufacturing process. It is crucial to determine the breakage and impurity ratios for assessing the quality and price of raw sugarcane in sugar refineries. However, the traditional manual sampling approach for detecting breakage and impurity ratios suffers from subjectivity, low efficiency, and result discrepancies.

Validation of source biasing method for its use in CSNS beamline shielding calculation.

The Chinese spallation neutron source (CSNS) is a high-performance pulsed neutron source, having 20 neutron beamlines for neutron scattering instruments. The shielding design of these beamlines is usually needed for Monte Carlo (MC) calculation, and the use of variance reduction methods is critical to carrying out an efficient, reliable MC shielding calculation. This paper discusses the source biasing method based on actual source term and geometry model of a CSNS neutron beamline.