Multiarc Generator
A multiarc generator, comprised of stacked metal contact disks, has been tested. Arc formation occurred in the gaps between each cylindrically shaped contact piece during 3-to 30-kA discharges through various stacked disk configurations. Severe current limiting was demonstrated by stacked disks whose axial length was less than their diameter. Current extinction time periods for discharges through the stacked disks were as much as 80 percent less than those measured for similar amplitude discharges through a solid metal rod. The energy dissipated per gap by arcing was calculated and found to increase as contact disk mass decreased. The i2dt action integral calculated for 3- to 12-kA discharges ranged from 53 A2s to 4210 A2s, respectively. The multiarc formation process is attributed to longitudinal electrodynamic forces separating the contact disks during current discharge.
Correlation of the electrical and space-time characteristics during electrical explosion of wires
The electrical and space-time characteristics during electrical explosion of wires is studied. (AIP)
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.