Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4,000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. A clinical performance test of 500 patients with malocclusion and/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.
For a High Temperature Superconducting (HTS) cable, a non-uniform AC current distribution among the multilayer conductors gives rise to increased AC losses. To get a uniform current distribution among the multilayer conductors, a constrained optimization model is constructed with continuous and discrete variables, such as the winding angle, radius and the winding direction of each layer. Under the constraints of the mechanical properties and critical current of the tape, the Particle Swarm Optimization (PSO) algorithm is employed for structural parameter optimization in both warm and cold dielectric type HTS cables. A uniform current distribution among layers is realized by optimizing the structural parameters. The perturbation analysis is employed to evaluate the parameters after optimization. It is found that the robust stabilizations are different among the various optimal results. The PSO is proved to be a more powerful tool than the Genetic Algorithm (GA) for structural parameter optimization.
Keywords-Current distribution, high temperature superconducting (HTS) cable, particle swarm optimization (PSO), perturbation analysis.I.
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