Publications by authors named "Jiuxin Wang"
The mechanical fault diagnosis of HVCB is important to ensure the stability of electric power systems. Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults.
View Article and Find Full Text PDFThe regular detection of weld seams in large-scale special equipment is crucial for improving safety and efficiency, and this can be achieved effectively through the use of weld seam tracking and detection robots. In this study, a wall-climbing robot with integrated seam tracking and detection was designed, and the wall climbing function was realized via a permanent magnet array and a Mecanum wheel. The function of weld seam tracking and detection was realized using a DeepLabv3+ semantic segmentation model.
View Article and Find Full Text PDFBackground: The age of glioma plays a unique role in prognosis. We hypothesized that age is not positively correlated with survival prognosis and explored its exact relationship.
Methods: Glioma was identified from the SEER database (between 2000 and 2018).
Introduction: World Health Organization (WHO) Grade III meningioma is a central nervous system tumor with a poor prognosis. In this retrospective cohort study, the authors constructed a nomogram for predicting the prognosis of WHO Grade III meningioma.
Methods: The patients of this nomogram were based on the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018.
Article Synopsis
- The study aimed to create a prognostic model for predicting 5- and 10-year survival rates in patients with ependymoma (EPN) using data from the SEER database.
- Seven key survival factors (age, gender, morphology, location, size, laterality, and resection) were identified through LASSO regression.
- The resulting nomogram showed moderate accuracy for predicting outcomes, which can help clinicians tailor treatment plans for EPN patients.