Predicting the weld width from high-speed successive images of the weld zone using different machine learning algorithms during laser welding. | LitMetric
The dynamic behavior of the keyhole and molten pool is associated with the quality of weld seam. In this study, an on-line visual monitoring system is devised to photograph the keyhole and molten pool during external magnetic field assisted laser welding on the AISI 2205 duplex stainless steel plates. Seven features are defined to describe the morphology of the keyhole and molten pool. Then, the principal component analysis (PCA) algorithm is applied to reduce the dimensions of these features to obtain different number of principal components (PCs). Three different machine learning algorithms, i.e. the back propagation neural network (BPNN), the radial based function neural network (RBFNN) and the support vector regression (SVR), are utilized to fit the relationship between the chosen PCs and the weld width. Finally, the global and local prediction accuracy of these three machine learning algorithms are compared under different number of PCs. Results illustrated that data dimensionality reduction is helpful to improve the modeling efficiency. Machine learning algorithms can be exploited to predict the weld quality during laser welding with high accuracy. Among them, the BPNN model performs best and SVR model performs better than RBFNN model in this research. This work aims to model the relation between the features in weld zone and the weld quality with different machine learning algorithms, and provides a guideline of model selection for laser welding on-line monitoring and a necessary foundation for realizing intelligent welding with advanced algorithm.
Background: Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.
Methods: We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases.
Alzheimer's Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million people worldwide. AD is characterised by the formation of plaques containing extracellular amyloid-β (Aβ) and neurofibrillary tangles composed of hyper-phosphorylated tau proteins (pTau).
Background: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hidden systemic biases such as GC bias, which can significantly affect the sensitivity and specificity of CNV detection. In many cases, the kit manifests provide only the genome coordinates of the targeted regions, and the exact bait design of the oligo capture baits is not available.
Background: Periodontitis is among the most prevalent inflammatory conditions and greatly impacts oral health. This study aimed to elucidate the role of basement membrane-related genes in the pathogenesis and diagnosis of periodontitis.
Methods: GSE10334 was used for identification of hub genes via the differential analysis, protein-protein interaction network, MCC and DMNC algorithms, and evaluation via LASSO regression and support vector machine analysis to identify basement membrane-related markers in patients with periodontitis.
Department of Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Background: Psychological disorders, such as anxiety and depression, are considered to be one of the causes of noncardiac chest pain (NCCP). And these patients can be challenging to differentiate from coronary artery disease (CAD), leading to a considerable number of patients still undergoing angiography. We aim to develop a practical prediction model and nomogram using cardiopulmonary exercise testing (CPET), to help identify these patients.
