68,666 research outputs found
An ensemble data-driven fuzzy network for laser welding quality prediction
This paper presents an Ensemble Data-Driven Fuzzy Network (EDDFN) for laser welding quality prediction that is composed of a number of strategically selected Data-Driven Fuzzy Models (DDFMs). Each model is trained by an Adaptive Negative Correlation Learning approach (ANCL). A monitoring system provides quality-relevant information of the laser beam spectrum and the geometry of the melt pool. This information is used by the proposed ensemble model to asist in the prediction of the welding quality. Each DDFM is based on three conceptual components, i.e. a selection procedure of the most representative welding information, a granular comprehesion process of data and the construction of a fuzzy reasoning mechanism as a series of Radial Basis Function Neural Networks (RBF-NNs). The proposed model aims at providing a fuzzy reasoning engine that is able to preserve a good balance between transparency and accuracy while improving its prediction properties. We apply the EDDFN to a real case study in manufacturing industry for the prediction of welding quality. The corresponding results confirm that the EDDFN provides better prediction properties compared to a single DDFM with an overal prediction performance > 78%
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
This work introduces a number of algebraic topology approaches, such as
multicomponent persistent homology, multi-level persistent homology and
electrostatic persistence for the representation, characterization, and
description of small molecules and biomolecular complexes. Multicomponent
persistent homology retains critical chemical and biological information during
the topological simplification of biomolecular geometric complexity.
Multi-level persistent homology enables a tailored topological description of
inter- and/or intra-molecular interactions of interest. Electrostatic
persistence incorporates partial charge information into topological
invariants. These topological methods are paired with Wasserstein distance to
characterize similarities between molecules and are further integrated with a
variety of machine learning algorithms, including k-nearest neighbors, ensemble
of trees, and deep convolutional neural networks, to manifest their descriptive
and predictive powers for chemical and biological problems. Extensive numerical
experiments involving more than 4,000 protein-ligand complexes from the PDBBind
database and near 100,000 ligands and decoys in the DUD database are performed
to test respectively the scoring power and the virtual screening power of the
proposed topological approaches. It is demonstrated that the present approaches
outperform the modern machine learning based methods in protein-ligand binding
affinity predictions and ligand-decoy discrimination
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