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Found 13 papers, 5 papers with code
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise
Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise.
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
Regarding the loss function, both intermediate and leaf nodes in the DAG graph are treated as target outputs during CINN training so as to drive co-learning of causal relationships among different types of nodes.
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network
In this paper, we propose a supervised contrastive learning approach with a class-aware loss function to enhance the feature extraction capability of neural networks for fault diagnosis.
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
To the best of our knowledge, this is the first instance of deploying a CNN-based bearing fault diagnosis model on an FPGA.
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection
The robustness of recommender systems under node injection attacks has garnered significant attention.
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives
This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions.
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies
In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Encouraged by this inspiring theoretical result on heterogeneous networks, we directly integrate conventional and quadratic neurons in an autoencoder to make a new type of heterogeneous autoencoders.
A generic physics-informed neural network-based framework for reliability assessment of multi-state systems
The gradient projection operation significantly mitigates the detrimental effects caused by the gradient interference when training PINN, thus accelerating the convergence speed of PINN to high-precision solutions to MSS reliability assessment.
A bio-inspired algorithm for fuzzy user equilibrium problem by aid of Physarum Polycephalum
The user equilibrium in traffic assignment problem is based on the fact that travelers choose the minimum-cost path between every origin-destination pair and on the assumption that such a behavior will lead to an equilibrium of the traffic network.
A Physarum-Inspired Approach to Optimal Supply Chain Network Design at Minimum Total Cost with Demand Satisfaction
A supply chain is a system which moves products from a supplier to customers.
An Adaptive Amoeba Algorithm for Shortest Path Tree Computation in Dynamic Graphs
When the edge weight changes, the proposed algorithm can recognize the affected vertices and reconstruct them spontaneously.
