4 research outputs found
Drude conductivity of a granular metal
We present a complete derivation of the granular analogue to Drude
conductivity using diagrammatic methods. The convergence issues arising when
changing the order of momentum and frequency summation are more severe than in
the homogeneous case. This is because there are now two momentum sums rather
than one, due to the intragrain momentum scrambling in tunnelling events. By
careful analytic continuation of the frequency sum, and use of integration by
parts, we prove that the system is in the normal (non-superconducting) state,
and derive the formula for the granular Drude conductivity expected from
Einstein's relation and Fermi's golden rule. We also show that naively
performing the momentum sums first gives the correct result, provided that we
interpret a divergent frequency sum by analytic continuation using the Hurwitz
zeta function.Comment: 18 pages, 5 figure
Filter-informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis
Intelligent fault diagnosis has been increasingly improved with the evolution
of deep learning (DL) approaches. Recently, the emerging graph neural networks
(GNNs) have also been introduced in the field of fault diagnosis with the goal
to make better use of the inductive bias of the interdependencies between the
different sensor measurements. However, there are some limitations with these
GNN-based fault diagnosis methods. First, they lack the ability to realize
multiscale feature extraction due to the fixed receptive field of GNNs.
Secondly, they eventually encounter the over-smoothing problem with increase of
model depth. Lastly, the extracted features of these GNNs are hard to
understand owing to the black-box nature of GNNs. To address these issues, a
filter-informed spectral graph wavelet network (SGWN) is proposed in this
paper. In SGWN, the spectral graph wavelet convolutional (SGWConv) layer is
established upon the spectral graph wavelet transform, which can decompose a
graph signal into scaling function coefficients and spectral graph wavelet
coefficients. With the help of SGWConv, SGWN is able to prevent the
over-smoothing problem caused by long-range low-pass filtering, by
simultaneously extracting low-pass and band-pass features. Furthermore, to
speed up the computation of SGWN, the scaling kernel function and graph wavelet
kernel function in SGWConv are approximated by the Chebyshev polynomials. The
effectiveness of the proposed SGWN is evaluated on the collected solenoid valve
dataset and aero-engine intershaft bearing dataset. The experimental results
show that SGWN can outperform the comparative methods in both diagnostic
accuracy and the ability to prevent over-smoothing. Moreover, its extracted
features are also interpretable with domain knowledge
Safety and Reliability - Safe Societies in a Changing World
The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management
- mathematical methods in reliability and safety
- risk assessment
- risk management
- system reliability
- uncertainty analysis
- digitalization and big data
- prognostics and system health management
- occupational safety
- accident and incident modeling
- maintenance modeling and applications
- simulation for safety and reliability analysis
- dynamic risk and barrier management
- organizational factors and safety culture
- human factors and human reliability
- resilience engineering
- structural reliability
- natural hazards
- security
- economic analysis in risk managemen