509 research outputs found
Jordan decomposition and geometric multiplicity for a class of non-symmetric Ornstein-Uhlenbeck operators
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
With the development of intelligent transportation systems, vehicles are
exposed to a complex network environment. As the main network of in-vehicle
networks, the controller area network (CAN) has many potential security
hazards, resulting in higher requirements for intrusion detection systems to
ensure safety. Among intrusion detection technologies, methods based on deep
learning work best without prior expert knowledge. However, they all have a
large model size and rely on cloud computing, and are therefore not suitable to
be installed on the in-vehicle network. Therefore, we propose a lightweight
parallel neural network structure, LiPar, to allocate task loads to multiple
electronic control units (ECU). The LiPar model consists of multi-dimensional
branch convolution networks, spatial and temporal feature fusion learning, and
a resource adaptation algorithm. Through experiments, we prove that LiPar has
great detection performance, running efficiency, and lightweight model size,
which can be well adapted to the in-vehicle environment practically and protect
the in-vehicle CAN bus security.Comment: 13 pages, 13 figures, 6 tables, 51 referenc
Productivity Prediction of Tight Sandstone Reservoir Based on BP Neural Network
To survey He-8 member tight sand reservoir with low porosity and permeability in Mizhi gas field in Ordos basin, using the conventional well log data, this paper proposes the tight sand reservoir productivity prediction model and classification criterion based on BP neural network, getting quick classification of gas well productivity. We can predict sand reserve quantitatively instead qualitatively with the methods.Applications show that the methods of productivity prediction are effective and practical
Anderson Localization from Berry-Curvature Interchange in Quantum Anomalous Hall System
We theoretically investigate the localization mechanism of the quantum
anomalous Hall effect (QAHE) in the presence of spin-flip disorders. We show
that the QAHE keeps quantized at weak disorders, then enters a Berry-curvature
mediated metallic phase at moderate disorders, and finally goes into the
Anderson insulating phase at strong disorders. From the phase diagram, we find
that at the charge neutrality point although the QAHE is most robust against
disorders, the corresponding metallic phase is much easier to be localized into
the Anderson insulating phase due to the \textit{interchange} of Berry
curvatures carried respectively by the conduction and valence bands. At the
end, we provide a phenomenological picture related to the topological charges
to better understand the underlying physical origin of the QAHE Anderson
localization.Comment: 6 pages, 4 figure
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