259 research outputs found
Hurricane-induced destratification and restratification in a partially-mixed estuary
Hurricane Isabel made landfall at the Outer Banks of North Carolina and moved past Chesapeake Bay on 18 –19 September 2003. The baroclinic response of this partially-mixed estuary to the passage of Isabel is investigated using the output from a regional atmosphere-ocean model. The hurricane-forced winds caused gradual deepening of the surface mixed layer, followed by rapid destratification in the water-column. The mixed-layer deepening appears to be driven by velocity shear and can be interpreted by a gradient Richardson number. Although strong winds caused complete mixing locally, a large longitudinal salinity gradient of about 10-4 psu m-1 persisted between the estuary\u27s head and mouth. After passage of the storm, the horizontal baroclinic pressure gradient drove restratification and a two-layer circulation in the estuary. The averaged buoyancy frequency increased linearly with time during an initial stage, and reached about 0.03 s-1 one day after the destratification. The model results are in good agreement with the theoretical prediction based on gravitational adjustment. Subsequently, turbulent diffusion works against the longitudinal advection to produce quasi-steady salinity distribution
IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network
Infrared and visible image fusion (IVIF) is used to generate fusion images
with comprehensive features of both images, which is beneficial for downstream
vision tasks. However, current methods rarely consider the illumination
condition in low-light environments, and the targets in the fused images are
often not prominent. To address the above issues, we propose an
Illumination-Aware Infrared and Visible Image Fusion Network, named as IAIFNet.
In our framework, an illumination enhancement network first estimates the
incident illumination maps of input images. Afterwards, with the help of
proposed adaptive differential fusion module (ADFM) and salient target aware
module (STAM), an image fusion network effectively integrates the salient
features of the illumination-enhanced infrared and visible images into a fusion
image of high visual quality. Extensive experimental results verify that our
method outperforms five state-of-the-art methods of fusing infrared and visible
images.Comment: Submitted to IEE
SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion
Most existing learning-based infrared and visible image fusion (IVIF) methods
exhibit massive redundant information in the fusion images, i.e., yielding
edge-blurring effect or unrecognizable for object detectors. To alleviate these
issues, we propose a semantic structure-preserving approach for IVIF, namely
SSPFusion. At first, we design a Structural Feature Extractor (SFE) to extract
the structural features of infrared and visible images. Then, we introduce a
multi-scale Structure-Preserving Fusion (SPF) module to fuse the structural
features of infrared and visible images, while maintaining the consistency of
semantic structures between the fusion and source images. Owing to these two
effective modules, our method is able to generate high-quality fusion images
from pairs of infrared and visible images, which can boost the performance of
downstream computer-vision tasks. Experimental results on three benchmarks
demonstrate that our method outperforms eight state-of-the-art image fusion
methods in terms of both qualitative and quantitative evaluations. The code for
our method, along with additional comparison results, will be made available
at: https://github.com/QiaoYang-CV/SSPFUSION.Comment: Submitted to IEE
Latency-Aware Collaborative Perception
Collaborative perception has recently shown great potential to improve
perception capabilities over single-agent perception. Existing collaborative
perception methods usually consider an ideal communication environment.
However, in practice, the communication system inevitably suffers from latency
issues, causing potential performance degradation and high risks in
safety-critical applications, such as autonomous driving. To mitigate the
effect caused by the inevitable latency, from a machine learning perspective,
we present the first latency-aware collaborative perception system, which
actively adapts asynchronous perceptual features from multiple agents to the
same time stamp, promoting the robustness and effectiveness of collaboration.
To achieve such a feature-level synchronization, we propose a novel latency
compensation module, called SyncNet, which leverages feature-attention
symbiotic estimation and time modulation techniques. Experiments results show
that the proposed latency aware collaborative perception system with SyncNet
can outperforms the state-of-the-art collaborative perception method by 15.6%
in the communication latency scenario and keep collaborative perception being
superior to single agent perception under severe latency.Comment: 14 pages, 11 figures, Accepted by European conference on computer
vision, 202
Novel feedback-Bayesian BP neural network combined with extended Kalman filtering for the battery state-of-charge estimation.
The state of charge estimation of lithium-ion batteries plays an important role in real-time monitoring and safety. To solve the problem that high non-linearity during real-time estimation of lithium-ion batteries who cause that it is difficult to estimate accurately. Taking lithium-ion battery as the research object, the working characteristics of lithium-ion ion battery are studied under various working conditions. To reduce the error caused by the nonlinearity of the lithium battery system, the BP neural network with the high approximation of nonlinearity is combined with the extended Kalman filtering. At the same time, to eliminate the over fitting of training, Bayesian regularization is used to optimize the neural network. Taking into account the real-time requirements of lithium-ion batteries, a feedback network is adopted to carry out real-time algorithm integration on lithium-ion batteries. A simulation model is established, and the results are analyzed in combination with various working conditions. Experimental results show that the algorithm has the characteristics of fast convergence and good tracking effect, and the estimation error is within 1.10%. It is verified that the Feedback-Bayesian BP neural network combined with the extended Kalman filtering algorithm can improve the accuracy of lithium-ion battery state-of-charge estimation
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