374 research outputs found
Heat Conduction In A Layered Structure With An Interface Crack Using The Dual Phase Lag Model
In this paper, the transient heat conduction in a layered composite with an insulated interface crack parallel to the boundaries is investigated by using the dual phase lag (DPL) model. Fourier and Laplace transforms are applied and the mixed boundary value problem for the cracked structure under temperature impact is reduced to solving a singular integral equation. The temperature field in time domain is obtained and the intensity factor of temperature gradient is defined. Numerical studies show that overshoot phenomenon may occur due to the combined effect of the insulated crack and application of the DPL heat conduction model. The thermal conductivity and the phase lag parameters have strong influence on the dynamic intensity factor of temperature gradients. The results obtained by the dual phase lag model can be reduced to that by the hyperbolic model and that by the parabolic model
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
The emergence of connected and automated vehicles (CAVs) promises smoother
traffic flow. In mixed traffic where human-driven vehicles (HDVs) also exist,
existing research mostly focuses on "looking ahead" (i.e., the CAVs receive
information from preceding vehicles) strategies for CAVs, while recent work
reveals that "looking behind" (i.e., the CAVs receive information from their
rear vehicles) strategies might provide more possibilities for CAV longitudinal
control. This paper presents a comparative study between these two types of
information flow topology (IFT) from the string stability perspective, with the
role of maximum platoon size (MPS) also under investigation. Precisely, we
provide a dynamical modeling framework for the mixed platoon under the
multi-predecessor-following (MPF) topology and the multi-successor-leading
(MSL) topology. Then, a unified method for string stability analysis is
presented, with explicit consideration of both IFT and MPS. Numerical results
suggest that MSL ("looking behind") outperforms MPF ("looking ahead" ) in
mitigating traffic perturbations. In addition, increasing MPS could further
improve string stability of mixed traffic flow.Comment: This paper has been accepted by 26th IEEE International Conference on
Intelligent Transportation Systems ITSC 202
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Recent text-to-image generative models can generate high-fidelity images from
text inputs, but the quality of these generated images cannot be accurately
evaluated by existing evaluation metrics. To address this issue, we introduce
Human Preference Dataset v2 (HPD v2), a large-scale dataset that captures human
preferences on images from a wide range of sources. HPD v2 comprises 798,090
human preference choices on 433,760 pairs of images, making it the largest
dataset of its kind. The text prompts and images are deliberately collected to
eliminate potential bias, which is a common issue in previous datasets. By
fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a
scoring model that can more accurately predict human preferences on generated
images. Our experiments demonstrate that HPS v2 generalizes better than
previous metrics across various image distributions and is responsive to
algorithmic improvements of text-to-image generative models, making it a
preferable evaluation metric for these models. We also investigate the design
of the evaluation prompts for text-to-image generative models, to make the
evaluation stable, fair and easy-to-use. Finally, we establish a benchmark for
text-to-image generative models using HPS v2, which includes a set of recent
text-to-image models from the academic, community and industry. The code and
dataset is available at https://github.com/tgxs002/HPSv2 .Comment: Revisio
Experimental Validation of DeeP-LCC for Dissipating Stop-and-Go Waves in Mixed Traffic
We present results on the experimental validation of leading cruise control
(LCC) for connected and autonomous vehicles (CAVs). In a mixed traffic
situation that is dominated by human-driven vehicles, LCC strategies are
promising to smooth undesirable stop-and-go waves. Our experiments are carried
out on a mini-scale traffic platform. We first reproduce stop-and-go traffic
waves in a miniature scale, and then show that these traffic instabilities can
be dissipated by one or a few CAVs that utilize Data-EnablEd Predicted Leading
Cruise Control (DeeP-LCC). Rather than identifying a parametric traffic model,
DeeP-LCC relies on a data-driven non-parametric behavior representation for
traffic prediction and CAV control. DeeP-LCC also incorporates input and output
constraints to achieve collision-free guarantees for CAVs. We experimentally
demonstrate that DeeP-LCC is able to dissipate traffic waves caused by
car-following behavior and significantly improve both driving safety and travel
efficiency. CAVs utilizing DeeP-LCC may bring additional societal benefits by
mitigating stop-and-go waves in practical traffic.Comment: 8 pages, 6 figure
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