335 research outputs found
Exact solutions for the macro-, meso- and micro-scale analysis of composite laminates and sandwich structures
A Simulation Way to Investigate the Reason for Congestion in Urban——A Case Study in Hohhot China
In the case of high density traffic flow, traditional traffic data statistical analysis methods, which not only have certain errors and lead to inaccurate data, but also have many limitations such as labor consumption, can no longer meet the demand for traffic analysis. Drones for traffic data, based on an aerial bird\u27s-eye view, no offset, and error-free complete statistics of urban road shooting section of all data, while greatly reducing cost consumption. A multi-dimensional simulation model is established for the UAV data to the Hohhot central urban area\u27s road simulation platform. This project will test and explore multidimensional data in the simulation platform to investigate the congestion problem in Hohhot\u27s central city, as well as motor vehicle driving characteristics, non-motor vehicle driving behavior, road setting design, and other aspects, and provide optimization solutions for data-driven intelligent traffic control and management.https://digitalcommons.odu.edu/gradposters2023_engineering/1005/thumbnail.jp
RMP: A Random Mask Pretrain Framework for Motion Prediction
As the pretraining technique is growing in popularity, little work has been
done on pretrained learning-based motion prediction methods in autonomous
driving. In this paper, we propose a framework to formalize the pretraining
task for trajectory prediction of traffic participants. Within our framework,
inspired by the random masked model in natural language processing (NLP) and
computer vision (CV), objects' positions at random timesteps are masked and
then filled in by the learned neural network (NN). By changing the mask
profile, our framework can easily switch among a range of motion-related tasks.
We show that our proposed pretraining framework is able to deal with noisy
inputs and improves the motion prediction accuracy and miss rate, especially
for objects occluded over time by evaluating it on Argoverse and NuScenes
datasets.Comment: IEEE International Conference on Intelligent Transportation Systems
(ITSC 2023
Truncated-ARQ aided adaptive network coding for cooperative two-way relaying networks: cross-layer design and analysis
Network Coding (NC) constitutes a promising technique of improving the throughput of relay-aided networks. In this context, we propose a cross-layer design for both amplifyand- forward (AF-) and decode-and-forward two-way relaying (DF-TWR) based on the NC technique invoked for improving the achievable throughput under specific Quality of Service (QoS) requirements, such as the maximum affordable delay and error rate.We intrinsically amalgamate adaptive Analog Network Coding (ANC) and Network Coded Modulation (NCM) with truncated Automatic Repeat reQuest (ARQ) operating at the different OSI layers. At the data-link layer, we design a pair of improved NC-based ARQ strategies based on the Stop-andwait and the Selective-repeat ARQ protocols. At the physical layer, adaptive ANC/NCM are invoked based on our approximate packet error ratio (PER). We demonstrate that the adaptive ANC design can be readily amalgamated with the proposed protocols. However, adaptive NC-QAM suffers from an SNR-loss, when the transmit rates of the pair of downlink (DL) channels spanning from the relay to the pair of destinations are different. Therefore we develop a novel transmission strategy for jointly selecting the optimal constellation sizes for both of the relay-to-destination links that have to be adapted to both pair of channel conditions. Finally, we analyze the attainable throughput, demonstrating that our truncated ARQ-aided adaptive ANC/NCM schemes attain considerable throughput gains over the schemes dispensing with ARQ, whilst our proposed scheme is capable of supporting bidirectional NC scenarios
The prevalence of adult attention-deficit hyperactivity disorder:A global systematic review and meta-analysis
Vibration Damping of Carbon Nanotube Assembly Materials
Vibration reduction is of great importance in various engineering
applications, and a material that exhibits good vibration damping along with
high strength and modulus has become more and more vital. Owing to the superior
mechanical property of carbon nanotube (CNT), new types of vibration damping
material can be developed. This paper presents recent advancements, including
our progresses, in the development of high-damping macroscopic CNT assembly
materials, such as forests, gels, films, and fibers. In these assemblies,
structural deformation of CNTs, zipping and unzipping at CNT connection nodes,
strengthening and welding of the nodes, and sliding between CNTs or CNT bundles
are playing important roles in determining the viscoelasticity, and elasticity
as well. Towards the damping enhancement, strategies for micro-structure and
interface design are also discussed
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