1,291 research outputs found
Application and Exploration of FLUENT Software in the Teaching of Engineering Thermophysics
Engineering thermophysics is a basic discipline for energy majors, but this course emphasizes the theoretical level and is difficult to understand. Students\u27 enthusiasm and participation in the learning process are low, and it is difficult to understand the course. Accordingly, the research team attempts to introduce Fluent software into the course teaching exploration. Specifically, Fluent software is adopted to provide a reliable physics teaching model, and to change the traditional teaching mode, so as to improve students\u27 daily learning ability and practical ability, and ultimately enable students to learn and practice
Learning When to See for Long-term Traffic Data Collection on Power-constrained Devices
Collecting traffic data is crucial for transportation systems and urban
planning, and is often more desirable through easy-to-deploy but
power-constrained devices, due to the unavailability or high cost of power and
network infrastructure. The limited power means an inevitable trade-off between
data collection duration and accuracy/resolution. We introduce a novel
learning-based framework that strategically decides observation timings for
battery-powered devices and reconstructs the full data stream from sparsely
sampled observations, resulting in minimal performance loss and a significantly
prolonged system lifetime. Our framework comprises a predictor, a controller,
and an estimator. The predictor utilizes historical data to forecast future
trends within a fixed time horizon. The controller uses the forecasts to
determine the next optimal timing for data collection. Finally, the estimator
reconstructs the complete data profile from the sampled observations. We
evaluate the performance of the proposed method on PeMS data by an RNN
(Recurrent Neural Network) predictor and estimator, and a DRQN (Deep Recurrent
Q-Network) controller, and compare it against the baseline that uses Kalman
filter and uniform sampling. The results indicate that our method outperforms
the baseline, primarily due to the inclusion of more representative data points
in the profile, resulting in an overall 10\% improvement in estimation
accuracy. Source code will be publicly available.Comment: Accepted by IEEE 26th International Conference on Intelligent
Transportation System
GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition
Gait recognition is an emerging biological recognition technology that
identifies and verifies individuals based on their walking patterns. However,
many current methods are limited in their use of temporal information. In order
to fully harness the potential of gait recognition, it is crucial to consider
temporal features at various granularities and spans. Hence, in this paper, we
propose a novel framework named GaitGS, which aggregates temporal features in
the granularity dimension and span dimension simultaneously. Specifically,
Multi-Granularity Feature Extractor (MGFE) is proposed to focus on capturing
the micro-motion and macro-motion information at the frame level and unit level
respectively. Moreover, we present Multi-Span Feature Learning (MSFL) module to
generate global and local temporal representations. On three popular gait
datasets, extensive experiments demonstrate the state-of-the-art performance of
our method. Our method achieves the Rank-1 accuracies of 92.9% (+0.5%), 52.0%
(+1.4%), and 97.5% (+0.8%) on CASIA-B, GREW, and OU-MVLP respectively. The
source code will be released soon.Comment: 14 pages, 6 figure
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