122 research outputs found
Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment
Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm
Few-shot learning for image-based bridge damage detection
Autonomous bridge visual inspection is a real-world challenge due to various materials, surface coatings, and changing light and weather conditions. Traditional supervised learning relies on massive annotated data to establish a robust model, which requires a time-consuming data acquisition process. This work proposes a few-shot learning (FSL) approach based on improved ProtoNet for damage detection with just a few labeled examples. Feature embedding is achieved through cross-domain transfer learning from ImageNet instead of episodic training. The ProtoNet is improved with embedding normalization to enhance transduction performance based on Euclidean distance and a linear classifier for classification. The approach is explored on a public dataset through different ablation experiments and achieves over 94% mean accuracy for 2-way 5-shot classification via the pre-trained GoogleNet after fine-tuning. Moreover, the proposed fine-tuning methods based on a fully connected layer (FCN) and Hadamard product are demonstrated with better performance than the previous method. Finally, the approach is validated using real bridge inspection images, demonstrating its capability of fast implementation for practical damage inspection with weakly supervised information
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is of great importance to transportation management and
public safety, and very challenging due to the complicated spatial-temporal
dependency and essential uncertainty brought about by the road network and
traffic conditions. Latest studies mainly focus on modeling the spatial
dependency by utilizing graph convolutional networks (GCNs) throughout a fixed
weighted graph. However, edges, i.e., the correlations between pair-wise nodes,
are much more complicated and interact with each other. In this paper, we
propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep
learning model for traffic forecasting. We first build the node-wise graph
according to the road network distance and the edge-wise graph according to
various edge interaction patterns. Then, we implement the interactions of both
nodes and edges using bicomponent graph convolution. The multi-range attention
mechanism is introduced to aggregate information in different neighborhood
ranges and automatically learn the importance of different ranges. Extensive
experiments on two real-world road network traffic datasets, METR-LA and
PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.Comment: Accepted by AAAI 202
Flavonoid intake and the risk of age-related cataract in China’s Heilongjiang Province
Background/Objectives: Epidemiological evidence suggests that diets rich in flavonoids may reduce the risk of developing age-related cataract (ARC). Flavonoids are widely distributed in foods of plant origin and the objective of this study was to evaluate retrospectively the association between the intakes of the five flavonoid subclasses and the risk of ARC. Subjects/Methods: A population-based case-control study (249 cases and 66 controls) was carried out in Heilongjiang province, which is located in the Northeast of China, and where intakes and availability of fresh vegetables and fruits can be limited. Dietary data gathered by food-frequency questionnaire (FFQ) were used to calculate flavonoid intake. Adjusted odds ratio (OR) and 95% confidence interval (CI) were estimated by logistic regression. Results: No linear associations between risk of developing ARC and intakes of total dietary flavonoids, anthocyanidins, flavon-3-ol, flavanone, total flavones or total flavonols were found, but quercetin and isorhamnetin intake was inversely associated with ARC risk (OR 11.78, 95% CI: 1.62-85.84, P<0.05, and OR 6.99, 95% CI:1.12-43.44, P<0.05, quartile 4 vs quartile 1, respectively). Conclusion: As quercetin is contained in many plant foods and isorhamnetin is only contained in very few foods, we concluded that higher quercetin intake may be an important dietary factor in the reduction of risk of age-related cataract
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
Knowledge tracing (KT) is the problem of predicting students' future
performance based on their historical interactions with intelligent tutoring
systems. Recent studies have applied multiple types of deep neural networks to
solve the KT problem. However, there are two important factors in real-world
educational data that are not well represented. First, most existing works
augment input representations with the co-occurrence matrix of questions and
knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday
terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly
integrate such intrinsic relations into the final response prediction task.
Second, the individualized historical performance of students has not been well
captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction
performance of the original deep knowledge tracing model with two auxiliary
learning tasks, i.e., \emph{question tagging (QT) prediction task} and
\emph{individualized prior knowledge (IK) prediction task}. Specifically, the
QT task helps learn better question representations by predicting whether
questions contain specific KCs. The IK task captures students' global
historical performance by progressively predicting student-level prior
knowledge that is hidden in students' historical learning interactions. We
conduct comprehensive experiments on three real-world educational datasets and
compare the proposed approach to both deep sequential KT models and
non-sequential models. Experimental results show that \emph{AT-DKT} outperforms
all sequential models with more than 0.9\% improvements of AUC for all
datasets, and is almost the second best compared to non-sequential models.
Furthermore, we conduct both ablation studies and quantitative analysis to show
the effectiveness of auxiliary tasks and the superior prediction outcomes of
\emph{AT-DKT}.Comment: Accepted at WWW'23: The 2023 ACM Web Conference, 202
A deep learning framework for intelligent fault diagnosis using AutoML-CNN and image-like data fusion
Intelligent fault diagnosis (IFD) is essential for preventative maintenance (PM) in Industry 4.0. Data-driven approaches have been widely accepted for IFD in smart manufacturing, and various deep learning (DL) models have been developed for different datasets and scenarios. However, an automatic and unified DL framework for developing IFD applications is still required. Hence, this work proposes an efficient framework integrating popular convolutional neural networks (CNNs) for IFD based on time-series data by leveraging automated machine learning (AutoML) and image-like data fusion. After normalisation, uniaxial or triaxial signals are reconstructed into -channel pseudo-images to satisfy the input requirements for CNNs and achieve data-level fusion simultaneously. Then, the model training, hyperparameter optimisation, and evaluation can be taken automatically based on AutoML. Finally, the selected model can be deployed on a cloud server or an edge device (via tiny machine learning). The proposed framework and method were validated via two case studies, demonstrating the framework’s availability for the automatic development of IFD applications and the effectiveness of the proposed data-level fusion method
Damage volumetric assessment and digital twin synchronization based on LiDAR point clouds
Point clouds are widely used for structure inspection and can provide damage spatial information. However, how to update a digital twin (DT) with local damage based on point clouds has not been sufficiently studied. This research presents an efficient framework for assessing and DT synchronizing local damage on a planar surface using point clouds. The pipeline starts from damage detection via DeepLabV3+ on the pseudo grayscale images from the point depth. It avoids the drawbacks of image and point cloud fusion. The target point cloud is separated according to the detected damage. Then, it can be converted into a 3D binary matrix through voxelization and binarization, which is highly lightweight and can be losslessly compressed for DT synchronization. The framework is validated via two case studies, demonstrating that the proposed voxel-based method can be easily applied to real-world damage with non-convex geometry instead of convex-hull fitting; finite-element (FE) models and BIM models can be updated automatically through the framework
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