A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data

Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods.

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Enabling deeper learning on big data for materials informatics applications

Scientific Reports ◽

10.1038/s41598-021-83193-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dipendra Jha ◽

Vishu Gupta ◽

Logan Ward ◽

Zijiang Yang ◽

Christopher Wolverton ◽

...

Keyword(s):

Neural Networks ◽

Big Data ◽

Deep Learning ◽

Deep Neural Networks ◽

Materials Science ◽

Prediction Models ◽

Model Performance ◽

Materials Informatics ◽

Learning Framework ◽

Significant Attention

AbstractThe application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

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Graph Convolutional Recurrent Neural Networks: A Deep Learning Framework for Traffic Prediction

Graph Data Mining - Big Data Management ◽

10.1007/978-981-16-2609-8_9 ◽

2021 ◽

pp. 189-204

Author(s):

Dongwei Xu ◽

Hongwei Dai ◽

Qi Xuan

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Traffic Prediction ◽

Learning Framework

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Simulating Dynamic Driving Behavior in Simulation Test for Unmanned Vehicles via Multi-Sensor Data

Sensors ◽

10.3390/s19071670 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1670 ◽

Cited By ~ 1

Author(s):

Danchen Zhao ◽

Yaochen Li ◽

Yuehu Liu

Keyword(s):

Driving Behavior ◽

Unmanned Vehicles ◽

Sensor Data ◽

The Road ◽

Driving Behaviors ◽

Slowing Down ◽

3D Space ◽

Geometric Consistency ◽

Virtual Vehicle ◽

Main Basis

Driving behavior is the main basis for evaluating the performance of an unmanned vehicle. In simulation tests of unmanned vehicles, in order for simulation results to be approximated to the actual results as much as possible, model of driving behaviors must be able to exhibit actual motion of unmanned vehicles. We propose an automatic approach of simulating dynamic driving behaviors of vehicles in traffic scene represented by image sequences. The spatial topological attributes and appearance attributes of virtual vehicles are computed separately according to the constraint of geometric consistency of sparse 3D space organized by image sequence. To achieve this goal, we need to solve three main problems: Registration of vehicle in a 3D space of road environment, vehicle’s image observed from corresponding viewpoint in the road scene, and consistency of the vehicle and the road environment. After the proposed method was embedded in a scene browser, a typical traffic scene including the intersections was chosen for a virtual vehicle to execute the driving tasks of lane change, overtaking, slowing down and stop, right turn, and U-turn. The experimental results show that different driving behaviors of vehicles in typical traffic scene can be exhibited smoothly and realistically. Our method can also be used for generating simulation data of traffic scenes that are difficult to collect.

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Multi-Task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6326 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8139-8146

Author(s):

Duong Le ◽

My Thai ◽

Thien Nguyen

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Word Sense Disambiguation ◽

Word Sense ◽

Knowledge Resources ◽

Useful Knowledge ◽

Learning Framework ◽

Task Learning ◽

Sense Disambiguation

The current deep learning works on metaphor detection have only considered this task independently, ignoring the useful knowledge from the related tasks and knowledge resources. In this work, we introduce two novel mechanisms to improve the performance of the deep learning models for metaphor detection. The first mechanism employs graph convolutional neural networks (GCN) with dependency parse trees to directly connect the words of interest with their important context words for metaphor detection. The GCN networks in this work also present a novel control mechanism to filter the learned representation vectors to retain the most important information for metaphor detection. The second mechanism, on the other hand, features a multi-task learning framework that exploits the similarity between word sense disambiguation and metaphor detection to transfer the knowledge between the two tasks. The extensive experiments demonstrate the effectiveness of the proposed techniques, yielding the state-of-the-art performance over several datasets.

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Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) ◽

10.1109/itsc.2017.8317835 ◽

2017 ◽

Cited By ~ 18

Author(s):

Khaled Saleh ◽

Mohammed Hossny ◽

Saeid Nahavandi

Keyword(s):

Neural Networks ◽

Data Fusion ◽

Recurrent Neural Networks ◽

Driving Behavior ◽

Sensor Data ◽

Sensor Data Fusion

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Predicting the quality of online health expert question-answering services with temporal features in a deep learning framework

Neurocomputing ◽

10.1016/j.neucom.2017.11.039 ◽

2018 ◽

Vol 275 ◽

pp. 2769-2782 ◽

Cited By ~ 10

Author(s):

Ze Hu ◽

Zhan Zhang ◽

Haiqin Yang ◽

Qing Chen ◽

Rong Zhu ◽

...

Keyword(s):

Deep Learning ◽

Question Answering ◽

Learning Framework ◽

Temporal Features ◽

Health Expert

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Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenic Alopecia

Acta Dermato Venereologica ◽

10.2340/actadv.v101.564 ◽

2021 ◽

Author(s):

Meng Gao ◽

Yue Wang ◽

Haipeng Xu ◽

Congcong Xu ◽

Xianhong Yang ◽

...

Keyword(s):

Neural Networks ◽

Image Analysis ◽

Deep Learning ◽

Quantitative Model ◽

High Accuracy ◽

Diameter Distribution ◽

Androgenic Alopecia ◽

Hair Density ◽

Learning Framework ◽

Quantitative Indicators

Since the results of basic and specific classification in male androgenic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a deep learning framework for automatic trichoscopic image analysis and a quantitative model for predicting basic and specific classification in male androgenic alopecia. A total of 2,910 trichoscopic images were collected and a deep learning framework was created on convolutional neural networks. Based on the trichoscopic data provided by the framework, correlations with basic and specific classification were analysed and a quantitative model was developed for predicting basic and specific classification using multiple ordinal logistic regression. The aim of this study was to develop a deep learning framework that can accurately analyse hair density and diameter distribution on trichoscopic images, and a quantitative model for predicting basic and specific classification in male androgenic alopecia with high accuracy.

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Attention-Based Convolutional and Recurrent Neural Networks for Driving Behavior Recognition Using Smartphone Sensor Data

IEEE Access ◽

10.1109/access.2019.2932434 ◽

2019 ◽

Vol 7 ◽

pp. 148031-148046 ◽

Cited By ~ 6

Author(s):

Jun Zhang ◽

Zhongcheng Wu ◽

Fang Li ◽

Jianfei Luo ◽

Tingting Ren ◽

...

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Driving Behavior ◽

Sensor Data ◽

Behavior Recognition ◽

Smartphone Sensor

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A novel deep capsule neural network for remaining useful life estimation

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x19866546 ◽

2019 ◽

Vol 234 (1) ◽

pp. 151-167 ◽

Cited By ~ 3

Author(s):

Andrés Ruiz-Tagle Palazuelos ◽

Enrique López Droguett ◽

Rodrigo Pascual

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Computation Time ◽

Remaining Useful Life ◽

Sensor Data ◽

Fault Diagnostics ◽

Data Set ◽

Life Estimation ◽

Useful Life ◽

Fault Prognostics

With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

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