Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting

Zhao, Wentian; Gao, Yanyun; Ji, Tingxiang; Wan, Xili; Ye, Feng; Bai, Guangwei

doi:10.1109/access.2019.2935504

Cited by 141 publications

(59 citation statements)

References 29 publications

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“…In building a monitoring system for a FDM 3D printer a temporal convolutional network with a two-stage sliding window strategy (TCN-TS-SW) is proposed to perform the estimation on the thermal state of the nozzle tip. This architecture is inspired by the temporal neural network (TCN) presented in [33], an artificial neural network architecture originally built to perform sequence modelling just like Nonlinear Autoregressive Neural Network with Exogenous Input (NARX) [34], LSTM [32] and CNN [35]. The temporal convolutional network (TCN) overcome the drawbacks of the canonical RNN on vanishing gradient problem.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks

2021

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A preventive maintenance embedded for the fused deposition modeling (FDM) printing technique is proposed. A monitoring and control integrated system is developed to reduce the risk of having thermal degradation on the fabricated products and prevent printing failure; nozzle clogging. As for the monitoring program, the proposed temporal neural network with a two-stage sliding window strategy (TCN-TS-SW) is utilized to accurately provide the predicted thermal values of the nozzle tip. These estimated thermal values are utilized to be the stimulus of the control system that performs countermeasures to prevent the anomaly that is bound to happen. The performance of the proposed TCN-TS-SW is presented in three case studies. The first scenario is when the proposed system outperforms the other existing machine learning algorithms namely multi-look back LSTM, GRU, LSTM, and the generic TCN architecture in terms of obtaining the highest training accuracy and lowest training loss. TCN-TS-SW also outperformed the mentioned algorithms in terms of prediction accuracy measured by the performance metrics like RMSE, MAE, and R2 scores. In the second case, the effect of varying the window length and the changing length of the forecasting horizon. This experiment reveals the optimized parameters for the network to produce an accurate nozzle thermal estimation.

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Section: Proposed Methodologymentioning

confidence: 99%

“…The effectiveness of the proposed two-stage sliding window technique is investigated as the proposed scheme is compared to the classic TCN model presented in [33] in terms of training loss and accuracy.…”

mentioning

confidence: 99%

Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks

2021

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“…The second analyzed deep architecture that we have implemented to correlate the visual features and the corresponding PPG signal, is based on the use of a temporal deep architecture. Specifically, ad-hoc 1D Temporal Deep dilated Convolutional Neural Network (1D-TDCNN) has been developed (Zhao et al, 2019). The main building block consists of a dilated causal convolution layer that operates over the time steps of each sequence (Zhao et al, 2019).…”

Section: The Car Driver Landmarks Deep Classifiermentioning

confidence: 99%

“…Specifically, ad-hoc 1D Temporal Deep dilated Convolutional Neural Network (1D-TDCNN) has been developed (Zhao et al, 2019). The main building block consists of a dilated causal convolution layer that operates over the time steps of each sequence (Zhao et al, 2019). The proposed 1D-TDCNN includes multiple residual blocks, each containing two sets of dilated causal convolution layers with the same dilation factor, followed by normalization, ReLU activation, and spatial dropout layers.…”

Section: The Car Driver Landmarks Deep Classifiermentioning

confidence: 99%

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

Rundo

Conoci

Spampinato

et al. 2021

Front. Neuroinform.

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In recent years, the automotive field has been changed by the accelerated rise of new technologies. Specifically, autonomous driving has revolutionized the car manufacturer's approach to design the advanced systems compliant to vehicle environments. As a result, there is a growing demand for the development of intelligent technology in order to make modern vehicles safer and smarter. The impact of such technologies has led to the development of the so-called Advanced Driver Assistance Systems (ADAS), suitable to maintain control of the vehicle in order to avoid potentially dangerous situations while driving. Several studies confirmed that an inadequate driver's physiological condition could compromise the ability to drive safely. For this reason, assessing the car driver's physiological status has become one of the primary targets of the automotive research and development. Although a large number of efforts has been made by researchers to design safety-assessment applications based on the detection of physiological signals, embedding them into a car environment represents a challenging task. These mentioned implications triggered the development of this study in which we proposed an innovative pipeline, that through a combined less invasive Neuro-Visual approach, is able to reconstruct the car driver's physiological status. Specifically, the proposed contribution refers to the sampling and processing of the driver PhotoPlethysmoGraphic (PPG) signal. A parallel enhanced low frame-rate motion magnification algorithm is used to reconstruct such features of the driver's PhotoPlethysmoGraphic (PPG) data when that signal is no longer available from the native embedded sensor platform. A parallel monitoring of the driver's blood pressure levels from the PPG signal as well as the driver's eyes dynamics completes the reconstruction of the driver's physiological status. The proposed pipeline has been tested in one of the major investigated automotive scenarios i.e., the detection and monitoring of pedestrians while driving (pedestrian tracking). The collected performance results confirmed the effectiveness of the proposed approach.

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“…It combines the advantage of CNN and RNN, in which, the causal convolution makes forecasts of future data don't leak, and the dilated causal convolution and residual structure can achieve a larger receptive field within a finite layer, and the calculation amount and training time of the model has no obvious increase. Relevant researchers soon applied TCN to time series forecasting problems, such as power timing prediction [29], traffic flow prediction [30,31], and bearing residual service life prediction [32]. The experimental results show the effectiveness of TCN.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Based on Temporal Convolutional Network and Transfer Learning

Liu

Zhang

et al. 2021

IEEE Access

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Accurate estimation of the state of charge (SOC) is critical for the normal use of lithium-ion battery equipment like electric vehicles. However, the SOC of lithium-ion battery is not available by direct measure, but can only indirectly be estimated by measurable variables. According to the nonlinear characteristics between the measured values and SOC during the working period of lithium-ion batteries, we propose a method to estimate the SOC of lithium-ion batteries with Temporal Convolutional Network (TCN). The measured values of voltage, current, and temperature during the use of lithium-ion batteries can be directly mapped to accurate SOC in this method without using a battery model or adaptive filter. The network can self-learning and update parameters by being fed datasets collected under various working conditions and then obtain a model that can correctly estimate SOC under different estimation conditions. In addition, it can also be applied to different types of lithium-ion batteries through transfer learning with only a small amount of battery data. At various ambient temperature conditions, the average MAE estimated by the proposed method is 0.67% for all the tests, which proves that the TCN network is an effective tool to estimate the SOC of lithium-ion batteries.

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Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting

Cited by 141 publications

References 29 publications

Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks

Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

State of Charge Estimation of Lithium-Ion Batteries Based on Temporal Convolutional Network and Transfer Learning

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