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In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.
Sen Zhang; Shaobo Li; Xiang Li; Yong Yao. Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations. Algorithms 2020, 13, 84 .
AMA StyleSen Zhang, Shaobo Li, Xiang Li, Yong Yao. Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations. Algorithms. 2020; 13 (4):84.
Chicago/Turabian StyleSen Zhang; Shaobo Li; Xiang Li; Yong Yao. 2020. "Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations." Algorithms 13, no. 4: 84.
The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering.
Yong Yao; Sen Zhang; Suixian Yang; Gui Gui. Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions. Sensors 2020, 20, 1233 .
AMA StyleYong Yao, Sen Zhang, Suixian Yang, Gui Gui. Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions. Sensors. 2020; 20 (4):1233.
Chicago/Turabian StyleYong Yao; Sen Zhang; Suixian Yang; Gui Gui. 2020. "Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions." Sensors 20, no. 4: 1233.
Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.
Sen Zhang; Yong Yao; Jie Hu; Yong Zhao; Shaobo Li; Jianjun Hu. Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks. Sensors 2019, 19, 2229 .
AMA StyleSen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu. Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks. Sensors. 2019; 19 (10):2229.
Chicago/Turabian StyleSen Zhang; Yong Yao; Jie Hu; Yong Zhao; Shaobo Li; Jianjun Hu. 2019. "Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks." Sensors 19, no. 10: 2229.
Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused by CNN channels without using an extra fusion algorithm. Our experiment results show that our method achieved much better performance on gear fault diagnosis compared with other traditional gear fault diagnosis methods involving feature engineering. A publicly available sound signal dataset for gear fault diagnosis is also released and can be downloaded as instructed in the conclusion section.
Yong Yao; Honglei Wang; Shaobo Li; Zhonghao Liu; Gui Gui; Yabo Dan; Jianjun Hu. End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals. Applied Sciences 2018, 8, 1584 .
AMA StyleYong Yao, Honglei Wang, Shaobo Li, Zhonghao Liu, Gui Gui, Yabo Dan, Jianjun Hu. End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals. Applied Sciences. 2018; 8 (9):1584.
Chicago/Turabian StyleYong Yao; Honglei Wang; Shaobo Li; Zhonghao Liu; Gui Gui; Yabo Dan; Jianjun Hu. 2018. "End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals." Applied Sciences 8, no. 9: 1584.
Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.
Shaobo Li; Yong Yao; Jie Hu; Guokai Liu; Xuemei Yao; Jianjun Hu. An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition. Applied Sciences 2018, 8, 1152 .
AMA StyleShaobo Li, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, Jianjun Hu. An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition. Applied Sciences. 2018; 8 (7):1152.
Chicago/Turabian StyleShaobo Li; Yong Yao; Jie Hu; Guokai Liu; Xuemei Yao; Jianjun Hu. 2018. "An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition." Applied Sciences 8, no. 7: 1152.
Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification.
Jie Hu; Shaobo Li; Yong Yao; Liya Yu; Guanci Yang; Jianjun Hu. Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification. Entropy 2018, 20, 104 .
AMA StyleJie Hu, Shaobo Li, Yong Yao, Liya Yu, Guanci Yang, Jianjun Hu. Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification. Entropy. 2018; 20 (2):104.
Chicago/Turabian StyleJie Hu; Shaobo Li; Yong Yao; Liya Yu; Guanci Yang; Jianjun Hu. 2018. "Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification." Entropy 20, no. 2: 104.