Wavelet‐based neural networks applied to automatic detection of road surface conditions using tire noise from vehicles.

2009 ◽  
Vol 125 (4) ◽  
pp. 2730-2730 ◽  
Author(s):  
Wuttiwat Kongrattanaprasert ◽  
Hideyuki Nomura ◽  
Tomoo Kamakura ◽  
Koji Ueda
Keyword(s):  
Neural Networks ◽  
Automatic Detection ◽  
Road Surface ◽  
Surface Conditions ◽  
Tire Noise

Detection of Road Surface Conditions Using Tire Noise from Vehicles

2009 ◽  
Vol 129 (7) ◽  
pp. 761-767 ◽  
Author(s):  
Wuttiwat Kongrattanaprasert ◽  
Hideyuki Nomura ◽  
Tomoo Kamakura ◽  
Koji Ueda
Keyword(s):  
Road Surface ◽  
Surface Conditions ◽  
Tire Noise

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

scholarly journals Automatic detection of contouring errors using convolutional neural networks

2019 ◽  
Vol 46 (11) ◽  
pp. 5086-5097 ◽  
Author(s):  
Dong Joo Rhee ◽  
Carlos E. Cardenas ◽  
Hesham Elhalawani ◽  
Rachel McCarroll ◽  
Lifei Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection

Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks

2020 ◽  
Vol 396 ◽  
pp. 514-521 ◽  
Author(s):  
Xulei Yang ◽  
Wai Teng Tang ◽  
Gabriel Tjio ◽  
Si Yong Yeo ◽  
Yi Su
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Detection ◽  
Anatomical Landmarks ◽  
Mr Scanning

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

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