Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Deep learning for monitoring of human gait: A review
AS Alharthi, SU Yunas, KB Ozanyan - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
The essential human gait parameters are briefly reviewed, followed by a detailed review of
the state of the art in deep learning for the human gait analysis. The modalities for capturing …
the state of the art in deep learning for the human gait analysis. The modalities for capturing …
A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults
In recent years, deep learning has become an emerging research orientation in the field of
intelligent monitoring and fault diagnosis for industry equipment. Generally, the success of …
intelligent monitoring and fault diagnosis for industry equipment. Generally, the success of …
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application
In recent years, an increasing popularity of deep learning model for intelligent condition
monitoring and diagnosis as well as prognostics used for mechanical systems and …
monitoring and diagnosis as well as prognostics used for mechanical systems and …
An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors
This paper presents a novel integrated approach to deal with the decision making and
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …
Interaction-aware graph neural networks for fault diagnosis of complex industrial processes
Fault diagnosis of complex industrial processes becomes a challenging task due to various
fault patterns in sensor signals and complex interactions between different units. However …
fault patterns in sensor signals and complex interactions between different units. However …
Data management in industry 4.0: State of the art and open challenges
Information and communication technologies are permeating all aspects of industrial and
manufacturing systems, expediting the generation of large volumes of industrial data. This …
manufacturing systems, expediting the generation of large volumes of industrial data. This …
Contactless fall detection using time-frequency analysis and convolutional neural networks
Automatic detection of a falling person based on noncontact sensing is a challenging
problem with applications in smart homes for elderly care. In this article, we propose a radar …
problem with applications in smart homes for elderly care. In this article, we propose a radar …
A new structural health monitoring strategy based on PZT sensors and convolutional neural network
Preliminaries convolutional neural network (CNN) applications have recently emerged in
structural health monitoring (SHM) systems focusing mostly on vibration analysis. However …
structural health monitoring (SHM) systems focusing mostly on vibration analysis. However …
A deep learning approach for multi-attribute data: A study of train delay prediction in railway systems
Dynamical systems that contain moving objects generate multi-attribute data, including
static, time-series, and spatiotemporal formats. The diversity of the data formats creates …
static, time-series, and spatiotemporal formats. The diversity of the data formats creates …