A new deep transfer learning based on sparse auto-encoder for fault diagnosis
Fault diagnosis plays an important role in modern industry. With the development of smart
manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods …
manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods …
Transfer learning and its extensive appositeness in human activity recognition: A survey
A Ray, MH Kolekar - Expert Systems with Applications, 2023 - Elsevier
In this competitive world, the supervision and monitoring of human resources are primary
and necessary tasks to drive context-aware applications. Advancement in sensor and …
and necessary tasks to drive context-aware applications. Advancement in sensor and …
A review of action recognition based on convolutional neural network
Y Jiaxin, W Fang, Y Jieru - Journal of Physics: Conference Series, 2021 - iopscience.iop.org
At present, the development of video action recognition is very rapid in many fields, such as
video understanding, intelligent monitoring, and human-computer interaction. However …
video understanding, intelligent monitoring, and human-computer interaction. However …
Generalized hidden-mapping transductive transfer learning for recognition of epileptic electroencephalogram signals
Electroencephalogram (EEG) signal identification based on intelligent models is an
important means in epilepsy detection. In the recognition of epileptic EEG signals, traditional …
important means in epilepsy detection. In the recognition of epileptic EEG signals, traditional …
Content-attention representation by factorized action-scene network for action recognition
During action recognition in videos, irrelevant motions in the background can greatly
degrade the performance of recognizing specific actions with which we actually concern …
degrade the performance of recognizing specific actions with which we actually concern …
Semi-supervised image-to-video adaptation for video action recognition
Human action recognition has been well explored in applications of computer vision. Many
successful action recognition methods have shown that action knowledge can be effectively …
successful action recognition methods have shown that action knowledge can be effectively …
A wavelet convolutional capsule network with modified super resolution generative adversarial network for fault diagnosis and classification
The study of fault diagnosis and classification has gained tremendous attention in various
aspects of modern industry. However, the performance of traditional fault diagnosis …
aspects of modern industry. However, the performance of traditional fault diagnosis …
A concise peephole model based transfer learning method for small sample temporal feature-based data-driven quality analysis
W Luo, J Zhang, P Feng, D Yu, Z Wu - Knowledge-Based Systems, 2020 - Elsevier
Insufficient samples and low analysis efficiency are two main problems for data-driven
quality analysis. To avoid negative transfer from source data to target data and improve …
quality analysis. To avoid negative transfer from source data to target data and improve …
Similarity domains machine for scale-invariant and sparse shape modeling
S Ozer - IEEE Transactions on Image Processing, 2018 - ieeexplore.ieee.org
We present an approach to extend the functionality and the use of kernel machines in image
processing applications. We introduce a novel way to design spatial kernel machines with …
processing applications. We introduce a novel way to design spatial kernel machines with …
Weakly-supervised action localization based on seed superpixels
In this paper, we present action localization based on weak supervision with seed
superpixels. In order to benefit from the superpixel segmentation and to learn a priori …
superpixels. In order to benefit from the superpixel segmentation and to learn a priori …