Vision transformer attention with multi-reservoir echo state network for anomaly recognition
Anomalous event recognition requires an instant response to reduce the loss of human life
and property; however, existing automated systems show limited performance due to …
and property; however, existing automated systems show limited performance due to …
ITran: A novel transformer-based approach for industrial anomaly detection and localization
X Cai, R Xiao, Z Zeng, P Gong, Y Ni - Engineering Applications of Artificial …, 2023 - Elsevier
Anomaly detection is currently an essential quality monitoring process in industrial
production. It is often affected by factors such as under or over reconstruction of images and …
production. It is often affected by factors such as under or over reconstruction of images and …
An effective zero-shot learning approach for intelligent fault detection using 1D CNN
Data-driven fault detection techniques have attracted extensive attention in engineering,
industry and many other areas in recent years. In many real applications, the following …
industry and many other areas in recent years. In many real applications, the following …
Image entropy equalization: A novel preprocessing technique for image recognition tasks
Image entropy is the metric used to represent a complexity of an image. This study considers
the hypothesis that image entropy differences affect machine learning algorithms' …
the hypothesis that image entropy differences affect machine learning algorithms' …
A novel probability confidence CNN model and its application in mechanical fault diagnosis
The development of artificial intelligence has brought new opportunities and challenges in
the field of mechanical fault diagnosis. Especially, data-driven intelligent fault diagnosis …
the field of mechanical fault diagnosis. Especially, data-driven intelligent fault diagnosis …
One-class ensemble classifier for data imbalance problems
Imbalanced data classification is an important issue in machine learning. Despite various
studies, solving the data imbalance problem is still difficult. Since the oversampling method …
studies, solving the data imbalance problem is still difficult. Since the oversampling method …
Defect classification on limited labeled samples with multiscale feature fusion and semi-supervised learning
Defect inspection is an essential part of ensuring the quality of industrial products. Deep
learning has achieved great success in defect inspection when a large number of labeled …
learning has achieved great success in defect inspection when a large number of labeled …
Generalized zero-shot emotion recognition from body gestures
In human-human interaction, body language is one of the most important emotional
expressions. However, each emotion category contains abundant emotional body gestures …
expressions. However, each emotion category contains abundant emotional body gestures …
OCSTN: One-class time-series classification approach using a signal transformation network into a goal signal
One-class classification (OCC) is a classification task where the training data have only one
class. The goal is to classify input data into one seen class or other unseen classes. This …
class. The goal is to classify input data into one seen class or other unseen classes. This …
Deep convolutional self-paced clustering
Clustering is a crucial but challenging task in data mining and machine learning. Recently,
deep clustering, which derives inspiration primarily from deep learning approaches, has …
deep clustering, which derives inspiration primarily from deep learning approaches, has …