Recent advances and application of machine learning in food flavor prediction and regulation
H Ji, D Pu, W Yan, Q Zhang, M Zuo, Y Zhang - Trends in Food Science & …, 2023 - Elsevier
Background Food flavor is a key factor affecting sensory quality. Predicting and regulating
flavor can result in exceptional flavor characteristics and improve consumer preferences and …
flavor can result in exceptional flavor characteristics and improve consumer preferences and …
Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis
S Liu, H Jiang, Z Wu, X Li - Mechanical Systems and Signal Processing, 2022 - Elsevier
Rolling bearing fault diagnosis is of great significance to the stable operation of rotating
machinery systems. However, the fault data collected in practical engineering is seriously …
machinery systems. However, the fault data collected in practical engineering is seriously …
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
Deep learning and its applications to machine health monitoring
Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …
redefining state-of-the-art performances in a wide range of areas such as object recognition …
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
Deep learning has attracted attentions in intelligent fault diagnosis of machinery because it
allows a deep network to accomplish the tasks of feature learning and fault classification …
allows a deep network to accomplish the tasks of feature learning and fault classification …
A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
Automatic and accurate identification of rolling bearings fault categories, especially for the
fault severities and fault orientations, is still a major challenge in rotating machinery fault …
fault severities and fault orientations, is still a major challenge in rotating machinery fault …
Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation
Intelligent machinery fault diagnosis system has been receiving increasing attention recently
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …
Challenges and opportunities of AI-enabled monitoring, diagnosis & prognosis: A review
Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …
prognosis, and health management, occupies an increasingly important position in reducing …
Normalized conditional variational auto-encoder with adaptive focal loss for imbalanced fault diagnosis of bearing-rotor system
The distribution of the health data monitored from mechanical system in the industries is
class imbalanced mainly. The amount of monitoring data for the normal condition is far more …
class imbalanced mainly. The amount of monitoring data for the normal condition is far more …