Towards long lifetime battery: AI-based manufacturing and management
Technologies that accelerate the delivery of reliable battery-based energy storage will not
only contribute to decarbonization such as transportation electrification, smart grid, but also …
only contribute to decarbonization such as transportation electrification, smart grid, but also …
The emerging trends of multi-label learning
Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
Multi-label learning from single positive labels
Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …
Compared to the standard multi-class case (where each image has only one label), it is …
Multi-task network anomaly detection using federated learning
Because of the complexity of network traffic, there are various significant challenges in the
network anomaly detection fields. One of the major challenges is the lack of labeled training …
network anomaly detection fields. One of the major challenges is the lack of labeled training …
Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine
J Liu, H Xu, X Peng, J Wang, C He - Reliability Engineering & System Safety, 2023 - Elsevier
With increasingly stringent in requirements on the reliability and safety of hydraulic systems,
data-driven fault diagnosis has emerged as a popular area of research. Hydraulic systems …
data-driven fault diagnosis has emerged as a popular area of research. Hydraulic systems …
Unsupervised concept drift detection for multi-label data streams
Many real-world applications adopt multi-label data streams as the need for algorithms to
deal with rapidly changing data increases. Changes in data distribution, also known as …
deal with rapidly changing data increases. Changes in data distribution, also known as …
ROULETTE: A neural attention multi-output model for explainable network intrusion detection
Abstract Network Intrusion Detection (NID) systems are one of the most powerful forms of
defense for protecting public and private networks. Most of the prominent methods applied to …
defense for protecting public and private networks. Most of the prominent methods applied to …
A review of federated learning in intrusion detection systems for iot
Intrusion detection systems are evolving into intelligent systems that perform data analysis
searching for anomalies in their environment. The development of deep learning …
searching for anomalies in their environment. The development of deep learning …
[HTML][HTML] Multi-target normal behaviour models for wind farm condition monitoring
A Meyer - Applied Energy, 2021 - Elsevier
The trend towards larger wind turbines and remote locations of wind farms fuels the demand
for automated condition monitoring strategies that can reduce the operating cost and avoid …
for automated condition monitoring strategies that can reduce the operating cost and avoid …