[HTML][HTML] Latent variable models in the era of industrial big data: Extension and beyond
A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …
technique in modern industry. Among various data-driven methods, latent variable models …
Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers.
The launch of ChatGPT in 2022 garnered global attention, marking a significant milestone in
the Generative Artificial Intelligence (GAI) field. While GAI has been in effect for the past …
the Generative Artificial Intelligence (GAI) field. While GAI has been in effect for the past …
A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition
Most supervised learning-based approaches follow the assumptions that offline data and
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
A data-driven self-supervised LSTM-DeepFM model for industrial soft sensor
Soft sensor, as an important paradigm for industrial intelligence, is widely used in industrial
production to achieve efficient monitoring and prediction of production status including …
production to achieve efficient monitoring and prediction of production status including …
Graph convolutional network soft sensor for process quality prediction
The nonlinear time-varying characteristics of the process industry can be modeled using
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
Artificial-intelligence-based design for circuit parameters of power converters
Parameter design is significant in ensuring a satisfactory holistic performance of power
converters. Generally, circuit parameter design for power converters consists of two …
converters. Generally, circuit parameter design for power converters consists of two …
A novel self-supervised deep LSTM network for industrial temperature prediction in aluminum processes application
This article studies the influence of pot temperature or electrolyte temperature in the
aluminum reduction production. Specifically, these indexes reflect the distribution of the …
aluminum reduction production. Specifically, these indexes reflect the distribution of the …
Multiscale dynamic feature learning for quality prediction based on hierarchical sequential generative network
In industrial processes, long short-term memory (LSTM) is usually used for temporal
dynamic modeling of soft sensor. The process data usually have various temporal …
dynamic modeling of soft sensor. The process data usually have various temporal …
Short-term electricity demand forecasting via variational autoencoders and batch training-based bidirectional long short-term memory
Electricity load forecasting is a key aspect for power producers to maximize their economic
efficiency in deregulated markets. So far, many solutions have been employed to forecast …
efficiency in deregulated markets. So far, many solutions have been employed to forecast …
[HTML][HTML] Using a vae-som architecture for anomaly detection of flexible sensors in limb prosthesis
Flexible wearable sensor electronics, combined with advanced software functions, pave the
way toward increasingly intelligent healthcare devices. One important application area is …
way toward increasingly intelligent healthcare devices. One important application area is …