Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …

In neural networks literature, there is a strong interest in identifying and defining activation
functions which can improve neural network performance. In recent years there has been a …

Evolutionary multitasking (EMT) is a newly emerging research topic in the community of
evolutionary computation, which aims to improve the convergence characteristic across …

In recent years, many applications are using various forms of deep learning models. Such
methods are usually based on traditional learning paradigms requiring the consistency of …

Fault diagnosis of a thermal system under varying operating conditions is of great
importance for the safe and reliable operation of a power plant involved in peak shaving …

Unsupervised domain adaptation addresses the problem of classifying data in an unlabeled
target domain, given labeled source domain data that share a common label space but …

In real industrial scenarios, deep learning-based fault diagnosis has been a popular topic
lately. Unfortunately, the source-trained model typically usually underperforms in target …

Recently, a number of domain adaptation (DA) methods have been proposed for knowledge
transfer in evolutionary multitasking (EMT). However, the learned mappings in these …

Network embedding is a highly effective method to learn low-dimensional node vector
representations with original network structures being well preserved. However, existing …

With the rapid development of power grid data, the data generated by the operation of the
power system is increasingly complex, and the amount of data increases exponentially. In …