Accurate fault diagnosis of rotating machinery is critical to avoid catastrophic accidents.
However, insufficient fault data seriously limit the performance of fault diagnosis in industrial …

Motivation How to partition a dataset into a set of distinct clusters is a ubiquitous and
challenging problem. The fact that data vary widely in features such as cluster shape, cluster …

Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to
avoid catastrophic accidents and ensure a stable power source. However, accurate fault …

Abstract Spectral Mixture Analysis is one of the fundamental subjects encountered when
dealing with remotely sensed hyperspectral images. Its goal is to identify constituent …

Accurate and transferable fault diagnosis methods have a critical role in constructing a
digital twin (DT) of wind turbine (WT). These methods can be utilized to predict premature …

Different from general spherical datasets, manifold datasets have a more complex spatial
manifold structure, which makes it difficult to distinguish sample points on different manifold …

Combination the spatial-contextual information in spectral unmixing as a preprocessing of
endmember extraction algorithms (EEAs) has been an important issue in hyperspectral …

The increasing importance and complexity of networks led to the development of network
fault management as a distinct field, providing support for network administrators with quality …

In this paper, hyperspectral image is taken as the research background. In order to solve the
problem of under_segmentation or over_segmentation caused by artificial empirical value …

ABSTRACT KNN is an extensively used classification algorithm owing to its simplicity, ease
of implementation and effectiveness. It is one of the top ten data mining algorithms, has been …