Uneven density data refers to data with a certain difference in sample density between
clusters. The local density of density peaks clustering algorithm (DPC) does not consider the …

Rapid sorting and reasonable regrouping of retired lithium-ion batteries (LIBs) are directly
related to the economy and safety of the second-life utilization. However, the efficiency and …

Abstract Gaussian Process Functional Regression (GPFR) is a powerful tool in functional
data analysis. In practical applications, functional data may be generated from different …

The mixture of Gaussian processes is effective for regression, but it cannot handle the non-
stationary curve clustering problem well. The two-layer mixture of Gaussian process …

Existing machine learning models can well handle common classes but struggle to detect
unfamiliar or unknown classes due to environmental variations. To address this challenge …

Uncertainties are normally unavoidable in engineering practice, which should be taken into
account in the structural design and optimization so as to reduce the relevant risks. Yet, the …

Plate shape recognition in the roller quenching process provides effective feedback signals
for plate shape control. Its accuracy directly affects the control effect. Most studies have …

Abstract The Density Peaks Clustering (DPC) algorithm is simple and efficient, but still has a
few limitations. For example, DPC needs manual selection of clustering centers and may …

Abstract Gaussian Mixture Models (GMMs) are one of the most potent parametric density
models used extensively in many applications. Flexibly-tied factorization of the covariance …

Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for
image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and …