The task of autonomous power sharing in a micro-grid is usually achieved by decentralized
droop control on individual interface power converters, which may suffer from the …

The support vector clustering (SVC) algorithm is a recently emerged unsupervised learning
method inspired by support vector machines. One key step involved in the SVC algorithm is …

The huge differences in the visual shapes of multi-angle shooting objects leads to the poor
performance of deep neural network (DNN). In this paper, an object detection model named …

A topological and dynamical characterization of the cluster structures described by the
support vector clustering is developed. It is shown that each cluster can be decomposed into …

This article presents a two-layer hybrid neural network framework, termed enhanced ELITE
(E-ELITE), for short-term load forecasting (STLF) with high-performance forecasting …

Following strong regulations such as the European General Data Protection Regulation
(GDPR), privacy protection in the financial sector has recently emerged as an urgent issue …

It is shown that if the Boolean hierarchy (BH) collapses, then there exists a sparse set S such
that co-NP⊆NP^S, and therefore the polynomial time hierarchy (PH) collapses to …

A novel three-stage methodology, termed the “consensus-based particle swarm optimization
(PSO)-assisted Trust-Tech methodology,” to find global optimal solutions for nonlinear …

In the measurement-based approach, given a load model structure, its parameter values are
derived using actual measurements. The issue of multiple local optimal solutions may arise …

In this letter, we develop a gaussian process model for clustering. The variances of
predictive values in gaussian processes learned from a training data are shown to comprise …