This paper reviews the recent developments of design optimization methods for
electromagnetic devices, with a focus on machine learning methods. First, the recent …

This paper presents a review of the recent trends and the current state of the art in the
application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the …

Two main contributions of this paper are 1) the energy consumption of ships (ECS) in port is
predicted, 2) reduction strategies for energy consumption of ships in port are discussed by …

Permanent-magnet vernier machine has an excellent potential for electric vehicles due to its
desirable features of a simple mechanical structure and high torque density, whereas the …

Employing machine learning techniques in predicting the parameters of metamaterial
antennas has a significant impact on the reduction of the time needed to design an antenna …

The k-nearest neighbors (KNN) regression method, known for its nonparametric nature, is
highly valued for its simplicity and its effectiveness in handling complex structured data …

This article proposes a multi-objective optimization method for the optimization design of a
new dual-stator arc permanent magnet machine (DS-APMM) which can be applied on the …

The sorption-enhanced steam gasification of biomass with an integrated carbon dioxide (CO
2) capture is a promising process for hydrogen production. By using machine learning (ML) …

The power market is a typical imperfectly competitive market where power suppliers gain
higher profits through strategic bidding behaviors. Most existing studies assume that a …

This article proposes a new machine learning technique based multi-level optimization (MLT-
MLO) method to optimize a dual-stator flux-modulated machine with dual-PM excitation (DS …