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Active damping strategy of the PMSM drive system with LC sine wave filter: Review and new expansion

The article reviews and expands the active damping strategy for permanent magnet motor drive systems with LC filters, analyzing its stability, dynamic performance, robustness, and algorithm complexity. A new expansion using capacitor current feedback with high-pass filter is proposed to overcome resonance suppression problems.

SourceCES Transactions on Electrical Machines and Systems·JournalCES Transactions on Electrical Machines and Systems·TypeExperimental study·DateDec 8, 2025

Improved analytical accuracy for permanent magnet torque machines: Accounting for armature magnetic field effects on magnetic circuit saturation

Researchers propose a novel analytical model that considers multiple nonlinear factors in motor performance calculation, reducing errors caused by magnetic saturation. The new method significantly improves design efficiency and prediction accuracy, cutting development time and cost.

SourceCES Transactions on Electrical Machines and Systems·JournalCES Transactions on Electrical Machines and Systems·TypeExperimental study·DateOct 19, 2025

Making immunizations more effective

Researchers have created two novel adjuvants that significantly enhance the immune response to vaccines. The new adjuvants target toll-like receptors and are designed to be tailored for every individual vaccine, potentially reducing side effects and increasing effectiveness.

SourceWiley·JournalAngewandte Chemie International Edition·TypeExperimental study·DateMar 27, 2023

Multi-spin flips and a pathway to efficient ising machines

A team of researchers from Waseda University developed a novel solution to efficiently solve complex optimization problems using Ising machines. Their hybrid algorithm reduces residual energy and reaches more optimal results in shorter time, increasing the machine's applicability across industries and sustainability practices.

SourceWaseda University·JournalIEEE Transactions on Computers·TypeComputational simulation/modeling·DateMay 31, 2022

The next generation of robots will be shape-shifters

Researchers at the University of Bath have developed a new coating method for soft robots that allows them to change shape and movement through human-controlled activity. This breakthrough in active matter could lead to the creation of machines governed by individual units that cooperate to determine movement and function.

SourceUniversity of Bath·JournalScience Advances·TypeExperimental study·DateMar 11, 2022

New tool can detect a precursor of engine-destroying combustion instability

A team of scientists from Tokyo University of Science has developed a machine learning-based tool to predict thermoacoustic oscillations in engines. The tool uses dynamical systems theory and can classify combustion into three states, identifying pressure fluctuations that indicate future combustion oscillations.

SourceTokyo University of Science·JournalAIAA Journal·TypeComputational simulation/modeling·DateNov 18, 2021

New algorithms train AI to avoid specific bad behaviors

Researchers at Stanford and UMass Amherst develop a new technique to create machine-learning algorithms that can learn to avoid undesirable outcomes such as gender bias and excessive risk. Their approach, called the Seldonian algorithm, enables users to specify what behaviors they want an AI system to avoid with high probability.

SourceStanford University·JournalScience·DateNov 21, 2019