Results 171 to 180 of about 122,227 (359)
Half-Bridge Full-Bridge AC–DC Resonant Converter for Bi-Directional EV Charger [PDF]
Behnam Koushki +2 more
openalex +1 more source
A machine learning‐guided self‐driving laboratory screened over 500 nickel‐based layered double‐hydroxide catalysts for alkaline oxygen evolution. Out of the eight metals, the robot uncovered a quaternary Ni–Fe–Cr–Co catalysts requiring only 231 mV overpotential to reach 20 mA cm−2.
Nis Fisker‐Bødker +3 more
wiley +1 more source
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj +2 more
wiley +1 more source
To enable versatile unconventional computing, a single SiOx threshold switching device is engineered to exhibit controllable dual‐mode oscillation. By modulating the input voltage, the device selectively provides stable full oscillation for oscillatory neural networks and stochastic probabilistic oscillation for probabilistic bits and true random ...
Hyeonsik Choi +3 more
wiley +1 more source
An Overview of Power Electronics Applications in Fuel Cell Systems: DC and AC Converters [PDF]
Mohsin Ali +3 more
openalex +1 more source
DSP-BASED CONTROL OF BOOST PFC AC-DC CONVERTERS USING PREDICTIVE CONTROL [PDF]
Haitham Z. Azazi +3 more
openalex +1 more source
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar +2 more
wiley +1 more source
Fuzzy Controlled Parallel AC-DC Converter for PFC
M. Subba Rao +3 more
openalex +1 more source
Asymmetric modulation of bridgeless single‐stage full‐bridge AC–DC converter for active power factor correction and zero voltage switching [PDF]
Junwei Liu +4 more
openalex +1 more source
Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining
Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson +3 more
wiley +1 more source

