Results 161 to 170 of about 337,141 (330)

EMI Execution Service implementation in ARC [PDF]

, 2012
Александр Константинов, M. Skou Andersen   +1 more
openalex   +1 more source

The Effects of Spread-Spectrum Techniques in Mitigating Conducted EMI to LED Luminance

, 2016
Mohammad Yanuar Hariyawan, Risanuri Hidayat, Eka Firmansyah   +2 more
openalex   +1 more source

Anomalous‐Energy Runaway Electrons Originating in Fast Ionisation Wave During Pulsed Gas Breakdown

High Voltage, EarlyView.
ABSTRACT Anomalous‐energy runaway electrons (RAEs), whose energy exceeds the maximum potential difference across the discharge gap, are widely observed in various plasma phenomena. This study investigates the origination of anomalous‐energy RAEs in fast ionisation waves (FIWs) formed during pulsed gas breakdown.
Bangdou Huang, Chenhua Ren, Cheng Zhang, Edl Schamiloglu, Tao Shao   +4 more
wiley   +1 more source

Inorganic High-Performance Fiber-Based Materials for Electromagnetic Interference Shielding: Fundamentals, Fabrications, and Emerging Applications. [PDF]

Nanomicro Lett
Qiao S, Shi Z, Tong A, Huang Z, He A, Wang B, He J, Wang J, Chen M, Huang Z, Hao L, Wu B, Jun Y, Tan YL, Ma P, Xu W, Chen F.   +16 more
europepmc   +1 more source

Enhanced Emi Shielding and Mechanical Stability Via Deformable Mxene-Rgo Conductive Networks in Superelastic Pdms Composite

Yaqiang Duan, Yuchen Gu, Weijun Yang, Pengwu Xu, Yunpeng Huang, Piming Ma   +5 more
openalex   +1 more source

A Non‐van der Waals Superlattice Breakthrough: Engineered Atomic Layer Assemblies

Rare Metals, EarlyView.
Qingmei Hu, Xiang Miao, Jiadong Zhou
wiley   +1 more source

Aluminum-Foil/Polyester Core-Spun Yarns Conductive Fabric Enabling High Electromagnetic Interference Shielding. [PDF]

Polymers (Basel)
Sun Y, Han X, Zhao K, Zhao W, He Z, He Z, Mo Y, Chu C, Natsuki T, Natsuki J.   +9 more
europepmc   +1 more source

Characteristics Analysis of the CM and DM Noise Separator in EMI

, 2016
Chansoo Park
openalex   +1 more source

Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials

IET Power Electronics, EarlyView.
This paper outlines the methodology for predicting power loss in magnetic materials. A neural network based method is introduced, which adopts a long short‐term memory network, expressing the core loss as a function of magnetic flux density in the frequency domain, temperature, frequency, and classification of the waveforms.
Dixant Bikal Sapkota, Puskar Neupane, Mecon Joshi, Shahabuddin Khan   +3 more
wiley   +1 more source

Flexible MXene nanosheet/multiwall carbon nanotube-reinforced poly (vinylidene fluoride-hexafluoropropylene)-polymethyl methacrylate composites for energy storage and EMI shielding. [PDF]

RSC Adv
Nath NK, Parida R, Parida BN, Das D, Parashar SKS, Nayak NC.   +5 more
europepmc   +1 more source