Machine Learning Test for Complementary Electrochromic Device
Author:
Funder
Division of Physics
Institute of Nuclear Energy Research
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9661583/9661588/09661615.pdf?arnumber=9661615
Reference14 articles.
1. Kinetics study and modelling of the electrochromic phenomenon in amorphous tungsten trioxide thin films in acid and lithium electrolytes
2. Electrochromic device modeling using an adaptive neuro-fuzzy inference system: A model-free approach
3. Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices
4. Efficient electrochromic properties of high-density and large-area arrays of one-dimensional NiO nanorods
5. Tantalum oxide film deposited by vacuum cathodic arc plasma with improved electrochromic performance
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1. Machine Learning Test for Modulation Range of Ammonium Metatungstate Based Liquid Electrochromic Devices;Lecture Notes in Electrical Engineering;2024
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