Results 111 to 120 of about 602,374 (329)

Machine learning–based extreme event attribution

Science Advances
The observed increase in extreme weather has prompted recent methodological advances in extreme event attribution. We propose a machine learning–based approach that uses convolutional neural networks to create dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature (GMT).
Jared T. Trok, Elizabeth A. Barnes, Frances V. Davenport, Noah S. Diffenbaugh   +3 more
openaire   +2 more sources

Review of Thin Lithium Metal Battery Anode Fabrication – Microstructure – Electrochemistry Relations

Advanced Materials, EarlyView.
Thin, lightweight lithium‐metal anodes are pivotal for practical high‐energy batteries. This review surveys processing routes that convert diverse Li precursors, e.g., ingots, melts, solutions, and vapor, into Li‐rich foils with controlled thickness, areal density, and tailored functionality.
Yuhang Hu, Zidong Chen, Yixian Wang, Haorui Hou, Bingcheng Chen, David Mitlin, Wei Liu   +6 more
wiley   +1 more source

Predicting China's Economic Running State Using Machine Learning

MATEC Web of Conferences, 2018
China's business index of macro-economic includes early warning index, coincidence index, leading index and lagging index, among which early warning index reflects the economic running state.
Luo Jianxiang, Fu Yonggang
doaj   +1 more source

Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing

, 2023
Botlagunta Preethish Nanan, Subrahmanyasarma Chitta   +1 more
openalex   +2 more sources

Electrochemical Carbon Dioxide Reduction to Methanol on Copper‐Based Catalysts: Mechanistic Insights and Industrial Prospects

Advanced Materials, EarlyView.
This review presents a focused and integrated perspective on copper‐based catalysts for the selective electrochemical reduction of CO2 to methanol. It elucidates active site dynamics, mechanistic pathways, and structure–activity relationships, while connecting fundamental insights with catalyst design, reactor engineering, and techno‐economic ...
Debabrata Bagchi, Carsten Walter, Venkata S. R. K. Tandava, Yasmin Lucero Cobos‐Becerra, Jack C. Q. Fletcher, Nico Fischer, Tobias Sontheimer, Prashanth W. Menezes   +7 more
wiley   +1 more source

Regularized manifold information extreme learning machine

Tongxin xuebao, 2016
By exploiting the thought of manifold learning and its theoretical method, a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed.
De-shan LIU, Yong-he CHU, De-qin YAN
doaj   +2 more sources

Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine [PDF]

, 2016
Xinyi Yang, Shan Pang, Wei Shen, Xuesen Lin, Keyi Jiang, Yonghua Wang   +5 more
openalex   +1 more source

Extreme Learning Machine-Based Power Forecasting in Photovoltaic Systems [PDF]

, 2023
Zeynep Bala Duranay
openalex   +1 more source

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

, 2020
Abdul Salam Shah, Haidawati Nasir, Muhammad Fayaz, Adidah Lajis, Israr Ullah, Asadullah Shah   +5 more
openalex   +1 more source