Results 181 to 190 of about 2,291,078 (312)

Wetland Information Extraction from RS Image Based on Wavelet Packet and the Active Learning Support Vector Machine [PDF]

, 2011
Pu Wang, Wenxing Bao
openalex   +1 more source

Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation [PDF]

, 2020
Meredith G. L. Brown, Sergii Skakun, Tao He, Shunlin Liang   +3 more
openalex   +1 more source

Machine Learning-Assisted Array Synthesis Using Active Base Element Modeling [PDF]

, 2021
Qi Wu, Weiqi Chen, Yu Chen, Haiming Wang, Wei Hong   +4 more
openalex   +1 more source

Structurally Colored Physically Unclonable Functions with Ultra‐Rich and Stable Encoding Capacity

Advanced Functional Materials, Volume 35, Issue 12, March 18, 2025.
This study reports a design strategy for generating bright‐field resolvable physically unclonable functions with extremely rich encoding capacity coupled with outstanding thermal and chemical stability. The optical response emerges from thickness‐dependent structural color formation in ZnO features, which are fabricated by physical vapor deposition ...
Abidin Esidir, Miaoning Ren, Sami Pekdemir, Mustafa Kalay, Nilgun Kayaci, Nail Gunaltay, Hakan Usta, Xian Huang, Mustafa Serdar Onses   +8 more
wiley   +1 more source

SEARCH FOR GAMMA-RAY-EMITTING ACTIVE GALACTIC NUCLEI IN THEFERMI-LAT UNASSOCIATED SAMPLE USING MACHINE LEARNING [PDF]

, 2014
M. Doert, M. Errando
openalex   +1 more source

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning [PDF]

, 2022
Dan Li, Yuxin Miao, Curtis J. Ransom, G. Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger, John F. Shanahan   +13 more
openalex   +1 more source

Electroplating of Wear‐ and Corrosion‐Resistant CrCoNi Medium‐Entropy Alloys beyond Hard Chromium Coatings

Advanced Functional Materials, EarlyView.
The electroplating of a CrCoNi medium‐entropy alloy is achieved using a mixture of an ionic liquid and an aqueous solution containing metal salts. The CrCoNi medium‐entropy alloy thin film exhibits high wear and corrosion resistance superior to conventional hard chromium coatings. Abstract High‐entropy alloys (HEAs) and medium‐entropy alloys (MEAs) are
Yuki Murakami, Shuhei Yoshida, Kosuke Ishii, Naoya Nishi, Akiyuki Ryoki, Nobuhiro Tsuji, Kuniaki Murase, Kazuhiro Fukami   +7 more
wiley   +1 more source

Ultra‐Fast Non‐Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing

Advanced Functional Materials, EarlyView.
A materials and device design concept that comprises a self‐assembled ultra‐thin epitaxial ion‐transporting layer, an amorphous oxide overcoat oxygen‐blocking layer, and a partial filament formed during an electroforming step is proposed for low‐current multilevel resistive switching devices.
Ming Xiao, Markus Hellenbrand, Nives Strkalj, Babak Bakhit, Zhuotong Sun, Nikolaos Barmpatsalos, Dovydas Joksas, Hongyi Dou, Zedong Hu, Ping Lu, Samip Karki, Sundar Kunwar, Jonathan D. Major, Aiping Chen, Haiyan Wang, Quanxi Jia, Adnan Mehonic, Judith L. MacManus‐Driscoll   +17 more
wiley   +1 more source

Advances in Radiative Heat Transfer: Bridging Far‐Field Fundamentals and Emerging Near‐Field Innovations

Advanced Functional Materials, EarlyView.
This review synthesizes the evolution of radiative heat transfer, emphasizing the transition from far‐field to near‐field regimes. Traditional frameworks, such as Planck's law, are revisited alongside modern innovations like fluctuational electrodynamics. Applications span nanoscale thermal management, energy harvesting, and thermophotovoltaic systems.
Ambali Alade Odebowale, Andergachew Berhe, Rasheed T. Ogundare, Salah Abdo, Amer Abdulghani, Haroldo T. Hattori, Andrey E. Miroshnichenko   +6 more
wiley   +1 more source

Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation [PDF]

arXiv
Despite significant advancements in active learning and adversarial attacks, the intersection of these two fields remains underexplored, particularly in developing robust active learning frameworks against dynamic adversarial threats. The challenge of developing robust active learning frameworks under dynamic adversarial attacks is critical, as these ...
arxiv