Computer Vision-Based Deep Learning Modeling for Salmon Part Segmentation and Defect Identification. [PDF]
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Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
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wiley +1 more source
Research Progress on Precision Tool Alignment Technology in Machining. [PDF]
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Integrative Approaches for DNA Sequence‐Controlled Functional Materials
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
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wiley +1 more source
Strength-Ductility Balance of HIP+HT-Treated LPBF GH3536 Alloy via In Situ EBSD: The Role of Annealing Twins. [PDF]
Zhang C, Cheng X, Lu J, Huang S, Chen B.
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Optical Design of a Large-Angle Spectral Confocal Sensor for Liquid Surface Tension Measurement. [PDF]
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Strength and Impact Toughness of Multilayered 7075/1060 Aluminum Alloy Composite Laminates Prepared by Hot Rolling and Subsequent Heat Treatment. [PDF]
Zhang H +6 more
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Smart Manufacturing for High-Performance Materials: Advances, Challenges, and Future Directions. [PDF]
Antony Jose S +5 more
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Local Rademacher Complexity Machine
Neurocomputing, 2019Abstract Support Vector Machines (SVMs) are a state-of-the-art and powerful learning algorithm that can effectively solve many real world problems. SVMs are the transposition of the Vapnik–Chervonenkis (VC) theory into a learning algorithm. In this paper, we present the Local Rademacher Complexity Machine (LRCM), a transposition of the Local ...
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