Results 111 to 120 of about 849,090 (345)
Uneven illumination surface defects inspection based on convolutional neural network
, 2019 Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface ...
Gao, Wenbin, Wu, Hao, Xu, Xiangrong
core
Fast‐Charging Solid‐State Li Batteries: Materials, Strategies, and Prospects
Advanced Materials, EarlyView.This review addresses challenges and recent advances in fast‐charging solid‐state batteries, focusing on solid electrolyte and electrode materials, as well as interfacial chemistries. The role of multiscale modeling and simulation in understanding Li+ transport and interfacial phenomena is emphasized, providing insights into materials, strategies, and ...
Jing Yu +7 more
wiley +1 more source
Convexified Convolutional Neural Networks [PDF]
arXiv, 2016 We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented as a low-rank matrix, which can be relaxed to ...
arxiv
Photonic Nanomaterials for Wearable Health Solutions
Advanced Materials, EarlyView.This review discusses the fundamentals and applications of photonic nanomaterials in wearable health technologies. It covers light‐matter interactions, synthesis, and functionalization strategies, device assembly, and sensing capabilities. Applications include skin patches and contact lenses for diagnostics and therapy. Future perspectives emphasize AI‐
Taewoong Park +3 more
wiley +1 more source
Challenges and Opportunities of Upconversion Nanoparticles for Emerging NIR Optoelectronic Devices
Advanced Materials, EarlyView.The special photo‐responsiveness of upconversion nanoparticles has opened up a new path for the advancement of near‐infrared (NIR)‐responsive optoelectronics. However, challenges such as low energy‐conversion efficiency and high nonradiative losses still persist.
Sunyingyue Geng +7 more
wiley +1 more source
Training convolutional neural networks with megapixel images [PDF]
arXiv, 2018 To train deep convolutional neural networks, the input data and the intermediate activations need to be kept in memory to calculate the gradient descent step. Given the limited memory available in the current generation accelerator cards, this limits the maximum dimensions of the input data.
arxiv
Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators
Advanced Materials, EarlyView.This study presents a DeepONet‐based machine learning framework for designing stochastic mechanical metamaterials with tailored nonlinear mechanical properties. By leveraging sparse but high‐quality experimental data from in situ micro‐mechanical tests, high predictive accuracy and enable efficient inverse design are achieved.
Hanxun Jin +7 more
wiley +1 more source
Convolutional Neural Networks: A Survey
Computers, 2023 Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of ...
openaire +2 more sources
Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
AlgorithmsThis paper presents a detailed analysis of a convolutional neural network based on multi-valued neurons (CNNMVN) and a fully connected multilayer neural network based on multi-valued neurons (MLMVN), employed here as a convolutional neural network in the
Igor Aizenberg, Alexander Vasko
doaj +1 more source
Bone tumor examination based on FCNN-4s and CRF fine segmentation fusion algorithm
Journal of Bone Oncology, 2023 Background and objective: Bone tumor is a kind of harmful orthopedic disease, there are benign and malignant points. Aiming at the problem that the accuracy of the existing machine learning algorithm for bone tumor image segmentation is not high, a bone ...
Shiqiang Wu +6 more
doaj