Results 41 to 50 of about 102,675 (264)

A robust deformed convolutional neural network (CNN) for image denoising

CAAI Transactions on Intelligence Technology, 2022
Abstract Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising.
Qi Zhang 0059, Jingyu Xiao, Chunwei Tian, Jerry Chun-Wei Lin, Shichao Zhang 0001   +4 more
openaire   +3 more sources

Structural Damage Detection Based on One-Dimensional Convolutional Neural Network

Applied Sciences, 2022
This paper proposes a structural damage detection method based on one-dimensional convolutional neural network (CNN). The method can automatically extract features from data to detect structural damage.
Zhigang Xue, Chenxu Xu, Dongdong Wen
doaj   +1 more source

Convolutional Neural Network (CNN) with Randomized Pooling

, 2022
Abstract Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it has been widely used in image processing for image classification, object identification, semantic segmentation etc. It has overcome the constraint of traditional machine learning approaches.
Hafiz Imran, Khalid Aamir, Muhammad Ilyas   +2 more
openaire   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Sensors, 2021
By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced.
Qiaodi Wen, Ziqi Luo, Ruitao Chen, Yifan Yang, Guofa Li   +4 more
doaj   +1 more source

AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
In order to address the overfitting problem caused by the small or simple training datasets and the large model’s size in Convolutional Neural Networks (CNNs), a novel Auto Adaptive Regularization (AAR) method is proposed in this paper. The relevant networks can be called AAR-CNNs. AAR is the first method using the “abstraction extent” (predicted by AE
Yao Lu 0008, Guangming Lu 0002, Yuanrong Xu, Bob Zhang 0001   +3 more
openaire   +1 more source

Performance Enhancement of Tin Chloride‐Incorporated Ferroelectric Polymer‐Based Artificial Synapse for Hardware Neural Networks

Advanced Functional Materials, EarlyView.
This paper proposes a highly efficient ferroelectric artificial synapse device based on an oxide semiconductor and SnCl2‐inserted P(VDF‐TrFE) gate dielectric layer. This FeFET significantly improved the synaptic performance due to the ion‐dipole interaction.
Hyun‐Soo Kim, Hyunkyu Lee, Yeonho Choi, Won Young Jang, Hyungmin Kim, Krishna Moorthy Ponnusamy, Hongseung Lee, Jaewook Yoo, Ji Ye Lee, S. Chandramohan, Seongin Hong, Hagyoul Bae, Hyeon‐Sik Jang, Sang Yeol Lee, Keun Heo   +14 more
wiley   +1 more source

Noise‐Limited Bit Precision in Ferroelectric Synaptic Transistors for High‐Resolution Neuromorphic Computing

Advanced Functional Materials, EarlyView.
Low‐frequency noise spectroscopy defines the resolvable conductance states of synaptic FeFETs by coupling read‐current fluctuation with usable dynamic range. The resulting noise‐limited bit precision establishes a universal, device‐agnostic reliability metric beyond the memory window, enabling quantitative benchmarking and rational design of high ...
Jaehong Park, Yeonseop Shin, Changhyeon Han, Sangwoo Kim, Eunchan Park, Youngchan Cho, Jaehong Min, Hyunjun Ahn, Jaekyoung Park, Cheol‐Woong Yang, Jangsaeng Kim, Daewoong Kwon, Wonjun Shin   +12 more
wiley   +1 more source

Wearable Kirigami‐Apertured Capacitive Sensors for Continuous Cardiac Volumetric Monitoring

Advanced Functional Materials, EarlyView.
A wearable kirigami‐apertured carbon nanotube‐paper composite (K‐CPC) capacitive sensor enables cardiac volumetric monitoring. The kirigami process creates a central aperture with cantilevered fibers that confine the electric field, enhancing both sensitivity and lateral resolution.
Yu‐Jen Cheng, Shawn Kim, Jonathan Avery, Younghoon Kwon, Jae‐Hyun Chung   +4 more
wiley   +1 more source

A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots

Entropy, 2020
Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear ...
Wenxing Lu, Haidong Rui, Changyong Liang, Li Jiang, Shuping Zhao, Keqing Li   +5 more
doaj   +1 more source