Results 111 to 120 of about 5,285,158 (212)

Research on Switching Current Model of GaN HEMT Based on Neural Network

Micromachines
The switching characteristics of GaN HEMT devices exhibit a very complex dynamic nonlinear behavior and multi-physics coupling characteristics, and traditional switching current models based on physical mechanisms have significant limitations.
Xiang Wang, Zhihui Zhao, Huikai Chen, Xueqi Sun, Shulong Wang, Guohao Zhang   +5 more
doaj   +1 more source

DT-CNN: Dilated and Transposed Convolution Neural Network Accelerator for Real-Time Image Segmentation on Mobile Devices

, 2019
A convolution neural network (CNN) accelerator is proposed for real-time image segmentation on mobile devices. The proposed CNN processor cuts down the redundant zero computations in dilated and transposed convolution for higher throughput.
Choi, Sungpill, Sungpill Choi, Hoi-Jun Yoo, Im, Dongseok, Kang, Sanghoon, Sanghoon Kang, Dongseok Im, Donghyeon Han, Han, Donghyeon, Yoo, Hoi-Jun   +9 more
core   +1 more source

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Shock and Vibration
Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability.
Baoquan Hu, Jun Liu, Yue Xu, Tianlong Huo   +3 more
doaj   +1 more source

Application of Convolutional Neural Networks (CNNs) in Agriculture

DMPedia Lecture Notes in Computer Science & Engineering
AI and machine learning applications are on the rise. Especially, deep learning is used very frequently in research these days. Convolutional Neural Networks (CNNs) are one such popularly used deep learning approach in the agricultural domain. In smart agriculture, CNNs find its use in identification, classification and mapping problems. It is used for
Akanksha Joshi, Rajeev Singh
openaire   +1 more source

ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos

, 2019
The task of crowd counting and density maps estimating from videos is challenging due to severe occlusions, scene perspective distortions and diverse crowd distributions.
Gao, Y, Han, J, Zhang, B, Miao, Y
core   +1 more source

Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis. [PDF]

J Digit Imaging, 2020
Raja H, Akram MU, Shaukat A, Khan SA, Alghamdi N, Khawaja SG, Nazir N.   +6 more
europepmc   +1 more source

Deep Learning and Convolutional Neural Networks (CNNs)

Deep Learning (DL), a subfield of Artificial Intelligence (AI) and Machine Learning (ML), has revolutionized computational intelligence by enabling machines to automatically learn hierarchical representations from large datasets. Among the various deep learning architectures, Convolutional Neural Networks have emerged as a dominant framework ...
V. V. Virginia, U.M. Mbanaso, E.S. Chaku, I.O. Clementina, Gilbert Aimufua   +4 more
openaire   +1 more source

HUMAN ACTIVITY RECOGNIZATION USING CONVOLUTION NEURAL NETWORK [PDF]

, 2020
Human improvement confirmation fuses mentioning times strategy information, assessed at inertial sensors, for example, accelerometers or whirligigs, into one of pre-portrayed works out.
Raju, S, Raju, Anthoti, Rajaram, Dr. J, Reddy, E Muralidhar   +3 more
core  

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics. [PDF]

Insects, 2020
Zhang Z, Zhan W, He Z, Zou Y.
europepmc   +1 more source

Convolution and max pooling layer accelerator for convolutional neural network [PDF]

, 2020
Convolutional Neural Network (CNN) are widely used in the field of computer vision and show its great advantages in image classification, object recognition, video surveillance.
Goh, Jinn Chyn
core