Optimal burn-in strategy for high reliable products using convolutional neural network

Optimal burn-in strategy for high reliable products using convolutional neural network

Burn-in test is widely used to improve the product reliability from the customer's perspective by identifying and screening out defective individuals before they are marketed. For those high reliable products whose failures are caused by gradual degradation, burn-in test not only could pick out...

Cur síos iomlán

Sonraí bibleagrafaíochta
Príomhchruthaitheoirí:	Lyu, Yi, Gao, Junyan, Chen, Ci, Jiang, Yijie, Li, Huachuan, Chen, Kairui, Zhang, Yun
Rannpháirtithe:	School of Electrical and Electronic Engineering
Formáid:	Journal Article
Teanga:	English
Foilsithe / Cruthaithe:	2021
Ábhair:	Engineering::Electrical and electronic engineering Burn-in Deep Learning
Rochtain ar líne:	https://hdl.handle.net/10356/145919

Míreanna comhchosúla

Physiotherapy for burns and plastic reconstruction of the hand /
de réir: 300264 Glassey, Nicole
Foilsithe / Cruthaithe: (2004)

Optimal multi-objective burn-in policy based on time-transformed Wiener degradation process
de réir: Lyu, Yi, et al.
Foilsithe / Cruthaithe: (2019)

Towards efficient and reliable neural networks
de réir: de Jorge Aranda, P
Foilsithe / Cruthaithe: (2024)

Occupational Medicine Forum : what is the current treatment of asphalt skin burns? /
de réir: 392225 Perry, Guy F.

Optimising convolutional neural networks for large-scale neuroimaging studies
de réir: Dinsdale, NK
Foilsithe / Cruthaithe: (2021)

Towards diverse generation and reliable classification using neural networks
de réir: Kulharia, V
Foilsithe / Cruthaithe: (2022)

Investigation of the molecular effects of cooling human burns
de réir: Wright, EH
Foilsithe / Cruthaithe: (2017)

Toward efficient deep learning with sparse neural networks
de réir: Lee, N
Foilsithe / Cruthaithe: (2020)

Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network
de réir: Li, Kexin, et al.
Foilsithe / Cruthaithe: (2024)

Neural networks as data
de réir: Dupont, E
Foilsithe / Cruthaithe: (2022)

Chemical plumbing, lead burning and oxy-acetylene welding for plumbers and heating engineers/
de réir: 176210 Partington, Ernest Bicknell
Foilsithe / Cruthaithe: (1945)

Efficient neural network verification and training
de réir: De Palma, A
Foilsithe / Cruthaithe: (2023)

On kernel and feature learning in neural networks
de réir: He, B
Foilsithe / Cruthaithe: (2022)

Water uptake by fresh Indonesian peat burning particles is limited by water-soluble organic matter
de réir: Chen, Jing, et al.
Foilsithe / Cruthaithe: (2018)

Learning from burnout : developing sustainable leaders and avoiding career derailment /
de réir: 395720 Casserley, Tim, et al.
Foilsithe / Cruthaithe: (2009)

Heat transfer model to predict skin burn injury for firefighters
de réir: Zainol, Zaina Norhallis
Foilsithe / Cruthaithe: (2023)

Increased burned area in the Pantanal over the past two decades
de réir: Correa, Danielle Blazys, et al.
Foilsithe / Cruthaithe: (2023)

Understanding Gaussian noise injections in neural networks
de réir: Camuto, A
Foilsithe / Cruthaithe: (2021)

Measuring Laser Beams with a Neural Network (Data)
de réir: Hofer, L, et al.
Foilsithe / Cruthaithe: (2022)

Towards robust machine learning with graph neural networks
de réir: Jaeckle, F
Foilsithe / Cruthaithe: (2022)

Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network
de réir: Chattopadhyay, Soham, et al.
Foilsithe / Cruthaithe: (2022)

DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes
de réir: Le, Nguyen Quoc Khanh, et al.
Foilsithe / Cruthaithe: (2022)

Identifying and exploiting structures for reliable deep learning
de réir: Sanyal, A
Foilsithe / Cruthaithe: (2021)

Neural networks for inference, inference for neural networks
de réir: Webb, S
Foilsithe / Cruthaithe: (2018)

A layer-wise theoretical framework for deep learning of convolutional neural networks
de réir: Nguyen, Huu-Thiet, et al.
Foilsithe / Cruthaithe: (2022)

Transfer learning on convolutional activation feature as applied to a building quality assessment robot
de réir: Liu, Lili, et al.
Foilsithe / Cruthaithe: (2018)

Burnout : the effect of Jungian type
de réir: Garden, Anna-Maria.
Foilsithe / Cruthaithe: (2009)

Burnout among social workers /
de réir: Gillespie, David F.
Foilsithe / Cruthaithe: (1987)

Hierarchically Local Tasks and Deep Convolutional Networks
de réir: Deza, Arturo, et al.
Foilsithe / Cruthaithe: (2020)

Leveraging structure for optimization in deep learning
de réir: Berrada, L
Foilsithe / Cruthaithe: (2019)

Estimation of Potential Aircraft Fuel Burn Reduction in Cruise Via Speed and Altitude Optimization Strategies
de réir: Lovegren, Jonathan, et al.
Foilsithe / Cruthaithe: (2011)

The use of generative adversarial network and graph convolution network for neuroimaging-based diagnostic classification
de réir: Huynh, Nguyen, et al.
Foilsithe / Cruthaithe: (2024)

Towards robust neural networks: evaluation and construction
de réir: Lu, J
Foilsithe / Cruthaithe: (2021)

Fingerprinting deep neural networks - a DeepFool approach
de réir: Wang, Si, et al.
Foilsithe / Cruthaithe: (2021)

Evaluation of keratin templates on dermal regeneration of full-thickness burns
de réir: Moay, Zi Kuang
Foilsithe / Cruthaithe: (2024)

Dense-TNT: efficient vehicle type classification neural network using satellite imagery
de réir: Luo, Ruikang, et al.
Foilsithe / Cruthaithe: (2025)

CayleyNets : graph convolutional neural networks with complex rational spectral filters
de réir: Levie, Ron, et al.
Foilsithe / Cruthaithe: (2020)

Metal concentrations and source apportionment of PM2.5 in Chiang Rai and Bangkok, Thailand during a biomass burning season
de réir: Kayee, Jariya, et al.
Foilsithe / Cruthaithe: (2021)

Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
de réir: Zhang, Zilong, et al.
Foilsithe / Cruthaithe: (2024)

OnionNet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells
de réir: Wang, Zechen, et al.
Foilsithe / Cruthaithe: (2022)