Results 221 to 230 of about 38,896 (265)

EventFlow: Real‐time neuromorphic event‐driven classification of two‐phase boiling flow regimes

Droplet, EarlyView.
We present a real‐time flow regime classification framework that integrates neuromorphic event‐driven sensing with deep recurrent neural networks. Unlike traditional frame‐based approaches, our system captures sparse event streams from an event‐based camera, representing only the dynamic brightness changes at the individual pixel level.
Sanghyeon Chang, Srikar Arani, Nishant Sai Nuthalapati, Youngjoon Suh, Nicholas Choi, Siavash Khodakarami, Md Rakibul Hasan Roni, Nenad Miljkovic, Aparna Chandramowlishwaran, Yoonjin Won   +9 more
wiley   +1 more source

Correction to: DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes. [PDF]

Brief Bioinform
europepmc   +1 more source

Correction: SpaMWGDA: Identifying spatial domains of spatial transcriptomes using multi-view weighted fusion graph convolutional network and data augmentation. [PDF]

PLoS Comput Biol
Yuan L, Meng B, Wang Q, Hu C, Wang C, Huang DS.   +5 more
europepmc   +1 more source

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

Deep Underground Science and Engineering, EarlyView.
The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +1 more source

Low-carbon supply chain logistics risk prediction using meta-learning-based graph convolutional network on prototype space. [PDF]

Sci Rep
Wang Y, Sun Y.
europepmc   +1 more source

Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Deep Underground Science and Engineering, EarlyView.
B1 is bord width 1, B2 is bord width 2, L is the pillar length, W is the pillar width, red color and letter A represent the pillars, and white color and number 1 represent excavated areas. Pstress is the average pillar stress; σv is the vertical component of the virgin stress, MPa; and e is the areal extraction ratio. e = B o B o + B P ${\rm{e}}=\frac{{
Tawanda Zvarivadza, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source

Smart comprehend gesture based emotions recognition system for people with hearing disability utilizing spatio temporal graph convolutional network techniques. [PDF]

Sci Rep
Alshahrani R, Alharbi AAK.
europepmc   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Heterogeneous graph convolutional network for rumor detection with multi-level interactive fusion and graph reconstruction. [PDF]

Sci Rep
Liu Y, Wang J, Yin M, Zhao C.
europepmc   +1 more source

Alzheimer's disease classification using mutual information generated graph convolutional network for functional MRI. [PDF]

J Alzheimers Dis
Fu Y, Jiang L, Detre J, Wang Z, Alzheimer's Disease Neuroimaging Initiative.   +4 more
europepmc   +1 more source