A Deep Learning Approach to the Acoustic Condition Monitoring of a Sintering Plant

2018 ◽  
Author(s):  
Shahab Pasha ◽  
Christian Ritz ◽  
David Stirling ◽  
Paul Zulli ◽  
David Pinson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Learning Approach ◽  
Sintering Plant

A Simulation-Driven Deep Learning Approach for Condition Monitoring of Hydrodynamic Journal Bearings. Part I: Diagnostics of Wear Faults

2019 ◽  
Author(s):  
Diogo Stuani Alves ◽  
Tiago Machado ◽  
Katia Lucchesi Cavalca Dedini ◽  
Ozhan Gecgel ◽  
João Paulo Dias ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Journal Bearings ◽  
Learning Approach

A Simulation-Driven Deep Learning Approach for Condition Monitoring of Hydrodynamic Journal Bearings. Part II: Diagnostics of Ovalization Faults

2019 ◽  
Author(s):  
Ozhan Gecgel ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias ◽  
Gregory Bregion Daniel ◽  
Diogo Stuani Alves ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Journal Bearings ◽  
Learning Approach

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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