scholarly journals Layout Optimization of Multi-Type Sensors and Human Inspection Tools With Probabilistic Detection of Localized Damages for Pipelines

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 90598-90614
Author(s):  
Amin Aria ◽  
Shapour Azarm ◽  
Mohammad Modarres
Keyword(s):  
Layout Optimization ◽  
Probabilistic Detection ◽  
Human Inspection

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Continuous layout optimization of urban fire station

2013 ◽  
Vol 32 (3) ◽  
pp. 852-854
Author(s):  
Hou-qing LU ◽  
Hui YUAN ◽  
Cheng LIU
Keyword(s):  
Layout Optimization ◽  
Urban Fire ◽  
Fire Station

scholarly journals Layout optimization of heaving Wave Energy Converters linear arrays in front of a vertical wall

2021 ◽  
Author(s):  
Eva Loukogeorgaki ◽  
Constantine Michailides ◽  
George Lavidas ◽  
Ioannis K. Chatjigeorgiou
Keyword(s):  
Wave Energy ◽  
Vertical Wall ◽  
Layout Optimization ◽  
Linear Arrays ◽  
Wave Energy Converters ◽  
Energy Converters

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

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