Presently gather Pipeline Welding Maintenance information without human mediation and mistakes, 24 x 7. Save the time your group spends on information assortment by means of an App.
Expand over the Time
Any IT and specially AI related product has ability to improve as IT technology improves over the time. Let alone the Machine Learning character of this product. In other words, as the time goes by, the data mined, coming out of Big Data, obviously improves the efficiency of the product.
Presently access your pipeline's well-being and symptomatic information from anyplace utilizing a cell phone.
We have utilized the most recent innovation while finding some kind of harmony among cost and components. It is intended to finish the work with less cost, successfully.
Beyond the time barriers
Our App predicts & help you in distinguishing welded joints that need consideration & the region where they need consideration. The development scientific instruments can assist you with doing fast & backing your choices.
This product is derived from Artificial Intelligence which is one of the modern branches of today science. AI has helped many industries to change and AI era is called another revolution in human science history.
Related Basic Technologies
Pre-processing takes place through Machine Learning (Supervised Learning). The basic idea is to mimic the way a human inspector would inspect radioscopic images.
Subsequently, a set of geometrical features is extracted from the source as input to a classifier (CNN). Image segmentation is a commonly used technique partitioning an image into multiple segments or regions.
Double Wall Double Image (DWDI) exposure technique is a typical arrangement adopted for taking radiographic images of the pipe with a diameter equal to or less than 80 mm, thereby not allowing any internal access for the insertion of the radiation source.
We make use of pre-trained DNNs to map the knowledge for Visual Recognition. As DNNs are machine learning mechanisms that comprise expanded Convolutional Neural Networks (CNNs or ConvNets), during feature extraction, image classification takes place through CNN or ConVets.
These networks are typically applied to image classification, regression and feature learning, including prediction of series with Deep Long Short-Term Memory Neural Networks.
The CNN layer processes elementary visual features, such as edges and corners, located at different regions of the input. Once the match is made, the results can be viewed on a computer monitor remotely, or a mobile device.
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Luminous Product (version 1.02.2021)
Luminous product is a software application to be offered on an annual subscription basis
WE TAKE CARE OF
. Administration dashboard settings
. Back up and restoration ability
. File management
. Organization definitions
. Saving the processed image(s) with all inputs & fields in data base for future reference
.Roles definitions for users as per:
.Access permission to users for each page
.User(s) profile definition
- Selecting Color
- Adding comment and Location…
. Preparation reports based on all parameters:
- Damages percentage
- Type of defect
. Optional features selecting while report generation
. Administration dashboard reports with settings ability
. Recently processed images view possibility
. Visibility of processed images and reports along with user/client profile
. Ability of clients feedback after using application on Web Service.