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Three practical applications of deep learning and IoT in oil and gas - IoT Agenda

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Deep learning and IoT are two game-changing technologies that have the potential to revolutionize the stakes for oil and gas companies facing profitmaking pressure in the face of the dramatic drop in price of oil. Deep learning algorithms can automatically detect pixel signatures from drone footage for cracks and leaks that humans can miss, thereby minimizing infrastructure risk. While providing remote diagnostic services to industrial assets, the conventional form of interaction is through traditional dashboard communications. With the advent of natural language processing algorithms powered by deep learning, field technicians can interact with the asset diagnostic applications through voice interactions just as bots help in customer service.


The top 20 industrial IoT applications

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The term "industrial Internet of Things" has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. The company has integrated sensors to tools and machines on the shop floor and given workers wearable technology -- including industrial smart glasses -- designed to reduce errors and bolster safety in the workplace. Gehring uses the same cloud-based real-time tracking to reduce downtime and optimize its own manufacturing productivity through monitoring its connected manufacturing systems, visualizing and analyzing data from its machine tools in the cloud. While it offers an IoT platform known as Lumada, Hitachi also makes a plethora of products leveraging connected technology, including trains, which the company is beginning to sell as a service.


Goodbye CFO? Bots and blockchain are taking over soon

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Chief financial officer (CFO) at global recruitment company Airswift, Tim Briant says artificial intelligence is going to disrupt finance departments completely, with bots replacing people. Chief financial officer at cloud accounting software company Xero, Sankar Narayan says artificial intelligence is powering his company's growth and enabling finance teams to spend more time conducting analysis. Technology has changed the role of the chief financial officer and artificial intelligence could erase it completely, predicts Todd Ford, CFO at spending management software company Coupa. The role of the CFO is to be steward of the company's capital and make decisions that maximise shareholder value CFOs of public companies need to embrace technological change to deliver shareholder value.


Video Friday: Isaac Plays Dominoes, iCub Cleans Up an Octopus, and Weaponized Plastic Fighting

IEEE Spectrum Robotics Channel

The Isaac robot simulator advances these tasks by providing an AI-based software platform that lets teams train robots in highly realistic virtual environments and then transfer that knowledge to real-world units. Under the hood, it demonstrated the ability to apply previously learned models of tool affordances, tool classification from vision, automatic tool pose detection, object segmentation and full/empty hand classification to achieve its task. By utilizing quadrotors attached to a mainframe via passive spherical joints as rotating-thrust generator, this SmQ (Spherically-connected multiple Quadrotor) system is fully-actuated (e.g., can resist sideway wind without tilting) and also backdrivable (e.g., impedance control possible for compliant interaction). With design optimization to address the tight weight-thrust margin of current rotor and battery technologies and proper control design, the ODAR system can exhibit such capability for "real" manipulation as 1) downward pushing force larger than 6kg (much larger than its own weight of 2.6kg) and 2) peg-in-hole teleoperation with radial tolerance of only 0.5mm, all unprecedented by other aerial manipulation systems (e.g., drone-manipulator).


Artificial Intelligence: The Future Of Oil And Gas

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In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance. By using AI software to analyze the company's large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods.


Callaghan forms Digital Energy Hub

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Callaghan Innovation will establish the Digital Energy Hub, an initiative to encourage early adoption and commercialisation by the energy sector of the next wave of digital technologies – namely Artificial Intelligence (AI), Big Data, Blockchain, Cloud Analytics and Internet of Things. In an interview with Idealog, Stu Christie explains that he sees the biggest opportunities for Artificial Intelligence (AI) technologies being in agriculture, manufacturing, infrastructure and transportation. From 2010 to 2017 the issue of digitalisation has been identified as one of the biggest movers among all issues displayed in the the World Energy Councils (WEC) energy issues maps. Have your say and play a part in forming New Zealand's energy issues map.


Machine-learning earthquake prediction in lab shows promise

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By listening to the acoustic signal emitted by a laboratory-created earthquake, a computer science approach using machine learning can predict the time remaining before the fault fails. To study the phenomena, the team analyzed data from a laboratory fault system that contains fault gouge, the ground-up material created by the stone blocks sliding past one another. Following a frictional failure in the labquake, the shearing block moves or displaces, while the gouge material simultaneously dilates and strengthens, as shown by measurably increasing shear stress and friction. "As the material approaches failure, it begins to show the characteristics of a critical stress regime, including many small shear failures that emit impulsive acoustic emissions," Johnson described.


Predicting Earthquakes with Machine Learning - insideHPC

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By listening to the acoustic signal emitted by a laboratory-created earthquake, a computer science approach using machine learning can predict the time remaining before the fault fails. Following a frictional failure in the labquake, the shearing block moves or displaces, while the gouge material simultaneously dilates and strengthens, as shown by measurably increasing shear stress and friction. "As the material approaches failure, it begins to show the characteristics of a critical stress regime, including many small shear failures that emit impulsive acoustic emissions," Johnson described. Read the paper: "Machine learning predicts laboratory earthquakes" Geophysical Research Letters.


Machine learning can predict simulated earthquakes by listening to fault lines

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The movement released energy in the form of seismic waves -- which was then analyzed by the team's artificial intelligence. Once this AI has been trained on an experiment, it can be used to make very accurate predictions of the time remaining before the next laboratory earthquake, for the same experiment but later on, or even for a different experiment. Concretely, even right after a laboratory earthquake, the AI can listen to the experiment for a very short duration, and make an accurate prediction of the time remaining before the next quake." Rouet-LeDuc observes that the fact that the work of the team in lab conditions provides hope that real world earthquake prediction may, in fact, be possible.


Machine Learning Could Improve Earthquake Prediction

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The team analyzed data from a laboratory fault system that contains fault gouge, the ground-up material created by the stone blocks sliding past one another. When a frictional failure occurred in the lab quake, the shearing block moves or displaces, while the gouge material simultaneously dilates and strengthens based on measurably increasing shear stress and friction. "These signals resemble Earth tremor that occurs in association with slow earthquakes on tectonic faults in the lower crust," said lead researcher Paul Johnson, in a statement. Findings from this experiment could not only have potential significance for earthquake forecasting, but could also potentially be applicable in all forms of failure scenarios, like non-destructive testing of industrial testing of industrial materials, avalanches, and other events.