3 Revolutionary Advances in IoT Machine Learning

Manuel Nau for IoT Business News: Oracle is taking the Internet of Things to a new level with the announcement that its IoT cloud service will now incorporate artificial intelligence and machine learning to provide clients with better business data insights than ever. Oracle’s upgrade will include digital twin and digital thread capability, providing companies with an integrated analytical perspective on their entire supply chain. Companies will also be able to develop their own IoT field apps, which can then be integrated into a central analytics platform to provide real-time insights into business processes. As part of this update, Oracle is also introducing services that will empower IoT smart connected factories, fleet management and remote monitoring and repair.

Oracle’s move reflects its interest in capturing the B2B segment of the growing machine learning market. The machine learning as a service market was worth nearly $1 billion in 2016, which will grow to $16.4 billion by 2024, reflecting an annual compound growth rate of 43.7 percent. Fueling this growth are pioneering innovations in machine learning that make it increasingly invaluable to use of the Internet of Things. Here’s a look at three areas where machine learning innovations are transforming the IoT. Full Article:

The FLIR Si1-LD is an easy-to-use acoustic imaging camera for locating and quantifying pressurized leaks in compressed air systems. This lightweight, one-handed camera is designed to help maintenance, manufacturing, and engineering professionals identify air leaks faster than with traditional methods. Built with a carefully constructed array of MEMS microphones for high sensitivity, the Si1-LD produces a precise acoustic image that visually displays ultrasonic information, even in loud, industrial environments. The acoustic image is overlaid in real time on a digital image, allowing you to accurately pinpoint the source of the sound, with onboard analytics which quantify the losses being incurred. The Si1-LD features a plugin that enables you to import acoustic images to FLIR Thermal Studio suite for offline editing, analysis, and advanced report creation. Field analysis and reporting can also be done using the FLIR Acoustic Camera Viewer cloud service. Transferring of images can be managed via memory stick or USB data cable. Through a regular maintenance routine, the FLIR Si1-LD can help facilities reduce their environmental impact and save money on utility bills.

3 Revolutionary Advances in IoT Machine Learning

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FLIR Si1-LD - Industrial Acoustic Imaging Camera for Compressed Air Leak Detection

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