Some of you may recall that back in October 2014, Hong Kong startup Ambi Labs unveiled its Ambi Climate as a gateway between your smartphone and your dumb air conditioner at home. But it isn't just about replacing your infrared remote control; what makes Ambi Climate unique is its machine learning capability, so that over time it learns your comfort preferences by way of various sensors, while also saving up to 20-percent energy according to user feedback. For instance, users in New York have a wider range of preferred temperatures, whereas users in Singapore peaked at around 77 degrees Fahrenheit (about 25 degrees Celsius) -- which is actually very close to what I prefer here in Hong Kong. On top of Comfort Mode, Ambi Climate also offers a Temperature Mode, an Away Mode (it only turns on the air conditioner to suit your settings) and a Manual Mode, as well as a timer and a scheduling feature.
The technology could also help human-driven and automated vehicles stay safe, for example by listening for emergency sirens or sounds indicating road surface quality. OtoSense has developed machine-learning software that can be trained to identify specific noises, including subtle changes in an engine or a vehicle's brakes. Under a project dubbed AudioHound, OtoSense has developed a prototype tablet app that a technician or even car owner could use to record audio for automated diagnosis, says Guillaume Catusseau, who works on vehicle noise in PSA's R&D department. Tests have shown that the system can identify unwanted noises from the engine, HVAC system, wheels, and other components.
More complex industrial equipment likely communicates over OPC or proprietary socket communications, but with all of their complexity, these machines still can't connect to the internet. Companies building IoT devices are solving this challenge by using gateways, also known as edge-based processing, to connect to cloud-based IoT platforms. However, connecting devices isn't as easy as updating software; instead, it's an investment in retrofitting old machines, replacing existing equipment, and enabling a workforce to leverage this equipment. After connecting devices and sending information to a data lake, we have to wait and observe what happens.
For example, the company's Software Environment for Neural Networks (SENN) is being continuously refined and adapted to new and evolving applications, including the optimization of gas turbines and wind turbines. Siemens Power Generation Services and CT have developed a system that continuously optimizes the operation and control of combustion in gas turbines. Based on AI from CT, the system, which is known as a Gas Turbine Autonomous Control Optimizer (GT-ACO), is currently being installed at a top customer in Asia. Improvements in overall gas turbine operation can be difficult to achieve because lower emissions characteristically result in shorter service life.
Falkner, Andreas (Siemens AG Austria) | Friedrich, Gerhard (University of Klagenfurt) | Haselböck, Alois (Siemens AG Austria) | Schenner, Gottfried (Siemens AG Austria) | Schreiner, Herwig (Siemens AG Austria)
The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period.
BuildingIQ, a provider of energy management software headquartered in California, provides tools to commercial building owners and managers to improve the energy efficiency of their building operations. BuildingIQ offers solutions that use technology originally developed by the Energy Division of the Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia's national lab.
German engineering powerhouse Siemens today announced that it has set up an innovation unit named'next47' (as the company was founded back in 1847) to "foster disruptive ideas more vigorously and to accelerate the development of new technologies", more specifically in the fields of artificial intelligence, blockchain, autonomous machines and what it calls'decentralized electrification'.
"We're going to see more complex products that consumers or different industries want us to manufacture," says Livio Dalloro, head of research for Siemens Corporate Technology. Dalloro gives an example of how the system, called SISPIs (Siemens Spiders), might eventually tackle a project. SISPIs are a proof of concept demonstrating that a flexible, autonomous robot team is possible--an idea first proposed by Siemens engineer Sinan Bank in 2014. Bank and Dalloro will use machine learning to analyze how the robots move and figure out better ways to do it.