IoTI.com Content Director Brian Buntz wrote recently about the resources Siemens is throwing at software, and while that's significant, I'm more interested in Siemens' AI and machine learning work. For the past decade, Siemens' AI efforts have been focused on improving control of industrial processes using deep learning and reinforcement learning. An example of this technology is Siemens' "self-optimizing" gas turbines that leverage reinforcement learning. You can extend the classical control loop with a machine-learning loop using neural networks, making it dynamic and thus creating a new control policy.
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.
Structures built with this system could be produced faster and less expensively than traditional construction methods allow, the researchers say. Following this approach for their initial work, the researchers showed that the system can be easily adapted to existing building sites and equipment, and that it will fit existing building codes without requiring whole new evaluations, Keating explains. Keating says the team's analysis shows that such construction methods could produce a structure faster and less expensively than present methods can, and would also be much safer. But the ability to design and digitally fabricate multifunctional structures in a single build embodies a shift from the machine age to the biological age -- from considering the building as a machine to live in, made of standardized parts, to the building as an organism, which is computationally grown, additively manufactured, and possibly biologically augmented."
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.
Skilled and experienced people are needed throughout the design and construction process. The number of such experts employed by many companies, however, is not sufficient to allow the full exploitation of the post recession UK market. Intelligent Systems or Knowledge Based techniques allow an organisation to capture key expertise within computer tools.
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.
Machine learning and data mining are research areas of computer science whose quick development is due to the advances in data analysis research, growth in the database industry and the resulting world construction market needs for methods that are capable of extracting valuable knowledge from large data stores. Knowledge discovery in database (KDD) is a process that combine data mining (DM) techniques from machine learning, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from a large database. Tarek Mahfouz proposed to create an automated decision support tool for productivity rate estimating through machine learning. The potential of machine learning modeling to be adopted for assigning productivity rates, in construction project databases, constructability analysis and other structural engineering methods making it a powerful tool for decision making and changing the image of a construction industry.
We analyzed those articles and compiled a list of 10 most interesting examples, where AI technology used for construction performance diagnostics, intelligent planning of construction projects or creating construction robot fleet management systems. The influences of various control parameters (including learning rate and momentum factor) and various network architecture factors (including the number of hidden units and the number of hidden layers) are examined. Knowledge representation and reasoning techniques derived from artificial intelligence research permit computers to generate plans, not merely analyze plans produced by humans. They were modeling a multistory office building project for construction planning, implementing SIPE to plan this project, and describing SIPE's performance in planning the construction of large-scale multistory buildings.