Accelerating the Industrial Internet of Things
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Siemens Case Studies Siemens | Using Machine Learning to Get Machines to Mimic Intuition

Siemens | Using Machine Learning to Get Machines to Mimic Intuition

Siemens | Using Machine Learning to Get Machines to Mimic Intuition
Renewable Energy
Autonomous Robots
Building Energy Management System (BEMS)

The ability to learn is a precondition for autonomy. With this in mind, Siemens researchers are developing knowledge networks based on deep learning-related simulated neurons and connections. Such networks can be used to generalize information by identifying associations between extraordinarily complex realms, such as the publicly accessible Internet and a company’s internal information systems. Far-reaching and generic, this technology appears to hold the potential of mimicking what humans call intuition.

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This specific case study analyzes the ramifications of neural networks on the renewable energy industry, specifically companies involved with wind turbines.  

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Biological systems that learn include everything from roundworms with approximately 300 nerve cells to adult elephants, whose brains contain 200 billion neurons. But regardless of whether you’re dealing with a fruit fly, a cockroach, a chimpanzee or a dolphin, the neurons of all of these creatures process and transmit information. Moreover, they do so for the same reasons: All organisms need to be able to discern and interpret their surroundings and then react appropriately in order to avoid danger and ensure their survival, as well as their ability to reproduce. They also need to be able to recall stimuli that signal risk or reward. In other words, learning is the key to survival in the natural environment.  Through creating a computational neural network that mimics the neurons in a human brain, Siemens' engineers are getting closer to creating virtual intuition.

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Cutting Edge (technology has been on the market for < 2 years)
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The capabilities of the latest machine learning systems are illustrated by AlphaGo, with which Google achieved a milestone in the development of self-learning machines and artificial intelligence in March 2016, when AlphaGo succeeded in defeating one of the world’s best Go players: Lee Sedol. The amazing thing is that up until Google’s accomplishment, this Asian strategy game had been considered to be too complex for a computer. 

Researchers at Corporate Technology (CT) are studying how machine learning techniques could be used to enable wind turbines to automatically adjust to changing wind and weather conditions, thus boosting their electricity output. The basis for self-optimizing wind turbines is the ability to derive wind characteristics from the turbines’ own operating data.  Up until now, this type of data has been used exclusively for remote monitoring and diagnosis; however, this same data can also be used to help improve the electricity output of wind turbines.”

Deep learning techniques are a new trend in machine learning. These techniques utilize up to 100,000 or more simulated neurons and ten million simulated connections —numbers that break all previous records in the field of artificial intelligence. Thanks to their many levels of artificial neurons, whereby each addresses a different level of abstraction of the material to be learned, deep learning techniques are expected, for instance, to enable new applications for automated image recognition.

Hardware that enables dual directional communication for data collection and control message delivery. Examples include cellular, Ethernet, and Wi-Fi.
Sensors transform energy into electrical data; they are the eyes and ears of IoT. Actuators transform electrical data into energy; they are the muscle of IoT.
Products used by end users that contain IoT technologies. Examples include enabled equipment, wearables, hand-held scanners, and tracking devices.
APIs are the market enabler for IoT. They allow users to manage devices, enable data transfer between software, and provide access capabilities.
Middleware integrates the diverse components of an IoT application by structuring communication, workflows, and business rules.
IoT analytics includes real-time or edge computing and batch analysis. Analytics can be behavioral, descriptive, predictive, or prescriptive.
Visualization solutions use dashboards, alerts, events, maps, and other tools to present easily comprehensible data to end users.
Data management solutions capture, index and store data in traditional database, cloud platforms, and fog systems for future use.
Security software provides encryption, access control, and identity protection to IoT solutions from data collection through end-user applications.
System integrators link IoT component subsystems, customize solutions, and ensure that IoT systems communicate with existing operational systems.
IoT data management consultancies help to make sense of big data, decide which data to maintain and for how long, and troubleshoot IT issues.
IoT hardware consultancies provide services such as solution specification, product design, connectivity setup, and partner identification.
IoT software consultancies support the development of data analytics, visualization solutions, and platforms, as well as integration into embedded systems.
Examples of business consulting services include go-to-market design and execution, business model development, channel development, and corporate M&A.
Connectivity as a service as provided by telecommunications companies, i.e. data transfer, radio waves.
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