Whitepapers

Whitepapers
Data-driven approach, what is it and why does it increase competitiveness

Why should a manufacturing company today, as well as any other organization, embark on a data-driven transformation journey? In 2020, research conducted by Statista measured a 12% increase, compared to 2018, in the adoption of a global data-driven approach for decision-making. Here's what is driving this change.

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Whitepapers
Smart Retrofitting by Design Thinking applied to an Industry 4.0 migration process in a steel mill plant

In this paper we propose a retrofitting methodology based on Design Thinking in a steel mill plant, and highlight how the retrofitting activity is important to allow even more companies to migrate to Industry 4.0, reducing the gap between SMEs and Large Industries for participation in the 4th Industrial Revolution.

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Whitepapers
Machine Learning at the Edge: a few applicative cases of Novelty Detection on IIoT gateways

In the paper we present the design and development of the Machine Learning (ML) modules for two case studies. In both cases we developed a ML model to learn the system’s normal behavior so to identify whichever abnormal condition may arise. Such a framework is usually referred to as Anomaly Detection (also known as Fault Detection or Novelty Detection). Our models succeeded at identifying the injected anomalies. In addition, no anomalies were observed when the model was fed with normal data. The results are discussed considering the trade-off between type of sensors, learning algorithm, training effort, computational demands.

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Whitepapers
Machine Learning and Artificial Intelligence: The Winning Formula all across the Companies

Maximum degree of automation, more flexibility in production, significantly lower personnel resources: innovative industrial companies hope for a prosperous future through Artificial Intelligence and Machine Learning.
And that is not all: PwC‘s consultants have
found out that such technologies are an
obligatory exercise in staying competitive.

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Whitepapers
ATM Protection Using Embedded Machine Learning Solutions

ATMs are an easy target for fraud attacks, like card skimming/trapping, cash trapping, malware and physical attacks. Attacks based on explosives are a rising problem in Europe and many other parts of the world. A report from the EAST association shows a rise of 80% of such attacks between the first six months of 2015 and 2016. This trend is particularly worrying, not only for the stolen cash, but also for the significant collateral damages to buildings and equipment.

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