Autonomously driving, digital medicine, robots in care, criminology or suicide prevention. Pattern recognition is the key factor in all steps towards artificial intelligence, neuronal applications, human-machine interaction or self-sufficiently acting robots.
The reason is that computers are initially stupid and completely without a brain, an empty hard drive. Insofar a computer which is newly created is like a baby: it can’t do anything, hasn’t got a grasp of anything, doesn’t know what it senses. Just like a baby, the computer hast to learn. By observing its environment, it slowly learns that a certain perception has a completely independent significance and requires a specific response, it becomes more and more capable of acting and is finally able to move safely in a complex world. Because the diversity of perceptions is infinite – there can always be new nuances and variations – life is permanent learning, or at least it should be. The more open a person is, the higher is the person’s willingness to approach new things, the more intelligent the person usually is.
The same applies to a computer and a machine. Before a certain independence or even the ability of decision making is reached, millions of patterns had to be learned and corresponding reactions had to be programmed. Road sign red or green, or is it a tree in autumn? A rolling ball, possibly a child which is not visible yet, danger to life, emergency stop? An enormous effort is required before a machine can even begin to understand or to decide anything. Since the storage capacities and processing speeds are literally exploding, the point in time for independently operating machines is getting closer. Machines even have a distinctive advantage over humans: The knowledge of a computer doesn’t get lost, it can be copied and further developed in other computer brains. Logically, this accelerates development exponentially.
Artificial intelligence is based on pattern recognition and the recognition of trends within patterns. The technical infrastructure of AI, i.e. neural networks, are constantly learning new patterns and linking them to more and more pattern packages, so that the ability to make decisions improves constantly with an ever increasing speed.
Economically interesting: The machine evaluates all available data and suggests a course of action.
We at QASS are convinced that measuring systems without pattern recognition are going to disappear from the market in the near future and production processes without pattern recognition will no longer be competitive. Therefore we have further developed the QASS Optimizer. We want to be able to provide our customers today with the industrial standard of tomorrow.
Our new measuring principle high-frequency pulse measurement (HFIM) allows a quantum leap in pattern recognition. As a new analysis and data management system, patterns in crack detection (crack patterns) are detected and recognized in real time and in-process. Adaptive frequency filters are used to distinguish between process and machine noises so that it can be unerringly decided if a noise is a crack or a pseudo crack.
- Higher product quality through selective crack detection
- Higher component safety
- In-line, in-process and in real time
- Fewer pseudo-rejects
- Savings potential and cost reduction
- User-friendly Plug & Play, comfortable conversion of CiS.01