Control systems are used to regulate an enormous variety of Machines, products, and
processes. They control quantities such as motion, temperature, heat flow, fluid flow,
fluid pressure, tension, voltage, and current. Most concepts in control theory are based
on having sensors to measure the quantity under control. In fact, control theory is
often taught assuming the availability of near-perfect feedback signals. Unfortunately,
such an assumption is often invalid. Physical sensors have shortcomings that can
degrade a control system.
Modern information technologies and the advent of Machines powered by artificial
intelligence (AI) have already strongly influenced the world of work in the 21st century.
Computers, algorithms and software simplify everyday tasks, and it is impossible
to imagine how most of our life could be managed without them. However, is it
also impossible to imagine how most process steps could be managed without
human force? The information economy characterised by exponential growth
replaces the mass production industry based on economy of scales
Inventors have long dreamed of creating Machines that think. This desire dates
back to at least the time of ancient Greece. The mythical figures Pygmalion,
Daedalus, and Hephaestus may all be interpreted as legendary inventors, and
Galatea, Talos, and Pandora may all be regarded as artificial life ( , Ovid and Martin
2004 Sparkes 1996 Tandy 1997 ; , ; , ).
We’re living through exciting times. The landscape of what computers can do is
changing by the week. Tasks that only a few years ago were thought to require
higher cognition are getting solved by Machines at near-superhuman levels of per-
formance. Tasks such as describing a photographic image with a sentence in idiom-
atic English, playing complex strategy game, and diagnosing a tumor from a
radiological scan are all approachable now by a computer. Even more impressively,
computers acquire the ability to solve such tasks through examples, rather than
human-encoded of handcrafted rules.
General paradigm in solving a computer vision problem is to represent a raw image
using a more informative vector called feature vector and train a classifier on top of
feature vectors collected from training set. From classification perspective, there are
several off-the-shelf methods such as gradient boosting, random forest and support
vector Machines that are able to accurately model nonlinear decision boundaries.
Hence, solving a computer vision problem mainly depends on the feature extraction
algorithm
German universities and scientists have repeatedly set the intermational standard in drive technology. Identification and active compensation of natural frequencies in oscillatory mechanics, status controls with monitoring structures incorporating acceleration sensors, adaptive compensation of measurement system deficiencies, self-adjusting detent torque compensation… everything invented with only a single aim in mind: to continue improv-ing the motion control, dynamics, precision and processing speed of your Machines. For the industrial applicabability of this technology scientific publications in proceedings and laboratory test rigs are not enough. These features consequenty need to be converted into cost-efficient and easily manageable products. That 's exactly what we have done.So in future, if you should need more than today ' smarket can offer you, now everything isgoing to be alright. With our new high-performance ServoOne drive series you will experi-ence
