NeuroTechnologijos installs a NeuroMem-powered monitoring system in a steel blaster furnace in Magnitogorsk – Russia. Its solution is composed of a bank of NT Adaptive Controllers designed and manufactured by NeuroTechnologijos and mounted in an industrial enclosure. Each NT Adaptive Controller receives signals from the machinery equipment and uses a NeuroMem neural network to verify that the signals stay within normal waveforms in amplitude, frequency and envelope. If novelties are detected, a second neural network can automatically learn the new waveforms for later review by a human supervisor.
Pisces Fish Machinery Inc. has developed and sold over 50 smart cameras powered by NeuroMem neurons to inspect fishes directly on the fileting lines on-board of fishing vessels. At the beginning of a new expedition, the fishermen perform the training of the neurons through a simple touch screen interface. The camera inspects 6 fishes per second with 98% accuracy and as a result the crew can be reduced leaving more storage space for the catch.
Surface inspection systems do not have to be a big investment in term of budget, resources and time.
General Vision has developed an AI camera powered by a NeuroMem network to detect defects and which can be assembled in-line with other identical cameras to cover any width of material passing on a belt or float. The cameras can be snapped on a simple din rail and spaced regularly, or not, to monitor 24/7 the quality of glass, plastic, vinyl, wood, paper and pulp, fabrics, printing, and more.
Depending on the material and installation, the training can be as simple as training on only one camera and then exporting the knowledge built by its neurons, to the neurons of the other cameras. Sometimes, training may require some tuning for the 2 cameras at each end of the line and this is where the real-time learning capabilities of the neurons is very practical.