Fachbereich Ingenieurwissenschaften und Kommunikation
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The proper use of protective hoods on panel saws should reliably prevent severe injuries from (hand) contact with the blade or material kickbacks. It also should minimize long-term lung damages from fine-particle pollution. To achieve both purposes the hood must be adjusted properly by the operator for each workpiece to fit its height. After a work process is finished, the hood must be lowered down completely to the bench. Unfortunately, in practice the protective hood is fixed at a high position for most of the work time and herein loses its safety features. A system for an automatic height adjustment of the hood would increase comfort and safety. If the system can distinguish between workpieces and skin reliably, it furthermore will reduce occupational hazards for panel saw users. A functional demonstrator of such a system has been designed and implemented to show the feasibility of this approach. A specific optical sensor system is used to observe a point on the extended cut axis in front of the blade. The sensor determines the surface material reliably and measures the distance to the workpiece surface simultaneously. If the distance changes because of a workpiece fed to the machine, the control unit will set the motor-adjusted hood to the correct height. If the sensor detects skin, the hood will not be moved. In addition a camera observes the area under the hood. If there are no workpieces or offcuts left under the hood, it will be lowered back to the default position.
This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.