Challenges in Automation, Robotics and Measurement by Roman Szewczyk, Cezary Zieliński, Malgorzata Kaliczyńska

By Roman Szewczyk, Cezary Zieliński, Malgorzata Kaliczyńska

This publication provides the set of papers permitted for presentation on the foreign convention Automation, held in Warsaw, 2-4 March of 2016. It offers the examine effects provided by means of most sensible specialists within the fields of business automation, keep watch over, robotics and dimension innovations. each one bankruptcy provides a radical research of a selected technical challenge that's frequently via numerical research, simulation, and outline of result of implementation of the answer of a true global challenge. The awarded theoretical effects, useful strategies and directions should be important for either researchers operating within the zone of engineering sciences and for practitioners fixing business difficulties.


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Extra info for Challenges in Automation, Robotics and Measurement Techniques: Proceedings of AUTOMATION-2016, March 2-4, 2016, Warsaw, Poland (Advances in Intelligent Systems and Computing)

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Dummy subject circuit is using as signal source with simulation of four main brain wave bands (Alpha, Beta, Gamma, Delta) and common-mode voltage source. Worst case electrode impedance for dry electrodes in the model is 150 kΩ with 12 kΩ impedance mismatch. 3 Hz and highest (−3 dB) cut-off frequency Fh = 100 Hz for the ideal components tolerances. More precision the Monte Carlo simulation considers resistors and capacitors tolerances. Figure 5 shows Monte Carlo simulation results for 1, 5, and 10 % passive components tolerances.

5) Analyzing the obtained vector basis of the frequency magnitude and generating the PWM signal. Classification of obtained frequency is shown in Table 1. As a the LED drivers the three general purpose widely available NPN Transistors 2N3904 are used. Transistors are connected in switching configuration. Resistors R7, R8 and R9 limiting the red, blue and green LEDs current to 30 mA. 5 Electrodes Design and Housing The best results in EEG measurement can be obtained by the use of factory-made electrodes.

For model training the efficient Broyden-Fletcher-Goldfarb-Shanno (BFGS) nonlinear optimisation algorithm is used, training is carried out in the parallel (recurrent) configuration. e. ???? = 1, nA = nB = 2) as in [6, 8]. In order to find a neural model with good accuracy and generalisation abilities the networks with K = 1, … , 7 hidden nodes are trained and compared. For each structure training is repeated 10 times (because of possible shallow local minima), all weights are initialised randomly. Table 1 presents properties of the best obtained models.

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