Break arcs are rotated with the radial magnetic field formed by a magnet embedded in a fixed cathode contact. The break arcs are generated in a 48VDC resistive circuit. The circuit current when the contacts are closed is 10A. The depth of the magnet varies from 1mm to 4mm to change the strength of the radial magnetic field for rotating break arcs. Images of break arcs are taken by two high-speed cameras from two directions and the rotational motion of the break arcs is observed. The rotational period of rotational motion of the break arcs is investigated. The following results are obtained. The break arcs rotate clockwise on the cathode surface seen from anode side. This rotation direction conforms to the direction of the Lorentz force that affects to the break arcs with the radial magnetic field. The rotational period gradually decreases during break operation. When the depth of magnet is larger, the rotational period becomes longer.

Break arcs are rotated with a radial magnetic field formed by a permanent magnet embedded in a fixed contact. The break arcs are generated in a 48VDC resistive circuit. The circuit current is 10A when the contacts are closed. The polarity of the fixed contact in which the magnet is embedded is changed. The rotational radius and the difference of position between the cathode and anode spots are investigated. The following results are obtained. The cathode spot is moved more easily than the anode spot by the radial magnetic field. The rotational radius of the break arcs is affected by the Lorentz force that is caused by the circumferential component of the arc current and the axial component of the magnetic field. The circumferential component of the arc current is caused by the difference of the positions of the rotating cathode and anode spots.

A number of studies have recently been published concerning chaotic neuron models and asynchronous neural networks having chaotic neuron models. In the case of large-scale neural networks having chaotic neuron models, the neural network should be constructed using analog hardware, rather than by computer simulation via software, due to the high speed and high integration of analog circuits. In the present study, we discuss the circuit structure of a chaotic neuron model, which is constructed on the basis of the mathematical model of an asynchronous chaotic neuron. We show that the pulse-type hardware chaotic neuron model can be constructed on the basis of the mathematical model of an asynchronous chaotic neuron. The proposed model is an effective model for the cell body section of the pulse-type hardware chaotic neuron model for ICs. In addition, we show the bifurcation structure of our composed model, and discuss the bifurcation routes and return maps thereof.