Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Lévy noise-induced self-induced stochastic resonance in a memristive neuron

All previous studies on self-induced stochastic resonance (SISR) in neural systems have only considered the idealized Gaussian white noise. Moreover, these studies have ignored one electrophysiological aspect of the nerve cell: its memristive properties. In this paper, first, we show that in the excitable regime, the asymptotic matching of the deterministic timescale and mean escape timescale of an $$\alpha $$ α -stable Lévy process (with value increasing as a power $$\sigma ^{-\alpha }$$ σ - α of the noise amplitude $$\sigma $$ σ , unlike the mean escape timescale of a Gaussian process which increases as in Kramers’ law) can also induce a strong SISR. In addition, it is shown that the degree of SISR induced by Lévy noise is not always higher than that of Gaussian noise. Second, we show that, for both types of noises, the two memristive properties of the neuron have opposite effects on the degree of SISR: the stronger the feedback gain parameter that controls the modulation of the membrane potential with the magnetic flux and the weaker the feedback gain parameter that controls the saturation of the magnetic flux, the higher the degree of SISR. Finally, we show that, for both types of noises, the degree of SISR in the memristive neuron is always higher than in the non-memristive neuron. Our results could guide hardware implementations of neuromorphic silicon circuits operating in noisy regimes.

Assessment of Partial Discharges in the Air by Application of Corona Camera

This paper reports the results of the analysis of measurements involving partial discharges (PD) occurring in the air using a corona camera (UV camera). The measurements were carried out in laboratory conditions and applied two electrode systems: needle–needle and needle–plate, in order to obtain various electric field distributions. The measurements of PDs, including a variety of alternatives, were carried out using a portable UV camera, taking into account the impact of the camera gain parameter and its distance from the PD sources. As a result, some important regularities and characteristics were identified that could significantly affect the ability to assess PDs by application of UV camera measurements. In addition, the results obtained can be employed for non-invasive diagnostic measurements performed on working power equipment and may be useful in further work on standardizing the result interpretation method obtained from measurements using a UV camera.

Lévy Noise-induced Self-induced Stochastic Resonance in a Memristive Neuron

Self-induced stochastic resonance (SISR) is a subtle resonance mechanism requiring a nontrivial scaling limit between the stochastic and the deterministic timescales of an excitable system, leading to the emergence of a limit cycle behavior which is absent without noise. All previous studies on SISR in neural systems have only considered the idealized Gaussian white noise. Moreover, these studies have ignored one electrophysiological aspect of the nerve cell: its memristive properties. In this paper, first, we show that in the excitable regime, the asymptotic matching of the mean escape timescale of an α-stable Lévy process (with value increasing as a power σ-α of the noise amplitude σ, unlike the mean escape timescale of a Gaussian process with the value increasing as in Kramers' law) and the deterministic timescale (controlled by the singular parameter) can also induce a strong SISR. In addition, it is shown that the degree of SISR induced by Lévy noise is not always higher than that of Gaussian noise. Second, we show that, for both types of noises, the two memristive properties of the neuron have opposite effects on the degree of SISR: the stronger the feedback gain parameter that controls the modulation of the membrane potential with the magnetic flux and the weaker the feedback gain parameter that controls the saturation of the magnetic flux, the higher the degree of SISR. Finally, we show that, for both types of noises, the degree of SISR in the memristive neuron is always higher than in the non-memristive neuron. Our results could find applications in designing neuromorphic circuits operating in noisy regimes.

Phototransistor: The Story So Far

The research paper prospects the theory of phototransistor ranging from the history of the device to its application in the real world. The research paper deep dives into the characteristics of the phototransistor while discussing its dependence on bias drive, bias voltage, and illumination intensity. The research paper includes a comparative study between the various types of phototransistors based on optical gain, spectral range, and efficiency. It also concludes the best illumination method for the phototransistor based on the optical gain parameter.

Genetic algorithm (GA)–based delay feedback control of chaotic behavior in the voltage mode controlled direct current (DC) drive system

In this paper, the chaotic behavior and chaos control in a voltage mode controlled DC drive system are investigated. The dynamical behavior of the system changing from the fundamental state to chaotic regime is obtained by the variation of some parameters. Two kinds of delay feedback controllers are designed to induce and control chaos in the voltage-mode DC drive system that exhibits chaotic behavior under certain conditions. The proposed control scheme is able to suppress chaos on the voltage mode controlled DC drive system operating in continuous conduction mode. With variation of controller parameters, the transition of dynamical behavior in the system has been demonstrated from different possible states to regular state, which may be named as period-1 operation. Unlike the traditional delay feedback control method, not only the feedback gain parameter K but also the delay parameter τ is used as variable parameters of the controller. Moreover, the genetic algorithm is used to simultaneously optimize both the feedback gain parameter K and delay parameter τ to improve the effectiveness of the controller. Numerical results show that the proposed method can control unstable periodic orbits and suppress chaos in the system, and also, optimized controller parameters provide fast response for transition from chaotic operation to normal operation.