Bremsstrahlung Radiation At a Vacuum Bubble Wall

When charged particles collide with a vacuum bubble, they can radiate strong electromagnetic waves due to rapid deceleration. Owing to the energy loss of the particles by this bremsstrahlung radiation, there is a non-negligible damping pressure acting on the bubble wall even when thermal equilibrium is maintained. In the non-relativistic region, this pressure is proportional to the velocity of the wall and could have influenced the bubble dynamics in the early universe.Comment: 6 pages, 2 figures, revtex, to appear in JKP

Feedback Linearization of Nonlinear Systems: Robustness and Adaptive Control.

Feedback linearization provides an effective means of designing nonlinear control systems. This method permits one to have an exactly equivalent linear system by using a coordinate transformation and state feedback. Once the nonlinear system is transformed to a linear system, one can proceed with well developed control technologies for linear systems. Feedback linearization is based on a model of the real system. If there is mismatch between the model and the real plant, feedback linearization does not yield an exactly linear system. The question of robustness then arises: will a controller based on the model be stable when applied to the real plant? We have developed a theoretical approach to analyze robustness of feedback linearization of SISO (Single-Input Single-Output) systems. We have also considered the dimensional reduction of a high dimensional model which is not a standard singularly perturbed system. Specifically we have found sufficient conditions for boundedness and convergence of the system trajectories when feedback linearization based on a nominal mathematical model is applied to an uncertain real plant which may have parametric and structural uncertainties as well as unmodeled dynamics. The developed approach does not require the restrictive conditions which are commonly used in the previously developed methods of robustness analysis. Furthermore, for parametric uncertainties a nonlinear adaptive control of feedback linearizable processes is proposed. The main feature of the proposed nonlinear adaptive control system is that it is relatively straightforward and simple. For this adaptive control system we have found sufficient conditions for stability of the output regulation and tracking of feedback linearizable systems using the second method of Lyapunov. Examples of the robustness analysis and the adaptive control for unstable chemical and biochemical reactors are given

Capturing scattered discriminative information using a deep architecture in acoustic scene classification

Frequently misclassified pairs of classes that share many common acoustic properties exist in acoustic scene classification (ASC). To distinguish such pairs of classes, trivial details scattered throughout the data could be vital clues. However, these details are less noticeable and are easily removed using conventional non-linear activations (e.g. ReLU). Furthermore, making design choices to emphasize trivial details can easily lead to overfitting if the system is not sufficiently generalized. In this study, based on the analysis of the ASC task's characteristics, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network, and therefore, we apply an element-wise comparison between different filters of a convolution layer's output. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power. Various experiments are conducted using the detection and classification of acoustic scenes and events 2020 task1-a dataset to validate the proposed methods. Our results show that the proposed system consistently outperforms the baseline, where the single best performing system has an accuracy of 70.4% compared to 65.1% of the baseline.Comment: Submitted to DCASE2020 worksho