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W. Jung

Publications and source records attributed to W. Jung.

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Characterizing expansivity through $C^*$-algebras

We study expansive homeomorphisms of a compact metric space $X$ through the lens of the commutative $C^*$-algebra $C(X)$ of continuous complex-valued functions, viewed as observables of the system. We introduce the notion of expansive observables: elements of $C(X)$ whose level sets distinguish distinct orbits. We prove that the expansive observables form an F$_\sigma$-subalgebra of $C(X)$, and we characterize them completely for connected equicontinuous homeomorphisms, showing that only constant observables are expansive in this setting. Furthermore, we establish that topologically conjugate homeomorphisms share the same algebra of expansive observables. Using this framework, we show that the set of periodic points intersects at most countably many level sets of any expansive observable. This provides $C^*$-algebraic proofs of well-known facts like for instance that the set of periodic points of an expansive homeomorphism is countable or that the sole continuum exhibiting homeomorphisms which are both expansive and equicontinuous are the degenerated ones. Finally, we prove that no homeomorphism of the circle or the unit interval admits a dense set of expansive observables, yielding a $C^*$-algebraic demonstration of the nonexistence of expansive homeomorphisms in these spaces.

math.DS

On the universal approximation of real functions with varying domain

We establish sufficient conditions for the density of shallow neural networks \cite{C89} on the family of continuous real functions defined on a compact metric space, taking into account variations in the function domains. For this we use the Gromov-Hausdorff distance defined in \cite{5G}.

math.GN

Thermal Effects on Damping Determination of Perpendicular MRAM Devices by Spin-Torque Ferromagnetic Resonance

We report device level damping measurements using spin-torque driven ferromagnetic resonance on perpendicular magnetic random-access memory (MRAM) cells. It is shown that thermal agitation enhances the apparent damping for cells smaller than about 55 nm. The effect is fundamental and does not reflect a true damping increase. In addition to the thermal effect, it is still found that device level damping is higher than film level damping and increases with decreasing cell size. This is attributed to edge damage caused by device patterning.

cond-mat.mes-hall

Role of Subgradients in Variational Analysis of Polyhedral Functions

Understanding the role that subgradients play in various second-order variational analysis constructions can help us uncover new properties of important classes of functions in variational analysis. Focusing mainly on the behavior of the second subderivative and subgradient proto-derivative of polyhedral functions, functions with polyhedral epigraphs, we demonstrate that choosing the underlying subgradient, utilized in the definitions of these concepts, from the relative interior of the subdifferential of polyhedral functions ensures stronger second-order variational properties such as strict twice epi-differentiability and strict subgradient proto-differentiability. This allows us to characterize continuous differentiability of the proximal mapping and twice continuous differentiability of the Moreau envelope of polyhedral functions. We close the paper with proving the equivalence of metric regularity and strong metric regularity of a class of generalized equations at their nondegenerate solutions.

math.OC

Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning

This paper proposes machine-independent feature engineering for winding inter-turn short circuit fault that uses electrical current signals. Electrical current signal collected from permanent magnet synchronous motor (PMSM) is subjected to different environmental and operational conditions. To solve these problems, robust current signal imaging method and deep learning-based feature extraction method are developed. The overall procedure includes the following three key steps: (1) transformation of a time-series current signal to two-dimensional image, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The proposed RP method develops from feature engineering that provides the dominant fault feature representations in a robust way. The proposed RP is designed that maximizes the features of inter-turn short fault and minimizes the effect of noise from systems with various capacities. To demonstrate the validity of the proposed method, two case studies are conducted using an artificial fault seeded testbed with two different capacities of motor. By calculating the feature using only the electrical current signal of the motor without the parameters related to the capacity of the motor, the proposed feature can be applied to motors with different capacities while maintaining the same performance.

eess.SP

Influence of graphene-substrate interactions on configurations of organic molecules on graphene: pentacene/epitaxial graphene/SiC

Pentacene has been used widely in organic devices, and the interface structure between pentacene and a substrate is known to significantly influence device performances. Here we demonstrate that molecular ordering of pentacene on graphene depends on the interaction between graphene and its underlying SiC substrate. The adsorption of pentacene molecules on zero-layer and single-layer graphene, which were grown on a Sifaced 6H-SiC(0001) wafer, was studied using scanning tunneling microscopy (STM). Pentacene molecules form a quasi-amorphous layer on zero-layer graphene which interacts strongly with the underlying SiC substrate. In contrast, they form a uniformly ordered layer on the single-layer graphene having a weak graphene-SiC interaction. Furthermore, we could change the configuration of pentacene molecules on the singlelayer graphene by using STM tips. The results suggest that the molecular ordering of pentacene on graphene and the pentacene/graphene interface structure can be controlled by a graphene-substrate interaction.

cond-mat.mes-hall