The pullback attractors for the Higher-order Kirchhoff-type equation with strong linear damping

The paper investigates pullback the attractors for the Higher-order Kirchhoff-type equation with strong linear damping:.Firstly, we do priori estimation for the equations to obtain the existence and uniqueness of the solution inby some assumptions the Galerkin method. Then, we prove existence of the pullback attractorsin

Oxidative Etching of Hexagonal Boron Nitride Toward Nanosheets with Defined Edges and Holes

Lateral surface etching of two-dimensional (2D) nanosheets results in holey 2D nanosheets that have abundant edge atoms. Recent reports on holey graphene showed that holey 2D nanosheets can outperform their intact counterparts in many potential applications such as energy storage, catalysis, sensing, transistors, and molecular transport/separation. From both fundamental and application perspectives, it is desirable to obtain holey 2D nanosheets with defined hole morphology and hole edge structures. This remains a great challenge for graphene and is little explored for other 2D nanomaterials. Here, a facile, controllable, and scalable method is reported to carve geometrically defined pit/hole shapes and edges on hexagonal boron nitride (h-BN) basal plane surfaces via oxidative etching in air using silver nanoparticles as catalysts. The etched h-BN was further purified and exfoliated into nanosheets that inherited the hole/edge structural motifs and, under certain conditions, possess altered optical bandgap properties likely induced by the enriched zigzag edge atoms. This method opens up an exciting approach to further explore the physical and chemical properties of hole-and edge-enriched boron nitride and other 2D nanosheets, paving the way toward applications that can take advantage of their unique structures and performance characteristics

Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

Breaking of brightness consistency in optical flow with a lightweight CNN network

Sparse optical flow is widely used in various computer vision tasks, however assuming brightness consistency limits its performance in High Dynamic Range (HDR) environments. In this work, a lightweight network is used to extract illumination robust convolutional features and corners with strong invariance. Modifying the typical brightness consistency of the optical flow method to the convolutional feature consistency yields the light-robust hybrid optical flow method. The proposed network runs at 190 FPS on a commercial CPU because it uses only four convolutional layers to extract feature maps and score maps simultaneously. Since the shallow network is difficult to train directly, a deep network is designed to compute the reliability map that helps it. An end-to-end unsupervised training mode is used for both networks. To validate the proposed method, we compare corner repeatability and matching performance with origin optical flow under dynamic illumination. In addition, a more accurate visual inertial system is constructed by replacing the optical flow method in VINS-Mono. In a public HDR dataset, it reduces translation errors by 93\%. The code is publicly available at https://github.com/linyicheng1/LET-NET.Comment: 7 pages,7 figure

Synchro-Transient-Extracting Transform for the Analysis of Signals with Both Harmonic and Impulsive Components

Time-frequency analysis (TFA) techniques play an increasingly important role in the field of machine fault diagnosis attributing to their superiority in dealing with nonstationary signals. Synchroextracting transform (SET) and transient-extracting transform (TET) are two newly emerging techniques that can produce energy concentrated representation for nonstationary signals. However, SET and TET are only suitable for processing harmonic signals and impulsive signals, respectively. This poses a challenge for each of these two techniques when a signal contains both harmonic and impulsive components. In this paper, we propose a new TFA technique to solve this problem. The technique aims to combine the advantages of SET and TET to generate energy concentrated representations for both harmonic and impulsive components of the signal. Furthermore, we theoretically demonstrate that the proposed technique retains the signal reconstruction capability. The effectiveness of the proposed technique is verified using numerical and real-world signals

Exponential attractor for the Higher-order Kirchhoff-type equation with nonlinear strongly damped term

We investigate the existence of exponential attractor for the Higher-order Kirchhoff-type equation with nonlinear strongly damped term: .For strong nonlinear damping σ(s) and φ(s) ,we assumptions .Under of the proper assume H1-H3, we first prove the squeezing property of the nonlinear semigroup associated with this equation, then the existence of exponential attractor is proved

Holey graphene: a unique structural derivative of graphene

Holey graphene (hG), also called graphene nanomesh, is a structural derivative of graphene. hG is formed by removing a large number of atoms from the graphitic plane to produce holes distributed on and through the atomic thickness of the graphene sheets. These holes, sometimes with abundant functional groups around their edges, impart properties that are uncommon to intact graphene but advantageous toward various applications. In this review, strategies to prepare hG and the related applications that take advantage of the unique structural motif of these materials are discussed. Prospects are then given for this emerging class of graphene derivatives