Observer-Based Robust Tracking Control for a Class of Switched Nonlinear Cascade Systems

This paper is devoted to robust output feedback tracking control design for a class of switched nonlinear cascade systems. The main goal is to ensure the global input-to-state stable (ISS) property of the tracking error nonlinear dynamics with respect to the unknown structural system uncertainties and external disturbances. First, a nonlinear observer is constructed through state transformation to reconstruct the unavailable states, where only one parameter should be determined. Then, by virtue of the nonlinear sliding mode control (SMC), a discontinuous nonlinear output feedback controller is designed using a backstepping like design procedure to ensure the ISS property. Finally, an example is provided to show the effectiveness of the proposed approach

Phy-chemical Attributes of Nano-scale V2O5/TiO2 Catalyst and Its’ Effect on Soot Oxidation

The V2O5 catalysts which supported on nano-scale TiO2 with variation of vanadium contents (5%, 10%, 20% and 40%) were prepared by an incipient-wetness impregnation method. The phase structures of nano-scale V2O5/TiO2 catalysts with different loading rates were characterized by Scanning electron microscope (SEM), X-Ray diffraction (XRD) and Fourier transform infrared (FT-IR) spectra. The oxidation activities of catalysts over diesel soot were performed in a themogravimetric analysis (TGA) system. The kinetics of the catalytic oxidation process were analyzed based on Flynn-Wall-Ozawa method. The characterization results showed that the phase structure of V2O5 supported on TiO2 depends heavily on the vanadium contents, which will put great effects on the catalytic performances for soot oxidation. At a low vanadium loading rates (V5-V20), active species exist as monomers and polymeric states. At a high loading rate (V40), the crystalline bulk V2O5 covers the surface of TiO2. The formed crystal structure occupied the active sites and led a decreasing in the catalytic effect. By comparing the characteristics temperatures of soot oxidation over V2O5 catalysts, the catalytic activities of catalysts with different loading rates for soot oxidation can be ranked as: V5 < V10 < V40 < V20. Via pyrolysis kinetics analysis, it is revealed that the activation energy of soot oxidation is minimum when the vanadium loading rates is 20%, which is fit well with the TG experimental results. The consistency of pyrolysis kinetics and TG experimental results confirm that the best activity catalyst is V20 in discussed catalysts of this paper, which is nearest to the monolayer dispersion saturated state of V2O5/TiO2 catalyst. Moreover, it convincingly demonstrate the obvious threshold effect in V2O5 catalysts.

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

Heterogeneous graph neural networks (HGNNs) have been widely applied in heterogeneous information network tasks, while most HGNNs suffer from poor scalability or weak representation when they are applied to large-scale heterogeneous graphs. To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning. Unlike traditional heterogeneous graph neural networks, we adopt the contrastive learning mechanism to deal with the complex heterogeneity of large-scale heterogeneous graphs. We first learn relation-aware node embeddings under the network schema view. Then we propose a novel positive sample selection strategy to choose meaningful positive samples. After learning node embeddings under the positive sample graph view, we perform a cross-view contrastive learning to obtain the final node representations. Moreover, we adopt the label smoothing technique to boost the performance of RHCO. Extensive experiments on three large-scale academic heterogeneous graph datasets show that RHCO achieves best performance over the state-of-the-art models

Facile Preparation, Characterization, and Highly Effective Microwave Absorption Performance of CNTs/Fe 3

A facile method has been developed to synthesize light-weight CNTs/Fe3O4/PANI nanocomposites. The formation route was proposed as the coprecipitation of Fe2+ and Fe3+ and an additional process of in situ polymerization of aniline monomer. The structure and morphology of CNTs/Fe3O4/PANI were characterized by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared (FTIR) spectroscopy. The TEM investigation shows that the CNTs/Fe3O4/PANI nanocomposites exhibit less intertwined structure and that many more Fe3O4 particles are attached homogeneously on the surface of CNTs, indicating that PANI can indeed help CNTs to disperse in isolated form. The wave-absorbing properties were investigated in a frequency of 2–18 GHz. The results show that the CNTs/Fe3O4/PANI nanocomposites exhibit a super absorbing behavior and possess a maximum reflection loss of −48 dB at 12.9 GHz, and the bandwidth below −20 dB is more than 5 GHz. More importantly, the absorption peak frequency ranges of the CNTs/Fe3O4/PANI composites can be tuned easily by changing the wax weight ratio and thickness of CNTs/Fe3O4/PANI paraffin wax matrix