Stability and control of nonlinear systems described by retarded functional equations: a review of recent results

This paper reports on recent results in a series of the work of the authors on the stability and nonlinear control for general dynamical systems described by retarded functional differential and difference equations. Both internal and external stability properties are studied. The corresponding Lyapunov and Razuminkhin characterizations for input-to-state and input-to-output stabilities are proposed. Necessary and sufficient Lyapunov-like conditions are derived for robust nonlinear stabilization. In particular, an explicit controller design procedure is developed for a new class of nonlinear time-delay systems. Lastly, sufficient assumptions, including a small-gain condition, are presented for guaranteeing the input-to-output stability of coupled systems comprised of retarded functional differential and difference equations.     

Polarization discrimination between repeater false-target and radar target

High fidelity repeater false-target badly affects a radar system’s detecting, tracking, and data processing. It is an available approach of confronting false-target for radar that discriminates firstly and then eliminates. Whereas for the technique progress about the repeater false-target jam, it is more and more difficult to discriminate this jam in the time-domain, frequency-domain, or space-domain. The technique using polarization information to discriminate the target and false-target is discussed in th...     

Optimal hole shape for minimum stress concentration using parameterized geometry models

The goal of this work is to obtain optimal hole shape for minimum stress concentration in two-dimensional finite plates using parameterized geometry models. The boundary shape for a hole is described by two families of smooth curves: one is a “generalized circular” function with powers as two parameters; the other one is a “generalized elliptic” function a and b are ellipse axes) with powers as two parameters and one of the ellipse axes as the third parameter. Special attention is devoted to the practicability of parameterized equations and the corresponding optimal results under the condition with and without the curvature radius constraint. A number of cases were examined to test the effectiveness of the parameterized equations. The numerical examples show that extremely good results can be obtained under the conditions with and without curvature radius constraint, as compared to the known solutions in the literature. The geometries of the optimized holes are presented in a form of compact parametric functions, which are suitable for use and test by designers. It is anticipated that the implementation of the suggested parameterized equations would lead to considerable improvements in optimizing hole shape with high quality.     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

Effect of Sintering Temperature on Magnetic and Electrical Properties of GdBaCo<Subscript>2</Subscript><Emphasis Type="Italic">O</Emphasis><Subscript>5 + <Emphasis Type="Italic">δ</Emphasis></Subscript>

In this study, we report the results of an investigation into the sintering temperature dependence of magnetic and transport properties for GdBaCo₂ O _{5 + δ} synthesized through a sol-gel method. The lowering of sintering temperature leads to the increase of oxygen content and the reduction of grain size. The increase of oxygen content results in the enhancement of magnetic interactions and the weakening of Coulomb repulsion effect, while the reduction of grain size improves the magnetoresistance effect. Metal-insulator transition accompanied with spin-state transition is observed in all samples.     

The impact of industrial agglomeration on industrial pollutant emission: evidence from China under New Normal

The purpose of this study is to analyze the environmental pollution effects elicited by industrial agglomeration and to devise necessary changes before and after China going into the New Normal, a contemporary phase of less rapid but more sustainable economic development. An empirical model is constructed based on the Copeland–Taylor model, and empirical research is conducted using statistical panel data derived from 285 Chinese cities between 2003 and 2014. To study the relationship between industrial agglomeration and industrial pollutant emission both before and after the ‘New Normal,’ the sample data are divided into two time periods: 2003–2008 and 2009–2014. Estimated results are as follows. First, industrial agglomeration exacerbates industrial pollution levels overall although the negative environmental effect of industrial agglomeration is weakened following China’s entry into the New Normal phase of economy. Second, both the interaction term of industrial agglomeration and foreign direct investment (FDI) and the interaction term of industrial agglomeration and environmental regulation are negatively related to industrial agglomeration. These findings indicate that FDI and environmental regulation can indirectly reduce industrial pollutant emissions by way of industrial agglomeration.     

Inverse analysis procedure to determine flow stress and friction data for finite element modeling of machining

This paper describes an inverse procedure to determine the constitutive constants and the friction conditions in the machining processes using Finite Elements (FE) simulations. In general, the FE modeling of machining processes is an effective tool to analyze the materials machinability under different cutting conditions. However, the use of reliable rheological and friction models represents the basis of a correct numerical investigation. The presented inverse procedure was based on the numerical results obtained using a commercial FE code and was developed considering a specific optimization problem, in which the objective functions that have to be minimized is the experimental/numerical error. This problem was performed by a routine developed in a commercial optimization software. In order to verify the goodness and the robustness of the methodology, it was applied on a Super Duplex Stainless Steel (SDSS) and on an Austenitic Stainless Steel (AUSS) orthogonal machining processes. This work, then, was focused on the identification of the Johnson-Cook (JC) coefficients (A,B,C, n and m) and on the calibration of a Coulomb friction model, in the specific cases of the SAF2507 SDSS and of an AISI 316 Based AUSS Alloy (AISI 316 ASBA). The identification phases were performed considering forces and temperatures experimental data, collected in two specific experimental tasks in which different orthogonal cutting tests were carried out under different cutting parameters conditions.     

A series of nanoparticles with phase-separated structures by 1,1-diphenylethene controlled one-step soap-free emulsion copolymerization and their application in drug release

A facile one-step approach to synthesize various phase-separated porous, raspberry-like, flower-like, core–shell and anomalous nanoparticles and nanocapsules via 1,1-diphenylethene (DPE) controlled soap-free emulsion copolymerization of styrene (S) with glycidyl methacrylate (GMA), or acrylic acid (AA) is reported. By regulating the mass ratio of S/GMA, transparent polymer solution, porous and anomalous P(S-GMA) particles could be produced. The P(S-GMA) particles turn from flower-like to raspberry-like and then to anomalous structures with smooth surface as the increase of divinylbenzene (DVB) crosslinker. Transparent polymer solution, nanocapsules and core–shell P(S-AA) particles could be obtained by altering the mole ratio of S/AA; anomalous and raspberry-like P(S-AA) particles are produced by adding DVB. The unpolymerized S resulted from the low monomer conversion in the presence of DPE aggregates to form nano-sized droplets, and migrates towards the external surfaces of the GMA-enriched P(S-GMA) particles and the internal bulk of the AA-enriched P(S-AA) particles. The nano-sized droplets function as in situ porogen, porous P(S-GMA) particles and P(S-AA) nanocapsules are produced when the porogen is removed. This novel, facile, one-step method with excellent controllability and reproducibility will inspire new strategies for creating hierarchical phase-separated polymeric particles with various structures by simply altering the species and ratio of comonomers. The drug loading and release experiments on the porous particles and nanocapsules demonstrate that the release of doxorubicin hydrochloride is very slow in weakly basic environment and quick in weakly acidic environment, which enables the porous particles and nanocapsules with promising potential in drug delivery applications.

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