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This paper is concerned with persistent identification of systems that involve deterministic unmodeled dynamics and stochastic observation disturbances, and whose unknown parameters switch values (possibly large jumps) that can be represented by a Markov chain. Two classes of problems are considered. In the first class, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains with transition rates sufficiently faster than adaptation rates of the identification algorithms. In this case, tracking real-time parameters by output observations becomes impossible and we show that an averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and can be estimated with periodic inputs and least-squares type algorithms. Upper and lower error bounds are established that explicitly show impact of unmodeled dynamics. In contrast, the second class of problems represents systems whose state transitions occur infrequently. An adaptive algorithm with variable step sizes is introduced for tracking the time-varying parameters. Convergence and error bounds are derived. Numerical results are presented to illustrate the performance of the algorithm. Index Terms--Error bounds, exogenous noise, parameter switching, parameter tracking, persistent identification, unmodeled dynamics.

Semantic segmentation is a hot topic in computer vision and various deep learning networks are designed to achieve higher accuracy on that by fully exploring the capability of neural networks. This paper aims to address the issue and proposes the substructures with novelty for popular networks. Meanwhile, we present a cross‐channel structure, which simultaneously reduces parameter while the kernel size becomes larger. After that, to overcome the weakness of insufficient dataset which refers to satellite image data, we propose a feature attention mechanism with generative adversarial network to enhance the images' features. We show the recognition result on the satellite image dataset with a large picture. This paper evaluates substructures on the PASCAL VOC2012 dataset and improves the mIOU from 74.68% to 88.15%. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2006.10.005 Byline: Jianbin Wei (a), Cheng-Zhong Xu (b) Keywords: Quality of service; Proportional delay differentiation; Fuzzy control; Rate allocation Abstract: Proportional delay differentiation (PDD) is an important service model for providing relative differentiated services on the Internet. It aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing rate-allocation approaches for PDD services assume the average queueing delay of a class is inversely proportional to its service rate. This assumption is not necessarily valid when the system is not heavily loaded. To provide consistent PDD services under various load conditions, in this paper, we propose a novel rate-allocation approach that applies fuzzy control theory to capture the nonlinear relationship between the queueing delay and the service rate. In the approach, a class's service rate is adjusted according to a set of fuzzy control rules defined over its error (the difference between the target delay ratio and the achieved one), the change of error, and the change of service rate. We prove that the fuzzy control system is stable and the service rate of a class converges to its equilibrium point at steady state. Simulation results demonstrate that, in comparison with other rate-allocation approaches, the fuzzy control approach is able to provide consistent PDD services under wide range load conditions. It is also shown robust under various system conditions, including with multiple classes, changing target delay ratios, changing load conditions, and different traffic patterns. Author Affiliation: (a) Mathematics and Computer Science Department, South Dakota School of Mines and Technology, Rapid City, SD 57701, United States (b) Department of Electrical and Computer Engineering, Wayne State University, 650 Merrick Street, Detroit, MI 48202, United States Article History: Received 20 November 2004; Revised 15 June 2006; Accepted 16 October 2006 Article Note: (miscellaneous) Responsible Editor: M. Buddhikot

This note addresses the stabilization of a one-dimensional wave equation with control at one end and noncoliocated observation at another end. A simple exponentially convergent observer is constructed. The dynamical stabilizing boundary output feedback is designed via the observed state. While the closed-loop system is nondissipative, we show its exponential stability using Riesz basis approach. Index Terms--Exact controllability, exponential stability, feedback control, observer, wave equation.