In this brief, the problem of composite anti-disturbance tracking control for a class of strict-feedback systems with unmatched unknown nonlinear functions and external disturbances is investigated. ...A disturbance-observer-based control (DOBC) in combination with a neural network scheme and back-stepping method is developed to achieve a composite anti-disturbance controller design that provides guaranteed performance. In the proposed method, a conventional disturbance observer and a radial basis function neural network (RBFNN) are combined into a new disturbance observer to estimate the unmatched disturbances. As compared with conventional DOBC methods, the primary merit of the proposed method is that the unknown nonlinear functions are approximated using the RBFNN technique, and not regarded as part of the disturbances or estimated by a conventional disturbance observer. Hence, the proposed method can obtain higher control accuracy than the conventional DOBC methods. This advantage is validated by simulation studies.
This paper is concerned with the problems of composite disturbance-observer-based control (DOBC) and ℋ∞ control for Markovian jump systems with nonlinearity and multiple disturbances. Our aim is to ...design a disturbance observer to estimate the disturbance generated by an exogenous system, then construct the control scheme by integrating the output of the disturbance observer with state-feedback control law, such that, the closed-loop system can be guaranteed to be stochastically stable, and different types of disturbances can be attenuated and rejected. By constructing a proper stochastic Lyapunov–Krasovskii functional, sufficient conditions for the existence of the desired observer and the state-feedback controller are established in terms of linear matrix inequalities (LMIs), which can be readily solved by standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed approaches.
The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image ...contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.
This article presents the innovative application of social network analysis to agenda setting research. It suggests that the approach of network analysis enables researchers to map out the ...interrelationships among objects and attributes both in the media agenda and the public agenda. Further, by conducting statistical analysis, researchers are able to compare the media agenda networks and public agenda networks in order to explore a third level of agenda setting effects. Concrete procedures for applying network analysis in agenda setting research are presented, and a set of hypotheses are suggested in this article.
Remote sensing image scene classification is an active and challenging task driven by many applications. More recently, with the advances of deep learning models especially convolutional neural ...networks (CNNs), the performance of remote sensing image scene classification has been significantly improved due to the powerful feature representations learnt through CNNs. Although great success has been obtained so far, the problems of within-class diversity and between-class similarity are still two big challenges. To address these problems, in this paper, we propose a simple but effective method to learn discriminative CNNs (D-CNNs) to boost the performance of remote sensing image scene classification. Different from the traditional CNN models that minimize only the cross entropy loss, our proposed D-CNN models are trained by optimizing a new discriminative objective function. To this end, apart from minimizing the classification error, we also explicitly impose a metric learning regularization term on the CNN features. The metric learning regularization enforces the D-CNN models to be more discriminative so that, in the new D-CNN feature spaces, the images from the same scene class are mapped closely to each other and the images of different classes are mapped as farther apart as possible. In the experiments, we comprehensively evaluate the proposed method on three publicly available benchmark data sets using three off-the-shelf CNN models. Experimental results demonstrate that our proposed D-CNN methods outperform the existing baseline methods and achieve state-of-the-art results on all three data sets.
The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In ...this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches.
Osteoporosis is widely regarded as one of the typical aging-related diseases due to the impairment of bone remodeling. The silent information regulator of transcription1 (SIRT1) is a vital regulator ...of cell survival and life-span. SIRT1 has been shown to be activated by resveratrol treatment, and also has been proved to prevent aging-related diseases such as osteoporosis. However, the role of SIRT1 about autophagy or mitophagy of osteoblasts in resveratrol-regulated osteoporotic rats remains unclear. This study seeks to investigate the role of SIRT1 about autophagy or mitophagy in osteoblasts through PI3K/Akt signaling pathway in resveratrol-regulated osteoporotic rats. The vivo experiment results have revealed that resveratrol treatment significantly improved bone quality and reduced the levels of serum alkaline phosphatase and osteocalcin in osteoporotic rats. Moreover, Western bolt analysis showed that expression of SIRT1, LC3, and Beclin-1 in osteoblasts increased, while p-AKT and p-mTOR were downregulated in osteoporosis rats with high dose resveratrol treatment. On the other hand, resveratrol treatment increased the SIRT1 activity, LC3 and Beclin-1 mRNA expression in the dexamethasone (DEX)-treated osteoblasts. More mitophagosomes were observed in the DEX-treated osteoblasts with resveratrol. Meanwhile, the TOM20, Hsp60, p-Akt and p-mTOR activities were decreased in the DEX-treated osteoblasts with resveratrol. Resveratrol treatment did not change the p-p38 and p-JNK activities in the osteoblasts. These results revealed that resveratrol treatment protected osteoblasts in osteoporosis rats by enhancing mitophagy by mediating SIRT1 and PI3K/AKT/mTOR signaling pathway.
Disturbance-observer-based control (DOBC) and related methods have been researched and applied in various industrial sectors in the last four decades. This survey, at first time, gives a systematic ...and comprehensive tutorial and summary on the existing disturbance/uncertainty estimation and attenuation techniques, most notably, DOBC, active disturbance rejection control, disturbance accommodation control, and composite hierarchical antidisturbance control. In all of these methods, disturbance and uncertainty are, in general, lumped together, and an observation mechanism is employed to estimate the total disturbance. This paper first reviews a number of widely used linear and nonlinear disturbance/uncertainty estimation techniques and then discusses and compares various compensation techniques and the procedures of integrating disturbance/uncertainty compensation with a (predesigned) linear/nonlinear controller. It also provides concise tutorials of the main methods in this area with clear descriptions of their features. The application of this group of methods in various industrial sections is reviewed, with emphasis on the commercialization of some algorithms. The survey is ended with the discussion of future directions.
As a typical application of deep learning, physics-informed neural network (PINN) has been successfully used to find numerical solutions of partial differential equations (PDEs), but how to improve ...the limited accuracy is still a great challenge for PINN. In this work, we introduce a new method, symmetry-enhanced physics informed neural network (SPINN) where the invariant surface conditions induced by the Lie symmetries or non-classical symmetries of PDEs are embedded into the loss function in PINN, to improve the accuracy of PINN for solving the forward and inverse problems of PDEs. We test the effectiveness of SPINN for the forward problem via two groups of ten independent numerical experiments using different numbers of collocation points and neurons per layer for the Korteweg-de Vries (KdV) equation, breaking soliton equation, heat equation, and potential Burgers equations respectively, and for the inverse problem by considering different layers and neurons as well as different numbers of training points with different levels of noise for the Burgers equation in potential form. The numerical results show that SPINN performs better than PINN with fewer training points and simpler architecture of neural network, and in particular, exhibits superiorities than the PINN method and the two-stage PINN method of Lin and Chen by considering the Sawada-Kotera equation. Furthermore, we discuss the computational overhead of SPINN in terms of the relative computational cost to PINN and show that the training time of SPINN has no obvious increases, even less than PINN for certain cases.
•Introducing the symmetry-enhanced PINN (SPINN) to improve the accuracy of the PINN.•SPINN outperforms than PINN with fewer training points and simpler architecture of neural network.•The training time of SPINN for PDEs has no obvious increases, even less than the one of PINN.
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