In this article, we introduce adaptive robust observers for the estimation of comprehensive state variables and model parameters of a leader system. In particular, we suppose that the communication network among follo...
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In this article, we introduce adaptive robust observers for the estimation of comprehensive state variables and model parameters of a leader system. In particular, we suppose that the communication network among followers suffers communication link faults (CLFs), and the leader contains uncertainty, which means no follower knows the model parameters of leader system. Then, by applying the proposed observers, we elaborate on the design of a robust model-independent controller tailored for the leader-follower consensus problem (LFCP) of multiple Euler-Lagrange systems (MELSs). In contrast to the controllers currently employed for the LFCP in MELSs, the controller developed herein exhibits robustness against bounded external disturbances. Besides, the controller does not rely on the specific structure or characteristics of the Euler-Lagrange (EL) system model. At last, we provide simulation examples to demonstrate the validity of the proposed observers and the robust controller in the context of the LFCP for MELSs.
作者:
Gao, JianHuang, XinhanLiu, BoHUST
Dept CSE Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China BFSU
Beijing 100089 Peoples R China
To improve the real-time performance, a quick scale-invariant interest point detecting approach based on the image color information is proposed in this paper. The approach uses the scale normalized Laplacian operator...
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To improve the real-time performance, a quick scale-invariant interest point detecting approach based on the image color information is proposed in this paper. The approach uses the scale normalized Laplacian operator to extract the interest points in the incomplete image pyramid. A new local descriptor is presented in the approach to compute the feature vector of each interest point. The descriptor is made up with several subregions like the SIFT (Scale-Invariant Feature Transform) descriptor, meanwhile, it chooses the mean values of different color components in each subregion as the feature vector's elements to differentiate color objects better and reduce the descriptor's dimension. Through the experiment, the detected interest points are robust to many image transformations and the approach is indicative of needing less computation than other interest point detecting algorithms. The research discloses that the approach can obtain both superior stability and real-time performance at the same time.
Drowsy driving is pervasive, and also a major cause of traffic accidents. Estimating a driver's drowsiness level by monitoring the electroencephalogram (EEG) signal and taking preventative actions accordingly may ...
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Drowsy driving is pervasive, and also a major cause of traffic accidents. Estimating a driver's drowsiness level by monitoring the electroencephalogram (EEG) signal and taking preventative actions accordingly may improve driving safety. However, individual differences among different drivers make this task very challenging. A calibration session is usually required to collect some subject-specific data and tune the model parameters before applying it to a new subject, which is very inconvenient and not user-friendly. Many approaches have been proposed to reduce the calibration effort, but few can completely eliminate it. This paper proposes a novel approach, feature weighted episodic training (FWET), to completely eliminate the calibration requirement. It integrates two techniques: feature weighting to learn the importance of different features, and episodic training for domain generalization. Experiments on EEG-based driver drowsiness estimation demonstrated that both feature weighting and episodic training are effective, and their integration can further improve the generalization performance. FWET does not need any labelled or unlabelled calibration data from the new subject, and hence could be very useful in plug-and-play brain-computer interfaces.
This paper presents an enhanced multi-level filter algorithm and its Very Large Scale Integration (VLSI) architecture for infrared imageprocessing. The modified multi-level filter algorithm resolves the splitting tar...
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This paper presents an enhanced multi-level filter algorithm and its Very Large Scale Integration (VLSI) architecture for infrared imageprocessing. The modified multi-level filter algorithm resolves the splitting targets problem using Gaussian pyramid processing. Owning three filtering paths, the proposed VLSI architecture of the filter can simultaneously enhance small targets with different sizes in infrared images. Some design techniques in implementing hardwired multiplication, subsample and asynchronous FIFO have been presented. This VLSI architecture has been implemented using Semiconductor Manufacturing International Corporation (SMIC) 0.35 mu m 4-layer CMOS technology. The simulation results show that it not only effectively suppresses background, eliminates noise and enhances small targets in an infrared image comparing with other small target detective methods, but also meets infrared image real-time processing requirements (5M similar to 10M pixels/s). The implemented filter chip consists of 60,284 gates and 8 K Static Random Access Memory (SRAM), operates at 50 MHz.
This paper describes structure of imaging system, imaging mechanism of microwave radiometer, and the microwave radiation image features and recognition. Results of image experiments show: on microwave radiation image,...
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This paper describes structure of imaging system, imaging mechanism of microwave radiometer, and the microwave radiation image features and recognition. Results of image experiments show: on microwave radiation image, the features of metal target, water surface and road among trees, grassland and jungle become extremely obvious.
Critical infrastructures (CIs) are essential for the national security, economy, and public safety, whose protection against cyberattacks has become the focus of significant attention. Impact assessment plays an impor...
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Critical infrastructures (CIs) are essential for the national security, economy, and public safety, whose protection against cyberattacks has become the focus of significant attention. Impact assessment plays an important role in this protection, which provides real-time situations for security managers. The assessment of CIs not only need to analyze the cyberattack's impact on any specific station, but also to consider the spreading process of the negative impact. However, few research in this domain consider the above two aspects simultaneously due to the complexity of CIs and their operation. To tackle this problem, a hierarchical flow model (HFM)-based impact assessment is presented. In this approach, a novel HFM method is proposed to describe a system from a function system perspective, which combines a flow model and hierarchical knowledge. By using this method, a CI model which considers cyber-physical interaction within a station, dependence among stations, and the topological structure of the physical network is established. Next, based on the CI model, an impact assessment is proposed to quantify the loss which is caused by the impact spreading within the CI network. Finally, case studies on a gas supply system are conducted to verify the effectiveness of the proposed approach.
The observer-based edge-consensus problem of networked continuous-time dynamical systems with edge state non-negative constraint and actuator saturation is considered in this paper. Based on line graph theory, low-gai...
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The observer-based edge-consensus problem of networked continuous-time dynamical systems with edge state non-negative constraint and actuator saturation is considered in this paper. Based on line graph theory, low-gain output-feedback technique and algebraic Riccati equation (ARE)-based method, two edge-consensus algorithms are designed to achieve the observer-based edge consensus, in which the specific mathematical expressions of the two algorithms are obtained. Meanwhile, sufficient conditions are obtained to meet the bounded inputs and the non-negative edge states by combining with the ARE-based low-gain output-feedback technique and the positive system theory. Moreover, the feedback-gain and observer-gain matrices which must meet the sufficient conditions at the same time are existed and easy to obtain. Finally, two simulation cases are introduced to show the effectiveness of the theoretical results.
An effective model for diesel engine in cold test has been developed in this paper. The mathematical model of diesel engine is mainly composed by four parts: turbocompressor model, intake manifold model, exhaust manif...
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An effective model for diesel engine in cold test has been developed in this paper. The mathematical model of diesel engine is mainly composed by four parts: turbocompressor model, intake manifold model, exhaust manifold model and the pressure model of cylinder in cold test. And then a new engine faults diagnosis method based on frequency domain analysis is presented. The typical faults of diesel engine in cold test, including the cylinder work phase fault and intake/exhaust valve phase fault, are distinguished by the amplitude frequency performance of engine torque. And the location of the engine faults is solved by the performance of the phases of low-frequency of engine torque signals. It has been proved to be an effective method to identify and locate the faults of the diesel engine in cold test.
Spiking neural P systems (SN P systems, for short) are known as a class of distributed parallel computing models, which are inspired by the way in which neurons process and communicate information with each other by m...
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Spiking neural P systems (SN P systems, for short) are known as a class of distributed parallel computing models, which are inspired by the way in which neurons process and communicate information with each other by means of spikes. In this work, we focus on a new variant of SN P systems, namely spiking neural P systems with communication on request (SNQ P systems, for short). We concentrate on searching for a small universal SNQ P system. The best known result is that 49 neurons are enough for constructing a Turing universal SNQ P system. Here, we construct a Turing universal SNQ P system with only 14 neurons, which answers an open problem whether the number of neurons for constructing a Turing universal SNQ P system can be further improved. (c) 2017 Elsevier B.V. All rights reserved.
There have been increasing interests in studying multiplex dynamical networks *** paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer *** a two-layer network model in ...
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There have been increasing interests in studying multiplex dynamical networks *** paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer *** a two-layer network model in which different layers have different coupling patterns,we propose novel methods to recover unknown topological structure of one layer,using the information of the other layer known as a *** proposed methods make full use of the measured evolutional states of the multiplex network itself,and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological *** with the traditional synchronization-based identiflcation method,the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer,and thus greatly reduce the cost of topology ***,numerical simulations validate the effectiveness of the proposed methods.
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