This paper addresses the stability of networked controlsystems with aperiodic sampling and time-varying network-induced delay. The sampling intervals are assumed to vary within a known interval. The transmission dela...
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Sparse Mobile Crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of S...
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Graph Neural Networks (GNN) has become a powerful graph data processing method, which has been widely used in node classification, link prediction, and other graph analysis tasks. Due to the diversity and complexity o...
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This paper addresses the stabilization of linear systems with multiple time-varying input delays. In scenarios where neither the exact delays information nor their bound is known, we propose a class of linear time-var...
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A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branc...
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Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algo...
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Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algorithm for moving object and region detection in video using a compressive sampling is *** algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background *** algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated *** leads to a computationally efficient method and a system compared with the existing motion estimation *** experimental results show that the sampling rate can reduce to 25% without sacrificing performance.
The positioning accuracy of a short-haul target-locating system,the inverse-GPS(IGPS) ,was analyzed in detail. The relationship between IGPS and the positioning error was discussed. The multiplicative error minimal bo...
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The positioning accuracy of a short-haul target-locating system,the inverse-GPS(IGPS) ,was analyzed in detail. The relationship between IGPS and the positioning error was discussed. The multiplicative error minimal bound of the geometric dilution of precision (GDOP) about the four-base-station IGPS was also investigated. In order to clarify the practical implementation of IGPS,the multiplicative and additive error factors which affect the positioning accuracy and theoretical estimation of positioning accuracy were presented. By analyzing the experiments of locating a target's position in virtual three-dimensional areas,the positioning performance of IGPS was illustrated. The results show that the multiplicative and additive error factors should be eliminated in IGPS to improve the positioning accuracy.
This paper is concerned with the optimal fusion of sensors with cross-correlated sensor *** taking linear transformations to the measurements and the related parameters, new measurement models are established, where t...
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This paper is concerned with the optimal fusion of sensors with cross-correlated sensor *** taking linear transformations to the measurements and the related parameters, new measurement models are established, where the sensor noises are decoupled. The centralized fusion with raw data, the centralized fusion with transformed data, and a distributed fusion estimation algorithm are introduced, which are shown to be equivalent to each other in estimation precision, and therefore are globally optimal in the sense of linear minimum mean square error(LMMSE). It is shown that the centralized fusion with transformed data needs lower communication requirements compared to the centralized fusion using raw data directly, and the distributed fusion algorithm has the best flexibility and robustness and proper communication requirements and computation complexity among the three algorithms(less communication and computation complexity compared to the existed distributed Kalman filtering fusion algorithms). An example is shown to illustrate the effectiveness of the proposed algorithms.
An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed...
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An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter (UKF) when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.
A backstepping method based adaptive robust dead-zone compensation controller is pro- posed for the electro-hydraulic servo systems (EHSSs) with unknown dead-zone and uncertain system parameters. Variable load is se...
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A backstepping method based adaptive robust dead-zone compensation controller is pro- posed for the electro-hydraulic servo systems (EHSSs) with unknown dead-zone and uncertain system parameters. Variable load is seen as a sum of a constant and a variable part. The constant part is regarded as a parameter of the system to be estimated real time. The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them. Compared with the traditional dead-zone compensation method, a dead-zone compensator is incor- porated in the EH$S without constructing a dead-zone inverse. Combining backstepping method, an adaptive robust controller (ARC) with dead-zone compensation is formed. An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure. Simulations show that the proposed method has a splendid tracking performance, all the uncertain parameters can be estimated, and the disturbance has been rejected while the dead-zone term is well estimated and compensated.
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