This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output d...
This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.
This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements. Such a problem is critical in various robotic applications such as robotic perceptive locomotion....
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In practical applications of cooperative navigation algorithms based on Kalman filtering, noise is often colored noise, which does not meet the requirement of Gaussian white noise for Kalman filtering. Therefore, an a...
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ISBN:
(数字)9798350384185
ISBN:
(纸本)9798350384192
In practical applications of cooperative navigation algorithms based on Kalman filtering, noise is often colored noise, which does not meet the requirement of Gaussian white noise for Kalman filtering. Therefore, an algorithm is proposed to identify and compensate for colored noise online using a BP neural network. Firstly, a cooperative navigation model based on Kalman filtering is established. Then, by studying the update process of Kalman filtering, the neural network is incorporated into the prediction calculation of the observations, following the online learning and updating of the Kalman filter. Mathematical simulations show that under conditions of colored noise, the results of the Kalman filter diverge. However, after correction by the BP neural network, the filtering converges, providing high reference value for improving the accuracy of cooperative navigation.
With the addition of UAVs, the perception and recognition of ground targets have also advanced significantly. UAV photography has gradually become an integral part of this field. Due to the flexibility and lightness o...
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Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling....
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Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm.
PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piR...
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The Agile Earth Observation Satellite scheduling selects and sequences satellite observations of possible targets on the Earth's surface, each with a specific profit and multiple time windows. The objective is to ...
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High-resolution medical images have important medical value,but are difficult to obtain *** by hardware equipment and patient’s physical condition,the resolution of directly acquired medical images is often not ***,m...
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High-resolution medical images have important medical value,but are difficult to obtain *** by hardware equipment and patient’s physical condition,the resolution of directly acquired medical images is often not ***,many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical ***,current super-resolution algorithms only work on a single scale,and multiple networks need to be trained when super-resolution images of different scales are *** definitely raises the cost of acquiring high-resolution medical ***,we propose a multi-scale superresolution algorithm using *** algorithm combines a metalearning approach with an enhanced depth of residual super-resolution network to design a meta-upscale *** meta-upscale module utilizes the weight prediction property of meta-learning and is able to perform the super-resolution task of medical images at any ***,we design a non-integer mapping relation for super-resolution,which allows the network to be trained under non-integer magnification *** to the state-of-the-art single-image super-resolution algorithm on computed tomography images of the pelvic *** meta-learning multiscale superresolution algorithm obtained a surpassing of about 2%at a smaller model *** on different parts proves the high generalizability of our ***-scale super-resolution algorithms using meta-learning can compensate for hardware device defects and reduce secondary harm to patients while obtaining high-resolution medical *** can be of great use in imaging related fields.
This work addresses the growing civilian applications of drone technology and the associated security risks, particularly with respect to public and air safety. To address these issues, we introduce an improved YOLOv1...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
This work addresses the growing civilian applications of drone technology and the associated security risks, particularly with respect to public and air safety. To address these issues, we introduce an improved YOLOv11-DEC model for real-time detection of small-target unmanned aerial vehicles (UAVs) in complex environments. The model incorporates SBA, group normalisation, and detail-enhanced convolution, and integrates RepViT with EMA attention to increase feature representation and detection accuracy. Additionally, we developed a lightweight LSDECD detection head to maintain model size while preserving accuracy. Experiments on the DUT dataset show that YOLOv11-DEC outperforms baseline models such as YOLOv11, in terms of detection accuracy, offering an effective solution for UAV monitoring and defence systems.
In this paper, the stability analysis of Load frequency control (LFC) systems with time-varying delay is conducted. Firstly, an augmented Lyapunov-Krasovskii (L-K) functional is designed to incorporate the relevant in...
In this paper, the stability analysis of Load frequency control (LFC) systems with time-varying delay is conducted. Firstly, an augmented Lyapunov-Krasovskii (L-K) functional is designed to incorporate the relevant information of the delay state. Then, the functional derivatives are estimated using an auxiliary-function-based integral inequality in conjunction with an improved reciprocally convex inequality. Furthermore, the correlation function is processed through a quadratic function negative definite derivation. Finally, case studies established on a one-area LFC system are conducted to demonstrate the superiority of the method, and its accuracy is verified through a simulation case.
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