We show that every flat nonlinear discrete-time system with two inputs can be transformed into a structurally flat normal form by state- and input transformations. This normal form has a triangular structure and allow...
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This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurement...
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This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurements of a continuous-time signal whenever it crosses fixed and regularly partitioned thresholds. The knowledge of the intersample behavior of the output data is exploited in this work to derive an expectation-maximization (EM) algorithm for parameter estimation of the state-space and noise covariance matrices. For this purpose, we use the incremental discrete-time equivalent of the system, which leads to EM iterations of the continuous-time state-space matrices that can be computed by standard filtering and smoothing procedures. The effectiveness of the identification method is tested via Monte Carlo simulations.
Efficient and accurate railway track obstacle detection is crucial for ensuring travel safety. In order to solve the problem of small amount of track obstacle data and variable types of obstacles, we introduce the con...
Efficient and accurate railway track obstacle detection is crucial for ensuring travel safety. In order to solve the problem of small amount of track obstacle data and variable types of obstacles, we introduce the concept of few-shot continual learning into the object detection algorithm. This paper introduces a novel memory mechanisms-based multi-Domain few-shot continual learning algorithm. By simulating working memory and long-term memory processes, our method combines few-shot learning with continual adaptation to varying obstacles. The proposed double-loop approach reduces catastrophic forgetting, enhances model generalization, and improves detection accuracy. This innovation holds promise for advancing railway safety through effective obstacle detection. The proposed method showcases remarkable efficiency and accuracy in the detection of railway obstacles. Our experimental results demonstrate significant improvements compared to other methods.
We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize transmit beamforming and reconfigurable intelligent surface (RIS) phase shifts in a multiuser multiple input single output (MU-MISO) ...
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In this paper,the hydrodynamic modeling and parameter identifcation of the RobDact,a bionic underwater vehide inspired byDactylopteridae,are carried out based on computational fluid dynamics(CFD)and force measurement ...
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In this paper,the hydrodynamic modeling and parameter identifcation of the RobDact,a bionic underwater vehide inspired byDactylopteridae,are carried out based on computational fluid dynamics(CFD)and force measurement ***,thePaper briely describes the RobDact,then establishes the kinematis model and rigid body dynamics model of the RobDactaccording to the hydrodynamic force and moment *** CFD simulations,the hydrodynamic force of theRobDact at diferent speeds is obtained,and then,the hydrodynamic model parameters are ***,themeasurement platform is developed to obtain the relationship between the thrust generated by the RobDact and the inputfluctuation parameters,Finally,combining the rigid body dynamics model and the fin'thrust mapping model,thehydrodynamic model of the RobDact at diferent motion states is constructed.
This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks based upon non-Euclidean contraction theory. The basic idea is to cast the robustness ...
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This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samp...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samples on the current model output. This is done by penalizing the sensitivity of the NARX model simulated output with respect to the past inputs. This promotes the stability of the estimated models and improves the obtained model quality. The effectiveness of the approach is demonstrated through a simulation example, where a neural network NARX model is identified with this novel method. Moreover, it is shown that the proposed regularization approach improves the model accuracy in terms of simulation error performance compared to that of other regularization methods and model classes.
A new 3D memristor system is constructed based on VB17 system. The basic dynamics of the system are analyzed by means of bifurcation analysis, phase orbit diagram and Lie index. Based on the idea of bias and the intro...
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A new 3D memristor system is constructed based on VB17 system. The basic dynamics of the system are analyzed by means of bifurcation analysis, phase orbit diagram and Lie index. Based on the idea of bias and the introduction of trigonometric functions, the attractor self-propagation phenomenon is generated in the system, which proves that the newly proposed memristor system has super many steady states. In addition, the system has the phenomenon of bias lift. Finally, the realization of the theory is proved by designing the circuit of 3D memory chaotic system. Multisim simulation verifies the validity the validity of the numerical analysis.
The paper presents structural diagrams of the control system of a robotic system with feedback on the patient's effort and without it, illustrates the conditions for using each of them;a method is prepared to incr...
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With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data...
With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data integrity attacks to compromise the measurements of certain sensors, causing the grid monitoring system to fail to grasp the correct system operating status. To solve the NP-hard suspicious sensor selection problem, this paper proposes an efficient attack detection scheduling algorithm, the Particle Swarm Optimization algorithm based on historical information directional guidance (HIDG-based PSO algorithm). The proposed algorithm is utilized with its unique evolutionary mechanism, which reduces the computational power requirements for sensor selection. For the problem of uncertainty in evolutionary algorithms, this paper uses historical information to guide the selection of suspicious sensors at the current moment. The simulation results show that the proposed algorithm can efficiently select suspicious sensors, which will greatly improve the efficiency of attack detection and ensure the security of information fusion of the power grid monitoring system.
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