This paper focuses on an adaptive fixed-time tracking control problem for nonstrict-feedback stochastic nonlinear systems with unknown control coefficients and output constraints. To overcome this problem, this articl...
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This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs) subject to model uncertainties and environmental disturbances. A safety-certified path-guided coordi...
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This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs) subject to model uncertainties and environmental disturbances. A safety-certified path-guided coordinated control method is proposed for multiple ASVs to achieve a distributed formation in obstacle environments. Specifically, a neural predictor with a high-order tuner is presented to approximate unknown nonlinearities with accelerated learning performance. Subsequently, control Lyapunov functions(CLFs) and control barrier functions(CBFs) are constructed for mapping stability constraints and safety constraints on states to control inputs. A quadratic optimization problem is constructed with the norm of control inputs as the objective function, CLFs and CBFs as constraints. Neurodynamic optimization is used to deal with the quadratic programming problem and generate the optimal kinetic control signals, thereby attaining the desired safe formation. Unlike the high-order CBF, a CBF backstepping method is proposed to establish safety constraints such that repeated time derivatives of system nonlinearities can be avoided. The multi-ASVs system is ensured to be input-to-state safe irrespective of high-order relative degree. Through the Lyapunov theory, the multi-ASVs system is proven to be input-to-state stable. Finally, simulation results are presented to validate the efficacy of the presented safety-certified distributed formation control for networked ASVs.
In this paper, a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentration...
In this paper, a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentrations. The cuvette is filled with the liquid to be measured and rotated by a certain angle. The change in the number of interference fringes, caused by comparing an empty cuvette with a liquid-filled cuvette, is used to calculate the refractive index of the liquid. A four-fold logic subdivision algorithm is then used to improve measurement resolution. The experimental results show that for pure water and different NaCl and glucose solutions concentrations, the average relative errors are 0.47%, 0.59%, and 2.17%, respectively, with the maximum relative error within ±2.54%. The standard deviation of all solutions is less than 3.4%.
Unmanned aerial vehicles (UAVs) can provide detection coverage service in many scenarios. The fair coverage is achieved by designing carefully UAVs' trajectories, which are established at each step by choosing the...
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Traditional face recognition techniques exhibit suboptimal accuracy when dealing with faces occluded by masks, particularly posing a challenge for masked face identification. Considering the application characteristic...
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The accuracy of wind power prediction is of great significance for optimizing power system scheduling and improving power output stability. In order to solve the established difficulties in wind prediction, a predicti...
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In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise, a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural ne...
In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise, a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural network can discriminate the direction of self-mixing fringes accurately and quickly. For experimental SMI signals, the 1D U-Net can be used for discriminant direction after a one-step normalization. Simulation and experimental results show that the proposed method is suitable for SMI signals with noise within the whole weak feedback regime, and can maintain a high discrimination accuracy for signals interfered by 5dB noise. Combined with fringe counting method, accurate and rapid SMI signal displacement reconstruction can be realized.
In this paper, we present a control strategy for bilateral teleoperators operated in the task space, which is designed to estimate the uncertain dynamics. To implement this proposed strategy, we develop a straightforw...
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The multiagent controllability of both node and edge dynamics depends on the communication topology, leader selection, as well as the weight adjustments. This paper deals with the controllability relationship between ...
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Most existing Multimodal Sentiment Analysis (MSA) models that are enhanced with external sentiment knowledge primarily focus on integrating this knowledge during the multimodal fusion stage, while overlooking its pote...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Most existing Multimodal Sentiment Analysis (MSA) models that are enhanced with external sentiment knowledge primarily focus on integrating this knowledge during the multimodal fusion stage, while overlooking its potential benefits during the multimodal representation learning process. In this study, we propose a Sentiment Knowledge-Enhanced Multimodal Sentiment Analysis (SKE-MSA) framework, which incorporates an external sentiment knowledge base—the VAD lexicon—to enhance MSA. SKE-MSA introduces the Sentiment-aware Encoding Loss (SAE_Loss), designed to leverage external sentiment knowledge to guide the representation learning of multimodal encoders. Building on this, we extract both common and unique features of multimodal representations and subsequently predict sentiment intensity based on these features. Extensive experiments conducted on three MSA datasets demonstrate the competitive performance of SKE-MSA.
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