Unsupervised Domain Adaptation (UDA) is a powerful solution to domain shift problems by transferring knowledge from labeled source domain to unlabeled target domain. Although UDA has gained much attention in visual ap...
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In this paper,the dynamic event-triggered networked predictive control problem is investigated for networked control systems(NCSs) under deception ***,the system states are estimated by the Luenberger ***,the networke...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In this paper,the dynamic event-triggered networked predictive control problem is investigated for networked control systems(NCSs) under deception ***,the system states are estimated by the Luenberger ***,the networked predictive control(NPC) strategy is developed to actively compensate for the time *** order to save the limited communication resource,the dynamic event-triggered control(DETC) method is proposed by using additional dynamic *** applying the Lyapunov method,sufficient conditions for the stability of the considered NCS with deception attacks and time delay are ***,the controller gain and observer gain matrices are obtained by solving the corresponding convex optimization ***,the effectiveness of the proposed control method is demonstrated via a simulation example.
The photovoltaic (PV) industry is developing rapidly, and electricity from decentralized or grid-connected systems is becoming increasingly important. Grid-connected PV systems with active and reactive power compensat...
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Mean field(MF) models have been widely applied to economics, control theory, and other fields. Its prominent feature is that the individual influence on the overall population is negligible and the impact of the entir...
Mean field(MF) models have been widely applied to economics, control theory, and other fields. Its prominent feature is that the individual influence on the overall population is negligible and the impact of the entire system to the single agent is significant and cannot be ignored. As a classical problem in control theory, linear quadratic(LQ) control for MF models has been widely investigated(e.g., [1, 2]).
Training machine learning models in virtual environments often fails to generalize effectively to real-world applications. The discrepancy between simulated and real environments results in suboptimal performance of t...
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Changing the N content in the Ti_(3)AlC_(2−y)N_(y) MAX phase solid solutions allows for the fine-tuning of their ***,systematic studies on the synthesis and properties of Ti_(3)AlC_(2−y)N_(y) solid solution bulks have...
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Changing the N content in the Ti_(3)AlC_(2−y)N_(y) MAX phase solid solutions allows for the fine-tuning of their ***,systematic studies on the synthesis and properties of Ti_(3)AlC_(2−y)N_(y) solid solution bulks have not been reported thus ***,previously reported Ti_(3)AlC_(2−y)N_(y) solid solution bulks(y=0.3,0.5,0.8,and 1.0)were synthesized via hot pressing of their powder counterparts under optimized *** prepared Ti_(3)AlC_(2−y)N_(y) bulks are dense and have a fine microstructure with grain sizes of 6–8μ*** influence of the N content on the mechanical properties,electrical conductivities,and coefficients of thermal expansion(CTEs)of the prepared Ti_(3)AlC_(2−y)N_(y) bulk materials was *** flexural strength and Vickers hardness values increased with increasing N content,suggesting that solid solution strengthening effectively improved the mechanical properties of Ti_(3)AlC_(2−y)N_(y).Ti_(3)AlCN(y=1)had the highest Vickers hardness and flexural strength among the studied samples,reaching 5.54 GPa and 550 MPa,***,the electrical conductivity and CTEs of the Ti_(3)AlC_(2−y)N_(y) solid solutions decreased with increasing N content,from 8.93×10^(−6) to 7.69×10^(−6) K^(−1) and from 1.33×10^(6) to 0.95×10^(6) S/m,*** work demonstrated the tunable properties of Ti_(3)AlC_(2−y)N_(y) solid solutions with varying N contents and widened the MAX phase family for fundamental studies and applications.
With the energy crisis and environmental pollution intensifying,lithium-ion batteries(LIBS) are widely used in new energy industries such as energy storage and electric *** is a consensus in these industries that the ...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
With the energy crisis and environmental pollution intensifying,lithium-ion batteries(LIBS) are widely used in new energy industries such as energy storage and electric *** is a consensus in these industries that the retirement of lithium batteries will usher in a peak in the next few ***,the capacity estimation and reutilization of retired LIBS has become a hot issue of social *** this paper,an accurate estimation model for state of health(SOH) estimation of retired LIBS is established,which is based on electrochemical impedance spectroscopy(EIS) and back propagation(BP) neural *** comparing the EIS curves under different SOH,we select the maximum impedance of the imaginary part and the impedance amplitude at 0.01 Hz and 0.1 Hz in the EIS as the inputs of BP neural network,and the actual SOH is used as the *** mean absolute error(MAE) and root mean square error(RMSE) of samples for verification are 0.59% and 1.38%,so the SOH estimation model has high accuracy and adaptability for retired lithium-ion batteries.
Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the outp...
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In this paper, an efficient graph-theoretic approach for state-feedback time-optimal stabilization of switched Boolean control networks (SBCNs) under constrains is proposed. Initially, the dynamic properties of the SB...
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This study focuses on enhancing the evasion capabilities of unmanned ground vehicles(UGVs) using Generative Adversarial Imitation Learning(GAIL). The UGVs are trained to evade unmanned aerial vehicles(UAVs). A decisio...
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This study focuses on enhancing the evasion capabilities of unmanned ground vehicles(UGVs) using Generative Adversarial Imitation Learning(GAIL). The UGVs are trained to evade unmanned aerial vehicles(UAVs). A decision-making neural network has been trained via GAIL to refine evasion strategies with expert demonstrations. The simulation environment was developed with OpenAI Gym and calibrated with real-world data for the improvement of accuracy. The integrated platform including the proposed algorithm was tested in flight experiments. Results showed that the UGVs could effectively evade UAVs in the complex and dynamic environment.
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