Thrust estimation is a significant part of aeroengine thrust control *** traditional estimation methods are either low in accuracy or large in *** further improve the estimation effect,a thrust estimator based on Mult...
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Thrust estimation is a significant part of aeroengine thrust control *** traditional estimation methods are either low in accuracy or large in *** further improve the estimation effect,a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN)is *** solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual *** the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further ***,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence ***,six neural network models are deployed in the embedded controller of the micro-turbojet *** Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running ***,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.
Missing values exist widely in real-world datasets, which restrict the performance of data mining. In this paper, we propose a joint optimization framework to mine attribute associations and category structures in inc...
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With the rapid advancement of social economies,intelligent transportation systems are gaining increasing *** to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the...
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With the rapid advancement of social economies,intelligent transportation systems are gaining increasing *** to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external *** research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition *** address this,this research has developed a new model for recognizing abnormal traffic *** model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature *** approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network ***,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at *** temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data *** research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test *** results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.
The conventional selective template etching method to fabricate yolk-shell microwave absorbers is inconvenient and inefficient,so the thermally-driven contraction strategy was used to prepare asymmetric yolk-shell MnS...
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The conventional selective template etching method to fabricate yolk-shell microwave absorbers is inconvenient and inefficient,so the thermally-driven contraction strategy was used to prepare asymmetric yolk-shell MnSe@C microsphere microwave absorbers via self-template directed *** self-templated oriented transformation enables compositional customization and enhances the template *** confinement effect of the carbon shell is crucial for realizing the thermally-driven contraction strategy and contributes to the strong conduction loss to MnSe@*** the other hand,the enhanced polarization loss benefits from the abundant heterogeneous interfaces and defects in the asymmetric yolk-shell MnSe@C *** rich cavities in the yolk-shell structure not only facilitate optimal impedance matching,but also promote the enhancement of multiple reflection loss(RL).As a result,the asymmetric yolk-shell MnSe@C microspheres obtain excellent microwave absorption perfor-mance,the minimum RL(RL_(min))and the maximum effective absorption bandwidth(EAB)reaching-54.4 dB and 5.1 GHz,respectively,at a thickness of 1.9 *** successful obtainment of the asymmetric yolk-shell MnSe@C microspheres paves the way for the convenient synthesis of the yolk-shell transitionmetal selenides(TMSs)microwave absorbers.
This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input *** the mod...
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This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input *** the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller ***,the control gain of ultra-local model is considered as a *** fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped ***,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is ***,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown ***,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown ***,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is ***,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov ***,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.
Unsupervised domain adaptation (UDA) for time series classification (TSC) is an important but challenging task. In the process of UDA, feature learning is most critical. Most of the existing works in this area are bas...
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In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter e...
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In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter estimation method for LFM signals with a Duffing oscillator based on frequency periodicity is proposed in this *** method utilizes the characteristic that the output signal of the Duffing oscillator excited by the LFM signal changes periodically with frequency,and the modulation period of the LFM signal is estimated by autocorrelation processing of the output signal of the Duffing *** this basis,the corresponding relationship between the reference frequency of the frequencyaligned Duffing oscillator and the frequency range of the LFM signal is analyzed by the periodic power spectrum method,and the frequency information of the LFM signal is *** results show that this method can achieve high-accuracy parameter estimation for LFM signals at an SNR of-25 dB.
Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image cl...
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Belts are mainly used for transporting coal. During the conveying process, the belt may be idling. Manual inspections not only lead to reduced efficiency, but also pose safety hazards. As a result, it is proposed a be...
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This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where th...
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This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where the system dynamics is not *** analyzing the value function iterations,the convergence of the model-based algorithm is *** equivalence of several types of value iteration algorithms is *** effectiveness of model-free algorithms is demonstrated by a numerical example.
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