Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Hybrid Adaptive Event‑Triggered Platoon Control with Package Dropout

A novel hybrid adaptive event-triggered platoon control strategy is proposed to achieve the balanced coordination between communication resource utilization and vehicle-following performance considering the effect of package dropout.To deal with the disturbance caused by the event-triggered scheme,the parameter space approach is adopted to derive the feasible region from which cooperative adaptive cruise control controller satisfies internal stability,distance accuracy,and string stability.Subsequently,the Bernoulli random distribution process is employed to depict the phenomenon of package drop-out,and the hybrid coefficient is proposed to realize the allocation between the adaptive trigger threshold strategy and the adaptive headway strategy.The simulation of a six-vehicle platoon is carried out to verify the effectiveness of the designed control strategy.Results show that about 78.76%of communication resources have been saved by applying the event-triggered scheme,while guaranteeing the desired vehicle-following performance.And in the non-ideal communication environment with frequent package dropouts,the hybrid adaptive strategy achieves the coordination among communication resource utilization,string stability margin,distance accuracy,and traffic efficiency.

Contribution to Harmonic Depollution Control by the Use of Adaptive Hybrid Filter Type TLC: Case of Control by Modulated Hysteresis

In this article, we propose a topology of a TLC-HAPF power filter as a harmonic compensator for an optimization of the pollution control of electrical networks. This filter consists of an active part and a passive part in order to reduce or limit switching losses during current injection into networks thanks to its TLC module. This topology also provides solutions dynamic performance issues, resonance and lack of compensation capacity for imbalance cases. It also offers a greater range of compensation than conventional active models which do not offer as well as an intermediate circuit voltage in the order of 105 V to 109 V relatively lower than others models (600 v). A modulated hysteresis control of this topology is therefore also developed in this article and allows to obtain a network analysis on the three phases at three levels: source side, load side, and finally at the connection of the filter to the network, allowing to specify for these different positions the value of the current spectrum and its THD at this well-defined moment.

Multi-sensor Hybrid Fusion Algorithm Based on Adaptive Square-root Cubature Kalman Filter

In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.

Hybrid force control of astronaut rehabilitative training robot under active loading mode

In order to mitigate the effects of space adaptation syndrome(SAS) and improve the training efficiency of the astronauts, a novel astronaut rehabilitative training robot(ART) was proposed. ART can help the astronauts to carry out the bench press training in the microgravity environment. Firstly, a dynamic model of cable driven unit(CDU) was established whose accuracy was verified through the model identification. Secondly, to improve the accuracy and the speed of the active loading, an active loading hybrid force controller was proposed on the basis of the dynamic model of the CDU. Finally, the actual effect of the hybrid force controller was tested by simulations and experiments. The results suggest that the hybrid force controller can significantly improve the precision and the dynamic performance of the active loading with the maximum phase lag of the active loading being 9° and the maximum amplitude error being 2% at the frequency range of 10 Hz. The controller can meet the design requirements.

High-quality quasi-monochromatic near-field radiative heat transfer designed by adaptive hybrid Bayesian optimization

The increasing demand for versatile and high-quality near-field radiative heat transfer(NFRHT) has created a critical need for a design approach that can handle numerous candidate structures. In this work, we employ and develop an adaptive hybrid Bayesian optimization(AHBO) algorithm to design the high-quality quasi-monochromatic NFRHT. The candidate materials include hexagonal boron nitride, silicon carbide, and doped silicon. The high-quality quasi-monochromatic NFRHT is optimized over 1.0 × 10^(8) candidate structures to maximize the evaluation factor. It is worth noting that only 2.6% of the candidate structures needed to be calculated to identify the optimal structure. The optimal structure of quasi-monochromatic NFRHT is an aperiodic multilayer metamaterial that differs from conventional periodic multilayer structures. Moreover, we investigate the robustness and mechanisms of the optimal quasi-monochromatic NFRHT with respect to the vacuum gap distance and the temperature difference between the emitter and receiver. In addition, the high-quality multi-peak NFRHT is designed using the AHBO algorithm by improving the definition of the evaluation factor. The results demonstrate that the AHBO algorithm is efficient in designing high-quality quasi-monochromatic and multi-peak NFRHT, and it can be further expanded to other structural designs in the field of energy conversion.

Hybrid approach used for extended image-based wavefront sensor-less adaptive optics

The stochastic paralld gradient descent （SPGD） algorithm is widely used in wavefront sensor-less adaptive optics （WSAO） systems. However, the convergence is relatively slow. Modal-based algorithms usually provide much faster convergence than SPGD; however, the limited actuator stroke of the deformable mirror （DM） often prohibits the sensing of higher-order modes or renders a closed-loop correction inapplicable. Based on a comparative analysis of SPGD and the DM-modal-based algorithm, a hybrid approach involving both algorithms is proposed for extended image-based WSAO, and is demonstrated in this experiment. The hybrid approach can achieve similar correction results to pure SPGD, but with a dramatically decreased iteration number.

daptive Hybridized Interior Penalty Discontinuous Galerkin Methods for H(curl)-Elliptic Problems

We develop and analyze an adaptive hybridized Interior Penalty Discontinuous Galerkin(IPDG-H)method for H(curl)-elliptic boundary value problems in 2D or 3D arising from a semi-discretization of the eddy currents equations.The method can be derived from a mixed formulation of the given boundary value problem and involves a Lagrange multiplier that is an approximation of the tangential traces of the primal variable on the interfaces of the underlying triangulation of the computational domain.It is shown that the IPDG-H technique can be equivalently formulated and thus implemented as a mortar method.The mesh adaptation is based on a residual-type a posteriori error estimator consisting of element and face residuals.Within a unified framework for adaptive finite element methods,we prove the reliability of the estimator up to a consistency error.The performance of the adaptive symmetric IPDG-H method is documented by numerical results for representative test examples in 2D.