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Lower limb exoskeleton robots offer an effective treatment for patients with lower extremity dysfunction. In order to improve the rehabilitation training effect based on the human motion mechanism, this paper proposes a humanoid sliding mode neural network controller based on the human gait. A humanoid model is constructed based on the human mechanism, and the parameterised gait trajectory is used as target to design the humanoid control system for robots. Considering the imprecision of the robot dynamics model, the neural network is adopted to compensate for the uncertain part of the model and improve the model accuracy. Moreover, the sliding mode control in the system improves the response speed, tracking performance, and stability of the control system. The Lyapunov stability analysis proves the stability of the control system theoretically. Meanwhile, an evaluation method using the similarity function is improved based on joint angle, velocity, and acceleration to evaluate the comfort of humans in rehabilitation training more reasonably. Finally, to verify the effectiveness of the proposed method, simulations are carried out based on experimental data. The results show that the control system could accurately track the target trajectory, of which the robot is highly similar to the human.

The effective classification methods of the small target objects in the no-fly zone are of great significance to ensure safety in the no-fly zone. But, due to the differences of the color and texture for the small target objects in the sky, this may be unobvious, such as the birds, unmanned aerial vehicles (UAVs), and kites. In this paper, we introduced the higher layer visualizing feature extraction method based on the hybrid deep network model to obtain the higher layer feature through combining the Sparse Autoencoder (SAE) model, the Convolutional Neural Network (CNN) model, and the regression classifier model to classify the different types of the target object images. In addition, because the sample numbers of the small sample target objects in the sky may be not sufficient, we cannot obtain much more local features directly to realize the classification of the target objects based on the higher layer visualizing feature extraction; we introduced the transfer learning in the SAE model to gain the cross-domain higher layer local visualizing features and sent the cross-domain higher layer local visualizing features and the images of the target-domain small sample object images into the CNN model, to acquire the global visualizing features of the target objects. Experimental results have shown that the higher layer visualizing feature extraction and the transfer learning deep networks are effective for the classification of small sample target objects in the sky.

In this paper, an operator-based robust perfect control for nonlinear semiconductor refrigeration device with uncertainties and perturbation is considered. For the research about the properties of the semiconductor refrigeration, an aluminum plate with Peltier device is very representative. Therefore, the perfect tracking control performance of semiconductor refrigeration can be investigated by using this aluminum plate with Peltier device. Moreover, the operator based robust right coprime factorization (RRCF) approach is convenient in analysis and designing control system of nonlinear plant with uncertainties and perturbation. Based on the above reasons, an operator-based robust tracking control design for nonlinear semiconductor refrigeration device with uncertainties and perturbation is investigated by using an operator-based robust right coprime factorization approach, where the operator-based disturbance and state observers based on nominal plant properties are designed to compensate the effect of uncertainties and perturbation. A realizable operator controller is designed to improve the control performance and to realize the perfect tracking. The sufficient condition of robust stability for the designed system is derived. The robust stability condition ensured that the output tracking performance is realized. Finally, the effectiveness of the proposed design scheme was illustrated by the simulation and experimental results.

In this paper, a robust nonlinear tracking control design for a bio-inspired robot arm with human-like motion mechanism is investigated, and the bio-inspired operator controller based on human multi-joint viscoelastic properties is designed by using operator-based robust right coprime factorization approach. The motion mechanism of human multi-joint arm is used, and the measurement uncertainties of human multi-joint arm viscoelasticity are considered in designing bio-inspired operator controller. Based on the proposed design scheme, the sufficient conditions for the robust stability are derived in considering the coupling effects and measurement uncertainties, and the output tracking performance is realized. The effectiveness of the proposed design scheme was confirmed by the simulation results based on experimental data, and the time-varying estimated experimental data of human multi-joint arm viscoelasticity is used in simulation.

[abstFig src='/00280004/14.jpg' width='300' text='Robot arm with micro-hand system' ] This work focuses on a robust nonlinear control design of a robot arm with micro-hand (RAMH) by using operator-based robust right coprime factorization (RRCF) approach. In the proposed control system, we can control the endpoint position of robot arm and obtain the desired force of micro-hand to perform a task, and a miniature pneumatic curling soft (MPCS) actuator which can generate bidirectional curling motions in different positive and negative pressures is used to develop the fingers of micro-hand. In detail, to control successively the precise position of robot arm and the desired force of three fingers according to the external environment or task involved, this paper proposes a double-loop feedback control architecture using operator-based RRCF approach. First, the inner-loop feedback control scheme is designed to control the angular position of the robot arm, the operator controllers and the tracking controller are designed, and the robust stability and tracking conditions are derived. Second, the complex stable inner-loop and micro-hand with three fingers are viewed as two right factorizations separately, a robust control scheme using operator-based RRCF approach is presented to control the fingers forces, and the robust tracking conditions are also discussed. Finally, the effectiveness of the proposed control system is verified by experimental and simulation results.

Due to the lack of other component information in traditional magnetic leakage signal defects and the low accuracy of prediction methods, this paper proposes an improved residual network for magnetic leakage detection defect recognition method that predicts defect size and different detection speeds. A new defect diagnosis method based on ResNet18 on the Convolutional Neural Network (CNN) is proposed in this study. This method transfers the pre-trained ResNet18 network and replaces the activation function in the transferred network structure. It extracts features from transformed two-dimensional images obtained by converting the original experimental signals and signals with added noise, removing the influence of manual features. The results demonstrated that the improved ResNet18 network model, after transfer learning, achieved 100% prediction accuracy for all 10,000 grayscale images generated with defect lengths of 50 mm; width of 2 mm; and depths of 2 mm, 4 mm, 6 mm, and 8 mm. Moreover, the prediction accuracies for the quasi-static, slow, compensated fast, and fast scanning speeds were 99.20%, 98.50%, 93.30%, and 94.00%, respectively, for defect depths of 2 mm, 4 mm, 6 mm, and 8 mm. These accuracies surpass those of other models, demonstrating the significant improvement in prediction accuracy achieved by this method.

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the \"transfer learning'' assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.

Discrete Fourier transform (DFT) codebook-based solutions are well-established for limited feedback schemes in frequency division duplex (FDD) systems. In recent years, data-aided solutions have been shown to achieve higher performance, enabled by the adaptivity of the feedback scheme to the propagation environment of the base station (BS) cell. In particular, a versatile limited feedback scheme utilizing Gaussian mixture models (GMMs) was recently introduced. The scheme supports multi-user communications, exhibits low complexity, supports parallelization, and offers significant flexibility concerning various system parameters. Conceptually, a GMM captures environment knowledge and is subsequently transferred to the mobile terminals (MTs) for online inference of feedback information. Afterward, the BS designs precoders using either directional information or a generative modeling-based approach. A major shortcoming of recent works is that the assessed system performance is only evaluated through synthetic simulation data that is generally unable to fully characterize the features of real-world environments. It raises the question of how the GMM-based feedback scheme performs on real-world measurement data, especially compared to the well-established DFT-based solution. Our experiments reveal that the GMM-based feedback scheme tremendously improves the system performance measured in terms of sum-rate, allowing to deploy systems with fewer pilots or feedback bits.

In this paper, we survey state-of-the-art research outcomes in the burgeoning field of reconfigurable intelligent surface (RIS) in view of its potential for significant performance enhancement for next generation wireless communication networks by means of adapting the propagation environment. Emphasis has been placed on several aspects gating the commercially viability of a future network deployment. Comprehensive summaries are provided for practical hardware design considerations and broad implications of artificial intelligence techniques, so are in-depth outlooks on salient aspects of system models, use cases, and physical layer optimization techniques.

Systematic studies of the RF properties of various high temperature superconductors were carried out. The experimental technique employed was a parallel plate resonator technique adapted for studying superconducting thin films. The penetration depth and the surface resistance of the samples were measured simultaneously. The YBa$\\rm\\sb2Cu\\sb3O\\sb{7-\\delta}$ films studied have a wide range of material properties: transition temperature (Tc), normal state resistivity, etc.. At low temperatures, their penetration depths were found to have an essentially quadratic temperature dependence with systematic deviations that were correlated with the Tc of the samples. For the dirtiest films (low Tc and low normal channel conductivity), possible exponential behavior was observed for the penetration depth at the lowest temperatures. At higher temperatures (above Tc/2), all the films exhibited the same temperature dependence as that of YBa$\\rm\\sb2Cu\\sb3O\\sb{6.95}$ single crystals. Further analysis revealed that the data are qualitatively consistent with the dirty d-wave model of the high Tc superconductors and that there exists a correlation between the Tc of the films and the amount of disorder present. A strong correlation between Tc and the normal channel conductivity was also observed in these films. The temperature dependence of the normal channel conductivity strongly suggests a rapidly decreasing scattering rate for the quasi-particles below Tc. Preliminary studies on Bi$\\rm\\sb2Sr\\sb2CaCu\\sb2O\\sb8$ single crystals revealed some interesting temperature dependencies for their penetration depths. However we suspect the data are contaminated by c-axis contributions to the transport due to the large anisotropy of the material. In Tl$\\rm\\sb2Ba\\sb2CaCu\\sb2O\\sb8$ thin films, the penetration depth was found to be nearly linear with temperature at low temperatures. One pair of the films exhibited the same temperature dependence as that of YBa$\\rm\\sb2Cu\\sb3O\\sb{6.95}$ single crystals over the whole temperature range below Tc. More work on this material is needed to establish the implications of this correlation.