Accurate segmentation of lung nodules is of great significance for the early diagnosis of lung cancer. However, due to the diverse shapes and small sizes of lung nodules, lung nodule segmentation is a difficult task. In this paper, we propose an improved U-Net network called SMR-UNet, which integrates self-attention, multi-scale features and residual structures for lung nodule segmentation. The framework replaces the U-Net’s convolutional units with residual units to ensure fast convergence, enhances the network’s global modeling capability with Transformer, restores more detailed information with PixelShuffle, and enlarges the receptive field with a multi-scale feature fusion module before upsampling. The experiments show that on the LIDC dataset, the SMR-UNet achieves a Dice index of 0.9187 and an IoU of 0.8688, which are improved by 1.33% and 2.36% espectively compared to U-Net. When tested on lung nodules provided by the Department of Medical Imaging of the Fourth Affiliated Hospital of Guangxi Medical University, the SMR-UNet achieves a Dice index of 0.7785 and an IoU of 0.6541, which are improved by 4.22% and 4% espectively compared to U-Net.

To address the problems of external interference during unmanned aerial vehicle (UAV) flight and the low accuracy and weak generalization ability of the current single fault diagnosis model, this work proposes a weighted ensemble deep learning UAV fault diagnosis method. First, considering the differences in training methods and fault feature recognition principles of deep networks with different structures, three hybrid fault diagnosis models are constructed. These models are constructed by combining convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and bidirectional gate recurrent unit (BiGRU). CNN is used to extract the features of the UAV flight data and the obtained feature information is fed into BiLSTM and BiGRU to explore the fault information inherent in the time series data. Then, the three hybrid fault diagnosis models are used as the individual model of the ensemble learning algorithm, and the weights of the three individual models are optimized using a random grid search algorithm to construct a UAV fault diagnosis model based on hybrid deep learning weighted ensemble, which further improves the fault diagnosis performance. Finally, it is demonstrated experimentally that the proposed hybrid deep learning weighted ensemble fault diagnosis model can effectively identify the fault of UAV with an accuracy of 99.22 % and 99.62 % on binary and multivariate classification, respectively, and reflects better generalization performance in the metrics of precision, recall, and F1 score.

In this paper, the problem of actuator and sensor faults of a quadrotor unmanned aerial vehicle (QUAV) system is studied. In the system fault model, time delay, nonlinear term, and disturbances of QUAV during the flight are considered. A fault estimation algorithm based on an intermediate observer is proposed. To deal with a single actuator fault, an intermediate variable is introduced, and the intermediate observer is designed for the system to estimate fault. For simultaneous actuator and sensor faults, the system is first augmented, and then two intermediate variables are introduced, and an intermediate observer is designed for the augmented system to estimate the system state, faults, and disturbances. The Lyapunov–Krasovskii functional is used to prove that the estimation error system is uniformly eventually bounded. The simulation results verify the feasibility and effectiveness of the proposed fault estimation method.

The accurate and rapid segmentation of different categories of lung nodules is of great importance for the diagnosis of early stage lung cancer and to assist physicians in the diagnosis and treatment of the disease. In the segmentation process, there are various types of lung nodules with different shape characteristics and occupying small volumes, so the process of segmenting lung nodules out is challenging. The DMC-UNet network proposed in this paper is an improved network based on UNet. The DMC-UNet network combines a lightweight residual structure, multiscale feature upsampling fusion and X/Y Channel Attention Module and Coordinate Attention (CCA) attention mechanism. The overall framework of the network firstly replaces the convolutional units of U-Net with residual units, and replaces the traditional convolution in the residual units with Depthwise Separable Convolution (DSC) to reduce the number of parameters and computation of the model and improve the efficiency of model training and prediction, and secondly replaces the transposed convolution and PixelShuffle in the upsampling process of U-Net with parallel direction fusion to replace the transposed convolution used in the original U-Net, which can enable the model to better capture information at different scales, and the addition of a multiscale feature fusion module before PixelShuffle improves the traditional Efficient Sub-Pixel Convolutional Neural(ESPCN) model, which aims to expand the perceptual field, and finally, the addition of a CCA attention mechanism after the upsampling fusion can better recover spatial information. It is shown by experiments that the IoU and F1-score of DMC-UNet are 65.52%±0.71% and 76.02%±0.63%, respectively, on the lung nodules provided by the Department of Medical Imaging of the Fourth Affiliated Hospital of Guangxi Medical University (FAHGMU), and the absolute gains of IoU and F1-score compared with U-Net are 2.78% and 2.91% on the Lung Image Database Consortium(LIDC) public dataset, and 83.36% and 89.92% on the IoU and F1-score, respectively, with a gain of 1.37% and 0.73% on the IoU and F1-score, respectively, compared to the U-Net.

A facile hydrothermal process has been developed to synthesize the α-Fe₂O₃ nanowire arrays with a preferential growth orientation along the [110] direction. The W/α-Fe₂O₃/FTO memory device with the nonvolatile resistive switching behavior has been achieved. The resistance ratio (R_HRS/R_LRS) of the W/α-Fe₂O₃/FTO memory device exceeds two orders of magnitude, which can be preserved for more than 10³s without obvious decline. Furthermore, the carrier transport properties of the W/α-Fe₂O₃/FTO memory device are dominated by the Ohmic conduction mechanism in the low resistance state and trap-controlled space-charge-limited current conduction mechanism in the high resistance state, respectively. The partial formation and rupture of conducting nanofilaments modified by the intrinsic oxygen vacancies have been suggested to be responsible for the nonvolatile resistive switching behavior of the W/α-Fe₂O₃/FTO memory device. This work suggests that the as-prepared α-Fe₂O₃ nanowire-based W/α-Fe₂O₃/FTO memory device may be a potential candidate for applications in the next-generation nonvolatile memory devices.

In this paper, a study related to H _∞ robust fault-tolerant control (FTC) of a six-rotor Unmanned Aerial Vehicle (UAV) control system considering stochastic time lags and modeling uncertainties in the case of sensor faults and actuator non-affine faults (NAFs) occurs. First, the system is extended to a Markov jump system (MJS) by considering the stochastic delay due to the influence of uncertain network environment on the system. Then, the sufficient conditions that can make the augmented system stochastically stable with H _∞ performance index are given in the form of matrix inequalities. After that, the optimal parameters and matrices of the system are calculated by convex optimization after decoupling the matrix inequalities to obtain the solution of the controller gain matrix. Finally, the effectiveness of the method proposed in this paper is verified by simulation comparison experiments.