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Purpose The purpose of this paper is to design a hybrid robust tracking controller based on an improved radial basis function artificial neural network (IRBFANN) and a novel extended-state observer for a quadrotor system with various model and parametric uncertainties and external disturbances to enhance the resiliency of the control system. Design/methodology/approach An IRBFANN is introduced as an adaptive compensator tool for model and parametric uncertainties in the control algorithm of non-singular rapid terminal sliding-mode control (NRTSMC). An exact-time extended state observer (ETESO) augmented with NRTSMC is designed to estimate the unknown exogenous disturbances and ensure fast states convergence while overcoming the singularity issue. The novelty of this work lies in the online updating of weight parameters of the RBFANN algorithm by using a new idea of incorporating an exponential sliding-mode effect, which makes a remarkable effort to make the control protocol adaptive to uncertain model parameters. A comparison of the proposed scheme with other conventional schemes shows its much better performance in the presence of parametric uncertainties and exogenous disturbances. Findings The investigated control strategy presents a robust adaptive law based on IRBFANN with a fast convergence rate and improved estimation accuracy via a novel ETESO. Practical implications To enhance the safety level and ensure stable flight operations by the quadrotor in the presence of high-order complex disturbances and uncertain environments, it is imperative to devise a robust control law. Originality/value A new idea of incorporating an exponential sliding-mode effect instead of conventional approaches in the algorithm of the RBFANN is used, which makes the control law resistant to model and parametric uncertainties. The ETESO provides rapid and accurate disturbance estimation results and updates the control law to overcome the performance degradation caused by the disturbances. Simulation results depict the effectiveness of the proposed control strategy.

Non-Small Cell Lung Cancer (N-SCLC) accounts for almost 85% of all diagnosed lung cancer and the prognosis remains poor usually because of assimilated drug resistance including cisplatin. The miR-497-5p family has been discovered to play a significant role in regulating biological functions in N-SCLC. The purpose of this study was to investigate the molecular mechanism of miR-497-5p and its target gene on modulating cisplatin chemosensitivity in N-SCLC cells. The enhanced chemosensitivity effect of miR-497-5p to cisplatin in A549 and H1299 cells was detected by MTT method. Dual luciferase reporter assay, quantitative Real-Time PCR (qRT-PCR) and Western blotting were performed to demonstrate that miR-497-5p directly targets CDCA4 to reduce the expression. Transwell, colony formation and flow cytometry assays showed that combination of miR-497-5p and cisplatin exerted stronger effects on inhibiting N-SCLC cells proliferation, migration and invasion as well as promoting apoptosis and G1 phase arrest than miR-497-5p and cisplatin alone. The same tendency was observed in the upregulation of apoptosis-related protein Bax and Cytochrome-C and downregulation of cycle-related proteins CyclinB1 and CDK1. Our results indicate that upregulation of miR-497-5p targets CDCA4 directly and may function as an important modifier to sensitize N-SCLC cells to cisplatin.

The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image-spectrum merging cube data can be intrinsically represented as a third-order tensor that integrates spectral information and spatial information. In this article, a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise-smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by the l2,1-norm regularization. In the designed method, all the priors are integrated into a unified convex framework, and the anomalies can be finally determined by the anomaly tensor. Experimental results validated on several real hyperspectral data sets demonstrate that the proposed algorithm outperforms some state-of-the-art anomaly detection methods.

Nowadays, due to the ability of extracting deep features, the deep learning-based anomaly detection (AD) methods for hyperspectral images (HSIs) have been widely studied. However, all these AD methods treat the tasks of feature extraction and AD separately. Besides, most of them also do not make use of abundant spatial information of HSIs. Thus, a spectral-spatial hyperspectral AD method via collaborative representation constraint stacked autoencoders (SSCRSAE) is proposed. First, the collaborative representation constraint is imposed on the stacked autoencoders to extract deep nonlinear features that are more suitable for the collaborative representation-based detector (CRD). Then, CRD is used to for obtaining the preliminary detection result, which is more convenient for real HSIs because of no need for assuming the distribution of the background. Finally, aiming at further improving the SSCRSAE detector's performance, a novel spectral-spatial AD procedure is designed for calculating the final detection result by considering the spatial information of an HSI. Experimental results express that the proposed SSCRSAE exceeds eight state-of-the-art anomaly detectors used for comparison.

The low-rank and sparse decomposition model (LSDM) has been widely studied by researchers and has successfully solved the problem of hyperspectral image (HSI) anomaly detection (AD). The traditional LSDM usually ignores the information of the low-rank matrix, which only detects the anomalous targets by using the sparse component. To utilize both the sparse component and the low-rank component comprehensively, an anomaly detector for HSIs based on LSDM with density peak guided collaborative representation (LSDDPCRD) is proposed in this article. First, the LSDM technique with the mixture of Gaussian model is used to decompose the original HSI, which can also alleviate the background noise contamination problem. Then, the low-rank matrix is detected by the density peak guided collaborative representation detection algorithm, while the sparse matrix is calculated according to the Manhattan distance. In addition, an entropy-based adaptive fusing method is designed to combine the results obtained from the low-rank matrix and the sparse component. It could choose the fusing weights adaptively according to the characteristics of an HSI. The experimental results indicate that the LSDDPCRD performs better than eight classical and state-of-the-art AD algorithms (GRX, LRX, SRX-Segmented, CRD, RPCA-RX, LSMAD, LRASR, and LSDM-MoG) on four real HSIs.

In recent years, several representation models based on total variation (TV) have been proposed for hyperspectral imagery (HSI) anomaly detection. However, the TV terms of these works are directly imposed on the representation coefficient matrix, which can destroy the spatial structure of an HSI to some extent. Besides, as the spatial resolution of an HSI is relatively low, mixed pixels existing in an HSI can lead to anomaly component contamination, which can make the difference between background and anomalies not significant enough. To address these issues, a novel enhanced total variation (ETV) with endmember background dictionary for hyperspectral anomaly detection is proposed. The ETV is designed to be used on the row vectors of the representation coefficient matrix to enhance the spatial structure of an HSI in the presentation process. Furthermore, the proposed ETV regularized representation model with endmember background dictionary (ETVEBD) method elaborates a background dictionary constructed by endmembers of background pixels, which are pure spectral signatures of background pixels. The proposed EBD can decrease the influence of anomaly components in mixed pixels, and the coefficient matrix of the endmember background dictionary has more physical meanings. The proposed method is evaluated on four hyperspectral data sets and the experiment results show its performance is best when it is compared with other seven state-of-the-art methods.

Convolutional neural networks are widely used in the field of hyperspectral image classification because of their excellent nonlinear feature extraction ability. However, as the sampling position of the regular convolution kernel is unchangeable, the regular convolution cannot distinctively extract the spatial and spectral information around the central pixel, which makes the classification results at the boundaries of ground objects over-smoothed and the classification performance degraded. Thus, we propose a novel superpixel guided deformable convolution network (SGDCN) for hyperspectral image classification. Firstly, the superpixel region fusion filter (SRF-Filter) is designed to fuse the initial superpixel region segmented by the simple linear iterative clustering (SLIC), making the fused superpixel region have a high homogeneity and also contain spatial features of diverse scales. Then, the superpixel guided deformable convolution (SGD-Conv) is proposed to make the shape of deformable convolution consistent with the real shape of land covers, and the SGD-Conv can extract pure neighborhood spatial-spectral features. Finally, a superpixel joint bilateral filter (SPJBF) is designed to solve the pixel-level and region-level misclassification problem, which can effectively utilize the superpixel region's homogeneity and improve the classification accuracy. Experiments on three HSI datasets indicate that the SGDCN can obtain better classification performance when compared with other twelve state-of-the-art methods.

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, a shape-fixed convolution kernel cannot extract appropriate spatial-spectral features. Thus, we propose a novel two-stage classification method based on kernel-guided deformable convolution networks and double-window joint bilateral filter (KDCDWBF) for HSIs. First, according to the calculated similarity map, the shape of the kernel-guided deformable convolution (KDC) is more consistent with the real shape of land covers, so the KDC can extract more pure neighborhood spatial-spectral information. Then, using the piecewise smoothness property of the HSI, a double-window joint bilateral filter (DWJBF) is designed to complete the coarse-to-fine classification stage, which can solve the misclassification problem of single pixels and small regions. Experiments on two HSI datasets demonstrate that the proposed network can achieve better classification performance when compared with other state-of-the-art methods.

Semi-supervised soft sensor has gradually become a more practical solution due to the difficulty in collecting labels for industrial soft sensor. Currently, the commonly-used manifold regularization assumes similar inputs result in similar outputs, but such similarity assumption fails when temporality exists. Moreover, the performance of soft sensor is vulnerable to anomaly, so anomaly is generally discarded before modelling. The latent information in anomaly has seldom been explored for modelling yet. To overcome the limitation of manifold regularization and explore the usability of anomaly, a novel semi-supervised soft sensor, named Consistent-Contrastive Network (CC-Net), is proposed to build a temporality-aware and robust-to-anomaly soft sensor. Specifically, CC-Net consists of two branches, namely label branch and feature branch, to map the input into label and feature respectively. In the label branch, labeled samples are used in supervised manner. For unlabeled samples, two regularization terms, consistency and temporal consistency, are designed to constrain the pseudo-labels consistent against noise and along time respectively, which can adapt to temporality. In the feature branch, the idea of contrastive learning is applied to separate normal samples and anomalies. Such design properly utilizes the distribution information in anomaly as regularizer. With the designed regularization terms and the utilization of anomalies, CC-Net implements a temporality-aware and robust-to-anomaly soft sensor, which is demonstrated by real denitrification and desulphurization cases.

3D shape reconstruction of the object-level building (SROLB) is one of the essential issues in remote sensing. Especially, utilizing the single remote sensing image (SRSI) to perform shape reconstruction can offer better scalability and transferability, in terms of simplifying input data. Recently, the methods of shape reconstruction based on neural networks have been widely studied. However, most of them generate models with irregular surfaces and few details. Besides, complex background in SRSI leads to a poor generalization of networks and reduces the quality of generated models. To solve the above problems, an implicit representation network (IRNet) is proposed in this letter. IRNet is composed of two parts: 3D space decoding and feature extraction. First, the signed distance function is employed to fit implicit representation better in the decoding module. Moreover, a multi-stage weight loss function is designed, making the network generating models with flatter surfaces and more details. Then, a channel attention module is added to the feature extraction network. It reduces the interference of the background in the image effectively and improves the generalization of the network. Finally, our method generates mesh models of the individual buildings. Experimental results show that a better accuracy can be obtained compared with state-of-the-art methods.

When training samples are scarce, the semantic embedding technique, i. e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, i. e., zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning; e. g., we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available. 1.

In recent years, many target detection methods based on tensor representation theory have been proposed and achieved good results for hyperspectral images (HSIs). However, these methods still have some deficiencies. For example, 3-D hyperspectral data are first transformed into 1-D vectors in these methods, which may destroy the spatial structure of HSI data and reduce the detection performance. Besides, when the number of training samples is small, the results of the target detection method usually become worse. To solve these problems, a hyperspectral target detection method based on nonlocal self-similarity and rank-1 tensor is proposed in this article. First, different from these traditional tensor representation-based methods, the third-order tensor data are directly used as the input of the proposed method to preserve the spatial information and structure of an HSI. Second, the tensor blocks related to the class are constructed by using the nonlocal self-similarity of HSI data. Finally, by taking advantage of rank-1 canonical decomposition attribute, the process of tensor operation can be simplified, and the number of training samples can be reduced. The proposed method is compared with six state-of-the-art hyperspectral target detection methods on four HSI data sets. The experimental results show that the proposed method can have better target detection results than other compared methods, especially in the case of fewer training samples.

Change detection for multitemporal hyperspectral images (HSIs) has always been a research hotspot of remote sensing. However, most current detection methods only use spectral information or spatial information separately, and there are many false detection areas in the detection results. Besides, the feature extraction method based on neural networks needs a huge amount of training samples, but collecting labeled training samples for change detection tasks is difficult. Therefore, this letter proposes a hyperspectral change detection method based on a simplified 3-D convolutional autoencoder (S3DCAECD). First, the framework is based on deep unsupervised autoencoder (AE), which can extract deep spectral-spatial features from bitemporal images without the need for prior information. Second, by adding a 3-D convolution kernel and eliminating the pooling layer, the structure of 3-D convolutional AE is simplified, which can reduce spectral redundancy and improve data processing speed. Finally, a softmax classifier with a 2-D convolutional layer added is used to obtain the detection result, and only a few label samples are needed to train the classifier. Three HSIs' experimental results indicate that the accuracy of the S3DCAECD is more than 95% on three experimental datasets and it has better detection results than several commonly used methods.

In this paper, an adaptive multi-rotor UAV system of dynamic target tracking and path planning is proposed for the problems of occlusion, lighting change, and similar target interference in an unstructured environment. A DTE-tracker module is designed, consisting of a detector, tracker, and examiner, and proposes a dynamic target capture mechanism to improve the robustness and continuity of target tracking in complex environments. A DWA local path planning algorithm based on a dynamic target tracking task is proposed to control the yaw of the UAV to accurately locate specific targets in the center of the image, and finally achieve the purpose of stable tracking. A UAV platform was built equipped with an onboard computer, laser sensor, and visual sensor, and a series of target tracking and path planning experiments were carried out to verify the effectiveness of the method and test the performance of the algorithm in a complex jungle environment.

PurposeThis paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.Design/methodology/approachThe proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.FindingsThe performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.Practical implicationsFor the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.Originality/valueThe proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

For real industrial processes, time-varying behaviors are quite common. Consequently, industrial processes usually possess nonstationary characteristics, which makes conventional monitoring methods suffer from the model mismatch problem. In this article, an exponential analytic stationary subspace analysis (EASSA) algorithm is proposed to develop an adaptive strategy for nonstationary process monitoring. It is recognized that although covered by nonstationary trends, some underlying components of the process may remain stationary, which can be used for reliable process monitoring. For this, an EASSA algorithm is first developed to estimate the stationary sources more accurately and numerically stably. Then, a monitoring strategy is developed on the estimated stationary sources to provide reliable monitoring results. Considering that the relationships between process variables are driven to change slowly by time-varying behaviors, an update strategy and an adaptive monitoring scheme are designed to accurately track the trajectory of nonstationary processes while reducing the update frequency as much as possible. To meet the need of the EASSA algorithm, the minimum update unit has been expanded from one sample to one batch and the update conditions are given, by which the model is prevented from erroneously adapting to incipient faults to a certain extent. Case study on both a simulation process and a real thermal power plant process demonstrates that the proposed method can distinguish the real faults from normal changes while being robust to the disturbances in the nonstationary process.

In industrial processes, soft sensor techniques are used to predict the hard-to-measure quality variables under the classic supervised learning paradigm. However, the data challenges, i.e., the widespread noises and inadequate labeled samples, usually make the data-driven sensors weak for application. In this paper, a simple but effective regularization method termed as adversarial smoothing regularization (ASR) is proposed, which measures the local smoothness of prediction around each input sample. By minimizing the divergence between the predictions for noisy and clean inputs, the proposed ASR regulates the learning model to be robust against local perturbation in a semi-supervised manner. Theoretical analysis explains the smoothing capability of ASR, and shows that it is helpful for the improvement of generalization performance. We also design a tri-regression framework to further use the information of unlabeled samples with pseudo labels and present the adversarial smoothing tri-regression (ASTR) model for soft sensor. Based on two industrial processes, comprehensive soft sensor experiments and noise tests are performed to show the robust semi-supervised learning capability of the proposed ASR and ASTR.