This article deals with the distributed state estimation for mixed delays system under unknown attacks. A new multichannel random attack model is established for the first time, where network attacks are considered to exist in three channels: the target-to-sensor channel, the senor-to-sensor channel, and the sensor-to-estimator channel. In the above model, transmitted packets are allowed to be attacked multiple times simultaneously, and when they are successfully attacked, the transmitted information is modified. Besides, the topology of the sensor network is considered to change dynamically according to the Markov chain. Based on the newly established distributed estimation model, the estimation error system is proven to be asymptotically mean-square stable under a given ${H_\infty }$ antidisturbance index by using a Lyapunov theory and a stochastic analysis technique; then, the estimator parameter matrices are solved utilizing a linearization method. Finally, several simulation examples are listed to testify the effectiveness of the designed algorithm.

In the real-time test fault diagnosis algorithm based on deep learning, it is difficult to guarantee that the training and testing data come from the same time series distribution. Inconsistent distribution will lead to a decline in diagnostic performance. In addition, the convolutional neural network is limited by the fixed shape of its convolution kernel, which makes it difficult to fully extract the spatial constraint features between fault data. To solve the above problems, this paper proposes a bearing fault diagnosis method based on inverted Mel-scale frequency cepstrum coefficients and deformable convolution networks. The core of traditional Mel-scale frequency cepstrum coefficients is to construct a non-uniformly distributed frequency-domain filter bank. It is characterized by the dense distribution of low-frequency regions and the sparse distribution of high-frequency regions. Considering that the features that can well characterize fault information are concentrated in the high-frequency part, we reconstruct the traditional Mel-scale frequency cepstrum coefficients filter bank and propose a feature extraction method of inverted Mel-scale frequency cepstrum coefficients. This method can obtain the frequency-domain characteristics of bearing vibration signals, highlight the fault information contained in the high-frequency region, and reduce the influence of time series distribution inconsistency between training samples and testing samples on the diagnosis accuracy. In order to further improve the spatial discrimination between different fault categories, the deformable convolution networks model is introduced to extract the spatial distribution information of fault features and improve the accuracy of fault diagnosis. Finally, two public data sets and data from an experimental platform verify that the method can achieve high-precision fault diagnosis, and that inverted Mel-scale Frequency cepstrum coefficients are effective in solving the problem of inconsistent distribution.

Multimodal remote sensing data can portray land-cover characteristics more comprehensively. Deep learning has powerful feature extraction capability. Therefore, deep learning-based methods have been widely used for collaborative classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data and achieve competitive classification performance. However, existing methods either overlook complementary information between multimodal data during feature extraction or overly highlight features of one modality during multimodal feature interaction. In addition, some methods integrate the extracted multimodal features using a straightforward cross-attention mechanism, it is difficult to adequately emphasize the relative importance of multimodal features and tends to lose the local detail information of intra-modal features. Therefore, this paper proposes a bilateral interactive hierarchical adaptive fusion network (BIHAF-Net) for collaborative classification of HSI and LiDAR data. First, the proposed model adopts a two-branch structure, where each branch sequentially connects multilevel convolutional neural network (CNN) feature extractor and spectral-spatial Transformer, which are used to mine discriminative high-level semantic information from HSI and LiDAR data respectively. Second, the bilateral interactive feedback module (BIFM) is designed to enhance the spatial feature representation ability of HSI information and the spectral feature representation ability of LiDAR information. Finally, a cross-modal hierarchical adaptive fusion module (CHAFM) is developed to dynamically fuse the extracted multimodal features, which not only highlights the relative importance of the multimodal features, but also preserves local detail information of intra-modal. Experiment is conducted on four benchmark HSI and LiDAR datasets, and the experimental results demonstrate the proposed BIHAF-Net performs better classification performance.

As one of the important equipment of motor transmission, bearings play an important role in the production and manufacturing industry, if there are problems in the manufacturing process will bring significant economic losses or even endanger personal safety, so its state prediction and fault diagnosis is of great significance. In bearing fault diagnosis, it is a challenge to eliminate the effect of data imbalance on fault diagnosis. Generative adversarial network (GAN) networks have achieved some success in data imbalance fault diagnosis, but GAN networks suffer from sample generation bias when balancing samples. To solve this problem, fusion attention mechanism and global feature cross GAN networks is proposed. Firstly, the spatial channel fusion attention mechanism is added to the generator, so that the generator selectively amplifies and processes sample features from different regions, which helps the generator learn more representative features from a few categories; secondly, the global feature cross module is added to the discriminator, so that the discriminator efficiently extracts features from different samples, and improves its ability of recognizing the sample discrepancy; at the same time, in order to improve the model’s anti-noise ability, an anti-noise module is added to the discriminator to improve the efficiency of the model’s data imbalance fault diagnosis; finally, this paper’s method is validated by using two public bearing datasets and one self-constructed dataset. The experimental results prove that this method can effectively overcome the effect of data imbalance on GAN networks, and has a high accuracy rate in real industrial fault diagnosis tasks, what’s more, it proves that the method in this paper has a very good anti-noise performance and practical application value.