Fault intensity diagnosis (FID) plays a pivotal role in intelligent manufacturing while neglecting dependencies among target classes hinders its practical deployment. This paper introduces a novel and general framework with deep hierarchical knowledge loss (DHK) to achieve hierarchical consistent representation and prediction. We develop a novel hierarchical tree loss to enable a holistic mapping of same-attribute classes, leveraging tree-based positive and negative hierarchical knowledge constraints. We further design a focal hierarchical tree loss to enhance its extensibility and devise two adaptive weighting schemes based on tree height. In addition, we propose a group tree triplet loss with hierarchical dynamic margin by incorporating hierarchical group concepts and tree distance to model boundary structural knowledge across classes. The joint two losses significantly improve the recognition of subtle faults. Extensive experiments are performed on four real-world datasets from various industrial domains (three cavitation datasets from SAMSON AG and one publicly available dataset) for FID, all showing superior results and outperforming recent state-of-the-art FID methods.

The unstructured, unordered and inherent irregular sampling properties presents difficulties for accurate and efficient realizing semantic segmentation of large-scale 3D point cloud. The complexity and the long-distance information exploitation are the key challenges for large-scale 3D point cloud semantic segmentation. Therefore, in order to efficiently exploit long-distance global information and improve the segmentation accuracy of point cloud data located at the edges of distinct categories, a novel Trigonometric Bilinear attention and Global-aware Aggregation network (TBGA-Net) is designed to integrate and supplement the local-global contextual features for large-scale point clouds segmentation. The proposed Global-aware Context Aggregation block (GCA) can implicitly excavate the global context for each 3D point by utilizing its surface-to-volume ratio of the neighborhood to the global point cloud. Furthermore, aim at refining local-global features, the Trigonometric Bilinear Attention block (TBA) utilizes trigonometric functions to embed the point cloud coordinates of local regions, and applies bilinear attention to realize feature enhancement for obtaining more discriminative local-global features. Additionally, we further designed a novel Dynamic-adjusting Cross-entropy Loss (DCLoss) to incorporate with TBGA-Net for addressing the issue of class imbalance in training data for large-scale 3D point cloud semantic segmentation. The experimental results on three 3D point cloud datasets demonstrates that the proposed algorithm indicates better segmentation accuracy especially for the point located at the boundary of the distinct categories.

Land cover classification (LCC) based on remote sensing image segmentation is a prominent task of remote sensing data interpretation. The commonly used optical data is susceptible to the weather, so it has the potential to utilize complementary features from the supplementary synthetic aperture radar (SAR) data to enhance segmentation performance. However, current multi-modal segmentation methods focus on the deep fusion of features, which usually ignores the significance of structural consistency information. In order to make use of the mutual correction and information exchange between multi-modal data, we propose DCIFNet, a dual-stream correction-interaction-fusion multi-modal LCC network. Specifically, we design a differential feature correction and enhancement module (DF-CEM) that leverages bidirectional differential features to correct multi-modal features. In addition, for corrected feature pairs, we deploy a parallel attention interaction module (PAIM) to focus on the pixel-level feature correlation and achieve effective information exchange in both channel and spatial dimensions. Through the expert fusion module (EFM), DCIFNet leverages the gate network to attain a flexible and compact feature fusion between multi-modal features. Experimental results show that our method achieves a superior performance compared with other multi-modal fusion segmentation methods on three optical-SAR datasets. The source code of DCIFNet is publicly available at https://gitee.com/asdwer2046/dcifnet.

With the exponential growth of satellite remote sensing data, land cover classification models must adapt continuously to new classes. However, conventional incremental learning methods face critical challenges: catastrophic forgetting degrades recognition of old classes, and the softmax function further suppresses old-class probabilities due to ”class crowding.” Existing distillation techniques also struggle to transfer features in irregular geospatial regions. To address these issues, we propose Soft Labels and Subregion Distillation (SLSRD). SLSRD mitigates class crowding by employing soft labels instead of hard labels, derived from a hybrid of softmax and sigmoid outputs that preserve richer probabilistic information. Concretely, the soft label is a convex combination of softmax- and sigmoid-based probabilities that preserves inter-class relations while relaxing over-confident exclusivity for newly introduced categories, and it supervises all pixels across stages. In parallel, a breadth-first search identifies subregions within each image, which are weighted by probability and size, and similarity between corresponding subregions of the old and new models is maximized. This dual strategy effectively transfers fine-grained knowledge and overcomes the limitations of conventional distillation methods, particularly for large-scale remote sensing imagery. Experiments on three benchmark datasets-Vaihingen, GID, and FBP-demonstrate that SLSRD outperforms traditional methods, significantly improving incremental land cover classification.

Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems.As current FID methods are based on chain of thought without considering dependencies among target classes.To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods.The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of interdependent global hierarchical classifiers, where each node is denoted by word embeddings of a class.These global hierarchical classifiers are applied to learned deep features extracted by representation learning, allowing the entire model to be end-toend learnable.In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix (SCM) which effectively guides the information sharing of nodes in graphical convolutional neural networks and avoids over-smoothing issues.The Re-HKCM is derived from the

Land cover classification (LCC) is an important application in remote sensing data interpretation. As two common data sources, SAR images can be regarded as an effective complement to optical images, which will reduce the influence caused by single-modal data. But common LCC methods are focusing on designing advanced network architectures to process single-modal remote sensing data. Few works have been oriented toward improving segmentation performance through fusing multi-modal data. In order to deeply integrate SAR and optical features, we propose SwinTFNet, a dual-stream deep fusion network. Through the global context modeling capability of Transformer structure, SwinTFNet models teleconnections between pixels in other regions and pixels in cloud regions for better prediction in cloud regions. In addition, a Cross-Attention Fusion Module (CAFM) is proposed to fuse features from optical and SAR data. Experimental results show that our method improves greatly in the classification of clouded images compared with other excellent segmentation methods and achieves the best performance on multi-modal data.

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential for the diagnosis and treatment design of high-incidence low back pain. However, the grading accuracy is still challenged by lacking the fine-grained degenerative details, which is mainly due to the existing grading methods are easily dominated by the salient nucleus pulposus regions in LIVD, overlooking the inconspicuous degeneration changes of the surrounding structures. In this study, a novel regional feature recalibration network (RFRecNet) is proposed to achieve accurate and reliable LIVD degeneration grading. Detection transformer (DETR) is first utilized to detect all LIVDs and then input to the proposed RFRecNet for the fine-grained grading. To obtain sufficient features from both the salient nucleus pulposus and the surrounding regions, a regional cube-based feature boosting and suppression (RC-FBS) module is designed to adaptively recalibrate the feature extraction and utilization from the various regions in LIVD, and a feature diversification (FD) module is proposed to capture the complementary semantic information from the multi-scale features for the comprehensive fine-grained degeneration grading. Extensive experiments were conducted on a clinically collected dataset, which consists of 500 MR scans with a total of 10225 LIVDs. An average grading accuracy of 90.5%, specificity of 97.5%, sensitivity of 90.8%, and Cohen's kappa correlation coefficient of 0.876 are obtained, which indicate that the proposed framework is promising to provide doctors with reliable and consistent fine-grained quantitative evaluation results of the LIVD degeneration conditions for the optimal surgical plan design.

Recently, due to the limitations of single sensor, it is hard to improve the performance of land cover classification. The traditional image segmentation methods can not process the optical remote sensing images effectively, especially when optical sensor is affected by complex weather conditions. However, as an active radar, synthetic aperture radar(SAR) has the advantage of not being restricted by weather conditions with the the penetrability of electromagnetic radiation. So multi-sensor data fusion provides a great potential for land cover classification. In this paper, a new fusion network called SegFusion is proposed to improve the performance of land cover classification. There are two main components in SegFusion which are hierarchical Transformer encoder and Swin-Fusion(SW-Fusion) module. First, a hierarchical Transformer encoder is used to extract multilevel feature of optical and SAR images. By integrating features from different layers, we can obtain powerful representation that combines both low-resolution fine-grained features and high-resolution coarse-grained features. Second, SW-Fusion module is used to fuse the features of optical and SAR data. In SW-Fusion, we use modified Swin Transformer [1] block with multi-head cross-attention mechanism to exchange information between features from different sources.

During incremental learning tasks, catastrophic forgetting occurs when old models are updated with new information. To address this issue, we propose a novel method called label strategy and adaptive weights (LSAW) that improves the incremental learning process. The label strategy introduces the old classes and solves the problem of how to reasonably use the wrong samples predicted by the old model. In the cross-entropy (CE) loss, we apply a threshold to filter the pseudolabels predicted by the old model. Subsequently, we merge the pixel samples with high probability with the current label. The probability here refers to the probability that the pixel belongs to the true class. This process enables the introduction of information from old classes that are not directly accessible in the current stage. Moreover, this information is relatively reliable, and the model exhibits confidence in its accuracy. For the remaining pixels, we retain all classes’ information through label smoothing. In the distillation function, the old class and background pixel samples are selected for distillation according to the prediction map of the old classes. The weights of the classes are adaptively updated and adjusted using specific label information from each batch and the different stages of incremental learning. As demonstrated by the results of our experiment, on three remote sensing image datasets: China Computer Federation (CCF), Potsdam, and Vaihingen, our method achieves the best results.

Anomalous sound detection (ASD) is one of the most significant tasks of mechanical equipment monitoring and maintaining in complex industrial systems. In practice, it is vital to efficiently identify abnormal status of the working mechanical system, which can further facilitate the failure troubleshooting. In this paper, we propose a multi-pattern adversarial learning one-class classification framework, which allows us to use both the generator and the discriminator of an adversarial model for efficient ASD. The core idea is to learn reconstructing the normal patterns of acoustic data through two different patterns from auto-encoding generators, which succeeds in generalizing the fundamental role of a discriminator from identifying real and fake data to distinguishing between regional and local pattern reconstructions. Moreover, we design a novel balanceable detection strategy using both generators and a discriminator to achieve anomaly detection efficiently. Furthermore, we present a global filter layer for long-term interactions in the frequency domain space, which directly learns from the original data without introducing any human priors. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG, and one existing publicly) for anomaly detection, all showing superior results and outperform recent state-of-the-art ASD methods.

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a novel mutli-scale average pooling (MSAP) network to exploit the changed information from the noisy difference image. Being different from traditional convolutional network with only an one-scale pooling kernel, in the proposed method, multi -scale pooling kernels are equipped in convolutional network to obtain the spatial context information on changed regions from the difference image. Finally, we verify our proposed method on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-art methods.

Product theory of stochastic matrices provides a powerful tool in the consensus analysis of discrete-time multiagent systems. However, the classic theory cannot deal with networks with general coupling coefficients involving negative ones, which have been discussed only in very few papers due to the technicalities involved. Motivated by these works, here we developed some new results for the products of matrices which generalize that of the classical stochastic matrices by admitting negative entries. Particularly, we obtained a generalized version of the classic Hajnal inequality on this generalized matrix class. Based on these results, we proved some convergence results for a class of discrete-time consensus algorithms with time-varying delays and general coupling coefficients. At last, these results were applied to the analysis of a class of continuous-time consensus algorithms with discrete-time controller updates in the existence of communication/actuation delays.

In this paper, we provide a theoretical analysis for nonlinear discontinuous consensus protocols in networks of multiagents over weighted directed graphs. By integrating the analytic tools from nonsmooth stability analysis and graph theory, we investigate networks with both fixed topology and randomly switching topology. For networks with a fixed topology, we provide a sufficient and necessary condition for asymptotic consensus, and the consensus value can be explicitly calculated. As to networks with switching topologies, we provide a sufficient condition for the network to realize consensus almost surely. In particular, we consider the case that the switching sequence is independent and identically distributed. As applications of the theoretical results, we introduce a generalized blinking model and show that consensus can be realized almost surely under the proposed protocols. Numerical simulations are also provided to illustrate the theoretical results.

In this paper, we discuss pinning consensus in networks of multiagents via impulsive controllers. In particular, we consider the case of using only one impulsive controller. We provide a sufficient condition to pin the network to a prescribed value. It is rigorously proven that in case the underlying graph of the network has spanning trees, the network can reach consensus on the prescribed value when the impulsive controller is imposed on the root with appropriate impulsive strength and impulse intervals. Interestingly, we find that the permissible range of the impulsive strength completely depends on the left eigenvector of the graph Laplacian corresponding to the zero eigenvalue and the pinning node we choose. The impulses can be very sparse, with the impulsive intervals being lower bounded. Examples with numerical simulations are also provided to illustrate the theoretical results.

In this brief, we discuss some variants of generalized Halanay inequalities that are useful in the discussion of dissipativity and stability of delayed neural networks, integro-differential systems, and Volterra functional differential equations. We provide some generalizations of the Halanay inequality, which is more accurate than the existing results. As applications, we discuss invariant set, dissipative synchronization, and global asymptotic stability for the Hopfield neural networks with infinite delays. We also prove that the dynamical systems with unbounded time-varying delays are globally asymptotically stable.

In this brief, we provide some theoretical analysis of the consensus for networks of agents via stochastically switching topologies. We consider both discrete-time case and continuous-time case. The main contribution of this brief is that the underlying graph topology is more general in both cases than those appeared in previous papers. The weight matrix of the coupling graph is not assumed to be nonnegative or Metzler. That is, in the model discussed here, the off-diagonal entries of the weight matrix of the coupling graph may be negative. This means that sometimes, the coupling may not benefit, but may prevent the consensus of the coupled agents. In the continuous-time case, the switching time intervals also take a more general form of random variables than those appeared in previous works. We focus our study on such networks and give sufficient conditions that ensure almost sure consensus in both discrete-time case and continuous-time case. As applications, we give several corollaries under more specific assumptions, i.e., the switching can be some independent and identically distributed (i.i.d.) random variable series or a Markov chain. Numerical examples are also provided in both discrete-time and continuous-time cases to demonstrate the validity of our theoretical results.