In industrial IOT environments, intelligent systems must continually learn from dynamic data streams to maintain diagnostic accuracy for emerging fault types. However, mainstream replay-based incremental learning methods typically depend on knowledge distillation with cumbersome teacher models to preserve past knowledge, resulting in excessive memory overheads that preclude their deployment on resource-constrained edge devices. To overcome this challenge, we propose ME-ACR, a memory-efficient framework driven by attributional consistency reinforcement. ME-ACR first employs cosine-normalized classifier to mitigate the inherent recency bias and establish a stable feature space. Then, two synergistic self-supervised paradigms are designed, which anchors old knowledge without a teacher model. Attributional consistency reinforcement paradigm enforces internal cohesion by promoting consistency among the attribution patterns of correctly classified exemplars. Prototype-distance-based attribution disentanglement paradigm ensures external separation by aligning the topological structure of attribution space with the global geometry of feature space. By preserving the structure of both intra-class and inter-class attributional knowledge, ME-ACR effectively prevents catastrophic forgetting. Extensive experimental results on multiple datasets demonstrate the superiority of the proposed ME-ACR in achieving robust and memory-efficient fault diagnosis under incremental learning scenarios.

Federated Learning (FL) is a promising paradigm for industrial fault diagnosis with distributed data in Internet of Things. Despite notable progress, existing FL-based diagnostic methods still face several critical challenges. First, these methods require frequent communication between clients and servers by transmitting model parameters, leading to high computational and communication costs. Second, condition monitoring data from different clients are often collected under varying operating conditions, resulting in data heterogeneity across clients that severely degrades the diagnostic performance of FL approaches. To address these challenges, we propose a global-local contrastive learning-assisted lightweight federated domain generalization network for machine fault diagnosis. The proposed approach significantly reduces computational and communication overhead through lightweight model design and prototype-based interaction strategy. To mitigate the adverse effects of data heterogeneity, we design a local prototype contrastive learning module that effectively eliminates cross-client feature distribution discrepancies and learns client-invariant features. To generate robust global prototypes during model aggregation, a global prototype self-learning module is designed, which enables self-correction of global optimal prototypes and significantly enhances the generalization capability of the global model for unseen target clients. Experimental results on three cases demonstrate the competitive performance of the proposed method, highlighting its potential for real-world fault diagnosis applications with limited resources.

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

Multi-source domain adaptation (MSDA) has demonstrated superior performance in intelligent fault diagnosis (IFD) compared to single-source domain adaptation (SSDA), as it can provide more comprehensive and diverse information from multiple fully-labeled source domains. However, in many real industrial scenarios, acquiring multiple fully-labeled source domains is challenging because labeling all the source domains is as expensive and laborious as labeling the target domain. Given this concern, a cross-supervised multisource prototypical network (CSMPN) is proposed for multi-source few-shot fault diagnosis. Specifically, a domain-shared and a domain-individual branch are constructed to realize shared domain alignment across all the source and target domains and individual domain alignment of source-target domain pairs, respectively. Within two branches, domain alignment is realized by the designed prototypical contrastive learning (PCL) module. In the PCL module, we propose a prototype calibration strategy to address the issue of biased prototype estimation owing to outlier samples. In addition, a two-stage pseudo-labeled sample selection mechanism is proposed to enhance the feature representation ability of two branches. At the end of the two branches, we design a cross-supervised learning (CSL) module to realize mutual and collaborative learning between the two branches, which can further improve the diagnosis performance on the target domain. Experiments on two different bearing datasets are implemented to verify the superiority of the proposed method compared with the comparison methods. Our code is available at https://github.com/YNWA-Zhang/CSMPN .

Machine learning models have been widely successful in the field of intelligent fault diagnosis. Most of the existing machine learning models are deployed in static environments and rely on precollected datasets for offline training, which makes it impossible to update the models further once they are established. However, in the open and dynamic environment in reality, there is always incoming data in the form of streams, including new categories of data that are constantly generated over time. In addition, the operating conditions of mechanical equipment are time-varying, which results in continuous stream data that are nonindependently and homogeneously distributed. In industrial applications, the diagnosis problem of nonindependent and identically distributed continuous streaming data is referred to as the cross-domain class incremental diagnosis problem. To address the cross-domain class incremental problem, a novel cross-domain class incremental broad network (CDCIBN) is proposed. Specifically, to solve the nonindependent identically distributed problem, a novel domain-adaptation learning loss function is first designed, which enables the conventional broad network to handle the category increment task well. Then, a cross-domain class incremental learning mechanism is designed, which learns new categories while retaining the knowledge of old categories well enough without replaying old category data. The effectiveness of the proposed method is evaluated through multiple mechanical failure increment cases. Experimental analysis demonstrates that the designed CDCIBN has significant advantages in the variable working condition class incremental application.

The metaplectic operator has shown to be a valid technique for generalizing the notion of cross Wigner distribution to achieve time-frequency superresolution. Inspired by the latest work (Cordero and Rodino, 2022), we revisit the notion of cross Wigner distribution in metaplectic transform domains by introducing two$2N\times 2N$symplectic matrices rather than integrating them into one$4N\times 4N$symplectic matrix. We style the derived general formulation as the cross metaplectic Wigner distribution and obtain its basic properties including time translation property, frequency modulation property, time translation and frequency modulation property, Moyal formula, complex conjugate symmetry, time reversal symmetry, and scaling property. We use it to define the so-called short-time metaplectic transform and clarify the equivalence between them. We establish the standard Heisenberg’s uncertainty principles for the cross metaplectic Wigner distribution, i.e., an attainable lower bound for two real-valued functions in cross metaplectic Wigner distribution domains and a sequence of attainable (unattainable) lower bounds for two complex-valued (one real-valued and the other complex-valued) functions in orthogonal, orthonormal, the minimum eigenvalue commutative and the maximum eigenvalue commutative cross metaplectic Wigner distribution domains. We further demonstrate the time-frequency superresolution superiority of the derived results over the conventional one through theoretical analyses and numerical experiments.

In recent years, the surge of artificial intelligence has propelled autonomous driving technology to the forefront, capturing growing interest and enthusiasm. As a sub-module within autonomous driving, research in traffic sign detection and recognition has significantly advanced with a growing focus on utilizing deep learning methods. Nevertheless, in complex real-world road scenarios, traffic signs often exhibit a long-tailed distribution, with the majority of instances concentrated in a few frequent categories and a scarcity in the remaining ones. Considering conventional Traffic Sign Detection and Recognition methods are crafted using manually curated datasets, the class imbalance may detrimentally impact the efficacy of the detection model. In this paper, we first propose a gradient-guided loss reweighting model that dynamically reweights the loss for positive and negative samples based on the cumulative gradients across each category. Additionally, a classification bias-based refinement module is proposed to fine-tune these weights during training, based on the false positive and false negative rate. This serves to suppress a drop in precision for tail categories resulting from the gradient-guided loss reweighting module, thus further balancing the entire training process for improved results. Extensive experiments are performed on the TT100K and GTSDB datasets, yielding significant advancements surpassing state-of-the-art methods.

Traffic sign detection and recognition (TSDR) has attracted extensive studies recently due to its broad application prospect in Intelligent Transport Systems. TSDR is still challenging due to the small size of traffic signs in the image. Besides, the traffic signs in the real world exhibit a long-tailed distribution (i.e., data for most categories are scarce while for others are abundant.), which will lead to a significant performance drop of the detection framework. In this paper, we propose a novel traffic sign detection framework to address these challenging problems. In order to detect small traffic signs, we propose an effective adaptive and attentive spatial feature fusion module which learns the spatial attention map to fuse different feature maps at each scale while emphasizing or suppressing the features at different regions. This module can significantly alleviate the inconsistency among features and enhance feature representations of small objects. Furthermore, to address the long-tailed data problem, a hierarchical group softmax head which constructs a label tree to divide categories into different groups is proposed, in this way, categories in each group have relatively similar frequencies, then the softmax is applied in each relatively balanced group to calculate the probability of each category. Extensive experiments conducted on the TT100K and GTSDB datasets demonstrate that the proposed method achieves notable improvement in both the small traffic signs and long-tailed detection problems in TSDR.

It is difficult to preserve diminishing weak structures and edges, and remove complex details simultaneously in the context of image smoothing. While most of existing methods only take either local or global features into consideration, the authors propose two methods taking advantage of both to achieve smoothing, both of which consist of two steps and share the same first step. In the first step, the authors use a scale‐aware approach to generate a guidance image by blurring the small‐scale components in the input image. Such approach, based on the rolling guidance framework with domain transform filter and bilateral filter, can prevent diminishing the corners of the main structures. Subsequently, the authors use the two proposed methods, with the guidance image as input, to remove blurry details. The first method introduces two data fidelity terms into L 0 gradient minimisation and removes high‐contrast details, which is a structure‐preserving method. The other method, an edge‐preserving method, uses an adaptive L 0 gradient minimisation technique, facilitating the preservation of the weak structures and edges. The smoothing factors in such technique are decide by the corresponding gradient of each pixel of the guidance image. The authors apply both methods to various image processing fields.

The development of convolutional neural networks (CNN) has led to revolutionary progress in the resolution of the offline handwritten Chinese character recognition (HCCR) problem. As the recognition rate on a standard offline HCCR testbed is outstanding, a few samples that remain misclassified have kindled our interest. In this paper, with the help of human recognition results, we present a comprehensive analysis of the samples misclassified by a state-of-the-art CNN model. We performed the analysis based on the top-1-votes, which are obtained from the statistical analysis of human recognition results, and derived the following conclusions: (1) the majority of samples with high top-1-votes were mis-labeled. Besides, by comparing the results of human recognition with that of CNN, some limitations of CNN that provide scope for further improvement are presented; (2) in the samples with medium top- 1-votes, it is shown that the samples with different confidence level have different characteristics. Specifically, some samples could be regarded as multi-label samples; (3) the samples with low top-1- votes are either wrongly written or written extensively in cursive style, which are difficult to match their given ground-truths; (4)the relationship between writing styles and misclassifications are also introduced in the paper. We believe this work should provide some insights and brings new clues on designing new classification methods to deal with these challenging samples.

Shape matching has always been a key issue in the field of computer vision. To obtain high recognition accuracy with low time complexity and to reduce the influence of contour deformation due to noise in shape matching, a novel shape matching method based on common base triangle area (CBTA) is proposed. First, a CBTA descriptor of each contour point is defined based on the area functions of the triangles formed by its two neighbour points and other contour points. Then, the descriptor is locally smoothed to keep it more compact and robust to noise. Secondly, a match cost matrix is obtained by computing the CBTA descriptors of all the contour points on two shapes. Finally, the similarity between the two shapes is measured on the basis of the match cost matrix by a dynamic programming algorithm. The experimental results on MPEG‐7, Kimia and an articulation shape database indicate that this method is robust to contour deformation, and both the computational efficiency and the retrieval rate are essentially improved.