Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of anomalous images. Nonetheless, these methods might face challenges related to the preservation of image categories and pixel-wise structural integrity in the more practical multi-class setting. To solve the above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion’s denoising network, and a feature-space pre-trained feature extractor. Firstly, The SG network is proposed for reconstructing anomalous regions while preserving the original image’s semantic information. Secondly, we introduce Spatial-aware Feature Fusion (SFF) block to maximize reconstruction accuracy when dealing with extensively reconstructed areas. Thirdly, the input and reconstructed images are processed by a pre-trained feature extractor to generate anomaly maps based on features extracted at different scales. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach which surpasses the state-of-the-art methods, e.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and detection respectively on multi-class MVTec-AD dataset. Code will be available at https://lewandofskee.github.io/projects/diad.

The multivariate empirical mode decomposition (MEMD) has been pioneered recently for adaptively processing of multichannel data. Despite its high efficiency on timefrequency analysis of nonlinear and nonstationary signals, high computational load and over-decomposition have restricted wider applications of MEMD. To address these challenges, a fast MEMD (FMEMD) algorithm is proposed and featured by the following contributions: (i) A novel concept, pseudo directionindependent Multivariate Intrinsic Mode Function (IMIMF) which allows the interchange of sifting and projection operations, is defined for the purpose of developing FMEMD, (ii) FMEMD is computationally efficient. Compared with MEMD, the number of time-consuming sifting operations reduces from K·p to K for each iteration, where K and p denote the number of projection directions and signal dimension, respectively, (iii) FMEMD is consistent with EMD in terms of the dyadic filter bank property and (iv) FMEMD is more effective in working at low sampling rate. Validity of the raised approach is demonstrated on a wide variety of real world applications.

In order to improve the performance of fault isolation and diagnosis of Principal Component Analysis (PCA) based methods, this article proposes a novel fault detection and isolation approach using the Structured Joint Sparse PCA (SJSPCA). The objective function involves two regularization terms: the $l_{2,1}$ norm(sum of the Euclidean norms of the rows of a matrix) and the graph Laplacian. By imposing the $l_{2,1}$ norm, SJSPCA is able to achieve row-wise sparsity, and the graph Laplacian incorporates variable correlation information. The row-sparsity property of $l_{2,1}$ norm ensures that the score indices associated with normal variables approach zero and the graph Laplacian helps the isolation of correlated faulty variables. Once a fault is detected, a two stage fault isolation strategy is considered and a score index is calculated for each variable. It is proved that the proposed two stage strategy is capable of isolating faulty variables. The improved fault isolation performance of SJSPCA is illustrated by a simulation example and a gas flow fault observed in an industrial blast furnace.

To successfully implement the platoon control of connected and automated vehicles (CAVs), the model uncertainties, external disturbances, and time delays must be addressed. In this study, we propose a distributed Model Predictive Control scheme to achieve offset-free tracking for CAVs with model uncertainties and mixed disturbances which contains both stochastic and deterministic noises. A simple Vehicle-to-Vehicle communication flow with the short-distance and low-volume information exchange is adopted to reduce the effect of the communication delay and dropout. First, the deterministic and stochastic perturbances are addressed separately in constraints handling with an integrated control law based on the mismatched prediction model. Additional constraints on coupling input and output are imposed to guarantee the assumption about forecast trajectory. Then an observer gives an unbiased estimate of the state and integrated disturbance. Based on the estimation, the target calculator eliminates the steady tracking offset by designing a new target in real-time and the Min-Max Model Predictive Control steers the uncertain systems to the new target under the worst case within the domain of disturbances. Lastly, the Optimal Control Problem is reformulated and can be efficiently solved by existing solvers. Simulation results indicate that the proposed method shows superiority in interference rejection and offset-free tracking.