Hyperspectral anomaly detection (HAD) has attracted widespread attention in the field of hyperspectral image processing. In recent years, model-driven and data-driven methods have been widely used in hyperspectral anomaly detection. However, existing methods either do not fully utilize the various data characteristics of the background or directly use the various coupled data characteristics of the background in the image domain or a single deep transform domain, where the coupling characteristics in these domains are not obvious and difficult to separate, resulting in poor detection performance. To solve this problem, we innovatively proposed a two-stage deep transformation network with latent features guidance (TDTLFG) for HAD, aiming to fully utilize the potential data characteristics of the background tensor in different transform domains, and train the network under purposeful and directional guidance with those latent features guidance. Specifically, a multi-scale feature extraction (MSFE) and a multi-scale feature fusion (MSFF) two-stage deep transform blocks are designed to adapt to the anomaly scale-varying scenarios; the tensor matrix factorization is embedded into the network after the first MDFE deep transformation stage to guide the network to exploit the low-rankness of the background in three dimensions; the total variation (TV) constraint is designed on the output of the second MSFF transformation stage to exploit the piecewise-smoothness of the background in three dimensions. After that, the fidelity constraints and TV constraints of the network output are iteratively solved by adopting the ADMM algorithm. Finally, the detection performance effectiveness of the proposed model is proven on several real hyperspectral datasets.

Data-driven methods for predicting remaining useful life (RUL) have received considerable attention in the field of degradation data analysis. The transfer learning (TL) method offers new possibilities for RUL tasks in various operational settings. However, in many engineering applications, challenges in TL arise mainly from the scarcity or high cost of labeled data in the target domain, coupled with incomplete degradation of RUL samples within the target domain. This article proposes an innovative model named deep cross-domain transfer learning for interpretable prediction The model effectively harnesses the advantages of domain adaptation (DA) techniques in mitigating domain distribution disparities and also uses the exceptional visualization capabilities inherent in the variational autoencoder (VAE) model. This method integrates the VAE framework with regression networks and utilizes DA techniques to align feature spaces, achieving cross-domain RUL prediction with unlabeled target domain data and cross-domain visualization of the entire degradation process. The reproducing kernel Hilbert space is considered in domain adaption to control the complexity of hypothesis space. The effectiveness of the proposed method is demonstrated by analyzing the real C-MAPSS dataset.

Anomaly detection in the hyperspectral image (HSI) has gradually become a hot topic in remote sensing. Recently, some tensor-based methods have been proposed to improve detection performance by exploiting the characteristic of HSI data existing in the inherent multi-dimensional. However, the existing tensor-based methods may only partially use the prior properties in both spatial and spectral dimensions. In this paper, we proposed a novel tensor ring (TR) decomposition with factors TV regularization model for hyperspectral anomaly detection. First, raw HSI data is decomposed into background and anomaly tensors. The tensor ring decomposition is adopted to exploit the low-rank property of the background existing in both spatial and spectral dimensions. Then, the total variation (TV) regularization is designed on the three dimensions of the background tensor to explore the piecewise smoothness of the background existing in both spatial and spectral dimensions. Further, this TV regularization is transferred to each factor tensor by exploiting the relationship between the background tensor and each factor tensor. Next, thel2,1norm regularization is designed on the anomaly tensor to exploit the group sparsity of anomaly pixels. Finally, the alternating direction method of multipliers (ADMM) scheme is adopted to update the involved variables. Experimental results validated on several real hyperspectral datasets demonstrate the effectiveness of the proposed algorithm.

Low-rank tensor decomposition and autoencoder (AE) have recently attracted much attention in the hyperspectral anomaly detection field. Although the low-rank tensor decomposition model is self-explainable, a low-rank prior may only partially match real data. In contrast, AE can automatically learn the discriminative features between anomaly and background, whereas AE is not self-explainable. To overcome these problems, a deep low-rank and piecewise-smooth constraint tensor model is proposed for hyperspectral anomaly detection in this paper, which makes full use of the low-rank and piecewise-smooth properties of the background tensor in model-driven tensor-based methods and combines the advantage of data-driven AE methods at the same time. Specifically, the background is reconstructed through the AE network, and the tensor nuclear norm and total variation regularization are designed on the network output to make it self-explainable to exploit the low-rank and piecewise-smooth properties of the background in the three dimensions. At the same time, an alternating direction method of multipliers (ADMM) based approach is given to solve the proposed model, making the low-rank and piecewise-smooth background tensor estimation process and the network training process into two sub-problems. Then, the background can be approximately reconstructed under the constraint of latent priors, while the anomaly is reconstructed with significant reconstruction errors. Finally, the reconstruction errors indicate the anomalous degree. Experimental results validated on several real hyperspectral datasets demonstrate the effectiveness of the proposed algorithm.

Target detection in sea clutter is of great significance in military radar research. Because the characteristics of sea clutter are complex and easily affected by wind direction, the method of suppressing sea clutter by using sea clutter characteristics to reproduce is not advantageous. To address this problem, this paper focuses on direct extraction of target echo. According to the difference of fractional characteristics between target echo and sea clutter, fractional Fourier transform is introduced to extract target echo. In this paper, a fractional domain change coefficient clustering method is established to identify the range units of target, and a fractional domain filtering method based on the minimax is proposed to extract the target spectrum of target dominated range units, so as to separate the target echo from sea clutter. Simulated and measured experiments prove that this method can accurately identify targets and extract target information under various sea state and different number of targets.

In this paper, we propose an approach for verifying the correctness of object-oriented (OO) programs with respect to the algebraic specifications. Compared to the functional specification that emphasizes on specifying what a single operation does, algebraic specification, which was proposed originally for specifying abstract data types, specifies what different operations of a class are related to each other. We first extend the algebraic specification of abstract data types to OO programs, and then prove the conformance of the implementation of programs to the algebraic specifications by taking functional specification as a bridge.

Professionals and end users of computers often experience being constantly surrounded by modern technology. One side effect of modern technology is termed technostress, which refers to the “negative impact on attitudes, thoughts, behaviors, or body physiology that is caused either directly or indirectly by technology” (Well and Rosen, 1997). Based on social cognitive theory, this study developed a conceptual model in which computer-related technostress was studied as consequences of computer self-efficacy and technology dependence. Results show that (a) employees with higher level of computer self-efficacy have lower level of computer-related technostress, (b) employees with higher level of technology dependence have higher level of computer-related technostress, and (c) employees under different individual situations may perceive different levels of technostress. Contributions of this research and implications for theory and managerial practice are also discussed.