Quantum Dempster-Shafer theory (QDST) derives a quantum mass function (QMF), a fuzzy metric obtained from multiple information sources based on quantum interference. In general, QMF effectively represents and processes uncertain information, but managing conflicts among multiple QMFs remains challenging. To address this issue, we propose a novel quantum conflict indicator (QCI) within the QDST framework. It is the first metric satisfying ideal conflict measurement properties, including non-negativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. Based on QCI, a novel quantum conflict fusion method (QCI-Fusion) is introduced to fuse highly conflicting QMFs. Moreover, traditional methods, including QCI-Fusion, typically constructs the Quantum Frame of Discernment (QFoD) based on predicted labels, which makes it difficult to cover unseen classes. Therefore, a new decision architecture, QCI-Decision, is proposed for unsupervised detection that rejects out-of-distribution (OOD) samples while maintaining in-distribution (ID) classification. Experimental results show that the classification accuracy of QCI-Decision deviates from the original model predictions by at most 1.49%. Meanwhile, compared with the latest OOD detection methods, QCI-Decision improves the Area Under the Receiver Operating Characteristic Curve (AUC) by up to 0.6% and reduces the False Positive Rate at 95% True Negative Rate (FPR) by up to 1.63%. Moreover, compared with QCI-Fusion, QCI-Decision achieves approximately threefold faster fusion speed with negligible performance degradation, offering a promising solution for open-world quantum information decision.

In order to process multiview multilabel, multilabel, and multiview data, current learning algorithms are designed on the basis of data characteristics, correlations, etc. While these algorithms cannot express correlations among different features, instances, labels in within-view, cross-view, and consensus-view representations self-adaptively and relative accurately. To this end, this study takes the classical multiple correlations-based model as the basis and explores some laws of self-adaptive change for those correlations in multiple representations. The proposed algorithm is called multiple self-adaptive correlation-based multiview multilabel learning (MuSC-MVML). Extensive experiments on 38 datasets demonstrate the superiority of MuSC-MVML and some conclusions are addressed. 1) MuSC-MVML outperforms most compared algorithms in statistical in terms of AUC and its performance is also stable; 2) the computational cost of MuSC-MVML is moderate and on most datasets, MuSC-MVML has a relatively fast convergence; and 3) introducing some laws of self-adaptive change for those correlations can improve the ability of MuSC-MVML to process multiview multilabel datasets effectively and express correlations in multiple representations better. Furthermore, this study explains the reason that why we use alternating optimization strategy to optimize the model of MuSC-MVML and provides some suggestions that how to modify the model of MuSC-MVML to process incomplete multiview multilabel datasets with noise.

Distributed scatterer interferometric synthetic aperture radar (DS InSAR) is a widely used technique for monitoring surface deformation, but its effectiveness is often compromised by temporal and spatial decorrelation, leading to degraded interferometric phase quality. Enhancing phase quality through phase linking (PL) is essential. However, existing PL methods struggle to produce high-quality sample coherence matrices (SCMs) due to the inhomogeneity and limited availability of low-coherence homogeneous samples. Consequently, accurately deriving phase matrices, sample coherence magnitude matrices (SCMMs), and precision matrices becomes challenging, significantly impacting the accuracy of PL estimation. To address these limitations, a two-step optimization-based PL (TSO-PL) method is proposed. TSO-PL integrates both the complex and real domain characteristics of the SCM and features two key innovations: 1) feature compression of the SCM (FC-SCM) to improve the signal-to-noise ratio of the phase and the accuracy of the coherence value in SCM and 2) adaptive nonlinear shrinkage of the SCMM (ANS-SCMM) to yield a more accurate SCMM by improving its structure. The simulation results demonstrate that TSO-PL is robust to variations in the estimation window size and homogeneous sample number, outperforming the phase triangulation algorithm (PTA), eigenvalue decomposition (EVD), and eigendecomposition-based maximum likelihood (EMI) methods in terms of accuracy and noise reduction. In a case study, TSO-PL improved the maximum deformation rate detection by 29.5%, 36.7%, and 31.1% compared with PTA, EVD, and EMI, respectively, with a significantly lower root mean square error (RMSE) of 11.3 mm. These findings demonstrate that TSO-PL effectively reduces phase noise, preserves fringe integrity, and enhances the identification of high-density monitoring points, leading to more accurate surface deformation assessments.

In recent years, the utilization of wearable sensors for Human Activity Recognition (HAR) has garnered significant interest in the fields of medical health monitoring and sports management. However, HAR often suffer the poor generalization from the insufficient labeled data for complex activities. To address this issue, the novel Transfer Component Analysis-Bidirectional Long Short-Term Memory network (TCA-BiLSTM) with the fused attention mechanism is presented in this paper. Specifically, TCA-BiLSTM first leverages the Maximum Mean Difference (MMD) within the Reproducing Kernel Hilbert Space (RKHS) to learn transfer components for sensor-based HAR. These derived transfer components align the data collected from sensors deployed on different body parts, facilitating the mapping of cross-domain HAR data. Then, the two-layer BiLSTM with the novel fused attention mechanism is given to classify the unseen activities, which aims to capture the multi-granularity activity information after the TCA-based domain adaptation. To evaluate the effectiveness of TCA-BiLSTM, a series of experiments were conducted using the DSADS and PAMAP2 datasets. The results demonstrate that TCA-BiLSTM outperforms the state-of-art methods such as DSAN and FNet, achieving performance improvements of 6.1% and 2.5%, respectively.

Underground mining activities often lead to large local surface displacements. In this case, the interferometric fringes are dense, the deformation gradient between adjacent pixels tends to exceed$\pi $, and traditional phase unwrapping (PU) methods that satisfy the phase continuity assumption have difficulty correctly retrieving the deformation. Deep learning-based PU methods can overcome the phase continuity assumption to a certain extent. However, deep learning-based PU methods also have shortcomings, such as weak generalization, difficulty in transfer, and lack of interpretability. To address these issues, this article presents a new PU method for large gradient deformation of mining areas that couples knowledge and a deep learning network (KLC-Net). This method integrates the knowledge of the mining area subsidence mechanism and the interferometric synthetic aperture radar (InSAR) phase prior knowledge into the learning network and constructs a knowledge-learning coupling framework of input sample constraints, objective function constraints, and network structure constraints. The simulation experiments show that when the noise level (NL) is less than$\pi $, the KLC-Net algorithm is suitable for interferograms with different imaging geometries. The actual engineering experimental results show that even under severe temporal decorrelation conditions, the KLC-Net algorithm can still effectively retrieve surface deformations up to 1.6 m with an average root mean square error (RMSE) of 23.5 mm. These experimental results show that the KLC-Net algorithm can effectively improves the ability and accuracy of PU under large gradient deformations in coal mining areas, and also improves the generalization performance and interpretability of deep learning-based PU methods.

Vector Polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote-sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this paper proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multi-scale extraction, cropping-with-inpainting approach. Next, datasets derived from pre- and post-temporal images are used for training and testing, respectively, and the training set is purified by using two-classifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs fine-grained scene classification. The change rules and post-processing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on dataset-1, respectively. Similarly, in dataset-2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method’s efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

The critical point for the success of spectral-type subspace clustering algorithms is to seek reconstruction coefficient matrices that can faithfully reveal the subspace structures of data sets. An ideal reconstruction coefficient matrix should have two properties: 1) it is block-diagonal with each block indicating a subspace; 2) each block is fully connected. We find that a normalized membership matrix naturally satisfies the above two conditions. Therefore, in this paper, we devise an idempotent representation (IDR) algorithm to pursue reconstruction coefficient matrices approximating normalized membership matrices. IDR designs a new idempotent constraint. And by combining the doubly stochastic constraints, the coefficient matrices which are close to normalized membership matrices could be directly achieved. We present an optimization algorithm for solving IDR problem and analyze its computation burden as well as convergence. The comparisons between IDR and related algorithms show the superiority of IDR. Plentiful experiments conducted on both synthetic and real-world datasets prove that IDR is an effective subspace clustering algorithm.

Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications. The existing spectral-type subspace clustering algorithms either focus on designing constraints for the reconstruction coefficient matrix or feature extraction methods for finding latent features of original data samples. In this paper, inspired by graph convolutional networks, we use the graph convolution technique to develop a feature extraction method and a coefficient matrix constraint simultaneously. And the graph-convolutional operator is updated iteratively and adaptively in our proposed algorithm. Hence, we call the proposed method adaptive graph convolutional subspace clustering (AGCSC). We claim that, by using AGCSC, the aggregated feature representation of original data samples is suitable for subspace clustering, and the coefficient matrix could reveal the subspace structure of the original data set more faithfully. Finally, plenty of subspace clustering experiments prove our conclusions and show that AGCSC11We present the codes of AGCSC and the evaluated algorithms on https://github.com/weilyshmtu/AGCSC. outperforms some related methods as well as some deep models.

In recent years, wearable sensor-based human activity recognition (HAR) is becoming more and more attractive, especially in health monitoring and sports management. However, in order to obtain high-quality HAR, it is often necessary to get sufficient labeled activity data, which is very difficult, time-consuming, and costly in a natural environment. To tackle this problem, multisource domain adaptation (DA) is a promising method that aims to learn enough multisource prior knowledge from labeled activity data, and then transfer this learned knowledge to the target unlabeled dataset. Thus, this article presents a novel multisource weighted DA with evidential reasoning (w-MSDAER) for HAR, which can effectively utilize complementary knowledge between multiple sources. Specifically, we first use the strategy of distribution alignment to learn local domain-invariant classifiers based on multisource domains. And then the reliabilities of these derived classifiers are comprehensively evaluated according to the belief function based technique for order preference by similarity to ideal solution (BF-TOPSIS). Finally, the discounting fusion method is used to fuse the local classification results. Comprehensive experiments are conducted on two open-source datasets, and the results show that the proposed w-MSDAER significantly outperforms other state-of-art methods.

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of "relation reconstruction." We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself. The neighborhood relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample's neighborhood relation could be reconstructed by the neighborhood relations of other data samples; then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.

The complexity of an optimization problem is determined by its decision and objective spaces. Over the past few decades, a large number of works have focused on the performance improvement of metaheuristic algorithms via the objective space, whereas studies related to the decision space have attracted little attentions. Moreover, metaheuristic algorithms may not obtain satisfactory results within an entire feasible region, even if sufficient computational resources are available. Therefore, reducing the search space (i.e., finding a trust region) may be an effective method to ensure that the convergence is sufficiently close to the global optimal region. However, inappropriate subspace size may also weaken the performance of algorithms except for ones with a sufficiently small search space. To alleviate aforementioned problems, a variable search space (VSS) strategy based on a sequential trust region determination approach is proposed in this paper. In the VSS, the entire optimization process is divided into two stages: the first stage is to use an optimization approach for sequentially finding the trust domain of each variable and then determine the best-matched subspace; the second stage is to employ the optimization method for searching an optimal/near-optimal solution within the found trust region. The effectiveness of the VSS is evaluated using two widely used test suites, that is, IEEE CEC2014 and BBOB2012. Experimental results indicate that improving the algorithm performance is an important method for tackling problems, but locating a trust region is also beneficial for metaheuristic algorithms to improve the solution precision, especially for complex optimization problems.

In the original belief function (BF) theory, a precise-valued belief structure has been widely used to represent uncertain information. However, this mentioned belief structure is difficult to effectively measure the specific hesitant situation, especially when decision makers have a set of possible values for the belief assignments of focal elements. In order to model the hesitant nature of the behavior of people to make a decision under uncertainty, we propose a hesitant fuzzy belief structure (HFBS) that is based on the BF theory and the recent hesitant fuzzy set theory. We also present the novel rule of combination of HFBS that is used and evaluated in a wearable human activity recognition (HAR) system coupled with an extreme learning machine. The evaluation of this new HFBS approach is done from two benchmark datasets. We clearly show its effectiveness and its superiority compared to various methods used classically for the wearable HAR.

Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA–SVR. The developed GASA–SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA–SVR and HGA–SVR, results show that the hybrid GASA–SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA–SVR model provides a promising alternative to monthly rainfall prediction.

Impervious surface percentage(ISP) is the key parameter for urban regional environment research. This paper proposes the method of ISP estimation by using support vector machine(SVM) on TM image: (1) extract the ISA pixels which occupies any portion of the constructed impervious class based on SVM classification for spatial inputs of ISP estimation (2) estimate ISP of ISA pixels by using SVM regression model, build sample-ISP regression model based on various spectral features inputs and apply ISP-model for regional imperviousness mapping. On the TM image of Tianjin urban area, select high resolution image(Quickbird) classification result of college, industrial and residential districts as training sample(7500 items) and testing sample(2000 items), the mean square error(RMSE) of SVM model is 15.4%; adding “greenness” of tasseled cap transform as SVM feature, the RMSE decrease to 12%. The results of the study indicate that SVM model is suitable for large area ISP mapping without insufficient sample because of the non-linear characteristic and good performance of small-sample generalization. Additionally, to build a typical sample library for large-area ISP mapping will be our future research directions.

Synthetic variable ratio (SVR) method was first introduced to fuse panchromatic (PAN) image and multi-spectral (MS) image by Muechika et al. in 1993, and was improved by Zhang Y. in 1999 and 2001 respectively. As for Muechika SVR method, it isn't suitable for fusing PAN and MS which cover large area. And for Zhang Y. SVR(ZY-SVR) method, on the one hand it needs to select a great number of pixels of different land coverage classes to conduct multiple regression analysis and thus it seems greatly empirical; on the other hand the coefficients obtained through regression lack physical meanings. This paper puts forward a new method called LAB-SRV which introduces the sensor spectral response into SVR method using the CIELab color space. Several experiments done in this paper indicate that this method can sharpen multi-spectral MS images without changing their spectral characters very much and have an advantage over ZY-SVR method with PAN and MS of IKONOS and QuickBird.