查看更多>>摘要：This study deals with component-based dynamic event-triggered control for nonlinear singularly perturbed systems with actuator faults. To mitigate the burden of data communication and improve the efficiency of information exchange, a component-based dynamicevent-triggered mechanism was established, whose triggering thresholds changed with individual sensor nodes. Under the framework of the component-based dynamic-eventtriggered mechanism, the controller received separately triggering instants of each sensor node. A nonhomogeneous actuator fault was applied, whose transition probabilities were time-varying and resided in a polytope set. Based on a gain-scheduling technique and the parameter-dependent Lyapunov functional, sufficient conditions were achieved to guarantee the stochastic stability of a closed-loop system. Finally, the effectiveness and applicability of the derived results were verified using a numerical example and a tunnel diode circuit model. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Multiframe detection can be effective for the detection of targets with low signal-to-noise ratios by integrating the target information in several consecutive frames. Traditional methods usually suffer from high computational complexity and insufficient use of available information. In this paper, we propose solving the multiframe detection problem through two sequential steps: Doppler-aided track formation (DTF) and data-driven track detection (DTD). By using the DTF, which considers the coupling relationship between the location measurements and Doppler measurements, the potential tracks can be extracted without introducing too many false tracks. Using DTD, which fuses multiple features along the extracted tracks through a deep neural network, the detection can be sequentially declared. Simulation results show that the detection performance of the proposed method is better than that of the traditional methods for weak targets, and that false alarms can be handled well. Both theoretical analysis and experimental studies corroborate the computational efficiency of the proposed method in real-time implementation. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Recent years has witnessed urgent needs for addressing the curse of dimensionality regarding multi-label data, which attracts wide attention for feature selection. Feature relevance terms are often constructed depending on the amount of information contributed by selected features or candidate features to the label set in previous multi-label feature selection approaches based on information theory. Although it is important to consider the amount of information, they ignore both the changed ratio for the undetermined amount of information and the changed ratio for the established amount of information, two types of changed ratios regarding feature relevance evaluation cannot be underestimated. To this end, we devise a new feature relevance term, Relevance based on Weight (RW), which is based on two types of changed ratios. Both two types of changed ratios have positive or negative impacts regarding feature relevance evaluation. A novel multi-label feature selection approach, Relevance based on Weight Feature Selection (RWFS), is proposed based on RW. To verify the effectiveness, the proposed approach is compared to eight state-of-the-art multi-label approaches on thirteen real-world data sets. The experimental results present that RWFS approach has superior performance than other eight compared approaches. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：As many data in practical applications occur or can be arranged in multiview forms, multiview clustering utilizing certain complementary and heterogeneous information in various views to promote the clustering performance, has received much attention recently. Among varieties of methods, graph-based unsupervised learning methods are an essential approach for learning intrinsic structure relations of multiview data for clustering. Most of them firstly integrate information from each view into a consensus graph, which is then fed into the classic spectral clustering to achieve clustering. Such a two-step clustering paradigm is difficult to obtain the optimal clustering results even though every step performs individual optimization. This paper integrates multi-graph construction, consensus graph construction, and clustering in a unified learning framework, which can simultaneously consider the consistency and complementarity of multiview data to provide the clustering results directly. Moreover, we treat each view differently by automatic weight learning. Specifically, multi-graph learning, consensus graph learning, and weight learning are seamlessly integrated so that the related variables can be iteratively updated in the unified optimization framework-the clustering results towards an overall optimum. Comprehensive experiments on real multiview datasets verify the superiority of the proposed method over other state-of-the-art baselines in terms of three clustering evaluation metrics. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Classifying faces is a difficult task due to image variations in illumination, occlusion, pose, expression, etc. Typically, it is challenging to build a generalised classifier when the training data is small, which can result in poor generalisation. This paper proposes a new approach for the classification of face images based on multi-objective genetic programming (MOGP). In MOGP, image descriptors that extract effective features are automatically evolved by optimising two different objectives at the same time: the accuracy and the distance measure. The distance measure is a new measure intended to enhance generalisation of learned features and/or classifiers. The performance of MOGP is evaluated on eight face datasets. The results show that MOGP significantly outperforms 17 competitive methods. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, we study the efficient Time-interval Augmented spatial Keyword (TASK) query problem, which considers the location, time-interval, and attribute value of keywords of spatial objects on road networks. We propose the concept of keyword hot value which is usually the value of textual attribute, such as popularity and price, and design a novel similarity function to evaluate the similarity between spatial textual objects and the query. A hierarchical index GI-tree is proposed to handle the TASK query with a best-first strategy. To improve the query efficiency, a novel hybrid index, termed SGI, is designed for pruning unqualified objects by utilizing their spatial, textual and temporal information simultaneously. Furthermore, a tight upper bound score, as well as the heuristic searches is presented to get further optimization, and a search framework is designed to obtain the top-k results in an efficient way. Finally, extensive experiments on real-world datasets demonstrate the efficiency and scalability of our proposed solution. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：User identity linkage (UIL) across social networks has recently attracted an increasing amount of attention due to its significant research challenges and practical value. Most of the existing methods use a single method to express different types of attribute features. However, the simplex pattern can neither cover the entire set of different attribute features nor capture the higher-level semantic features in the attribute text. This paper establishes a novel semisupervised model, namely the multilevel attribute embedding for semisupervised user identity linkage (MAUIL), to seek the common user identity across social networks. MAUIL includes two components: multilevel attribute embedding and regularized canonical correlation analysis (RCCA)-based linear projection. Specifically, the text attributes for each network are first divided into three types: character-level, word-level, and topic-level attributes. Second, unsupervised approaches are employed to extract the corresponding three types of text attribute features, and user relationships are embedded as a complimentary feature. All the resultant features are combined to form the final representation of each user. Finally, target social networks are projected into a common correlated space by RCCA with the help of a small number of prematched user pairs. We demonstrate the superiority of the proposed method over state-of-the-art methods through extensive experiments on two real-world datasets. All the datasets and codes are publicly available online. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. More recently, combining logic with probability has been of major interest. Rough set methods have been proposed for modeling incompleteness and imprecision based on indiscernibility and its generalizations and there is a large body of work in this direction. More recently, the classical theory has been generalized to include probabilistic rough set methods of which there are also a great variety of proposals. Pragmatic, easily accessible, and easy to use tools for specification and reasoning with this wide variety of methods is lacking. It is the purpose of this paper to fill in that gap where the focus will be on probabilistic rough set methods. A landscape of (probabilistic) rough set reasoning methods and the variety of choices involved in specifying them is surveyed first. While doing this, an abstract generalization of all the considered approaches is derived which subsumes each of the methods. One then shows how, via this generalization, one can specify and reason about any of these methods using PROBLOG, a popular and widely used probabilistic logic programming language based on PROBLOG. The paper also considers new techniques in this context such as the use of probabilistic target sets when defining rough sets and the use of partially specified base relations that are also probabilistic. Additionally, probabilistic approaches using tolerance spaces are proposed. The paper includes a rich set of examples and provides a framework based on a library of generic PROBLOG relations that make specification of any of these methods, straightforward, efficient and compact. Complete, ready to run PROBLOG code is included in the Appendix for all examples considered. (C) 2021 The Author(s). Published by Elsevier Inc.
查看更多>>摘要：In this paper, the H-infinity observer design problem is investigated for discrete-time Hamiltonian systems subject to missing measurement and sensor saturations governed by Bernoulli distributed random variables. Our purpose is to design an observer such that the error dynamics of the state estimation is exponentially mean-square stable with prescribed H-infinity performance. By resorting to the Lyapunov function and the Hamiltonian system property, sufficient conditions are derived to guarantee the existence of the desired observer. Moreover, observer gains are designed in forms of the solutions to certain matrix inequalities. Finally, an illustrative example is utilized to testify the effectiveness of our observer design scheme. (C) 2022 Published by Elsevier Inc.
查看更多>>摘要：Most existing imbalanced data classification models mainly focus on the classification performance of majority class samples, and many clustering algorithms need to manually specify the initial cluster centers and the number of clusters. To solve these drawbacks, this study presents a novel feature reduction method for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors (AWKNN). First, the similarity between samples is evaluated by the difference and smaller value between samples on each dimension, a similarity measure matrix is then developed to measure the similarity between clusters, after which a new hierarchical clustering model is constructed. By combining the cluster center of each sample cluster with its nearest neighbor, new samples are generated. Then, a hybrid sampling model based on similarity measure is presented by putting the generated samples into imbalanced data and removing samples from majority classes. Thus, a balanced decision system is constructed based on generated samples and minority class samples. Second, to address the issues that the traditional symmetric uncertainty only considers the correlation between features, and mutual information ignores the added information after classification, the normalized information gain is introduced to design new symmetric uncertainty between each feature and the other features; then, the ordered sequence and the average of the symmetric uncertainty difference of each feature are provided to adaptively select the k-nearest neighbors of features. Moreover, the weight of the k-th nearest neighbor of features is defined to present the AWKNN density of features and their ordered sequence for clustering features. Finally, by combining the weighted average redundancy with the symmetric uncertainty between features and decision classes, the maximum relevance between each feature and decision classes, and the minimum redundancy among features in the same cluster is presented to select the optimal feature subset from the feature clusters. Experiments applied to 29 imbalanced datasets show that the developed algorithm is effective and can select the optimal feature subset with high classification accuracy for imbalanced data. (C) 2022 Elsevier Inc. All rights reserved.
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