Ye, JinZhan, JianmingDing, WeipingFujita, Hamido...
30页
查看更多>>摘要：Given that the three-way decision (3WD) theory provides a scientific solution and reasonable semantic interpretation for solving multi-criteria decision-making (MCDM) problems, this paper presents a new 3WD model and applies it to realistic MCDM problems. Since the fuzzy neighborhood operator is effective in handling uncertain data, we first define a pair of probabilistic approximation operators via the constructed alpha-fuzzy neigh borhood class. Then, a 3WD model is explored in decision information systems to address the related issues in the proposed fuzzy probabilistic rough set model. In light of the above works, we establish a novel approach to classify and rank applicants for enterprise talent recruitment problems. Instead of the rule of tie-breaking, a way is given to separate applicants into three parts by combining decision attributes, and the feasibility of the approach is confirmed as well. Finally, by using a data set in the UCI database, the results of comparative and experimental analyses demonstrate that the constructed MCDM approach owns better performances in terms of effectiveness and stability. (C) 2021 Elsevier Inc. All rights reserved.
Lin, ZhuangbiLiu, ZhiZhang, YunChen, C. L. Philip...
19页
查看更多>>摘要：An adaptive neural inverse optimal consensus control method is presented for a class of multi-agent systems (MASs) with uncertain dynamics and time-varying disturbance. The auxiliary system is constructed for every agent, and then, based on backstepping technique, neural networks are utilized to approximate the unknown function and develop the distributed controllers. Additionally, there is only one unknown parameter need to be learned for each agent. It is proved that the proposed control scheme can ensure that all states of MASs are bounded and each agent is Input-to-state stabilizable (ISS). In the meantime, the inverse optimal controller also minimizes a meaningful cost function given in advance, which contains system input and disturbance. That is, the control scheme also reduces the cost of input. Simulation experiments illustrate the feasibility of the proposed method. (c) 2021 Published by Elsevier Inc.
查看更多>>摘要：In the era of big data, clustering based on multi-source data fusion has become a hot topic in data mining field. Existing studies mainly focus on fusion models and algorithms of data sets in the same domain, but few studies consider imbalanced data sets from different domains. Furthermore, studies on imbalanced data sets mostly focus on classification and less on clustering problems. Therefore, we propose a novel clustering algorithm for mining fused location data. This algorithm can deal with imbalanced data sets with large density differences, find clusters generated by the minority class data, and reduce the time complexity of the clustering process. Since current evaluation indices are not suitable for evaluating clustering results of imbalanced data sets, we present a new comprehensive evaluation metric used in the clustering validity judgment. Urban hotspots mining is used as an example, and the effectiveness of the proposed method is validated using GPS trajectory data from the transport domain and check-in data from the social network. The experimental results demonstrate that the performance of the proposed algorithm outperforms that of the state-of-the-art clustering algorithms, and it can simultaneously discover urban hotspots formed by the majority and minority class data. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：As a universal phenomenon, conflicts exist widely in various walks of life, such as politics, economic lives, military operations, and cultural exchanges. In order to sove more complex and uncertain-conflict problems than what is possible within the confinement of current knowledge, this paper proposes a three-way conflict analysis model and conflict-degree based decision-theoretic rough set model for conflict resolution under Pythagorean fuzzy information. Firstly, we define a novel conflict distance and conflict function with Pythagorean fuzzy information from the perspective of absolute and relative conflict. We examine respectively a trisection of the conflict relation between a pair of agents, that among agents, that among disputes, and that among alliances. We also introduce the concepts of maximal strong alliances and maximal weak alliances. All of these efforts are then used to explain the reason for the emergence of conflicts. Secondly, to find an optimal feasible strategy for a given conflict situation, we propose a Pythagorean fuzzy decision theoretic rough set model based on conflict functions and define the score function for each feasible strategy. Finally, for verifying our proposed model, a three-way conflict analysis and resolution are employed to address a governance issue of a local government. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Vacating room after encryption (VRAE) is a popular framework of reversible data hiding for encrypted images (RDHEI). Most VRAE based RDHEI methods do not make a desirable payload. To address this issue, this paper proposes a novel data hiding technique using adaptive difference recovery (ADR) and exploits this novel technique to design an efficient VRAE based RDHEI method. Specifically, the proposed ADR-based data hiding technique adaptively determines the range of original difference between a cover pixel and a reference pixel with correlation according to the marked difference generated by bit substitution technique. The cover pixel can be recovered losslessly with the range of original difference and the reference pixel. In the proposed RDHEI method, the encrypted image is generated by block permutation and block-based modulation so that spatial redundancy within encrypted image blocks is preserved. Then the ADR-based data hiding technique is applied to the encrypted blocks. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average embedding rates of the proposed RDHEI method on the BOSSbase, BOWS-2 and UCID datasets are 3.2045, 3.1145 and 2.4633 bpp, respectively. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, we consider the predefined-time distributed optimization problems of homo-geneous and heterogeneous linear multi-agent systems under undirected and connected communication topologies, in which, all agents share coupled equality constraint. In order to make all agents converge to the optimal output at predefined-time cooperatively, we propose two distributed algorithms for homogeneous and heterogeneous multi-agent sys-tems according to time-base generator technology and output feedback, therein, the opti-mal output can make the global cost function reach minimum. In the design of the algorithms, the control gains are not required, which can avoid the requirement of some global information in advance. Furthermore, all agents converge to the optimal output with exponential speed, and we can set the convergence time arbitrarily. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Fuzzy concept has been an important methodology for data analysis, especially in the classification research. Particularly, fuzzy concept could directly process the continuous data through contrasting the numerical data into the membership degree of object to attri-bute. However, the classical fuzzy concept only focuses on the positive information, that is, the information about membership degree, while ignoring non-membership degree. Meanwhile, since the limitations of individual cognition and cognitive environment, the concept learning is progressive. Inspired by these thoughts, we design an incremental learning mechanism based on progressive fuzzy three-way concept for object classification in dynamic environment. In this paper, the object and attribute learning operators are first defined to obtain fuzzy three-way concept. Then, a progressive fuzzy three-way concept and its corresponding concept space are learned considering the progressive process of concept learning. Moreover, the object classify mechanism and dynamic update mecha-nism based on the progressive concept space are proposed, and their effectiveness is ver-ified by numerical experiments. Finally, an incremental learning mechanism is further designed for dynamic increased data and compared with other fuzzy classify methods. All the experimental results carried on ten datasets from UCI and KEEL illustrate the proposed learning mechanism is an excellent object classify algorithm. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：This paper addresses the event-triggered control problem of discrete-time uncertain networked control systems under random deception attacks. In order to deal with a network resource constraint, a novel two-event-generator scheme over both sensor-to-controller and controller-to-actuator channels is proposed to reduce the transmission frequency of system states and control commands, while maintaining desired system performance. More specifically, one of the event-generators is a time-varying memory-based event triggered scheme (METS), and the other is the traditional event-triggered scheme (ETS). On the other hand, in order to incorporate network security, a new model of memory based NCSs is established under random deception attacks. Then, sufficient conditions for preserving the asymptotic stability of uncertain NCSs are proposed by using the Lyapunov functional method. Furthermore, co-design criteria are derived for determining both the memory-based state feedback controller gains and event-triggering parameter matrices. Finally, the effectiveness of the proposed methods is verified by a quarter-car suspension system. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Food recommendation has attracted increasing attentions to various food-related applications and services. The food recommender models aim to match users' preferences with recipes, where the key lies in the representation learning of users and recipes. However, ranging from early content-based filtering and collaborative filtering methods to recent hybrid methods, the existing work overlooks the various food-related relations, especially the ingredient-ingredient relations, leading to incomprehensive representations. To bridge this gap, we propose a novel model Food recommendation with Graph Convolutional Network (FGCN), which exploits ingredient-ingredient, ingredient-recipe, and recipe-user relations deeply. FGCN employs the information propagation mechanism and adopts multiple embedding propagation layers to model high-order connectivity across different food related relations and enhance the representations. Specifically, we develop three types of information propagation: (1) ingredient-ingredient information propagation, (2) ingredient-recipe information propagation, and (3) recipe-user information propagation. To validate the effectiveness and rationality of FGCN, we conduct extensive experiments on a real-world dataset. The results show that the proposed FGCN outperforms the state-of-the-art baselines. Further in-depth analyses reveal that FGCN could alleviate the sparsity issue in food recommendation. (c) 2021 Elsevier Inc. All rights reserved.
NSTL
Elsevier