查看更多>>摘要：The mixed noise such as Gaussian noise together with the abrupt noise widely exists in the indoor environment, which always leads to the problem of performance degradation of the positioning system under the Internet of Things (IoT). In this paper, a novel kernel function named generalized Student's t kernel (GSt) and a resulting sparse generalized Student's t kernel adaptive filter (SGStKAF) is proposed to attack this problem. The proposed SGStKAF utilizes the kernel mean p-power error criterion (KMPE) with the L-1-norm penalty. The proposed SGStKAF has three significant features. Firstly, the generalized Student's t kernel can suppress the abrupt noise effectively. Secondly, the L-1-norm penalty guarantees that the fixed-point sub-iteration is available so that the more precise solution can be obtained in a few iterations. At last, a sparse structure of neural networks for the implementation of the proposed method can also be obtained via the L-1 constraint. Three experiments and comparisons are carried out to prove the effectiveness of the proposed positioning framework in terms of accuracy and robustness in both the simulation situation and the real-world indoor environments. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In recent decades, an avalanche of chaos-based cryptosystems have been proposed for information security. Most of these systems are not immune to the dynamical degradation of digital chaos and many have been shown to suffer from a lack of security. In this paper, a stream cipher system based on an analog-digital hybrid chaotic system is presented. The hybrid model can construct digital chaotic maps without degeneration and guarantee synchronization of analog chaotic systems for successful decryption. Moreover, focusing on the characteristics of low-dimensional chaotic maps, a modified three-dimensional Logistic map is proposed to improve the weaknesses of uneven distribution, low complexity and limited parameter space. Combining the three-dimensional Logistic map and the hybrid model, the proposed stream cipher has advantages of huge key space, virtually infinite cycle length and tight security. In particular, it is not affected by the dynamical degradation. Performance and security analyses indicate that the proposed stream cipher is highly resistant to various chaos-based attacks and cryptanalytic attacks.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Least Squares Regression (LSR) is a powerful method for learning the transform from high dimensional space into a low-dimensional label subspace. Due to the excellent performance of low-rank matrix decomposition based methods in exploring low-dimensional subspace structure, many extended algorithms of LSR impose low-rank constraints to make intra-class regression targets more comparative and similar. However, the low-rank constraints imposed on the original space may destroy the data structure and adversely affect the model of capturing the manifold structure. In this paper, a Denoising Low-Rank Discrimination based Least Squares Regression (DLRDLSR) model is proposed to eliminate noise in label space. Firstly, we decompose the data into a low-rank matrix and a sparse matrix in label subspace. Secondly, some constraints are imposed onto the low-rank matrix and sparse matrix to preserve the details, and then we use the low-rank matrix for classification. Moreover, e-dragging technique is introduced to enlarge the distance between different classes to enhance discrimination, and l2-norm constraint is also introduced to avoid overfitting. The experiments on a variety of image databases demonstrate that the proposed method is superior to other state-of-the-art methods. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：The total uncertainty measurement of basic probability assignment (BPA) in Dempster Shafer evidence theory (DSET) has always been an open issue. Although some scholars put forward various measurements and entropies of BPA, due to the existence of discord and non-specificity, there is no method can measure BPA reasonably. In order to utilize BPA to practical decision-making, pignistic probability transformation of BPA is a significant method. In the paper, we simulate the pignistic probability transformation (PPT) process based on the fractal idea, which describes PPT process in detail and shows the process of information volume changes during transformation intuitively. Based on transformation process, we propose a new belief entropy called fractal-based belief (FB) entropy. After verification, FB entropy is superior to all existing belief entropies in terms of total uncertainty measurement and physical model consistency.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Due to the uncertain circumstances in which decisions are made, the moderator may be unable to provide an accurate unit adjustment cost to each decision maker, making the unit adjustment cost uncertain. This paper formulates a consensus approach to reduce the adverse effects of parameter uncertainty and estimation errors. First, the mean-variance (MV) theory is employed to build the risk minimum cost consensus model (RMCCM) to reflect the risk preference of the moderator in the consensus progress. Second, due to existing estimation errors of the mean and covariance matrix of unit adjustment cost in RMCCM, new RMCCM variants are proposed using robust optimization. To identify a solution that performs best in the worst case, the robust counterpart RMCCM problem is equivalent to a tractable problem. Finally, a case study of urban housing demolition in China is used to verify the feasibility and applicability of the proposed method, and then sensitivity analysis and comparative analysis are presented.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In many practical applications, G-Skyline query is an important operation to return the best tuple groups, which are not g-dominated by other tuple groups of the same size, from a potentially huge data space. It is found that the existing G-Skyline algorithms cannot deal well with massive data due to high I/O cost and high computation cost. This paper proposes a novel GPR algorithm, which is based on presorting and reuse principle, to compute G -Skyline groups on massive data efficiently. The execution of GPR consists of two phases: acquisition of the candidate tuples and computation of G-Skyline groups. The sublinear-I/O method is proposed in phase 1 to scan the presorted table, which is proved to hold early termination property. This paper devises the basic framework of phase 2 and analyzes its execution cost. The SR strategy is utilized to reuse the subset computation results effec-tively and reduce the execution cost of phase 2 considerably. The extensive experimental results, conducted on synthetic and real-life data sets, show that GPR outperforms the existing algorithms significantly.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, we show that Minimum triangular norm (Min t-norm) always leads to unrealizable fuzzy PID (FPID) controllers. To show this, mathematical models of two FPID controllers are obtained. Two fuzzy sets, Negative (N) and Positive (P), for fuzzification of each of the three input variables (error, change of error, and double change of error), and four fuzzy sets N, Negative Small (NS), Positive Small (PS), and P on the output variable (change of control effort) are considered. Min t-norm, Maximum (Max) s-norm, triangular membership functions, Larsen Product (LP)/ Mamdani Minimum (MM) inference, and Centre of Area (Centre of Gravity) (CoA (CoG)) defuzzification are used for mathematical modeling of the controllers. Then upon thoroughly analyzing the properties of the mathematical models of the controllers, we show that Min t-norm leads to unrealizable FPID controllers.(c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：This paper attempts at formalizing a novel type of cellular automata (CAs) in the framework of distributed systems under asynchronous communication. Called asynchronous communicating cellular automata (ACCAs), our models allow each cell to exchange states with its neighboring cells independently at random times, via a specific protocol for asynchronous communication. This can actually facilitate the separation of communication between cells from the cells state transitions in an ACCA. The effect of asynchronous communication on dynamical behavior will be analyzed on the well-studied elementary class of cellular automata, especially on their robustness. Despite the unpredictable randomness in both communication and state transitions, computational equivalence between ACCAs and conventional CAs can be achieved, based on effective methods for transforming every synchronous CA into an equivalent ACCA. (C) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In the fields of rough set and machine learning, attribute reduction has been demonstrated to be effective in removing redundant attributes with clear explanations. Therefore, not only the generalization performances of the derived reducts, but also the efficiencies of searching reducts have drawn much attention. Immediately, various accelerators for quickly deriving reducts have been designed. However, most of the existing solutions merely speed up the procedure of searching reduct from one and only one perspective, it follows that the efficiencies of those accelerators may be further improved with a fusion view. For such a reason, a framework called Fusing Attribute Reduction Accelerators (FARA) is developed. Our framework is specifically characterized by the following three aspects: (1) sample based accelerator, which is realized by gradually reducing the volume of samples based on the mechanism of positive approximation; (2) attribute based accelerator, which is realized by adding multiple qualified attributes into the potential reduct for each iteration; (3) granularity based accelerator, which is realized by ignoring the candidate attributes within coarser granularity. By examining both the efficiencies of the searchings and the effectiveness of the searched reducts, comprehensive experiments over 20 public datasets fairly validated the superiorities of our framework against 5 popular accelerators. (C) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：A fuzzy extension of a Multi-Criteria Decision Analysis (MCDA) method implies a choice of an approach to estimating corresponding functions of fuzzy variables and a method for ordering alternatives based on ranking of fuzzy quantities. The objective of this paper is the development and comparison of Fuzzy MCDA (FMCDA) models, which represent different approaches to fuzzy extensions of an ordinary MCDA method. To do so, different approaches to assessing functions of fuzzy numbers are considered along with several methods for ranking of fuzzy numbers. Distinctions in ranking alternatives, including the number and significance of distinctions based on a granulation of the output information, are explored for different FMCDA models by using Monte Carlo simulating input scenarios of fuzzy multi-criteria problems. In addition, both intra-distinctions and inter-distinctions are explored. According to the results, distinctions in ranking alternatives by different FMCDA models may be considered as significant both for ranking and choice multi criteria problematiques. This research is of fundamental and applied importance and has no analogues. (C) 2021 The Author(s). Published by Elsevier Inc.
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