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In recent years, feature selection methods based on sparse regression have attracted much attention from researchers, and how to select more representative feature is the key point. In this paper, an unsupervised feature selection method based on redundancy learning and sparse regression(RSUFS) is proposed. Firstly, to make the model robust to outliers, this paper uses the l 2 , 1 -norm regression model as the loss function to learn the feature weight matrix. Secondly, in order to get exact k top features, l 2 , 0 -norm constraint is introduced. At the same time, the cosine similarity between features is taken into account to select more valuable features by reducing the redundancy between features. Finally, an efficient algorithm based on Augmented Lagrangian method is derived to solve the above optimization problem. Comparison experiments are made with some benchmark datasets and seven well-known unsupervised feature selection algorithms and the results show that the given algorithm is effective. • l 2 , 1 -norm regression is used as the loss function to effectively avoid the influence of outliers. • Pseudo-labels are constructed through spectral analysis to preserve the data geometry. • The original sparse problem is dealt with directly by the l 2 , 0 -norm constraint. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of encoding data into repeat-free sequences in which sequences are imposed to contain any k-tuple at most once (for predefined k). First, the capacity of the repeat-free constraint are calculated. Then, an efficient algorithm, which uses two bits of redundancy, is presented to encode length-n sequences for k = 2 + 2 log (n). This algorithm is then improved to support any value of k of the form k = a log (n), for 1 < a, while its redundancy is o(n). We also calculate the capacity of repeat-free sequences when combined with local constraints which are given by a constrained system, and the capacity of multi-dimensional repeat-free codes. [ABSTRACT FROM AUTHOR]

In this paper, two novel approaches for unsupervised feature selection are proposed based on the spectral clustering. In the first proposed method, spectral clustering is employed over the features and the center of clusters is selected as well as their nearest-neighbors. These features have a minimum similarity (redundancy) between themselves since they belong to different clusters. Next, samples of data sets are clustered employing spectral clustering so that to the samples of each cluster a specific pseudo-label is assigned. After that according to the obtained pseudo-labels, the information gain of the features is computed that secures the maximum relevancy. Finally, the intersection of the selected features in the two previous steps is determined that simultaneously guarantees both the maximum relevancy and minimum redundancy. Our second proposed approach is very similar to the first one whose only but significant difference with the first method is that it selects one feature from each cluster and sorts all the features in terms of their relevancy. Then, by appending the selected features to a sorted list and ignoring them for the next step, the algorithm continues with the remaining features until all the features to be appended into the sorted list. Both of our proposed methods are compared with state-of-the-art methods and the obtained results confirm the performance of our proposed approaches especially the second one. [ABSTRACT FROM AUTHOR]

In recent years, multi-view clustering has been widely concerned and studied. Being its data generally has high dimension and noise, how to effectively deal with redundant features in data from various views and improve the clustering effect is an important issue in multi-view clustering. In this paper, a robust multi-view clustering algorithm Multi-view K -means clustering algorithm based on redundant and sparse feature learning (RSFMVKM) considering redundancy and sparse feature learning is proposed, which can effectively reduce the dimensions of data from different views. Firstly, the K -means algorithm is extended to multi-view clustering, and the importance of each views is represented by learning the weight of each view. Secondly, the sparse feature representation is obtained by applying the norm constraint to the projection matrix in a single view, and the cosine similarity is used to depict the redundancy matrix, which can remove the redundant information and improve the clustering effect. Moreover, the proposed algorithm can effectively avoid the generation of empty classes, and the clustering index matrix is discrete, so the clustering results can be obtained directly. Finally, the proposed algorithm is compared with six state-of-the-art multi-view clustering algorithms on six public datasets, and the experimental results show that the algorithm is effective. • By feature learning, the dimensionality of data is effectively reduced and redundant features are removed. • Control the importance of each view by learning weights. • Using the coordinate descent method, the clustering results can be obtained directly and no empty class is generated. [ABSTRACT FROM AUTHOR]

Mutually uncorrelated (MU) codes are a class of codes in which no proper prefix of one codeword is a suffix of another codeword. These codes were originally studied for synchronization purposes and recently, Yazdi et al. showed their applicability to enable random access in DNA storage. In this paper,we follow the research of Yazdi et al. and study MU codes along with their extensions to correct errors and balanced codes. We first review a well-known construction of MU codes and study the asymptotic behavior of its cardinality. This task is accomplished by studying a special class of run-length limited codes that impose the longest run of zeros to be at most some function of the codewords length. We also present an efficient algorithm for this class of constrained codes and show how to use this analysis for MU codes. Next, we extend the results on the run-length limited codes in order to study $(d_{h},d_{m})$ -MU codes that impose a minimum Hamming distance of $d_{h}$ between different codewords and $d_{m}$ between prefixes and suffixes. In particular, we show an efficient construction of these codes with nearly optimal redundancy. We also provide similar results for the edit distance and balanced MU codes. Last, we draw connections to the problems of comma-free and prefix synchronized codes. [ABSTRACT FROM AUTHOR]

Fractional interpolation is used to provide sub-pixel level references for motion compensation in the interprediction of video coding, which attempts to remove temporal redundancy in video sequences. Traditional handcrafted fractional interpolation filters face the challenge of modeling discontinuous regions in videos, while existing deep learning-based methods are either designed for a single quantization parameter (QP), only generating half-pixel samples, or need to train a model for each sub-pixel position. In this paper, we present a one-for-all fractional interpolation method based on a grouped variation convolutional neural network (GVCNN). Our method can deal with video frames coded using different QPs and is capable of generating all sub-pixel positions at one sub-pixel level. Also, by predicting variations between integer-position pixels and sub-pixels, our network offers more expressive power. Moreover, we perform specific measurements in training data generation to simulate practical situations in video coding, including blurring the down-sampled sub-pixel samples to avoid aliasing effects and coding integer pixels to simulate reconstruction errors. In addition, we analyze the impact of the size of blur kernels theoretically. Experimental results verify the efficiency of GVCNN. Compared with HEVC, our method achieves 2.2% in bit saving on average and up to 5.2% under low-delay P configuration. [ABSTRACT FROM AUTHOR]

Additional check bits, which are commonly attached to the message’s input data, are normally used to minimize the error during data transmission. The receiver system implements a checking algorithm to determine if an error was occurred in the received data. This algorithm will correct a corrupted bit and recover the original message. An enhanced error detection correction code was presented to better detect and correct the corrupted conveyed bits. It improves the existing limitations of utilizing cyclic redundancy checking (CRC), Hamming code, and other checksum techniques. Also, it reduced the length of the redundancy bits which exists in CRC, the overhead of interspersing of the redundancy bits in a typical Hamming code, and the system resources such as processor time and bandwidth in checksum techniques. This paper was synthesized and simulated using the Xilinx Spartan 6 (XC7Z020-2CLG4841) FPGA. Results show that the resource utilization of the designed memory architecture using EEDC is lower compared to CRC, Hamming, and Checksum algorithms. [ABSTRACT FROM AUTHOR]

We consider the problem of constructing codes that can correct $\delta $ deletions occurring in an arbitrary binary string of length $n$ bits. Varshamov–Tenengolts (VT) codes, dating back to 1965, are zero-error single deletion $(\delta =1)$ correcting codes and have an asymptotically optimal redundancy. Finding similar codes for $\delta \geq 2$ deletions remains an open problem. In this paper, we relax the standard zero-error (i.e., worst-case) decoding requirement by assuming that the positions of the $\delta $ deletions (or insertions) are independent of the code word. Our contribution is a new family of explicit codes, that we call Guess & Check (GC) codes, that can correct with high probability up to a constant number of $\delta $ deletions (or insertions). GC codes are systematic; and have deterministic polynomial time encoding and decoding algorithms. We also describe the application of GC codes to file synchronization. [ABSTRACT FROM AUTHOR]

In this paper, binary Hall-effect sensor faults are investigated in rectangular-current-fed brushless dc (BLDC) drives and a very effective methodology for their detection, identification, and compensation is explored. It is shown that these faults cause erroneous commutation, generally leading to unstable operation. Using a fault detection and identification technique proposed by the authors in a recent paper on low-cost field-oriented drives, it is possible to pinpoint the faulty sensors. In this paper, it is demonstrated that the destabilizing effect of these faults on motion-state estimation can be compensated for in any position and speed estimation algorithm, as long as it is properly readapted. To this end, it is shown how to incorporate such fault-compensation in three state-of-the-art estimation algorithms: 1) the zeroth-order algorithm (ZOA); 2) the hybrid observer (HO); and 3) the vector-tracking observer (VTO). Comparative experimental tests are performed and it is verified that stable operation is achieved with three, two, or only a single Hall-effect sensor functioning correctly. These results show that the classical BLDC drive with three Hall-effect sensors has an inherent double redundancy to position-sensor faults. With the proposed method, this property can be exploited in systems that require very high reliability, such as in aerospace and automotive applications. Redundancy can be increased, using more than three Hall-effect sensors; reduced using two sensors; or eliminated using a single sensor, in ultra low-cost applications where redundancy is not a requirement. [ABSTRACT FROM AUTHOR]

Knowledge representation and reasoning using propositional logic is an important component of AI systems. A propositional formula in Conjunctive Normal Form (CNF) may contain redundant clauses — clauses whose removal from the formula does not affect the set of its models. Identification of redundant clauses is important because redundancy often leads to unnecessary computation, wasted storage, and may obscure the structure of the problem. A formula obtained by the removal of all redundant clauses from a given CNF formula F is called a Minimal Equivalent Subformula (MES) of F . This paper proposes a number of efficient algorithms and optimization techniques for the computation of MESes. Previous work on MES computation proposes a simple algorithm based on iterative application of the definition of a redundant clause, similar to the well-known deletion-based approach for the computation of Minimal Unsatisfiable Subformulas (MUSes). This paper observes that, in fact, most of the existing algorithms for the computation of MUSes can be adapted to the computation of MESes. However, some of the optimization techniques that are crucial for the performance of the state-of-the-art MUS extractors cannot be applied in the context of MES computation, and thus the resulting algorithms are often not efficient in practice. To address the problem of efficient computation of MESes, the paper develops a new class of algorithms that are based on the iterative analysis of subsets of clauses, and a lightweight pruning technique based on the computation of backbones. The experimental results, obtained on representative problem instances, confirm the effectiveness of the proposed methods. The experimental results also reveal that many CNF instances obtained from the practical applications of SAT exhibit a large degree of redundancy. [ABSTRACT FROM AUTHOR]

In this paper, global optimization model is designed for grid computing system. It is provided as a promising model for grid resource scheduling algorithm. It aims at solving the problem of optimally allocating services on the grid to optimize the grid service reliability, deadline and cost. In this paper, the problem of optimizing the reliability of grid systems has been modeled as a multi-objective optimization problem where apart from the grid system reliability; the system cost, deadline and redundancy are also considered as its constraints. The algorithm considers failure rate of computational and network resources to do the reliability analysis of the grid system. Based on the service reliability of the grid system, the proposed RORS algorithm selects the set of optimal resources among the candidate resources based on reliability, application execution time and cost that achieves optimal performance using a genetic algorithm. The proposed algorithm has been demonstrated using Java-based GridSim tool. [ABSTRACT FROM AUTHOR]

Detecting hidden communities is important for the analysis of complex networks. However, many algorithms have been designed for single layer networks (SLNs) while just a few approaches have been designed for multiplex networks (MNs). In this paper, we propose an algorithm based on layer reduction for detecting communities on MNs, which is termed as LRCD-MNs. First, we improve a layer reduction algorithm termed as neighaggre to combine similar layers and keep others separated. Then, we use neighaggre to find the community structure hidden in MNs. Experiments on real-life networks show that neighaggre can obtain higher relative entropy than the other algorithm. Moreover, we apply LRCD-MNs on some real-life and synthetic multiplex networks and the results demonstrate that, although LRCD-MNs does not have the advantage in terms of modularity , it can obtain higher values of surprise , which is used to evaluate the quality of partitions of a network. [ABSTRACT FROM AUTHOR]

This paper considers 1-out-of- N : G warm standby (WS) systems subject to multiple-phased mission requirements, where performance, failure behavior, operation cost, replacement time, and cost of system elements can vary from phase to phase due to changing working conditions and stress levels. The system succeeds if its elements can accomplish a specified mission task within the maximum allowed time. A numerical algorithm is proposed for evaluating mission indices, including system reliability, expected mission cost, and expected uncompleted work of the heterogeneous, dynamic standby system considered. The influence of the maximum allowed mission time on mission indices is investigated. As demonstrated through examples, the proposed algorithm can also facilitate a determination of mission interruption based on reliability or cost-oriented criteria. Based on the proposed algorithm, an unconstrained optimal standby element sequencing problem is then formulated and solved for heterogeneous WS multiphase systems. The problem is to find the optimal activation sequence of system elements maximizing system reliability, or minimizing expected mission cost, or minimizing expected uncompleted work. The constrained optimization problems of minimizing expected mission cost subject to providing a desired level of mission reliability or uncompleted work are also solved. Illustrative examples of these optimization problems and their applications in estimating the role of each element in the mission success are presented. [ABSTRACT FROM PUBLISHER]

Access control policies may contain anomalies such as incompleteness and inconsistency, which can result in security vulnerabilities. Detecting such anomalies in large sets of complex policies automatically is a difficult and challenging problem. In this paper, we propose a novel method for detecting inconsistency and incompleteness in access control policies with the help of data classification tools well known in data mining. Our proposed method consists of three phases: firstly, we perform parsing on the policy data set; this includes ordering of attributes and normalization of Boolean expressions. Secondly, we generate decision trees with the help of our proposed algorithm, which is a modification of the well-known C4.5 algorithm. Thirdly, we execute our proposed anomaly detection algorithm on the resulting decision trees. The results of the anomaly detection algorithm are presented to the policy administrator who will take remediation measures. In contrast to other known policy validation methods, our method provides means for handling incompleteness, continuous values and complex Boolean expressions. In order to demonstrate the efficiency of our method in discovering inconsistencies, incompleteness and redundancies in access control policies, we also provide a proof-of-concept implementation. [ABSTRACT FROM AUTHOR]

Redundancy is related to the amount of functionality that the structure can sustain in the worst-case scenario of structural degradation. This paper proposes a widely-applicable concept of redundancy optimization of finite-dimensional structures. The concept is consistent with the robust structural optimization and with the quantitative measure of structural redundancy on the basis of the information-gap theory. A derivative-free algorithm is proposed on the basis of the sequential quadratic programming (SQP) method, in which the finite-difference method that adaptively varies the difference increment is used. Preliminary numerical experiments show that an optimal solution of the redundancy optimization problem possibly has multiple worst-case scenarios. [ABSTRACT FROM AUTHOR]

Synthesis of thinned linear array geometry with low redundancy is an important problem for one-dimensional aperture synthesis radiometers (ASRs) with high spatial resolution and satisfactory sensitivity. In this paper, a synthesis method of low-redundancy linear array (LRLA) with large numbers of antennas is proposed. Two LRLA patterns are suggested to significantly reduce the tremendous solution space of large LRLA search problem and a strategy is proposed to speed up exploration. Further, particle swarm optimization (PSO) algorithm exploiting the suggested patterns and strategy is applied to the array synthesis. The advantages of the proposed method, in terms of both generating more new large LRLA configurations and reducing computational cost for the large LRLA configuration synthesis problem, are demonstrated by comparing its performance with that of the previously proposed methods. Finally, a number of newly found large LRLA configurations for 28–37 elements are also presented. [ABSTRACT FROM PUBLISHER]

This paper compares the performance of the popular adaptive h -refinement ( h\text{R} ) technique for the finite-element method (FEM) with the adaptive version of the recently presented operator-customized wavelet basis (OCWB). This new method is a combination of the second-generation wavelet theory with hierarchical basis, which is a multi-resolution basis, and, when applied to the FEM, the solution is split into different levels of detail. These levels, also referred to as scales, are composed by compactly supported functions, allowing the detail to be added only on the chosen regions. Although this strategy reduces the total system dimension with sufficiently small error, all the scales are naturally coupled, which means that there are interactions between functions composing different detail levels. This coupling forces the whole multi-resolution system to be recomputed when a detail is added, a kind of redundancy that also occurs on the h\text{R}$ method. Using the second-generation wavelet theory, functions are custom-designed to decouple the system, which means the interactions between the functions of different levels are eliminated, and it is possible to solve for further detail independently of previous scales. This property significantly increases the performance of the algorithm. Conversely, the formulation—and, consequently, the algorithm design—complexity is also increased, which is the reason why there are such few applications on the subject. Like the h\text{R} method, an adaptive OCWB can be programmed with various strategies; since the results are shown in terms of processing time on a regular PC, both the algorithms have been developed with similar structures. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid modulation strategy for asymmetrical cascaded multilevel converters operating under normal and faulty conditions on power cells. The proposed algorithm explores the advantages of both space vector (SV) and carrier-based modulation approaches. An SV is used for higher voltage cells to properly choose their voltage vectors in a way such as to avoid converter saturation, particularly under fault conditions. A generic algorithm to identify the domains of higher voltage vectors in which a reference lies, without the need for separation lines, is proposed. Then, to simplify the implementation of the algorithm, carrier-based modulation is employed for lower voltage cells, which switch with pulsewidth modulation. This suppresses the need for defining multiple switching sequences for different operational conditions, and it avoids converter saturation as much as possible, particularly under fault conditions on power cells. Simulation and experimental results for normal and faulty operational conditions are given, validating the theoretical analysis and demonstrating the good performance of the proposed hybrid modulation approach. [ABSTRACT FROM AUTHOR]

This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants. [ABSTRACT FROM PUBLISHER]

Clock trees are commonly used to deliver clock signals to sequential elements in circuits. However, by construction, tree structures are inherently prone to failure caused by variations. The robustness of a clock tree can be improved by inserting redundancy in the form of cross links or multilevel fusion trees. Such near-tree structures can provide robustness at low cost. In this paper, we establish that the locations of the inserted redundancy are crucial in providing cost-effective robustness. We present two methods to systematically insert redundancy. The redundancy is realized by either inserting cross links or performing local merges. Moreover, we present a vertex reduction method that reduces the amount of redundancy that needs to be inserted in our near-tree structures. Empirical results show that our structures are more robust to variations and have lower power consumption compared to the state-of-the-art clock networks. Furthermore, our near-tree structures provide smooth trade-offs between cost and robustness, reducing clock skews by 11%–39% at an expense of 3%–68% higher power consumption. [ABSTRACT FROM PUBLISHER]

The systems dynamics and control engineering (FDI) and the artificial intelligence diagnosis (DX) communities have developed complementary approaches that exploit structural relations in the system model to find efficient solutions for the residual generation and residual evaluation steps in fault detection and isolation in dynamic systems. This paper compares three different structural fault diagnosis techniques, two from the DX community and one from the FDI community. To simplify our comparison, we start with bond graphs as the common system modeling language and develop a graph-based framework using temporal causal graphs as the basis for analyzing the three fault isolation approaches. This framework allows for systematic comparison of the diagnosability properties of the three algorithms. The three-tank system is used as a running example to illustrate our concepts and algorithms. [ABSTRACT FROM AUTHOR]