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OPTIMUM AND HEURISTIC algorithms FOR AN APPROACH TO FINITE STATE MACHINE DECOMPOSITION

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1991年第3期10卷 296-310页

作者： ASHAR, P DEVADAS, S NEWTON, AR MIT DEPT ELECT ENGN & COMP SCICAMBRIDGEMA 02139

Techniques have been proposed in the past for various types of finite state machine (FSM) decomposition that use the number of states or edges in the decomposed circuits as the cost function to be optimized. Such measures are not reflective of the true logic complexity of the decomposed circuits. In addition, previous methods have been mainly heuristic in nature and offer limited guarantees as to the quality of the decomposition. In this paper, optimum and heuristic algorithms for the general decomposition of FSM's such that the sum total of the number of product terms in the one-hot coded and logic minimized submachines is minimum or minimal, are presented. This cost function is much more reflective of the area of an optimally state-assigned and minimized submachine than the number of states/edges in the submachine. The problem of optimum two-way FSM decomposition is formulated as one of symbolic output partitioning and it is shown that this is an easier problem than optimum state assignment. A procedure of constrained prime implicant generation and covering is described that represents an optimum FSM decomposition algorithm, under the specified cost function. It is shown that by means of this formulation, arbitrary decomposition topologies can be targeted by suitably modifying the encodeability constraints during the covering. Exact procedures are not viable for large problem instances because of limitations imposed by currently available computing resources. To overcome these limitations, a novel iterative optimization strategy of symbolic implicant expansion and reduction, modified from two-level Boolean minimizers, that represents a heuristic algorithm based on our exact procedure, is presented. Reduction and expansion are performed on functions with symbolic, rather than binary-valued outputs. The heuristic procedure can be used for problems of any size. Preliminary experimental results have been presented that illustrate both the efficacy of the proposed algo

关键词： Heuristic algorithms Automata Programmable logic arrays Cost function Clocks Electronics packaging Logic devices Logic circuits partitioning algorithms Topology

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SCHUR algorithms FOR HERMITIAN TOEPLITZ, AND HANKEL-MATRICES WITH SINGULAR LEADING PRINCIPAL SUBMATRICES

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1991年第11期39卷 2464-2480页

作者： ZAROWSKI, CJ Department of Electrical Engineering Queen's University Kingston ONT Canada

This paper shows how a simple matrix algebraic procedure may be used to induce Schur-type algorithms for the solution of certain Toeplitz and Hankel linear systems of equations when given Levinson-Durbin-type algorithms for such problems. The motivation for doing so is that Schur algorithms may be efficiently implemented on linearly connected parallel computer systems (of the sort considered by Kung and Hu, or Brent and Luk), while the Levinson-Durbin algorithms may not. The algorithm of Delsarte, Genin, and Kamp for Hermitian Toeplitz matrices in the singular case is used to induce a Schur algorithm for such matrices. An algorithm due to Heinig and Rost for Hankel matrices in the singular case is used to induce a Schur algorithm for such matrices. Finally, the Berlekamp-Massey algorithm is viewed as a kind of Levinson-Durbin algorithm and so is used to induce a Schur algorithm for the minimal partial realization problem. The Schur algorithm for Hermitian Toeplitz matrices in the singular case is shown to be amenable to implementation on a linearly connected parallel processor array of the sort considered by Kung and Hu, and in fact generalizes their result to the singular case.

关键词： Matrices partitioning algorithms Linear systems Equations Polynomials Concurrent computing Councils Inverse problems

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Low-Rank Tensor Regularized Fuzzy Clustering for Multiview Data

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第12期28卷 3087-3099页

作者： Wei, Huiqin Chen, Long Ruan, Keyu Li, Lingxi Chen, Long Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Indiana Univ Purdue Univ Dept Elect & Comp Engn Indianapolis IN 46202 USA Indiana Univ Purdue Univ Transportat Act Safety Inst Indianapolis IN 46202 USA Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China

Since data are collected from a range of sources via different techniques, multiview clustering has become an emerging technique for unsupervised data classification. However, most existing soft multiview clustering methods only consider the pairwise correlations and ignore high-order correlations among multiple views. To integrate more comprehensive information from different views, this article innovates a fuzzy clustering model using the low-rank tensor to address the multiview data clustering problem. Our method first conducts a standard fuzzy clustering on different views of the data separately. Then, the obtained soft partition results are aggregated as the new data to be handled by a Kullback-Leibler (KL) divergence-based fuzzy model with low-rank tensor constraints. The KL divergence function, which replaces the traditional minimized Euclidean distance, can enhance the robustness of the model. More importantly, we formulate fuzzy partition matrices of different views as a third-order tensor. So, a low-rank tensor is introduced as a norm constraint in the KL divergence-based fuzzy clustering to obtain dexterously high-order correlations of different views. The minimization of the final model is convex and we present an efficient augmented Lagrangian alternating direction method to handle this problem. Specially, the global membership is derived by using tensor factorization. The efficiency and superiority of the proposed approach are demonstrated by the comparison with state-of-the-art multiview clustering algorithms on many multiple-view data sets.

关键词： Tensile stress Clustering methods Clustering algorithms Data models Correlation Standards partitioning algorithms Fuzzy clustering Kullback– Leibler (KL) divergence low-rank tensor multiview data

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Detection and Defense of Cache Pollution Attacks Using Clustering in Named Data Networks

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2020年第6期17卷 1310-1321页

作者： Yao, Lin Fan, Zhenzhen Deng, Jing Fan, Xin Wu, Guowei Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116600 Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian 116600 Peoples R China Dalian Univ Technol Sch Software Dalian 116600 Peoples R China Univ North Carolina Greensboro UNCG Dept Comp Sci Greensboro NC 27412 USA

Named Data Network (NDN), as a promising information-centric networking architecture, is expected to support next-generation of large-scale content distribution with open in-network cachings. However, such open in-network caches are vulnerable against Cache Pollution Attacks (CPAs) with the goal of filling cache storage with non-popular contents. The detection and defense against such attacks are especially difficult because of CPA's similarities with normal fluctuations of content requests. In this work, we use a clustering technique to detect and defend against CPAs. By clustering the content interests, our scheme is able to distinguish whether they have followed the Zipf-like distribution or not for accurate detections. Once any attack is detected, an attack table will be updated to record the abnormal requests. While such requests are still forwarded, the corresponding content chunks are not cached. Extensive simulations in ndnSIM demonstrate that our scheme can resist CPA effectively with higher cache hit, higher detecting ratio, lower hop count, and lower algorithm complexity compared to other state-of-the-art schemes.

关键词： Pollution Fans Clustering algorithms Computer architecture partitioning algorithms Classification algorithms Resists Cache pollution attack clustering named data networks

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STOCHASTIC-MODELS FOR WIREABILITY ANALYSIS OF GATE ARRAYS

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1986年第1期5卷 52-65页

作者： SASTRY, S PARKER, AC Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

A stochastic model for estimating measures of placement and routing on gate arrays is presented. Three important problems are addressed: estimating the dimensions of the routing channels, estimating the routability of a channel given the number of tracks, and determining the distribution and moments of wire lengths. In the context of wiring space estimation, exact and asymptotic formulas for the dimensions of routing channels are presented. Next, an expression for the probability that a routing channel with a given number of tracks will be routable is derived, and its asymptotic properties are examined. Finally, a model that characterizes the relationship between wire length distributions and partitioning of logic is developed. The model provides a firm mathematical basis for Rent's rule from which the distribution of wire lengths can be determined. That is, Rent's rule, or in general any similar relation, contains all the information about wire lengths. Based on this, estimates for the average wire length are derived. Numerical results from both simulated and real chips are presented.

关键词： Stochastic processes Wire Routing Wiring partitioning algorithms Length measurement Very large scale integration Logic arrays Mathematical model Process design

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ON CLUSTER VALIDITY FOR THE FUZZY C-MEANS MODEL

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 1995年第3期3卷 370-379页

作者： PAL, NR BEZDEK, JC Department of Computer Science University of West Florida Pensacola FL USA

Many functionals have been proposed for validation of partitions of object data produced by the fuzzy c-means (FCM) clustering algorithm. We examine the role a subtle but important parameter-the weighting exponent m of the FCM model-plays in determining the validity of FCM partitions, The functionals considered are the partition coefficient and entropy indexes of Bezdek, the Xie-Beni, and extended Xie-Beni indexes, and the Fukuyama-Sugeno index. Limit analysis indicates, and numerical experiments confirm, that the Fukuyama-Sugeno index is sensitive to both high and low values of m and may be unreliable because of this. Of the indexes tested, the Xie-Beni index provided the best response over a wide range of choices for the number of clusters, (2-10), and for m from 1.01-7, Finally, our calculations suggest that the best choice for rn is probably in the interval [1.5, 2.5], whose mean and midpoint, m = 2, have often been the preferred choice for many users of FCM.

关键词： Clustering algorithms Fuzzy sets partitioning algorithms Entropy Testing Unsupervised learning Prototypes Equations Computer science Fuzzy logic

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An Efficient Network Reduction Method for Transmission Expansion Planning Using Multicut Problem and Kron Reduction

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第6期33卷 6120-6130页

作者： Ploussard, Quentin Olmos, Luis Ramos, Andres Inst Res Technol Madrid 28015 Spain Comillas Pontifical Univ Inst Res Technol Madrid 28015 Spain Univ Pontificia Comillas Inst Invest Tecnol Madrid 28015 Spain

This paper provides a new methodology to compute a reduced but accurate network representation in a transmission expansion planning (TEP) context. Considering this reduced network should lead to the same investment decisions as if the whole original network were considered. A set of relevant lines to be preserved is defined based on a proxy of the TEP solution. An optimal network partition, resulting from solving the multicut problem, is computed in such a way that the two ends of each of these relevant lines are allocated to two different areas. An iterative Kron reduction is then applied to each area to eliminate most of the buses that are not connected to any interarea line. This two-step process results in a compact but representative reduced network. Our algorithm has been implemented in General Algebraic Modelling Software and Matrix Laboratory and has been tested on the standard IEEE 118 bus system and a case study based on the European power system. The method produces very promising results and, in the considered case studies, leads to the same, or equally efficient, investment decisions and essentially the same total costs as when considering the whole original network.

关键词： Clustering dimension reduction integer linear programming network theory (graphs) partitioning algorithms transmission expansion planning relaxation methods

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Haery: A Hadoop Based Query System on Accumulative and High-Dimensional Data Model for Big Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第7期32卷 1362-1377页

作者： Song, Jie He, HongYan Thomas, Richard Bao, Yubin Yu, Ge Northeastern Univ Software Coll Shenyang 110819 Liaoning Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China

Column-oriented stores, known for their scalability and flexibility, are a common NoSQL database implementation and are increasingly used in big data management. In column-oriented stores, a "full-scan" query strategy is inefficient and the search space can be reduced if data is well partitioned or indexed;however, there is no pre-defined schema for building and maintaining partitions and indexes at lower cost. We leverage an accumulative and high-dimensional data model, a sophisticated linearization algorithm, and an efficient query algorithm, to solve the challenge of how a pre-defined and well-partitioned data model can be applied to flexible and time-varied key-value data. We adapt a high-dimensional array as the data model to partition the key-value data without additional storage and massive calculation;improve the Z-order linearization algorithm, which map multidimensional data to one dimension while preserving locality of the data points, for flexibility;efficiently build an expansion mechanism for the data model to support time-varied data. The result is Haery, a column-oriented store, based on a distributed file system and computing framework. In experiments, Haery is compared with Hive, HBase, Cassandra, MongoDB, PostgresXL, and HyperDex in terms of query performance. With results indicating Haery on average performs 4.57x, 4.23x, 3.55x, 1.79x, 1.82x, and 120.6x faster, respectively.

关键词： Data models Big Data partitioning algorithms NoSQL databases Indexing Key-value data column-oriented store multi-dimensional data model linearization accumulation

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A NEW RELAXATION ALGORITHM AND PASSIVE SENSOR DATA ASSOCIATION

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1992年第2期37卷 198-213页

作者： PATTIPATI, KR DEB, S BARSHALOM, Y WASHBURN, RB ALPHATECH INC TECH STAFFBURLINGTONMA 01803

This paper is concerned with the static problem of associating measurements at a given time from three angle-only sensors in the presence of clutter, missed detections, and an unknown number of targets. The measurement-target association problem is formulated as one of maximizing the joint likelihood function of the measurement partition. Mathematically, this formulation of the data association problem leads to a generalization of the three-dimensional (3-D) assignment (matching) problem, which is known to be NP hard;that is, the complexity of an optimal algorithm increases exponentially with the size of the problem, since no known polynomial time algorithm exists. Suboptimal algorithms of quantifiable accuracy are therefore of considerable importance. The new solution to the optimization problem developed in this paper is a Lagrangian relaxation technique that successively solves a series of generalized two-dimensional (2-D) assignment problems, with the worst case complexity of O(3kn3), where k is the number of relaxation iterations and n is the number of reports from each sensor. The dual optimization problem is solved via an acclerated subgradient method. A useful feature of the relaxation approach is that the resulting dual optimal cost is a lower bound on the feasible cost and, hence, provides a measure of how close the feasible solution is to the (perhaps unknowable) optimal solution. For the passive sensor data association problem, the feasible solution costs are typically within 1% of their corresponding dual optimal costs. A comparison of our relaxation algorithm to the optimal branch-and-bound and suboptimal row-column algorithms demonstrates that our algorithm is superior to these alternative approaches in terms of computational complexity and/or measurement-target association accuracy. Finally, the algorithm is illustrated via several application examples.

关键词： Cost function Target tracking Radar tracking Noise measurement Current measurement State estimation Time measurement partitioning algorithms Polynomials Lagrangian functions

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Robust Structural Balance in Signed Networks Using a Multiobjective Evolutionary Algorithm

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2020年第2期15卷 24-35页

作者： Wang, Shuai Liu, Jing Jin, Yaochu Xidian Univ Sch Artificial Intelligence Xian Peoples R China Univ Surrey Dept Comp Sci Guildford Surrey England

The aim of network structural balance is to find proper partitions of nodes that guarantee equilibrium in the system, which has attracted considerable attention in recent decades. Most of existing studies focus on reducing imbalanced components in complex networks without considering the tolerance of these balanced networks against attacks and failures. However, as indicated by some recent studies, the robustness of structurally balanced networks is also important in real applications, which should be emphasized in balancing processes. Currently, it remains challenging to define suitable robustness measures for signed networks, and few performance enhancement strategies have been designed. In this paper, two measures are designed to numerically evaluate the robustness of structurally balanced networks. Furthermore, the simultaneous enhancement on these two measures is modeled as a multiobjective optimization problem, and a multiobjective evolutionary algorithm, MOEA/D-RSB, is developed to successfully solve this problem. Experiments on synthetic and real-world networks demonstrate the good performance of MOEA/D-RSB in finding robust balanced candidates. In addition, the features of partitions with different robustness performances are analyzed to show the impact of different balancing strategies on network robustness. The obtained results are valuable in dealing with some problems arising in social and natural dynamics.

关键词： Robustness Optimization partitioning algorithms Complex networks Social network services Sociology Measurement

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