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Robust computation of aggregates in wireless sensor networks: Distributed randomized algorithms and analysis

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2006年第9期17卷 987-1000页

作者： Chen, Jen-Yeu Pandurangan, Gopal Xu, Dongyan Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in hostile environments that are prone to link and node failures. Computing aggregates such as average, minimum, maximum and sum is fundamental to various primitive functions of a sensor network, such as system monitoring, data querying, and collaborative information processing. In this paper, we present and analyze a suite of randomized distributed algorithms to efficiently and robustly compute aggregates. Our Distributed Random Grouping (DRG) algorithm is simple and natural and uses probabilistic grouping to progressively converge to the aggregate value. DRG is local and randomized and is naturally robust against dynamic topology changes from link/node failures. Although our algorithm is natural and simple, it is nontrivial to show that it converges to the correct aggregate value and to bound the time needed for convergence. Our analysis uses the eigenstructure of the underlying graph in a novel way to show convergence and to bound the running time of our algorithms. We also present simulation results of our algorithm and compare its performance to various other known distributed algorithms. Simulations show that DRG needs far fewer transmissions than other distributed localized schemes.

关键词： probabilistic algorithms randomized algorithms distributed algorithms sensor networks fault tolerance graph theory aggregate data query stochastic processes

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Natural Character Posing from a Large Motion Database

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2011年第3期31卷 69-77页

作者： Wu, Xiaomao Tournier, Maxime Reveret, Lionel Crytek R&D Dept Frankfurt Studio Frankfurt Germany

An interactive inverse kinematics approach robustly generates natural poses in a large human-reachable space. It employs adaptive kd clustering to select a representative frame set from a large motion database and emp... 详细信息

关键词： 3D graphics and realism Acceleration Animation Approximation algorithms artificial intelligence Carbon capture and storage computer graphics Databases Educational institutions graphics and multimedia Kinematics kinematics and dynamics Packaging probabilistic algorithms robotics Robustness

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Modeling partially reliable information sources: A general approach based on Dempster-Shafer theory

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INFORMATION FUSION 2006年第4期7卷 361-379页

作者： Haenni, Rolf Hartmann, Stephan London Sch Econ Dept Philosophy London WC2A 2AE England Univ Bern Inst Comp Sci & Appl Math CH-3012 Bern Switzerland Univ Konstanz Ctr Jr Res Fellows D-78457 Constance Germany

Combining testimonial reports from independent and partially reliable information sources is an important epistemological problem of uncertain reasoning. Within the framework of Dempster-Shafer theory, we propose a general model of partially reliable sources, which includes several previously known results as special cases. The paper reproduces these results on the basis of a comprehensive model taxonomy. This gives a number of new insights and thereby contributes to a better understanding of this important application of reasoning with uncertain and incomplete information. (C) 2005 Elsevier B.V. All rights reserved.

关键词： Dempster-Shafer theory probabilistic argumentation combining testimonial reports unreliable sources sensor fusion judgment aggregation probabilistic algorithms

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THE DESIGN AND ANALYSIS OF BUCKETSORT FOR BUBBLE MEMORY SECONDARY STORAGE

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IEEE TRANSACTIONS ON COMPUTERS 1985年第3期34卷 218-234页

作者： LINDSTROM, EE VITTER, JS BROWN UNIV DEPT COMP SCIPROVIDENCERI 02912

BucketSort is a new external sorting algorithm for very large files that is a substantial improvement over merge sorting with disks. BucketSort requires an associative secondary storage device, which can be realized by large disk drives with logic-per-track capabilities or by magnetic bubble memory (MBM). This paper describes and analyzes a hypothetical Bucket-Sort implementation that uses bubble memory. A new software marking technique is introduced that reduces the effective time for an associative search.

关键词： Analysis of algorithms associative memory database systems external sorting magnetic bubble memory probabilistic algorithms secondary storage vector processing

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Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2013年第1期24卷 8-21页

作者： Gkalelis, Nikolaos Mezaris, Vasileios Kompatsiaris, Ioannis Stathaki, Tania Ctr Res & Technol Hellas CERTH Inst Informat Technol Thermi 57001 Greece Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England CERTH Inst Informat Technol Thermi 57001 Greece

In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and a restricted Gaussian model is first presented. Then, two further discriminant analysis (DA) methods, i.e., fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the expectation maximization (EM) framework (EM-MSDA), which simultaneously derives the discriminant subspace and the maximum likelihood estimates. The two other proposed methods generalize MSDA in order to solve problems inherited from conventional DA. FSMSDA solves the subclass separation problem, that is, the situation in which the dimensionality of the discriminant subspace is strictly smaller than the rank of the inter-between-subclass scatter matrix. This is done by an appropriate weighting scheme and the utilization of an iterative algorithm for preserving useful discriminant directions. On the other hand, KMSDA uses the kernel trick to separate data with nonlinearly separable subclass structure. Extensive experimentation shows that the proposed methods outperform conventional MSDA and other linear discriminant analysis variants.

关键词： Classification clustering discriminant analysis feature extraction machine learning mixture of gaussians pattern recognition probabilistic algorithms

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Statistical Alignment with a Sequence Evolution Model Allowing Rate Heterogeneity along the Sequence

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2009年第2期6卷 281-295页

作者： Arribas-Gil, Ana Metzler, Dirk Plouhinec, Jean-Louis Univ Carlos III Madrid Dept Estadist E-28903 Getafe Spain Univ Frankfurt Fachbereich Informat & Math D-60054 Frankfurt Germany CNRS IEM UMR6218 Inst Transgenose F-45071 Orleans 2 France

We present a stochastic sequence evolution model to obtain alignments and estimate mutation rates between two homologous sequences. The model allows two possible evolutionary behaviors along a DNA sequence in order to determine conserved regions and take its heterogeneity into account. In our model, the sequence is divided into slow and fast evolution regions. The boundaries between these sections are not known. It is our aim to detect them. The evolution model is based on a fragment insertion and deletion process working on fast regions only and on a substitution process working on fast and slow regions with different rates. This model induces a pair hidden Markov structure at the level of alignments, thus making efficient statistical alignment algorithms possible. We propose two complementary estimation methods, namely, a Gibbs sampler for Bayesian estimation and a stochastic version of the EM algorithm for maximum likelihood estimation. Both algorithms involve the sampling of alignments. We propose a partial alignment sampler, which is computationally less expensive than the typical whole alignment sampler. We show the convergence of the two estimation algorithms when used with this partial sampler. Our algorithms provide consistent estimates for the mutation rates and plausible alignments and sequence segmentations on both simulated and real data.

关键词： Markov processes probabilistic algorithms mathematics and statistics sequence evolution biology and genetics

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Sparse Bayesian learning for efficient visual tracking

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2005年第8期27卷 1292-1304页

作者： Williams, O Blake, A Cipolla, R Univ Cambridge Dept Engn Machine Intelligence Lab Cambridge CB2 1PZ England Microsoft Res Cambridge CB3 0FB England Univ Cambridge Dept Engn Cambridge CB2 1PZ England

This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using a fully probabilistic Relevance Vector Machine (RVM) to generate observations with Gaussian distributions that can be fused over time. Rather than adapting a recognizer, we build a displacement expert which directly estimates displacement from the target region. An object detector is used in tandem, for object verification, providing the capability for automatic initialization and recovery. This approach is demonstrated in real-time tracking systems where the sparsity of the RVM means that only a fraction of CPU time is required to track at frame rate. An experimental evaluation compares this approach to the state of the art showing it to be a viable method for long-term region tracking.

关键词： probabilistic algorithms robust regression tracking object recognition

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On the Distribution of the Number of Cycles in the Breakpoint Graph of a Random Signed Permutation

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第5期8卷 1411-1416页

作者： Grusea, Simona Univ Aix Marseille 1 LATP UMR CNRS 6632 Equipe EBM F-31077 Toulouse 4 France Univ Toulouse Dept GMM Inst Math Toulouse INSA Toulouse F-31077 Toulouse 4 France

We use the finite Markov chain embedding technique to obtain the distribution of the number of cycles in the breakpoint graph of a random uniform signed permutation. This further gives a very good approximation of the... 详细信息

关键词： Markov processes probabilistic algorithms distribution functions biology and genetics

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Variance Analysis of Multi-sample and One-sample Multiple Importance Sampling

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COMPUTER GRAPHICS FORUM 2016年第7期35卷 451-460页

作者： Sbert, M. Havran, V. Szirmay-Kalos, L. Tianjin Univ Tianjin Peoples R China Univ Girona Girona Spain Czech Tech Univ Fac Elect Engn Prague Czech Republic Budapest Univ Technol & Econ Budapest Hungary

We reexamine in this paper the variance for the Multiple Importance Sampling (MIS) estimator for multi-sample and one-sample model. As a result of our analysis we can obtain the optimal estimator for the multi-sample model for the case where the weights do not depend on the count of samples. We extend the analysis to include the cost of sampling. With these results in hand we find a better estimator than balance heuristic with equal count of samples. Further, we show that the variance for the one-sample model is larger or equal than for the multi-sample model, and that there are only two cases where the variance is the same. Finally, we study on four examples the difference of variances for equal count as used by Veach, our new estimator, and a recently introduced heuristic.

关键词： Categories and Subject Descriptors (according to ACM CCS) G.3 [Computer Graphics]: Mathematics of Computing / PROBABILITY AND STATISTICS&mdash probabilistic algorithms

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Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

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ENERGIES 2024年第3期17卷 550页

作者： Marzbani, Fatemeh Abdelfatah, Akmal Amer Univ Sharjah Dept Ind Engn POB 26666 Sharjah U Arab Emirates Amer Univ Sharjah Dept Civil Engn POB 26666 Sharjah U Arab Emirates

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field.

关键词： economic dispatch conventional optimization probabilistic algorithms artificial intelligence metaplastic algorithms hybrid approaches

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