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检索条件"主题词=Expectation-maximisation algorithm"

共 493 条记录，以下是311-320 订阅

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Learning Energy Consumption Profiles from Data 5

Learning Energy Consumption Profiles from Data

引用

IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Andreoli, Jean-Marc Xerox Res Ctr Europe Grenoble France

ISBN: (纸本)9781479945184

A first step in the optimisation of the power consumption of a device infrastructure is to detect the power consumption signature of the involved devices. In this paper, we are especially interested in devices which spend most of their time waiting for a job to execute, as is often the case of shared devices in a networked infrastructure, like multi-function printing devices in an office or transaction processing terminals in a public service. We formulate the problem as an instance of power disaggregation in non intrusive load monitoring (NILM), with strong prior assumptions on the sources but with specific constraints: in particular, the aggregation is occlusive rather than additive. We use a specific variant of Hidden Semi Markov Models (HSMM) to build a generative model of the data, and adapt the expectation-maximisation (EM) algorithm to that model, in order to learn, from daily operation data, the physical characteristics of the device, separated from those linked to the job load or the device configurations. Finally, we show some experimental results on a multifunction printing device.

关键词： energy conservation expectation-maximisation algorithm hidden Markov models power consumption printers EM algorithm HSMM NILM device infrastructure hidden semi Markov models learning energy consumption profiles multifunction printing device networked infrastructure nonintrusive load monitoring physical characteristics power consumption signature power disaggregation public service transaction processing terminal Data models Energy consumption Hidden Markov models Markov processes Optimization Power demand Printing expectation-maximisation algorithm Hidden Markov models Power consumption printers Power demand data models Printing energy consumption Physical characteristics Markov chain Energy conservation

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DREAMS: DFM Rule EvAluation using Manufactured Silicon

DREAMS: DFM Rule EvAluation using Manufactured Silicon

引用

32nd IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

作者： Blanton, R. D. Wang, F. Xue, C. Nag, P. K. Xue, Y. Li, X. Carnegie Mellon Univ Adv Test Chip Lab ECE Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781479910717

DREAMS (DFM Rule EvAluation using Manufactured Silicon) is a comprehensive methodology for evaluating the yield-preserving capabilities of a set of DFM (design for manufacturability) rules using the results of logic diagnosis performed on failed ICs. DREAMS is an improvement over prior art in that the distribution of rule violations over the diagnosis candidates and the entire design are taken into account along with the nature of the failure (e. g., bridge versus open) to appropriately weight the rules. Silicon and simulation results demonstrate the efficacy of the DREAMS methodology. Specifically, virtual data is used to demonstrate that the DFM rule most responsible for failure can be reliably identified even in light of the ambiguity inherent to a non-ideal diagnostic resolution, and a corresponding rule-violation distribution that is counter-intuitive. We also show that the combination of physically-aware diagnosis and the nature of the violated DFM rule can be used together to improve rule evaluation even further. Application of DREAMS to the diagnostic results from an in-production chip provides valuable insight in how specific DFM rules improve yield (or not) for a given design manufactured in particular facility. Finally, we also demonstrate that a significant artifact of DREAMS is a dramatic improvement in diagnostic resolution. This means that in addition to identifying the most ineffective DFM rule(s), validation of that outcome via physical failure analysis of failed chips can be eased due to the corresponding improvement in diagnostic resolution.

关键词： design for manufacture elemental semiconductors expectation-maximisation algorithm failure analysis integrated circuit design integrated circuit reliability integrated circuit testing integrated circuit yield silicon

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Joint estimation of glottal source and vocal tract for vocal synthesis using Kalman smoothing and EM algorithm

Joint estimation of glottal source and vocal tract for vocal...

引用

Workshop on Applications of Signal Processing to Audio and Acoustics

作者： Jinachitra, P Smith, JO Stanford Univ CCRMA Stanford CA 94305 USA

ISBN: (纸本)0780391543

In this paper, a joint parameter estimation of the derivative glottal source waveform and the vocal tract filter is presented where aspiration noise and observation noise are taken into account within a state-space model. The Rosenberg-Klatt glottal model is used in Conjunction with an all-pole filter to model voice production. The EM algorithm is employed to iteratively estimate the model parameters in a maximum-likelihood sense. utilizing a Kalman smoother in the expectation step. The model and estimator allow for improved estimates of model parameters for resynthesis, yielding an output which sounds natural and remains flexible for modification, a desirable property for expressive vocal synthesis.

关键词： Kalman filters expectation-maximisation algorithm smoothing methods speech synthesis EM algorithm Kalman smoothing Rosenberg-Klatt glottal model all-pole filter aspiration noise glottal source waveform joint parameter estimation maximum-likelihood sense

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Learning Multiple Non-Linear Sub-Spaces using K-RBMs

Learning Multiple Non-Linear Sub-Spaces using K-RBMs

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chandra, Siddhartha Kumar, Shailesh Jawahar, C. V. IIIT Hyderabad CVIT Hyderabad Andhra Pradesh India Google Hyderabad Andhra Pradesh India

ISBN: (纸本)9780769549897

Understanding the nature of data is the key to building good representations. In domains such as natural images, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, preserving most of the relevant information. Feature learning can thus be seen as a form of dimensionality reduction. In this paper, we describe a feature learning scheme for natural images. We hypothesize that image patches do not all come from the same distribution, they lie in multiple non-linear subspaces. We propose a framework that uses K Restricted Boltzmann Machines (K-RBMS) to learn multiple non-linear subspaces in the raw image space. Projections of the image patches into these subspaces gives us features, which we use to build image representations. Our algorithm solves the coupled problem of finding the right non-linear subspaces in the input space and associating image patches with those subspaces in an iterative EM like algorithm to minimize the overall reconstruction error. Extensive empirical results over several popular image classification datasets show that representations based on our framework outperform the traditional feature representations such as the SIFT based Bag-of-Words (BoW) and convolutional deep belief networks.

关键词： Boltzmann machines expectation-maximisation algorithm feature extraction image classification image representation learning (artificial intelligence) natural scenes

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Handwritten Line Detection via an EM algorithm

Handwritten Line Detection via an EM algorithm

引用

12th International Conference on Document Analysis and Recognition (ICDAR)

作者： Cruz, Francisco Ramos Terrades, Oriol Univ Autonoma Barcelona Comp Vis Ctr E-08193 Barcelona Spain

ISBN: (纸本)9780769549993

In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results.

关键词： document image processing expectation-maximisation algorithm handwriting recognition image segmentation

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algorithms for iterative phase noise estimation based on a truncated DCT expansion

Algorithms for iterative phase noise estimation based on a t...

引用

12th IEEE International Workshop on Signal Processing Advances in Wireless Communications

作者： Bhatti, Jabran Noels, Nele Moeneclaey, Marc Univ Ghent Dept Telecommun & Informat Proc Ghent Belgium

ISBN: (纸本)9781424493326

We present two phase noise estimation algorithms for single-carrier burst communications. The first and second estimation techniques (denoted as PEA1 and PEA2 respectively) are based on a truncated discrete cosine transform (DCT) basis expansion of the phase noise and of the corresponding phasor, respectively and do not require any knowledge of the phase noise statistics. An initial pilot-based estimate is iteratively improved by means of the expectation-maximization algorithm, yielding a soft-decision-directed phase noise estimate. The performances of PEA1 and PEA2 are compared in terms of the mean-square phase error and bit-error rate. Numerical results show that PEA2 out performs PEA1 for practical values of the signal-to-noise ratio.

关键词： algorithm design and analysis Bit error rate Discrete cosine transforms Estimation Phase estimation Phase noise Signal processing algorithms bit-error rate channel estimation discrete cosine transforms error statistics expectation-maximisation algorithm expectation-maximization algorithm iterative phase noise estimation algorithm phase noise signal-to-noise ratio single-carrier burst communications telecommunication networks truncated DCT expansion truncated discrete cosine transform

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Markov chain Monte Carlo data association for target tracking 25

Markov chain Monte Carlo data association for target trackin...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Bergman, N Doucet, A Linkoping Univ Dept Elect Engn Div Automat Control SE-58183 Linkoping Sweden

ISBN: (纸本)0780362934

We consider the estimation of the state of a discrete-time Markov process using observations which are sets of measurements from a finite number of known linear models. The measurement to model association is unknown and false measurements that do not yield any information about the Markov process are contained in the measurement set. The objective is to perform data association between the detected measurements and the models and determine optimal estimates of the state of the Markov process. The application of this problem is found in over the horizon target tracking. We derive iterative deterministic and stochastic algorithms based on Gibbs sampling. Rao-Blackwellisation allows us to solve the problem efficiently, yielding methods with computational complexity linear in the number of recieved data sets. Contrary to recent approaches based on the EM algorithm, the novel procedures we propose do not require an introcution of a missing data set and consequently their range of applicability is wider. A simulation study shows that the new algorithms are superior to previously proposed methods.

关键词： Markov chain Target tracking data association Markov chain monte carlo methods State estimation expectation-maximisation algorithm Markov missing data beyond visual range

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Blind Modulation Classification for MIMO systems using expectation Maximization 33

Blind Modulation Classification for MIMO systems using Expec...

引用

33rd Annual IEEE Military Communications Conference on Affordable Mission Success - Meeting the Challenge (MILCOM)

作者： Zhu, Zhechen Nandi, Asoke K. Brunel Univ Sch Engn & Design Dept Elect & Comp Engn London UB8 2PL England

ISBN: (纸本)9781479967704

In this paper, we propose a blind modulation classifier for multiple-input multiple-output (MIMO) systems. The assumption of unknown channel matrix and noise variance has not been considered prior to this work. For each modulation candidate, the channel parameters are jointly estimated via expectation maximization (EM). The resulting estimation is used for the likelihood evaluation of the corresponding modulation candidate. Classification decision is reached using the maximum likelihood (ML) criterion. Classification performance is validated in simulated fading channel with white Gaussian noise. The proposed classifiers achieves robust classification accuracy in most scenarios for BPSK, QPSK, and 16-QAM modulations.

关键词： MIMO communication expectation-maximisation algorithm fading channels quadrature amplitude modulation quadrature phase shift keying 16-QAM modulations BPSK MIMO systems QPSK blind modulation classification expectation maximization maximum likelihood criterion noise variance simulated fading channel unknown channel matrix white Gaussian noise Accuracy Binary phase shift keying Channel estimation Estimation MIMO

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Greedy Maximal Scheduling in Wireless Networks

Greedy Maximal Scheduling in Wireless Networks

引用

IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Li, Qiao Negi, Rohit Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781424456383

In this paper we consider greedy scheduling algorithms in wireless networks, i.e., the schedules are computed by adding links greedily based on some priority vector. Two special cases are considered: 1) Longest Queue First (LQF) scheduling, where the priorities are computed using queue lengths, and 2) Static Priority (SP) scheduling, where the priorities are preassigned. We first propose a closed-form lower bound stability region for LQF scheduling, and discuss the tightness result in some scenarios. We then propose an lower bound stability region for SP scheduling with multiple priority vectors, as well as a heuristic priority assignment algorithm, which is related to the well-known expectation-Maximization (EM) algorithm. The performance gain of the proposed heuristic algorithm is finally confirmed by simulations.

关键词： closed-form lower bound stability region expectation-maximisation algorithm expectation-maximization algorithm greedy algorithms greedy maximal scheduling algorithm heuristic priority assignment algorithm longest queue first scheduling priority vector rad

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Robot Motor Skill Coordination with EM-based Reinforcement Learning

Robot Motor Skill Coordination with EM-based Reinforcement L...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Kormushev, Petar Calinon, Sylvain Caldwell, Darwin G. Italian Inst Technol IIT Adv Robot Dept I-16163 Genoa Italy

ISBN: (纸本)9781424466757

We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the user's demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.

关键词： EM-based reinforcement learning WAM robotic arm collision avoidance correlation information dynamic movement primitives dynamic pancake-flipping task dynamical systems expectation-maximisation algorithm expectation-maximization based reinforcement learning learning (artificial intelligence) manipulators motor control obstacle avoidance robot motor skill coordination skill learning torque control torque control

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