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

共 493 条记录，以下是281-290 订阅

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A Smart Meter Based Approach to Power Reliability Index for Enterprise-Level Power Grid

A Smart Meter Based Approach to Power Reliability Index for ...

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IEEE 3rd International Conference on Smart Grid Communications (SmartGridComm)

作者： Gamroth, Catherine Wu, Kui Marinakis, Dimitri Univ Victoria Dept Comp Sci Victoria BC Canada

ISBN: (纸本)9781467309110;9781467309103

We aim to develop a new, practical power reliability index (PRI) for enterprise-level power grids. Largely different from traditional evaluation methods that assume the exponential distribution of time between failures and use theoretical metrics such as mean time between failures (MTBF), our approach is data-driven and takes advantage of smart meters that are capable of recording various power quality indicators at selected monitoring points. The basic idea of our approach is to estimate the instantaneous network reliability, a concept describing current network reliability status, based on measurements from a limited number of smart meters in the power grid. We develop algorithms to solve the technical challenges in the accurate estimation of instantaneous network reliability, using expectation Maximization (EM) and Monte Carlo expectation Maximization (MCEM). The effectiveness of our algorithms is validated with empirical evaluation.

关键词： Monte Carlo methods expectation-maximisation algorithm power system measurement power system reliability smart meters

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Mixture Periodic GARCH models: Applications to Exchange Rate Modeling

Mixture Periodic GARCH models: Applications to Exchange Rate...

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International Conference on Modeling, Simulation and Applied Optimization

作者： Faycal Hamdi Said Souam Faculte de Mathematiques USTHB Universite Paris Ouest Nanterre La Defense (EconomiX) and CREST

ISBN: (纸本)9781467358125

In this paper, we propose an extension of a mixture periodic ARCH model (MPARCH) to a mixture periodic GARCH model (MPGARCH), and provide some probabilistic properties of this class of models. An estimation method based on the expectation-Maximization (EM) algorithm is proposed. Finally, it is applied to model the spot rates of the Algerian Dinar against the U.S.-Dollar and Euro. The empirical analysis demonstrates that the proposed mixture model yields the best performance among the competing models.

关键词： Periodic Mixture models Statistical analysis optimum performance DOLLARS Arch expectation-maximisation algorithm Modeling

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List-mode Reconstruction for the Biograph mCT with Probabilistic Line-of-Response Positioning and Event-by-Event Motion Correction

List-mode Reconstruction for the Biograph mCT with Probabili...

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IEEE Nuclear Science Symposium / Medical Imaging Conference Record (NSS/MIC) / 19th Room-Temperature Semiconductor X-ray and Gamma-ray Detector Workshop

作者： Jin, Xiao Chan, Chung Mulnix, Tim Liu, Chi Carson, Richard E. Yale Univ PET Ctr New Haven CT 06520 USA

ISBN: (纸本)9781467320306;9781467320283

In this study, we address two issues that degrade spatial resolution in PET. First, conventionally, the Siemens Biograph mCT scanner collects data in 32-bit list mode, so each LOR is defined as a sinogram bin (instead of detector pair) with axial span and transaxial mashing of adjacent angles reducing the number of LORs, thus degrading resolution. In this work, we proposed a probabilistic assignment of LOR positions (pLOR) that addresses axial and transaxial LOR grouping in 32-bit data. Secondly, respiratory motion results in image artifacts in whole-body scans. Gated reconstruction does not correct for intra-gate motion. We have previously demonstrated for brain imaging that event-by-event correction can eliminate intra-frame motion. Therefore, we developed a list-mode reconstruction algorithm for the Siemens Biograph mCT with pLOR positioning and event-by-event motion correction, based on MOLAR (Motion-compensation OSEM List-mode algorithm for Resolution-Recovery Reconstruction). This algorihtm was compared to the manufacturer's PSF+TOF algorithm. Static phantom and animal studies demonstrated that MOLAR with pLOR gives faster contrast recovery than the PSF+TOF algorithm that uses the average LOR positioning for all sub-LORs within each sinogram bin. Moving phantom and a whole-body human study suggested that event-by-event motion correction is able to reduce image blurring introduced by the intra-gate motion. We conclude that list-mode reconstruction with pLOR positioning and event-by-event motion correction can generate high quality images for the meT, and has great potential to optimally recover fine structures in whole-body PET scans with motion.

关键词： expectation-maximisation algorithm image resolution image restoration medical image processing motion compensation phantoms positron emission tomography probability

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Development of GPU Based Image Reconstruction Method for Clinical SPECT

Development of GPU Based Image Reconstruction Method for Cli...

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IEEE Nuclear Science Symposium / Medical Imaging Conference Record (NSS/MIC) / 19th Room-Temperature Semiconductor X-ray and Gamma-ray Detector Workshop

作者： Liu, Hui Ma, Tianyu Chen, Si Liu, Yaqiang Wang, Shi Jin, Yongjie Tsinghua Univ Dept Engn Phys Minist Educ Key Lab Particle & Radiat Imaging Beijing 100084 Peoples R China

ISBN: (纸本)9781467320306;9781467320283

The aim of this work is to develop, validate and preliminarily evaluate two graphic processing units (GPU) implementations of the OS-EM iterative image reconstruction algorithms for a clinical SPECT system. The Monte Carlo software SIMSET was used to simulate the data acquisition of a typical SPECT camera with a parallel-hole collimator for the Shepp-Logan phantom. CUD A was adopted for the GPU programming of the OS-EM algorithm with high complexity. For the first implementation, the system matrix used for reconstruction was pre-computed by Monte Carlo simulation. A major task in the GPU programing in this case was to efficiently access every element of the pre-stored system matrix and compute the projection and back-projection for each image update. In the second implementation, the system matrix was calculated on-the-fly. A rotation-based projector and back-projector was employed, which reduced the complexity for the GPU programming and can achieve higher efficiency theoretically than other methods of computing projector and back-projectors. We modeled the depth-dependent collimator detector response in the matrix computation. The results showed that, for both GPU implementations, there are seldom differences of images reconstructed by the GPU and CPU implementation of the OS-EM algorithm. In the first matrix computation strategy, an acceleration ratio of 31.9 was achieved by using the GPU implementation compared with the CPU implementation of the same algorithm. In the second strategy, the acceleration ratio was 33.9. The images reconstructed with the first strategy demonstrated higher quantitative accuracy due to the more accurate modeling of the system matrix. But the corresponding reconstructed time was more than the second strategy. We also applied the second GPU implementation of the OS-EM method to clinical SPECT patient cardiac data and achieved the reconstruction of a 64x64x64 image within 8 seconds. Compared with the FBP reconstruction image, the OS-EM r

关键词： Monte Carlo methods biomedical equipment cameras cardiology collimators data acquisition expectation-maximisation algorithm graphics processing units image reconstruction medical image processing parallel architectures phantoms single photon emission computed tomography

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Accelerating expectation-Maximization algorithms with Frequent Updates

Accelerating Expectation-Maximization Algorithms with Freque...

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IEEE International Conference on Cluster Computing

作者： Yin, Jiangtao Zhang, Yanfeng Gao, Lixin Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9781467324229

expectation Maximization is a popular approach for parameter estimation in many applications such as image understanding, document classification, or genome data analysis. Despite the popularity of EM algorithms, it is challenging to efficiently implement these algorithms in a distributed environment. In particular, many EM algorithms that frequently update the parameters have been shown to be much more efficient than their concurrent counterparts. Accordingly, we propose two approaches to parallelize such EM algorithms in a distributed environment so as to scale to massive data sets. We prove that both approaches maintain the convergence properties of the EM algorithms. Based on the approaches, we design and implement a distributed framework, FreEM, to support the implementation of frequent updates for the EM algorithms. We show its efficiency through three well-known EM applications: k-means clustering, fuzzy c-means clustering and parameter estimation for the Gaussian Mixture model. We evaluate our framework on both a local cluster of machines and the Amazon EC2 cloud. Our evaluation shows that the EM algorithms with frequent updates implemented on FreEM can run much faster than those implementations with traditional concurrent updates.

关键词： expectation-maximisation algorithm Parameter estimation algorithms Image understanding acceleration Distribute

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EM Reconstruction of Dual Isotope PET with Staggered Injections and Prompt Gamma Positron Emitters

EM Reconstruction of Dual Isotope PET with Staggered Injecti...

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IEEE Nuclear Science Symposium / Medical Imaging Conference Record (NSS/MIC) / 19th Room-Temperature Semiconductor X-ray and Gamma-ray Detector Workshop

作者： Andreyev, Andriy Sitek, Arkadiusz Celler, Anna Univ British Columbia Med Imaging Res Grp Vancouver BC V5Z 1M9 Canada Brigham & Womens Hosp Harvard Med Sch Dept Radiol Boston MA 02115 USA

ISBN: (纸本)9781467320306;9781467320283

The aim of Dual Isotope Positron Emission Tomography (DIPET) is to create two independent images of two co-injected radiotracers. DIPET shortens the duration of the study, reduces patient discomfort and produces perfectly co-registered images. Its implementation is difficult because positron decay of any isotope creates only 511 keV photons. Recently, we have proposed a DIPET technique that uses a combination of radiotracer A which is a pure positron emitter (such as F-18 or C-11) and radiotracer B in which positron decay is followed by the emission of a high-energy (HE) prompt gamma (such as K-38 or Cu-60). Events that are detected as triple coincidences of HE gammas with the corresponding two 511 keV photons identify the line-of-responses (LORs) that originate from isotope B (similar to 10% of all events). These LORs are used to separate the two intertwined distributions, using a dedicated image reconstruction algorithm based on expectation Maximization (EM). However, this approach is suboptimal as it combines high-statistics data (radiotracers A and B), with low-statistics (radiotracer B) to create separate images of radiotracers A and B. We hypothesize that from the performance of DIPET could be improved if an independent estimate of radiotracer A distribution is available by using staggered injections method. Here we investigate this hypothesis by developing and testing an EM-based algorithm for image reconstruction of such data. Our algorithm is general, i.e. can reconstruct staggered data, positron and HE gamma data or a combination of both. Reconstruction of DIPET data obtained from Monte-Carlo simulations shows that the combined method provides the best results in terms of separation of images corresponding to radiotracers A and B. Our EM algorithm provides not only good image separation for the combined method but also when only staggered injections or only prompt gamma data are used.

关键词： Monte Carlo methods expectation-maximisation algorithm image reconstruction medical image processing positron emission tomography

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Estimating Multiple Concurrent Processes

Estimating Multiple Concurrent Processes

引用

IEEE International Symposium on Information Theory

作者： Acharya, Jayadev Das, Hirakendu Jafarpour, Ashkan Orlitsky, Alon Pan, Shengjun Univ Calif San Diego ECE San Diego CA USA

ISBN: (纸本)9781467325790

We consider two related problems of estimating properties of a collection of point processes: estimating the multiset of parameters of continuous-time Poisson processes based on their activities over a period of time t, and estimating the multiset of activity probabilities of discrete-time Bernoulli processes based on their activities over n time instants. For both problems, it is sufficient to consider the observations' profile-the multiset of activity counts, regardless of their process identities. We consider the profile maximum likelihood (PML) estimator that finds the parameter multiset maximizing the profile's likelihood, and establish some of its competitive performance guarantees. For Poisson processes, if any estimator approximates the parameter multiset to within distance epsilon with error probability delta, then PML approximates the multiset to within distance 2 delta with error probability at most delta . e(4)root(t.S), where S is the sum of the Poisson parameters, and the same result holds for Bernoulli processes. In particular, for the L-1 distance metric, we relate the problems to the long- studied distribution-estimation problem and apply recent results to show that the PML estimator has error probability e(-(t.S)0.9) for Poisson processes whenever the number of processes is k = O(tS log(tS)), and show a similar result for Bernoulli processes. We also show experimental results where the EM algorithm is used to compute the PML.

关键词： Poisson process Error probability Bernoulli expectation-maximisation algorithm concurrent process

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Learning Basis Skills by Autonomous Segmentation of Humanoid Motion Trajectories

Learning Basis Skills by Autonomous Segmentation of Humanoid...

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12th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Lee, Sang Hyoung Suh, Il Hong Calinon, Sylvain Johansson, Rolf Hanyang Univ Dept Comp Sci & Engn Seoul South Korea Hanyang Univ Dept Elect & Comp Engn Seoul South Korea IIT Dept Adv Robot Genoa Italy Lund Univ LTH Dept Automat Lund Sweden

ISBN: (纸本)9781467313698

Manipulation tasks are characterized by continuous motion trajectories containing a set of key phases. In this paper, we propose a probabilistic method to autonomously segment the motion trajectories for estimating the key phases embedded in such a task. The autonomous segmentation process relies on principal component analysis to adaptively project into one of the low-dimensional subspaces, in which a Gaussian mixture model is learned based on Bayesian information criterion and expectation-maximization algorithms. The basis skills are estimated by a set of Gaussians approximating quasi-linear key phases, and those times spent calculated from the segmentation points between two consecutive Gaussians representing the local changes of dynamics and directions of the trajectories. The basis skills are then used to build novel motion trajectories with possible motion alternatives and optional parts. After sequentially reorganizing the basis skills, a Gaussian mixture regression process is used to retrieve smooth motion trajectories. Two experiments are presented to demonstrate the capability of the autonomous segmentation approach.

关键词： expectation-maximisation algorithm humanoid robots learning (artificial intelligence) manipulators

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A MAP-Based Channel Estimation algorithm for SIMO Systems over Extended SV Channel Model

A MAP-Based Channel Estimation Algorithm for SIMO Systems ov...

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IEEE International Conference on Communication Systems (ICCS)

作者： Zhang, Li Chew, Yong Huat Wong, Wai-Choong Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Agcy Sci Res & Technol Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781467320542;9781467320528

In this paper, a maximum a posterior (MAP) based channel estimation algorithm is proposed to estimate both the temporal and spatial domain channel parameters for single-input multiple-output (SIMO) systems transmitting over an extended Saleh-Valenzuela (SV) channel. The proposed algorithm leverages on prior knowledge of the statistical distributions of signal clusters and rays within a cluster, and uses the expectation-maximization (EM) algorithm to resolve the high dimensional optimization problem into iteratively solving the multiple 3-dimensional optimizations. The successive interference cancellation (SIC) method is applied in the initialization stage to obtain the initial guess to the EM algorithm. Simulations are carried out in two typical indoor scenarios following the extended SV model. The proposed algorithm is shown to outperform the maximum likelihood (ML) based algorithm for SIMO systems.

关键词： channel estimation channel model expectation-maximisation algorithm statistical distribution algorithms initialization Prior Learning rays rays single input-multiple output

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Channel Estimation in Underwater Cooperative OFDM System with Amplify-and-Forward Relaying

Channel Estimation in Underwater Cooperative OFDM System wit...

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IEEE Global Communications Conference (GLOBECOM)

作者： Senol, Habib Panayirci, Erdal Erdogan, Mustafa Uysal, Murat Kadir Has Univ Dept Elect Elect Engn TR-34083 Istanbul Turkey Ozyegin Univ Dept Elect Elect Engn TR-34794 Istanbul Turkey

ISBN: (纸本)9781467309219;9781467309202

This paper is concerned with a challenging problem of channel estimation for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. We exploit the sparse structure of the channel impulse response to improve the performance of the channel estimation algorithm, due to the reduced number of taps to be estimated. The resulting novel algorithm initially estimates the overall sparse channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The correlated non-Gaussian effective noise is modeled as a Gaussian mixture. Based on the Gaussian mixture model, an efficient and low complexity algorithm is developed based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the noise distribution parameters in an iterative way. The proposed SAGE algorithm is designed in such a way that, by choosing the admissible hidden data properly on which the SAGE algorithm relies, a subset of parameters is updated for analytical tractability and the remaining parameters for faster convergence Computer simulations show that underwater acoustic (UWA) channel is estimated very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance.

关键词： Gaussian processes OFDM modulation amplify and forward communication channel estimation cooperative communication error statistics expectation-maximisation algorithm transient response underwater acoustic communication

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