Distribution system state estimation (DSSE) is an essential tool in active distribution grids in order to enhance the awareness of the power system operators regarding the state of the distribution system. One of the ...
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ISBN:
(纸本)9781479976959
Distribution system state estimation (DSSE) is an essential tool in active distribution grids in order to enhance the awareness of the power system operators regarding the state of the distribution system. One of the peculiarities of the DSSE is the lack of adequate real-time measurements. Thus, in this paper a three-phase DSSE algorithm enhanced with an advanced approach for modeling pseudomeasurements is proposed. The major innovation of the proposed methodology is the development of a DSSE algorithm based on limited real-time measurements. The proposed DSSE algorithm takes into account the unbalanced nature of the distribution systems and the presence of virtual measurements. The pseudomeasurements are obtained from historical data, through a Gaussian Mixture Model (GMM) methodology, improved by the Calinski & Harabasz (CH) criterion and the expectationmaximization (EM) algorithm. The performance of the proposed methodology is illustrated on the IEEE 13 node test feeder and compared with the case where the pseudomeasurements are modeled through a normal distribution.
This paper deals with the problem of nonlinear dual-rate system identification with random time delay. The proposed approach adopts the multiple modeling framework, and the global LPV model is represented by a combina...
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This paper deals with the problem of nonlinear dual-rate system identification with random time delay. The proposed approach adopts the multiple modeling framework, and the global LPV model is represented by a combination of various local models weighted by a probability function. The considered structure of the local model is in a state space from, and the process has fast rate inputs and slow rate outputs with random time delay. The expectation maximization algorithm is utilized to formulate and solve the problem of interest. The parameters of the local models and the weighting functions are estimated simultaneously. The particle smoothing technique is adopted to handle the computation of expectation functions. The effectiveness of the proposed approach is further illustrated through a simulation example. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle...
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ISBN:
(纸本)9781728190549
In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.
Change detection is a key topic in land use/land cover related studies and significant efforts have been made in the development of methods for change detection. In this article a multivariate analysis method based on...
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ISBN:
(纸本)081944684X
Change detection is a key topic in land use/land cover related studies and significant efforts have been made in the development of methods for change detection. In this article a multivariate analysis method based on canonical transformation is introduced into change detection using multi-temporal remote sensing imageries. Afterwards an automatic unsupervised discriminating technique based on the Bayes-Rule of Minimum Error is employed for changed areas identification in the difference image. Experimental results of a case study using Landsat TM imageries are presented to demonstrate the effectiveness of our method.
Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based te...
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ISBN:
(数字)9781510617421
ISBN:
(纸本)9781510617421
Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.
Robust estimation methods are useful in mitigating the impact of anomalies in clock data. Such anomalous clock data is assumed to be well modeled by a Student's t-distribution. This paper derives a lower bound on ...
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ISBN:
(纸本)9789464593617;9798331519773
Robust estimation methods are useful in mitigating the impact of anomalies in clock data. Such anomalous clock data is assumed to be well modeled by a Student's t-distribution. This paper derives a lower bound on the performance of the misspecified Gaussian model using the theory of the Misspecified Cramer-Rao bound (MCRB). The results of these derivations are verified by analyzing the Mean Square Error (MSE) of the misspecified Gaussian Maximum Likelihood Estimator (MLE) when using data generated by the Student's t-distribution. The derived MCRB indicates a constraint on the MSE when assuming a Gaussian distribution. The MLE for the mean of the Student's t-distribution is obtained with an expectation maximization algorithm and is shown to obtain a lower MSE than the MCRB and hence, the misspecified estimator. This indicates an improvement in performance if anomalous clock data is appropriately accounted for in the statistical model.
Each node in wireless sensor network has limited energy for communication and computation, so how to reduce the energy consumption efficiently is an important research problem. In this paper, we propose a fully distri...
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ISBN:
(纸本)9781479928255;9781479928279
Each node in wireless sensor network has limited energy for communication and computation, so how to reduce the energy consumption efficiently is an important research problem. In this paper, we propose a fully distributed expectation maximization algorithm (FDEM) for acoustic source localization to reduce communication cost significantly. It just requires local data transmission and simple computation. Each sensor node transmits energy observations to its nearest neighbors (nodes within one-hop communication range) and also receives energy observations from its nearest neighbors. Long range wireless communication is avoided and the energy is saved. Simulation results for a static source localization and a moving source tracking demonstrate that the FDEM algorithm can provide a good tradeoff between localization accuracy and communication cost.
Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the obj...
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ISBN:
(纸本)9780819472960
Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the object. Three virus objects, two spherical and one helical, are considered. Solution of the maximum likelihood problem by expectation maximization algorithms or by direct maximization of the log likelihood requires large scale computing and end-to-end codesign of biological problem formulation, statistical models, algorithms, and software design and implementation have contributed to achieving practical results.
Classical wireless communication technologies are threatened with so many challenges for meeting the desires of ubiquity and mobility for the cellular systems. Hostile wireless channels features and restricted frequen...
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ISBN:
(纸本)9781509002108
Classical wireless communication technologies are threatened with so many challenges for meeting the desires of ubiquity and mobility for the cellular systems. Hostile wireless channels features and restricted frequency bandwidths are obstacles in future generation systems. In order to deal with these limitations, different advanced signal processing approaches, such as expectation-maximization (EM) algorithm, SAGE algorithm, Baum-Welch algorithm, Kalman filters and their extensions etc. were proposed. In this paper, estimation of unknown channel parameter and detection of data at receiver end has been performed. MIMO Rayleigh and Rician channels are taken for wireless communication. To find the initial point for EM algorithm FCM clustering algorithm is used. In this work, algorithm is implemented using MATLAB R2012a. The performance matrices of the algorithm are bit error rate (BER) and mean square error (MSE) at different values of signal to noise ratio.
In order to improve the accuracy of time series anomaly detection and classification, aiming at the problem of large feature dimensions of high-dimensional data, on the basis of studying the traditional feature select...
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