scholarly journals A Harmonic Impedance Estimation Method Based on the Cauchy Mixed Model

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Zhirong Tang ◽  
Huaqiang Li ◽  
Fangwei Xu ◽  
Qin Shu ◽  
Yue Jiang
Keyword(s):  
Expectation Maximization ◽  
Mixed Model ◽  
Expectation Maximization Algorithm ◽  
Data Distribution ◽  
Estimation Method ◽  
Measured Data ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Point Of Common Coupling ◽  
Common Coupling

In this paper, a new method without any tradition assumption to estimate the utility harmonic impedance of a point of common coupling (PCC) is proposed. But, the existing estimation methods usually are built on some assumptions, such as, the background harmonic is stable and small, the harmonic impedance of the customer side is much larger than that of utility side, and the harmonic sources of both sides are independent. However these assumptions are unpractical to modern power grid, which causes very wrong estimation. The proposed method first uses a Cauchy Mixed Model (CMM) to express the Norton equivalent circuit of the PCC because we find that the CMM can right fit the statistical distribution of the measured harmonic data for any PCC, by testing and verifying massive measured harmonic data. Also, the parameters of the CMM are determined by the expectation maximization algorithm (EM), and then the utility harmonic impedance is estimated by means of the CMM’s parameters. Compared to the existing methods, the main advantages of our method are as follows: it can obtain the accurate estimation results, but it is no longer dependent of any assumption and is only related to the measured data distribution. Finally, the effectiveness of the proposed method is verified by simulation and field cases.

scholarly journals Two-hop distance estimation in wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Estimation Method ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Distance Information ◽  
Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

Mixed model for interoccurrence times of earthquakes based on the expectation-maximization algorithm

2011 ◽  
Vol 59 (5) ◽  
pp. 872-890 ◽  
Author(s):  
Nadia Tahernia ◽  
Morteza Khodabin ◽  
Noorbakhsh Mirzaei
Keyword(s):  
Expectation Maximization ◽  
Mixed Model ◽  
Expectation Maximization Algorithm

scholarly journals Estimating properties of the fast and slow adaptive processes during sensorimotor adaptation

2018 ◽  
Vol 119 (4) ◽  
pp. 1367-1393 ◽  
Author(s):  
Scott T. Albert ◽  
Reza Shadmehr
Keyword(s):  
Motor Learning ◽  
Expectation Maximization ◽  
State Space Model ◽  
Expectation Maximization Algorithm ◽  
Measured Data ◽  
Sensorimotor Learning ◽  
Adaptive Processes ◽  
Time Courses ◽  
Hidden States ◽  
Generalized Expectation

Experience of a prediction error recruits multiple motor learning processes, some that learn strongly from error but have weak retention and some that learn weakly from error but exhibit strong retention. These processes are not generally observable but are inferred from their collective influence on behavior. Is there a robust way to uncover the hidden processes? A standard approach is to consider a state space model where the hidden states change following experience of error and then fit the model to the measured data by minimizing the squared error between measurement and model prediction. We found that this least-squares algorithm (LMSE) often yielded unrealistic predictions about the hidden states, possibly because of its neglect of the stochastic nature of error-based learning. We found that behavioral data during adaptation was better explained by a system in which both error-based learning and movement production were stochastic processes. To uncover the hidden states of learning, we developed a generalized expectation maximization (EM) algorithm. In simulation, we found that although LMSE tracked the measured data marginally better than EM, EM was far more accurate in unmasking the time courses and properties of the hidden states of learning. In a power analysis designed to measure the effect of an intervention on sensorimotor learning, EM significantly reduced the number of subjects that were required for effective hypothesis testing. In summary, we developed a new approach for analysis of data in sensorimotor experiments. The new algorithm improved the ability to uncover the multiple processes that contribute to learning from error. NEW & NOTEWORTHY Motor learning is supported by multiple adaptive processes, each with distinct error sensitivity and forgetting rates. We developed a generalized expectation maximization algorithm that uncovers these hidden processes in the context of modern sensorimotor learning experiments that include error-clamp trials and set breaks. The resulting toolbox may improve the ability to identify the properties of these hidden processes and reduce the number of subjects needed to test the effectiveness of interventions on sensorimotor learning.

A stochastic estimation procedure for intermittently-observed semi-Markov multistate models with back transitions

2017 ◽  
Vol 28 (3) ◽  
pp. 770-787
Author(s):  
Hilary Aralis ◽  
Ron Brookmeyer
Keyword(s):  
Expectation Maximization ◽  
Likelihood Function ◽  
Expectation Maximization Algorithm ◽  
Estimation Procedure ◽  
Elderly Subjects ◽  
Accurate Estimation ◽  
Parameter Estimates ◽  
Multistate Models ◽  
Observation Process ◽  
Wide Range

Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition intensities of the underlying continuous-time process. When transition intensities depend on the time elapsed in the current state and back transitions between states are possible, this intermittent observation process presents difficulties in estimation due to intractability of the likelihood function. In this manuscript, we present an iterative stochastic expectation-maximization algorithm that relies on a simulation-based approximation to the likelihood function and implement this algorithm using rejection sampling. In a simulation study, we demonstrate the feasibility and performance of the proposed procedure. We then demonstrate application of the algorithm to a study of dementia, the Nun Study, consisting of intermittently-observed elderly subjects in one of four possible states corresponding to intact cognition, impaired cognition, dementia, and death. We show that the proposed stochastic expectation-maximization algorithm substantially reduces bias in model parameter estimates compared to an alternative approach used in the literature, minimal path estimation. We conclude that in estimating intermittently observed semi-Markov models, the proposed approach is a computationally feasible and accurate estimation procedure that leads to substantial improvements in back transition estimates.

scholarly journals Fitting Precipitation Particle Size–Velocity Data to Mixed Joint Probability Density Function with an Expectation Maximization Algorithm

2020 ◽  
Vol 37 (5) ◽  
pp. 911-925
Author(s):  
Yuta Katsuyama ◽  
Masaru Inatsu
Keyword(s):  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Estimation Method ◽  
Joint Probability ◽  
Terminal Velocity ◽  
Observation Data ◽  
Fall Velocity ◽  
Test Population ◽  
Original Number ◽  
Joint Probability Density

AbstractThis paper proposes an estimation method of joint size and terminal velocity distribution on the basis of sampling data of precipitation particles containing multiple types. Assuming that the velocity follows the normal distribution and the size follows the gamma distribution, the method searches a locally maximum logarithmic likelihood within a realistic parameter range using the expectation–maximization algorithm. Several test populations were prepared with a realistic number of elements, and then the method was evaluated by retrieving the populations from their sample. The results showed that the original parameters were successfully estimated in most cases of the test population containing some of liquids, graupels, and rimed and unrimed aggregates. The original number of elements was also estimated with an adjustment of the number of elements in a manner such that each of their minority fractions exceeded a threshold. Applied to the two-dimensional disdrometer observation data, the method was helpful to discard frequently observed erroneous data with unrealistically large fall velocity.

scholarly journals Improved Expectation Maximization Algorithm for Gaussian Mixed Model Using the Kernel Method

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Mohd Izhan Mohd Yusoff ◽  
Ibrahim Mohamed ◽  
Mohd Rizam Abu Bakar
Keyword(s):  
Expectation Maximization ◽  
Mixed Model ◽  
Likelihood Function ◽  
Kernel Method ◽  
Expectation Maximization Algorithm ◽  
Information Criteria ◽  
Maximum Likelihood Estimates ◽  
Modified Method ◽  
Telecommunication Companies ◽  
Future Work

Fraud activities have contributed to heavy losses suffered by telecommunication companies. In this paper, we attempt to use Gaussian mixed model, which is a probabilistic model normally used in speech recognition to identify fraud calls in the telecommunication industry. We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. Firstly, we look at a mechanism for the determination of the initial number of Gaussian components and the choice of the initial values of the algorithm using the kernel method. We show via simulation that the technique improves the performance of the algorithm. Secondly, we developed a procedure for determining the order of the Gaussian mixed model using the log-likelihood function and the Akaike information criteria. Finally, for illustration, we apply the improved algorithm to real telecommunication data. The modified method will pave the way to introduce a comprehensive method for detecting fraud calls in future work.

scholarly journals Simulation and comparison of two fusion methods for macroscopic fundamental diagram estimation

2019 ◽  
Vol 51 (3) ◽  
pp. 35-48 ◽  
Author(s):  
Xiaohui Lin ◽  
Jianmin Xu ◽  
Chengtao Cao
Keyword(s):  
Road Network ◽  
Estimation Method ◽  
Weighted Averaging ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Fusion Method ◽  
Fundamental Diagram ◽  
Macroscopic Fundamental Diagram ◽  
Fusion Methods ◽  
Estimation Fusion

Accurate estimation of macroscopic fundamental diagram (MFD) is the precondition of MFD’s application. At present, there are two traditional estimation methods of road network’s MFD, such as the loop detector data (LDD) estimation method and the floating car data (FCD) estimation method, but there are limitations in both traditional estimation methods. In order to improve the accuracy of road network MFD estimation, a few scholars have studied the fusion method of road network MFD estimation, but there are still some shortcomings on the whole. However, based on the research of adaptive weighted averaging (AWA) fusion method for MFD estimation of road network, I propose to use the MFD’s two parameters of road network obtained by LDD estimation method and FCD estimation method, and establish a back-propagation neural network data fusion model for MFD parameters of road network (BPNN estimation fusion method), and then the micro-traffic simulation model of connected-vehicle network based on Vissim software is established by taking the intersection group of the core road network in Tianhe District of Guangzhou as the simulation experimental area, finally, compared and analyzed two MFD estimation fusion methods of road network, in order to determine the best MFD estimation fusion method of road network. The results show that the mean absolute percent error (MAPE) of the parameters of road network’s MFD and the average absolute values of difference values of the state ratio of road network’s MFD are both the smallest after BPNN estimation fusion, which is the closest to the standard MFD of road network. It can be seen that the result of BPNN estimation fusion method is better than that of AWA estimation fusion method, which can improve the accuracy of road network MFD estimation effectively.

scholarly journals Measurement-Based Distribution Grid Harmonic Impedance Models and Their Uncertainties

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4259
Author(s):  
Ravi Shankar Singh ◽  
Vladimir Ćuk ◽  
Sjef Cobben
Keyword(s):  
Estimation Method ◽  
Distribution Grid ◽  
Harmonic Model ◽  
Equivalent Models ◽  
Grid Impedance ◽  
Before And After ◽  
Point Of Common Coupling ◽  
Simulated Network ◽  
Common Coupling ◽  
Impedance Models

Aggregated Norton’s equivalent models, with parallel impedance and current injection at different harmonic frequencies are used to model the distribution grid in harmonic studies. These models are derived based on measurements and/or prior knowledge about the grid. The measurement-based distribution (sub-)grid impedance estimation method uses harmonic phasors of 3-phase current and voltage measurements to capture the response of the distribution (sub-)grid before and after an event in the utility side of the grid. However, due to increasing non-linear components in the grid, knowledge about uncertainty in parameters of such equivalent models which intrinsically describe a linear grid becomes important. The aim of this paper is to present two novel methods to calculate the uncertainty of the measurement-based Norton’s equivalent harmonic model of the distribution (sub-)grids as seen from the utility side at the Point of Common Coupling (PCC). The impedance and the uncertainty calculations are demonstrated on a simulated network.

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