Order Estimation Method Research Articles

State-of-art survey of fractional order modeling and estimation methods for lithium-ion batteries

Abstract This paper presents a state-of-art survey of the research on fractional-order (FO) modeling with parameter identification, and FO estimation methods for state of charge (SOC), state of health (SOH), and remaining usage life (RUL) of lithium-ion batteries (LIBs) mainly in recent five years. FO electrochemical models and six different types of FO equivalent circuit models (ECMs) are introduced in detail. Then, the corresponding tuning algorithm for parameters of these FO models are also provided in brief. Moreover, FO estimation methods for SOC are listed and analyzed, mainly including FO observers, and FO Kalman filters (FO-KFs). SOH and RUL estimation is another vital aspect for LIBs ageing and degradation monitoring, thus FO estimation methods proposed in recent research within five years are all listed. Finally, some suggestions that may be helpful for further research are proposed in conclusion.

Model order estimation methods for low frequency oscillations in power systems

Estimation of low frequency oscillation modes is important for taking remedial actions to ensure the small signal stability of the power system. This paper proposes three approaches to estimate the model order of low frequency oscillations using ringdown data from Phasor Measurement Units. The first approach uses a sequential cumulative sum test on the eigenvalues of the autocorrelation matrix of the signal data to estimate the model order. The second approach estimates the model order based on the eigenvalue contribution in the trace of the autocorrelation matrix. The third algorithm uses sum of squares of the eigenvalues to estimate the model order. All the methods are compared to ascertain their efficacy in estimating the model. The algorithms are tested on oscillation data obtained for WSCC 9 bus system simulated on RTDS and the field data from a generating station in India.

An application of a semi-hidden Markov model in wireless communication systems

Stochastic processes are approved presentation of real systems which its development in space or time can be supposed as random. A semi-hidden Markov model as a type of stochastic processes is a modification of hidden Markov models with states that are no longer totally unobservable and are less hidden. This mathematical model is employed for modeling data sequences with long runs, memory and statistical inertia. In this article, we investigate the theory of the semi-hidden Markov model along with its parameter estimation and order estimation methods. Moreover, the proposed model is applied to model the error traces generated by the wireless channels. A new Markov-based trace analysis algorithm is suggested to divide a non-stationary network error trace into stationary parts. By means of the best semi-hidden Markov model and fitting probability distribution, we would be able to model these parts accurately. Calculating the information measure criteria and the autocorrelation function by running the modified Baum–Welch algorithm several times help us to find the optimal order of the semi-hidden Markov model.

MapReduce-based fuzzy very fast decision tree for constructing prediction intervals

We propose the fuzzy version of very fast decision tree (VFDT) to predict prediction intervals and compared them with those generated by traditional VFDT. The proposed fuzzy VFDT is able to capture intrinsic features of VFDT as well as uncertainties available in data. The VFDT and fuzzy VFDT were trained using the lower upper bound estimation (LUBE) method in order to generate prediction intervals. We also implemented VFDT; developed and implemented fuzzy VFDT using Apache Hadoop MapReduce framework, where multiple slave nodes build a VFDT and fuzzy VFDT model. The developed models were tested on six datasets taken from the web. We conducted sensitivity analysis by studying the influence of the window size of the data stream, number of bins in discretisation on the final results. Results demonstrated that the proposed MapReduce-based fuzzy VFDT and VFDT can construct high-quality prediction intervals precisely and quickly.

Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

Intensity fluctuation-invariant maximum likelihood estimation of speckle order.

We present a speckle order estimation method invariant to mean intensity variations inside the sample. It is based on the acquisition of two statistically independent speckle images. The Cramer-Rao lower bound and the maximum likelihood estimator in the presence of pure speckle noise are derived and shown to have performance very close to classical estimation methods that are not invariant to mean intensity fluctuations. This method is also shown to be robust to Poisson shot noise and additive noise, and is validated on an optical experiment with a standard camera. Thus, it is useful for estimating speckle order in real-world coherent images.

Comparison of Highway Capacity Estimation Methods

The capacity of a freeway segment is a critical factor for planning, design, and operational analysis of freeway facilities. This research aimed to perform a comparison among well-known freeway capacity estimation methods in order to investigate their application, as well as their advantages and disadvantages. Single estimate capacity methods such as the Van Aerde method, and breakdown probability methods, such as the product limit method (PLM), the Highway Capacity Manual (HCM) method, and the sustainable flow index (SFI) method, were applied at six merge bottleneck locations in the Kansas City area. The results from all methods were compared and the advantages and the disadvantages of each method were discussed. The HCM results showed a significant variability in the estimated breakdown probability function and the resulting capacities. The HCM method was also found to be sensitive to the breakdown probability ratios as a single breakdown observation can significantly shift the fitted distribution and the corresponding capacity estimate. The PLM model provided the highest capacity estimates, followed by the Van Aerde model capacities. The Van Aerde capacities were also found to be closer to the average pre-breakdown flow rates. Finally, the PLM and the SFI method showed consistent performance in comparison to the remaining methods, and flexibility in being applied on different sites with various characteristics.

An improved signal envelope estimation method for analysis of acoustic signals emitted by remotely piloted helicopters

This study presents a new signal envelope estimation method in order to improve the performance of the Empirical Mode Decomposition (EMD), especially in dealing with transient signals. An important application of this method is the analysis of signals containing spikes such as acoustic signals. For this purpose, both the spline interpolation and control point selection algorithm are adapted. To construct signal envelopes, the Catmull-Rom spline interpolation is employed due to its desired features. Also, a new algorithm is utilized in order to select control points prior to the iterative sifting process. Afterward, the proposed method is verified by some benchmark signals. Performance indices indicate that the proposed EMD outperforms the traditional one in handling transient signals. Finally, the proposed method is applied to acoustic signals gathered during some flight tests of a remotely piloted helicopter. The results demonstrate that the proposed method is able to isolate acoustic signals emitted by dissimilar sources.

Suppression of performance degradation in traveling direction estimation by using IMU and PCA for PDR

In this paper, we propose a new method to estimate a traveling direction for a pedestrian with a smart phone by using the Inertial Measurement Unit (IMU) and principal component analysis (PCA). Our estimator does not restrict a holding status of the smart phone. It is possible to estimate an attitude of the smart phone based on the IMU filter which makes use of values from sensors embedded in itself. We estimate a traveling direction of the pedestrian based on the PCA by using the output of the filter. However, this method essentially includes an indeterminacy problem which allows a direction reversal because the component of the PCA is non-directed graph. In this paper, we investigate a modified traveling direction estimation method in order to suppress a performance degradation caused by this problem. From experimental results, we show that the proposed estimator outperforms the conventional one.

Spatial modelling of zonality elements based on compositional nature of geochemical data using geostatistical approach: a case study of Baghqloom area, Iran

Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the correlation coefficient of a closed number system and comparing it with an open number system, one can see an increase in the values of the closed number system, which is false. Such features of this data prevent the application of standard statistical techniques to process the data. Therefore, several methods have been proposed for transforming the data from closed to open number systems. There are various geostatistical methods consisting of estimation and simulation methods in order to model a deposit. Geostatistical simulations can produce various models for a deposit with different probability percentages. The most applicable geostatistical simulation method is the sequential Gaussian simulation technique, which is highly flexible. In this work, 392 Litho-geochemical data of the Baghqloom region of Kerman in Iran consisting of 20 elements were at first converted using an open number system. Afterwards, the elements that were helpful for exploring the area and were normally standard were simulated for 100 times. After the simulations, the valid output was chosen using geostatistical validation. The maps derived from the simulations revealed the enriched concentrations of mineralization elements in the central regions.

Determination of the health state of fuel cell vehicle for a clean transportation

The penetration of Fuel Cell vehicle in a large scale market requires efficiency, long life cycle use and health state monitoring of the key powertrain element: the fuel cell stack. This paper presents three novel methods for an efficient and rapid estimation of the Electrochemical Impedance Spectrum (EIS) of the Fuel Cell. The EIS is a very useful technique for fault detection of electrochemical systems as FC. To perform EIS, measurements at high frequencies are quite easy and rapid to perform but the measurements at low (and very low) frequencies can take a long time to proceed. This long delay to obtain those low-frequency measurements can limit the applicability of the methodology, especially when considering that stationary operating conditions of FC are quite difficult to maintain, nevertheless required for guarantying the quality of the measurements. A method allowing an accurate estimation of the FC EIS at low and very low frequencies could be of a great interest to solve this issue. Authors present different FC EIS estimation methods in order to overcome the drawback of the EIS method used in the fault detection. The three different methods are validated experimentally and the performances are compared.

Order Estimation of Superimposed Nonlinear Complex Cisoid Model Using Adaptively Penalizing Likelihood Rule: Consistency Results

Recently a novel approach of model order selection based on penalizing adaptively the likelihood (PAL) function was introduced in [1]. In this paper, we use the PAL method for order estimation of complex valued nonlinear exponential (cisoid) model and study its asymptotic statistical properties. We investigate the asymptotic statistical properties for the 1-dimensional cisoid model under the assumption of circularly symmetric gaussian error distribution and establish that the PAL estimator is consistent. We also present simulation examples to compare the performance of PAL rule with the commonly used information criteria based rules.

Reservoir Characterization and Geostatistical Modeling of Ilam & Sarvak Formations in One of Oil Fields in Southwest of Iran

Exploration and exploitation of hydrocarbon reservoirs have been always time consuming with high risk and high cost. In this regard, assessment of reservoir characterizations and information about spatial distribution of its parameters play an important role in adaptation of suitable strategies for hydrocarbon resources management. There are only few numbers of oil wells cored in every oil field due to high cost, time-consuming process, and other drilling problems. Therefore, it is required to use alternative estimation methods in order to achieve the petro-physical parameter in total space of reservoir. In this research, geostatistical methods have been applied as a new approach to calculate and estimate porosity and permeability of reservoir in one of southwestern oil fields of Iran. The information obtained from 86 wells in one of southwestern oil fields of Iran has been available in this study. Physical parameters of porosity and permeability are vital parameters that should be estimated in studied reservoir. This study indicated that Gaussian Variogram Model is the best model to predict porosity and permeability values in field. Error means of actual values of porosity are equal to 6.9% and for permeability are 11.21% using Gaussian Model. Also, after prediction of porosity and permeability values for field, distribution of these parameters in field was illustrated in two-dimensional and three-dimensional modes besides distribution and location of wells in field in order to determine the best drilling spots and reduce risk of drilling operations.

Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties

Recently, the paper [2] introduced a method for model order estimation based on penalizing adaptively the likelihood (PAL). In this paper, we use the PAL based order estimation method for a nonlinear sinusoidal model and study its asymptotic statistical properties. We prove that the estimator of the model order using the PAL rule is consistent. Simulation examples are presented to illustrate the performance of the PAL method for small sample sizes and to compare it with that of three information criterion-based methods.

Intrinsic elastography and its dependence on arterial flow volume.

We propose a new estimation method of tissues stiffness from deformation induced by arterial pulsation. The new method was named Intrinsic Elastography (IE). Under two assumptions that the deformation induced by pulsation propagates throughout the body, and the propagation velocity (PV) is closely related to the mechanical properties of tissues, the IE can evaluate the tissue stiffness based on the PV. However, this method has an issue that the observed PVs change with the changes of amounts of the flow. In this paper, we propose the new estimation method in order to estimate true shear elasticity in spite of the changes. The results was compared to the results of SWI. The results was almost identical to the result of SWI, or showed the similar results.

Buckley–James-type estimation of quantile regression with recurrent gap time data

In longitudinal studies, an individual may potentially undergo a series of repeated recurrence events. The gap times, which are referred to as the times between successive recurrent events, are typically the outcome variables of interest. Various regression models have been developed in order to evaluate covariate effects on gap times based on recurrence event data. The proportional hazards model, additive hazards model, and the accelerated failure time model are all notable examples. Quantile regression is a useful alternative to the aforementioned models for survival analysis since it can provide great flexibility to assess covariate effects on the entire distribution of the gap time. In order to analyze recurrence gap time data, we must overcome the problem of the last gap time subjected to induced dependent censoring, when numbers of recurrent events exceed one time. In this paper, we adopt the Buckley–James-type estimation method in order to construct a weighted estimation equation for regression coefficients under the quantile model, and develop an iterative procedure to obtain the estimates. We use extensive simulation studies to evaluate the finite-sample performance of the proposed estimator. Finally, analysis of bladder cancer data is presented as an illustration of our proposed methodology.

Compatible merchantable stem volume and taper equations for eucalyptusplantations in the Eastern Mediterranean Region of Turkey

Abstract: Eucalyptus plantations are an important source of raw materials for the forest products industry in Turkey. Despite its economic and ecological value, there is little reference information regarding the growth and yield of eucalyptus plantations. Thus, a segmented compatible stem taper and merchantable tree volume equation system was developed for eucalyptus plantations in the Eastern Mediterranean Region of Turkey. The data used in this study come from 190 Eucalyptus grandis (EG) and 149 Eucalyptus camaldulensis (EC) destructively sampled from even-aged plantations. The equation systems were fitted by the simultaneous estimation of parameters by maximum likelihood with the full information estimation method in order to optimize fitting while simultaneously minimizing the errors in a combined way. The equation systems produced similar results for the two species analyzed in this study. Based on the overall fit statistics, the taper and volume equations showed consistent performance for different sections of stem using relative height classes, different tree height, and diameter classes in estimating stem diameter and merchantable volume. Differences in the taper equations among eucalyptus species were examined and tested using the F-test. The result of the F-test indicated differences in species-specific taper equations for the EG and EC species. A different taper equation should therefore be used for each eucalyptus species. The segmented taper and merchantable stem volume equations can help forest managers to estimate the stem diameter and volumes of standing trees of both eucalyptus species, which is important in practical forestry applications.

Evaluation of Covariates Influence on Urapidil Pharmacokinetics using Non-Linear Mixed Effect Model

Drug development is a very laborious, expensive and time consuming process. Inadequate pharmacokinetic knowledge on the drug candidate is one of the reasons for failure during drug development. The in-vivo absorbability of drugs categorized as BCS Class II is very difficult to predict because of the large variability in the absorption or dissolution kinetics. Urapidil comes under the category of BCS Class II. Thus, present study was aimed to assess the influence of covariates on pharmacokinetics of Urapidil from typical pharmacokinetics studies using population pharmacokinetic model. In this study one compartment model incorporating subject specific parameters was developed and evaluated. Results demonstrated that the one compartment absorption model without lag time under first order estimation method best describes the pharmacokinetics of Urapidil. The final model described the body weight influence on apparent oral clearance of Urapidil and 27.60% of the inter-individual variability was explained by the covariate body weight. Thus, it can be concluded that body weight was found to be the most important covariate for clearance of Urapidil. The projected model shall further developed in patients treated for Urapidil and results from this study should interpret cautiously while any dose adjustment for Urapidil treatment in patient population.

Skewness of Gaussian Mixture Absolute Value GARCH(1, 1) Model

This paper studies the skewness of the absolute value GARCH(1, 1) models with Gaussian mixture innovations (Gaussian mixture AVGARCH(1, 1) models). The maximum estimated-likelihood estimator (MELE) employed (a two- step estimation method in order to estimate the skewness of Gaussian mixture AVGARCH(1, 1) models. Through the real data analysis, the adequacy of adopting Gaussian mixture innovations is exhibited in reflecting the skewness of two major Korean stock indices.

Statistical Analysis of Results from Laboratory Studies in Experimental Economics: A Critique of Current Practice

Drawing on examples of recently published and widely-cited studies in experimental economics, we show that behavioral games are frequently analyzed in a manner that is prone to biased causal inference. First, deficiencies in design and implementation jeopardize the crucial assumption that treatments are statistically independent of potential outcomes. Researchers frequently do not randomly assign treatments or do not focus on randomly assigned factors when interpreting results. Second, many analyses of second mover behaviors in two-stage games, such as the ultimatum game and the trust game, are susceptible to bias. Third, uncontrolled stimuli, such as face-to-face interaction among subjects or the presentation of subjects’ photos, may also cause bias. Fourth, we discuss the limits of causal inference in repeated games, such as the public goods game. We recommend adjusting laboratory procedures and estimation methods in order to lessen reliance on substantive assumptions not grounded in experimental design.