Parameter Estimation Process Research Articles

A Randles Circuit Parameter Estimation of Li-Ion Batteries With Embedded Hardware

Accurate modelling of electrochemical sources is very important to predict how a source will perform in specific applications related to the load or environmental parameters. A Randles circuit is considered as a reliable equivalent electrical circuit in studying and modelling various electrochemical systems and processes. The classical parameter estimation approach based on use of software packages (ZSim, MEISP, LEVMW, etc.) requires for the high computational performance processing units, decreasing the reliability as proper maintenance actions can be delayed because of offline analysis. Advancing the state of the art, we propose a low-complexity approach for embedded hardware-based parameter estimation of the Randles circuit. Our non-iterative method uses only the measured real and imaginary part of impedance, with numerical approximation of the first derivative of real/imaginary part quotient, to create closed-form expressions with a unique solution. The initial estimated values are available from partial dataset (after measurement at only 3 frequencies). Moreover, it is not software platform-specific, which enables a high level of portability. The presented method is verified with theoretical, numerical and experimental analysis, with more than 1000 datasets. We also demonstrated the applicability in parameter estimation of the Randles circuit of a Li-ion battery. Finally, we verified suitability for embedded hardware platforms, with deployment on a microcontroller-based platform with a clock speed of 16 MHz and 8 kB of SRAM. Reliable parameter estimation processing of a 100-points dataset was performed in just 106 milliseconds with 1% relative error, requiring less than 53 mJ of energy.

Bayesian Convolution for Stochastic Epidemic Model

Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reliable risk map for intervention. The aim of this study is to develop a stochastic convolution susceptible, infective, recovered-susceptible, infective (SIR-SI) model describing the dynamics of the relationship between humans and Aedes aegypti mosquitoes. This model involves temporal trend and uncertainty factors for both local and global heterogeneity. Bayesian approach was applied for the parameter estimation of the model. It has an intrinsic recurrent logic for Bayesian analysis by including prior distributions. We developed a numerical computation and carry out simulations in WinBUGS, an open-source software package to perform Markov chain Monte Carlo (MCMC) method for Bayesian models, for the complex systems of convolution SIR-SI model. We considered the monthly DHF data of the 2016–2018 periods from 10 districts in Kendari-Indonesia for the application as well as the validation of the developed model. The estimated parameters were updated through to Bayesian MCMC. The parameter estimation process reached convergence (or fulfilled the Markov chain properties) after 50000 burn-in and 10000 iterations. The deviance was obtained at 453.7, which is smaller compared to those in previous models. The districts of Wua-Wua and Kadia were consistent as high-risk areas of DHF. These two districts were considered to have a significant contribution to the fluctuation of DHF cases.

Design of Dynamics Invariant LSTM for Touch Based Human–UAV Interaction Detection

The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform. The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved an accuracy of 96\%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs.

Orbit determination methods for interplanetary missions: development and use of the Orbit14 software

In the last years, a new generation of interplanetary space missions have been designed for the exploration of the solar system. At the same time, radio-science instrumentation has reached an unprecedented level of accuracy, leading to a significant improvement of our knowledge of celestial bodies. Along with this hardware upgrade, software products for interplanetary missions have been greatly refined. In this context, we introduce Orbit14, a precise orbit determination software developed at the University of Pisa for processing the radio-science data of the BepiColombo and Juno missions. Along the years, many tools have been implemented into the software and Orbit14 capitalized the experience coming from simulations and treatment of real data. In this paper, we present a review of orbit determination methods developed at the University of Pisa for radio-science experiments of interplanetary missions. We describe the basic theory of the process of parameters estimation and refined methods necessary to have full control on experiments involving spacecraft orbiting millions of kilometers far from the Earth. Our aim is to give both an extensive description of the treatment of radio-science experiments and step-to-step instructions for those who are approaching the field of orbit determination in the context of space missions. We show also the work conducted on the Juno and BepiColombo missions by means of the Orbit14 software. In particular, we summarize the recent results obtained with the gravity experiment of Juno and the simulations performed so far for the gravimetry-rotation and relativity experiments of BepiColombo.

Coherence and quantum Fisher information in general single-qubit parameter estimation processes

Quantum metrology is superior to classical metrology in parameter estimation. This is because quantum states have coherence that, however, does not exist in classical states. Previous works were limited to the study of the relationship between quantum Fisher information (QFI) and coherence of the initial probe state only for a specific single-qubit parameter estimation process (e.g., a process described by a single-qubit spin angular momentum operator ${J}_{z}$). Here, we consider a more general case and study the relationship between QFI and coherence of the initial probe state for a single-qubit general parameter estimation process, which is described by a single-qubit spin angular momentum operator ${J}_{\stackrel{P\vec}{n}}$ with a general unit vector $\stackrel{P\vec}{n}$. We find that the QFI is proportional to the square of coherence of the initial probe state, when the single-qubit bases are eigenstates of the spin angular momentum operator ${J}_{\stackrel{P\vec}{n}}$ describing the parametrization process. We also design and conduct a linear optical experiment to support our theory. The experimental results are in good agreement with the theoretical ones. This work provides an important guideline for enhancing the precision of parameter estimation.

Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

Abstract Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on the EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).

The Effect of Weighting Data on the Goodness of Fit Indicators of the Six Sigma Structural Equation Modeling

The main core of Structural Equation Modeling (SEM) is the parameter estimation process. This process implies a variance-covariance matrix Σ that is close as possible to the sample variance-covariance matrix of data input (S). The six Sigma survey uses ordinal (rank) values from 1 to 5. There are several weighted correlation coefficients that overcome the problems of assigning equal weights to each rank and provide a locally most powerful rank test. This paper extends the SEM estimation method by adding the ordinal weighted techniques to enhance the goodness of fit indicators. A two data sets of the Six Sigma model with different statistics properties are used to investigate this idea. The weight 1.3 enhances the goodness of fit indicators with data set that has a negative skewness, and the weight 0.7 enhances the goodness of fit indicators with data set that has a positive skewness through treating the top-rankings.

Monte Carlo Simulation in Quantile Regression Parameter for Sparsity Estimate

Monte Carlo is a method used to generate data according to the distribution and resampling until the parameters of the method used became convergen. The purpose of this simulation is first to prove that quantile regression with the estimated sparsity function parameter can model the data according to the non-uniform distribution of the data. Secondly, it’s to prove that the quantile regression is a developed method from the linear regression. The pattern of data which is not uniform is generally referred to as heterogeneous data, while the pattern of uniform data distribution is called homogeneous data. Data in this study will be generated for small and large samples on homogeneous and heterogeneous data. Uniformity of variance will be carried out on both heterogeneous and homogeneous data types, namely 0.25,1 and 4. The parameter estimation process and data generation will be resampled 1000 times. Thus, in conclusion of the simulation studies was the parameter estimates in the classical regression will be the same as the parameter estimates in the quantile regression at quantile 0.5. In the simulation, it is decided that the quantile regression method can be used on heterogeneous and homogeneous data to the unconstrained number of samples and variances.

Kinetic data analysis and mathematical modeling of intra (wild type vs. engineered) and inter species (Saccharomyces cerevisiae vs. Zymomonas mobilis) dependency for bioethanol production from glucose, xylose or their combination

This study investigates intra (wild type vs engineered) and inter (S. cerevisiae vs Z. mobilis) species kinetic parameters relationship for bioethanol production with glucose, xylose or their combination as a carbon source when the same xylose utilization pathway is engineered in these strains. Kinetic constants of wild type and engineered strains of S. cerevisiae were compared. Through the comparison, and due to the introduction of the xylose pathway in the engineered strain (intra species dependency), a set of correction factors were obtained. The correction factors were then used to determine process parameters in silico (e.g., biomass and product rates) of wild type Z. mobilis from experimental kinetic parameters obtained from an engineered Z. mobilis strain presented in the literature (inter species dependency). The estimated process parameters for wild type Z. mobilis were validated using experimental data generated by the authors and showed good correlation. Results showed a relationship between wild type and engineered strains of the same microorganism. For example, the substrate limitation constants in the engineered strain of xylose (Ks,xyl, Kp,xyl, Kx,xyl) and of glucose (Ks,glu, Kp,glu, Kx,glu) were nine and three times higher in the engineered strain compared with that of the wild type (Ks,glu, Kp,glu, Kx,glu) respectively. This study suggests that these correction factors are conserved and can be used as a guide to estimate kinetic constant values for species or strains for which they are not available.

Two-stage integrated process for bio-methanol production coupled with methane and carbon dioxide sequestration: Kinetic modelling and experimental validation

The study demonstrates a two-stage integrated process for bio-methanol production using Methylosinus trichosporium NCIMB 11131, coupled with sequestration of methane and carbon dioxide. The first stage involved generation of methanotrophic biomass via sequestration of methane; which was used as biocatalyst to reduce carbon dioxide into methanol in the second stage. Maximum biomass titer of 3.39 g L−1 and productivity of 0.60 g L−1 d−1 were achieved in semi-batch stirred tank reactor with methane concentration in the inlet gas mixture of 2.5% v/v and gas flow rate of 0.5 vvm. Methane fixation rate was estimated to be 0.32 g L−1 d−1. Maximum methanol titer of 0.58 g L−1 was achieved at headspace carbon dioxide concentration of 50% v/v and liquid to headspace volume ratio 10:90. Subsequently, a kinetic model was developed to predict and understand the system behaviour in terms of dynamic profile of growth, methanol formation, concentration of dissolved methane or carbon dioxide in the aqueous phase and headspace carbon dioxide concentration, in response to varying process parameters. The model can serve as a tool for estimation of process parameters and aid in overall production optimization.

감마과정 기반 양방향 접합 트랜지스터 잔여수명 예측

Purpose: The gamma process was used to predict the remaining useful life of a bipolar junction transistor, which is a widely used electronic component.<BR>Methods: The proposed method consists of six major steps: 1) data collection via accelerated degradation test, 2) data conversion for the gamma process, 3) parameter estimation of the gamma process, 4) shape function estimation using a fitting procedure, 5) degradation model generation using the inverse power law and Arrhenius relation, and 6) prediction of the remaining useful life by estimating the failure criterion.<BR>Results: The degradation characteristic of a bipolar junction transistor is the beta (β) value (current gain), and the transistor is considered a failure when the β value deteriorates by 33.3%.<BR>Conclusion: This paper proposes a new method for predicting the remaining life of a bipolar junction transistor.

Nonintrusive optimal parameters estimation for a switch mode power supply equivalent circuit model: 120 V 60 Hz CFLs Case study

The large-scale use of home appliances based on Switch Mode Power Supplies (SMPSs) raises the interest in power quality problems they might cause. Accurate modeling of SMPS-based loads allows predicting the collective impact caused on low voltage networks. However, model parameter values for a specific load are not often available. This paper proposes a nonintrusive Optimal Parameter Estimation (OPE) process for an SMPS Equivalent Circuit Model (ECM) through an optimization problem formulation using only current measurements. This OPE process is composed of the Narrow Search (NS) and Data Fitting (DF) methods. The NS method advantage is that it limits the parameter region using datasheets, measurements, and manufacturing standards to obtain optimal parameters for a specific supply voltage. The DF method leading convenience is that it computes the parameters from equations, as a function of nominal voltage and rated power by using data regression. The parameters estimated by the NS method induce errors that exceed about five times the DF method; in addition, the DF method results improve by about 10% methods available in the state-of-the-art. Thereby, the OPE process is helpful in practical applications related to demand-side management or collective harmonic impact for a wide range of SMPS-based loads.

Two-stage sampling exponential and chain ratio type estimators in the presence of measurement error

Currently one of the most important topics that are discussed in survey methodology is the accuracy of statistics or survey errors that may occur in the parameters estimation process and measurement error is one of the most important non-sampling error. In the present study general class of exponential and chain ratio type estimators are considered under two stage sampling for the estimation of population mean by using two auxiliary variables in the presence of measurement error. The bias and mean square error of these estimator has been obtained. Moreover, two artificial populations are generated to check the properties of proposed estimator among other estimator. The relative efficiency is also computed and the results revealed that proposed estimators are more efficient than the ratio estimator and by increasing sample size efficiency of suggested estimators are improved.

The double ratio geometric process for the analysis of recurrent events

AbstractSince its introduction, the geometric process (GP) has attracted extensive research attention from authors in various research communities, including probability, statistics, and reliability mathematics. However, the GP can only model a process with its gap times (i.e., times between events/failures) having a monotonic trend (either increasing or decreasing). It also implicitly assumes that the level of the modification on the hazard rate functions and that on the age after the occurrence of an event are the same, which is too restrictive and may limit its application. To overcome these drawbacks, this paper extends the GP to a new stochastic model. Probabilistic properties of the proposed model are investigated. The maximum likelihood method is used to estimate the parameters in the model. Case studies are performed to illustrate the parameter estimation process and obtain favorable performance.

New approaches to parameter estimation with statistical censoring by means of the CEV algorithm: Characterization of its properties for high-performance normal processes

The process of parameter estimation in order to characterize a population using algorithms is in constant development and perfection. Recent years show that data-based decision-making is complex when there is uncertainty generated by statistical censoring. The purpose of this article is to evaluate the effect of statistical censoring on the normal distribution, which is common in many processes. Parameter estimation properties will be characterized with the conditional expected value algorithm, using different censoring percentages and sample sizes. The estimation properties chosen for the study will focus on the monitoring and decision-making related to industrial processes with the presence of censoring.

Interest Rate Deregulation and Performance of Deposit Money Banks: Time Series Evidence from Nigeria

This work investigated the relationship between interest rate deregulation and performance of Nigerian deposit money banks for the period 1996-2018. Interest rate deregulation was disaggregated into prime lending rate, maximum lending rate, 3-months deposit rate and over 12-months deposit rate while return on assets (ROA) was used as a proxy for deposit money banks’ performance. Data on the above variables were sourced from the Central Bank of Nigeria Statistical Bulletin (2018 edition) and the World Bank data base. The data were tested for stationarity using the Dickey-Fuller (D-F) test, for long-run relationship using Bound’s co-integration test, and for reliability of ARDL results using serial correlation, heteroscedasticity and normality tests. The results of the tests revealed that all the variables were integrated of order zero or one, and that a long-run relationship exists between the variables. Consequently, ARDL model for parameter estimation process revealed that only prime lending rate was positively related to ROA of banks while none of the explanatory variables was statistically significant. The researcher then submitted that there is no significant relationship between interest rate deregulation and the performance of Nigerian deposit money banks for the period considered. Hence, deposit money banks should strive to mobilize adequate savings from surplus spenders by offering them deposit rates that are capable of inducing savers to increase their savings and boost the availability of loanable funds. Also, there is urgent need to restructure the Nigerian financial system whereby policies by the monetary authorities will achieve pre-determined goals. In essence, to make interest rate policies meaningful, there is need to curtail financial transactions that escape the banking system.

A Simplified Fractional Order Modeling and Parameter Identification for Lithium-Ion Batteries

Abstract With the widespread development of new energy, the study of power lithium-ion batteries (LIBs) has broad prospects and great academic significance. The model and parameters are two essential prerequisites for LIB state estimation, which are used to provide a guarantee for the secure and convenient handling of LIBs. To obtain the reliable model and parameters, a simplified fractional order equivalent circuit model (FO-ECM) with high precision is presented in this article. The dynamic external electrical characteristic of LIBs is represented by the one-order FO-ECM, and then, the FO-ECM parameters are identified by the combination of Grunwald–Letnikov (G-L) definition-based factional order numerical calculation and noise compensation-based forgetting factor recursive least squares (FFRLS) method. The simplified FO-ECM can better characterize the nonlinear dynamic behaviors of LIBs, and the G-L definition-based FO-FFRLS algorithm can maintain good accuracy in the parameter estimation process. The results show that the simplified FO-ECM can improve the modeling precision and parameter identification performance compared with the common integer-order ECM in different test cycles.

A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data

Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the nature of clusters in the data. Because it is expected that the observations of same cluster are more similar than the observations from different clusters. In this study, a method has been proposed for the analysis of spatially clustered areal data based on generalized estimating equations which were originally developed for analyzing longitudinal data. The performance of the model for known clusters is tested in terms of how well it estimates the regression parameters and how well it captures the true spatial process. These results are presented and compared with the conditional auto-regressive model which is the most frequently used spatial model. In the simulation study, the proposed generalized estimating equations approach yields better results than the popular conditional auto-regressive model from the both perspectives of parameter estimation and spatial process capturing. A real life data on the vitamin A supplement coverage among postpartum women in Bangladesh is then analyzed for demonstration of the method. The existing divisional clustering behavior of vitamin A supplement coverage in Bangladesh is identified more accurately by the proposed approach than that by the conditional auto-regressive model.

Basel regulatory capital formula revised

Credit capital requirement is a key component of Basel implementation to assess a bank’s capital adequacy. Under the Internal Rating-Based approach, some risk parameters, including Asset Correlation, are implicit assumptions that cannot be observed directly. While some heuristic formulae of Asset Correlation for different business segments are provided by Basel, they may not be fully consistent with each bank’s loss experience and thus may cause systematic underestimation of banks’ capital requirement. To address this issue, we derive an equivalent capital formula in such way that the unobservable Asset Correlation is replaced by an observable and well-understood parameter called Default Volatility, which can be calibrated based on banks’ historical loss experience. This new approach simplifies parameter estimation process without requiring additional data, as well as making risk analysis such as stress testing more credible.

Relating land-use/land-cover patterns to water quality in watersheds based on the structural equation modeling

Understanding the relationships between land-use/land-cover (LULC) patterns in watersheds and water quality is of great significance for exploring the causes of non-point source pollution and scientific planning of land use. Multivariate regression methods have often been used to examine the response of specific water contaminants to LULC patterns. However, this also resulted in overlooking the ecological process and biased parameter estimates caused by redundancy where some landscape metrics are strongly correlated. In this study, based on “source-sink” landscape theory, Redundancy Analysis (RDA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) were used to investigate the relationship between LULC patterns and water quality in the surrounding sub-watersheds of the Danjiangkou Reservoir in the dry and rainy seasons. The results showed that water quality parameters were characterized by seasonal differences. In the dry season, only the proportion of the watershed occupied by urban landscapes (P_Urban) had a significant impact on water quality in watersheds (β = 0.835, p < 0.001), so the water quality was only affected by point source pollution in the dry season. In the rainy season, specific water contaminants were categorized as organic and inorganic pollutants. The “sources” of organic and inorganic pollutants were agricultural and urban land, respectively. However, natural vegetation was not only a “sink” of inorganic pollutants, but also a “source” of organic pollutants. Furthermore, agriculture and natural vegetation mediated the effects of landscape spatial configuration on the water quality, yet landscape aggregation negatively moderated the relationship between urban and organic pollution. Therefore, watersheds with high landscape aggregation have small agricultural landscapes and large natural vegetation landscapes where water quality is affected by low levels of inorganic pollution and high levels of organic pollution. However, organic pollution from urban sources was aggravated by watersheds with low landscape aggregation. This study revealed the relationships between LULC patterns and water quality in watersheds and reflected the ecological process to a certain extent.