Proposed Estimation Method Research Articles

Settling-Time Estimation for Finite-Time Stabilization of Fractional-Order Quaternion-Valued Fuzzy NNs

This article solves the problems of finite-time control and settling-time estimation for fractional-order quaternion-valued fuzzy neural networks (FQFNNs) with time delays. A novel fractional differential inequality is established to estimate the settling time of the addressed system, which is more general and less conservative than the existing results. In addition, owing to the noncommutativity of multiplication of quaternions, the decomposition method is usually adopted to discuss the finite-time stabilization (FTS) of quaternion-valued neural networks, which inevitably doubles the dimensionality of the system and brings a great computational burden. To avoid the aforementioned issues, some new properties of the quaternion-valued signum function are presented for exploring the FTS of FQFNNs by the direct quaternion method without any decomposition. Then, 1-norm and 2-norm control strategies are designed to stabilize the addressed system in finite time, and several sufficient criteria are derived to ensure the FTS of FQFNNs. Finally, the validity of the obtained theoretical results and the superiority of the proposed estimation method are illustrated by numerical simulations.

A SDTOGI based speed control method for SPMSG in WECS

Abstract The sensorless techniques are incorporated to optimize the cost and performance of the Surface Mounted Permanent Magnet Synchronous Generator (SPMSG) in the Wind Energy Conversion System (WECS) in this manuscript. Strengthen Dual third-Order Generalized Integrator (SDTOGI) with Synchronous Reference Frame-Phase Locked Loop (SRF-PLL) is applied in SPMSG to estimate the speed & position of the measured generator stator current. The proposed technique is applied in low-speed direct-driven 1.5 MW SPMSG. SDTOGI with SRF-PLL technique is compared with conventional techniques to investigate the efficacy of the proposed sensorless technique due to rapid change in the torque of the turbine. The stability of the proposed system is higher, as shown in the Nyquist plot. Further, the obtained simulation result shows that the proposed method estimates position and speed efficiently. The results are verified in the OPAL-RT 4510 real-time simulator environment to ensure the efficacy of the proposed technique.

The New Prediction Methodology for CO2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach

Energy is one of the most fundamental elements of today’s economy. It is becoming more important day by day with technological developments. In order to plan the energy policies of the countries and to prevent the climate change crisis, CO2 emissions must be under control. For this reason, the estimation of CO2 emissions has become an important factor for researchers and scientists. In this study, a new hybrid method was developed using optimization methods. The Shuffled Frog-Leaping Algorithm (SFLA) algorithm has recently become the preferred method for solving many optimization problems. SFLA, a swarm-based heuristic method, was developed in this study using the Levy flight method. Thus, the speed of reaching the optimum result of the algorithm has been improved. This method, which was developed later, was used in a hybrid structure of the Firefly Algorithm (FA). In the next step, a new Artificial Neural Network (ANN)-based estimation method is proposed using the hybrid optimization method. The method was used to estimate the amount of CO2 emissions in Türkiye. The proposed hybrid model had the RMSE error 5.1107 and the R2 0.9904 for a testing dataset, respectively. In the last stage, Türkiye’s future CO2 emission estimation is examined in three different scenarios. The obtained results show that the proposed estimation method can be successfully applied in areas requiring future estimation.

Evaluating the strength of a beam-type electric switch mechanism housing

Aim. Choice of method and its validation for the purpose of confirming the feasibility of safe operation of a switch mechanism housing submitted to rolling stock axle loads of 30 tf. Method. As of today, there are neither regulations regarding the strength parameters of load-carrying elements of electric switch mechanisms, nor verification procedures. Given the above, when evaluating the strength of electric switch mechanism housing, the authors used the method used for the purpose of evaluating the strength of load-carrying elements of locomotives per GOST R 55513-2013. That method has a long history of application as part of calculation and testing of motive power and has shown good results. According to the method, strength is evaluated by comparing the safety factor of fatigue strength n with the allowed value for steel structures [n] = 2.0. Result. The activities resulted in the validation of the proposed method of strength estimation by the criterion of fatigue strength. The value of fatigue endurance of electric switch mechanism housing was estimated based on the results of fatigue benchmark tests of three items. Each was stressed by stepping up the amplitude of the applied force after the base number of cycles had been reached. At the first stage, the loading was equivalent to the operational value. After the base number of functions had been reached for each item, the following step was initiated. The tests continued until cracks have been found. In the process of testing, the amplitude of stress was recorded at each level of loading. For the purpose of identifying the fatigue strength characteristics, the reduced fatigue strength of the switch mechanism housing was calculated under the hypothesis of linear addition of fatigue damage subject to the condition of deterministic loading and subsequent processing of the findings using statistical methods. The value of fatigue limit has been obtained for the housing of a tie-type electric switch mechanism equal to 48.4 MPa. The safety factor of fatigue strength was found to be equal to 2.86, which is above the minimal allowed value. Conclusion. It has been shown that the housing of a tie-type electric switch mechanism has a sufficient strength as regards the operational static and dynamic loads. Its safe operation when exposed to axle loads of 30 tf has also been confirmed.

A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies.

The hazard ratio (HR) is often reported as the main causal effect when studying survival data. Despite its popularity, the HR suffers from an unclear causal interpretation. As already pointed out in the literature, there is a built-in selection bias in the HR, because similarly to the truncation by death problem, the HR conditions on post-treatment survival. A recently proposed alternative, inspired by the Survivor Average Causal Effect, is the causal HR, defined as the ratio between hazards across treatment groups among the study participants that would have survived regardless of their treatment assignment. We discuss the challenge in identifying the causal HR and present a sensitivity analysis identification approach in randomized controlled trials utilizing a working frailty model. We further extend our framework to adjust for potential confounders using inverse probability of treatment weighting. We present a Cox-based and a flexible non-parametric kernel-based estimation under right censoring. We study the finite-sample properties of the proposed estimation methods through simulations. We illustrate the utility of our framework using two real-dataexamples.

Joint state estimation of lithium-ion batteries combining improved equivalent circuit model with electrochemical mechanism and diffusion process

Accurate state estimation plays a key role for guaranteeing the safety and reliability of lithium-ion batteries. This paper develops a novel joint state estimation method for lithium-ion batteries based on a hybrid model combining improved equivalent circuit model (IECM) with electrochemical mechanism and diffusion process, it mainly includes state-of-charge, state-of-health, state-of-power and state-of-energy. Firstly, an IECM by combining the internal electrochemical mechanism and traditional equivalent circuit model is established to simulate the battery dynamic characteristics. The model parameters are offline identified by an electrochemical mechanism decoupling approach and online updated with the battery aging. Next, a hybrid model is presented by combining the diffusion process-based empirical aging model and the IECM, a co-estimation algorithm for state-of-charge and state-of-health by applying the dual extended Kalman filter is proposed. Then, the peak current is online calculated to evaluate the state-of-power under the model limitation, and the future working condition is predicted to evaluate the state-of-energy. Finally, several case studies are implemented to verify the effectiveness of developed method, the results indicate that the proposed state joint estimation method has higher accuracy and stronger robustness, and the root mean square error does not exceed 1 % with the battery aging.

First-order binomial autoregressive processes with Markov-switching coefficients

In this article, a new autoregressive process for finite-range time series of counts is proposed to analyse the finite-range integer-valued data based on an invisible Markov chain. We derive the probabilistic and statistical properties of the model. Conditional least squares (CLS) method and conditional maximum likelihood (CML) method are employed to estimate the parameters of interest. Furthermore, the forecasting problem is addressed. In addition, multiple simulation studies are performed to investigate the finite sample performance of parameter estimators and to compare the proposed estimation methods. The proposed model is applied to a finite-range data series of measles infections in Germany in 2004–2005.

Penalized additive neural network regression

In this study, we develop a penalized additive regression estimation method based on a neural network architecture. An additive neural network model is constructed by using a linear combination of univariate neural networks, or equivalently functional components. We use a B-spline activation function, which is useful to capture local features of data, for nodes that constitute the model. A penalty function is adopted to induce sparsity in functional components and nodes on each component simultaneously. This enables us to obtain a sparse representation, which in turn improves accountability of the model. To implement the proposed estimation method, we devise an efficient iterative algorithm based on a coordinate-wise updating process. An initialization scheme specialized for the B-spline activation function is proposed. The initialization approach enables the proposed method to achieve better performance compared with random initialization scheme. Numerical studies show that the fitted functional components of our estimator adapt to local and sparse structures based on a given dataset.

Pretest and shrinkage estimators in generalized partially linear models with application to real data

AbstractSemiparametric models hold promise to address many challenges to statistical inference that arise from real‐world applications, but their novelty and theoretical complexity create challenges for estimation. Taking advantage of the broad applicability of semiparametric models, we propose some novel and improved methods to estimate the regression coefficients of generalized partially linear models (GPLM). This model extends the generalized linear model by adding a nonparametric component. Like in parametric models, variable selection is important in the GPLM to single out the inactive covariates for the response. Instead of deleting inactive covariates, our approach uses them as auxiliary information in the estimation procedure. We then define two models, one that includes all the covariates and another that includes the active covariates only. We then combine these two model estimators optimally to form the pretest and shrinkage estimators. Asymptotic properties are studied to derive the asymptotic biases and risks of the proposed estimators. We show that if the shrinkage dimension exceeds two, the asymptotic risks of the shrinkage estimators are strictly less than those of the full model estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite‐sample performance of the proposed estimation methods. We then apply our proposed methods to two real data sets. Our simulation and real data results show that the proposed estimators perform with higher accuracy and lower variability in the estimation of regression parameters for GPLM compared with competing estimation methods.

Envelope-based constrained model for multiparameter pre-stack seismic inversion

Pre-stack seismic inversion is an effective method to estimate multiparameter from seismic reflection data in the oil–gas reservoir exploration. In order to get a reliable inverted parameter result, a constrained model is commonly used to reduce the multi-solution in the seismic inversion. However, it is hard to get an appropriate constrained model by conventional well-log data interpolation or velocity field in offshore oil–gas exploration with sparse wells. Envelope inversion has been successfully applied to get a good initial model with low-frequency components in full-waveform inversion. In order to fully exploit the rich information contained in seismic data, we introduce envelope inversion into background modeling based on convolution theory and propose a multiparameter pre-stack seismic inversion with envelope prior constraint. Both inversion results of the model and field data indicate the effectiveness and adaptability of the proposed method in multiparameter estimation.

Estimation method for mixture copula models in hydrological context

Hydrological extreme events are characterized by several correlated variables. For a better associated risk assessment, the dependence structure between these variables must be taken into account by considering copulas. On the other hand, extreme events are generated from different phenomena. In such cases, the margins and/or copula may be affected. Hence, mixture copula should be considered. Recently, there have been an increasing number of studies dealing with the parameter estimation of mixture copula. However, existing methods have several drawbacks. To overcome these drawbacks, we propose a new parameter estimation approach for the mixture copula models, based on the maximum pseudo-likelihood using a metaheuristic algorithm. A simulation study is conducted to evaluate the performance of the proposed method and to compare it with those of the widely used existing method. Results indicate that the proposed method estimates more accurately the parameters even with small sample sizes compared to the existing ones. An application to a real data set is also provided and validated with the available data.

Development of a method for estimating particles mixing curves in short DEM simulation time

A new method for estimating particles mixing curves by simulating the particles behavior for a short period of time using discrete element method (DEM) was developed. The mixing curve is the time variation of the mixing degree, and can be divided into the following two stages: one is the mixing degree increasing stage, and the other is the mixing degree stagnation stage. The mixing curves could be estimated from time variations of mixing and de-mixing rates by assuming that the stagnation occurs when the mixing and de-mixing are balanced. Assuming the mixing and de-mixing rates are represented by the first-order exponential functions, each rate was estimated by the simulations of particles behavior for a short period of time. The estimated mixing curves agree with experimental ones and the proposed estimation method reduced the calculation time to approximately one-fifth of the conventional one that does not use a method to reduce the mixing time. Therefore, the proposed estimation method could estimate the mixing curve in a short period of time.

Data augmentation based estimation for the censored quantile regression neural network model

Quantile regression neural network (QRNN) model has received wide attentions in recent years to explore complex nonlinear problems. However, when the responses yi are subject to censoring (left censoring, right censoring and interval censoring might occur), predictions by using observed data, will lead to unbelievable results. Thus, new method for QRNN model with censored data is appealing. In this paper, we propose an iterative approach based on the data augmentation method for censored QRNN model estimation. Firstly the censored data are imputed through a data augmentation process, then the QRNN model is updated with the imputed data, finally we make predictions through the updated QRNN model. It is worth mentioning that simulation studies and real data illustrations show the superiority of our proposed method. Using the results based on full uncensored data as the benchmark, we compare the estimation efficiency of the proposed method with the existing ones. Our method outperforms others in terms of quantile loss and prediction interval width, yielding prediction results that are much closer to the benchmark. The proposed estimation method for censored QRNN model can be easily adapted to deal with different censoring types including left censoring, right censoring and interval censoring, remedying the defect that existing method is only suitable for right censoring type.

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.

Composite quantile regression for heteroscedastic partially linear varying-coefficient models with missing censoring indicators

This paper is concerned with composite quantile regression for partially linear varying-coefficient models with heteroscedasticity when data are right censored and censoring indicators are missing at random. We construct estimators of parametric regression coefficients and nonparametric varying-coefficient functions in the proposed models based on regression calibration, imputation and inverse probability weighted approaches. The asymptotic normality of the proposed estimators is proved. Meanwhile, an adaptive LASSO penalized variable selection method and its oracle property are considered. We also demonstrate the performance of the proposed estimation method and variable selection procedure through comprehensive simulations and a real-data application.

Sequential Transient Detection for RF Fingerprinting

In this paper, a sequential transient detection method for radio frequency (RF) fingerprinting used in the identification of wireless devices is proposed. To the best knowledge of the authors, sequential detection of transient signals for RF fingerprinting has not been considered in the literature. The proposed method is based on an approximate implementation of the generalized likelihood ratio algorithm. The method can be implemented online in a recursive manner with low computational and memory requirements. The transients of wireless transmitters are detected by using the likelihood ratio of the observations without the requirement of any a priori knowledge about the transmitted signals. The performance of the method was evaluated using experimental data collected from 16 Wi-Fi transmitters and compared to those of two existing methods. The experimental test results showed that the proposed method can be used to detect the transient signals with a low detection delay. Our proposed method estimates transient starting points 20-times faster compared to an existing robust method, as well as providing a classification performance of a mean accuracy close to 95%.

Analog instrument pointer monitoring and parameter estimation via line scan vision

Analog instruments are widely used in energy engineering. However, an analog instrument is only human-readable because it does not have a built-in digital communication interface. In power system, it is necessary not only to record the final output value but also to monitor the associated dynamic power process. The present paper develops an analog instrument pointer monitoring and parameter estimation system via line scan vision. A line scan camera is used to collect the dynamic process data of the analog instrument. The captured images can be regarded as a discrete temporal sequence. The light-spot centroid method is implemented to extract the initial pointer position. Data normalization is used to process the initial data. By analysing the system step response function, we construct a cost function, and Least-squares identification algorithm is used to estimate the damp and natural frequency. The proposed method monitors the dynamic process of the analog instrument with various inputs, such as sine signals and random signals, thereby enabling condition monitoring. Experimental prediction results show that the proposed estimation method is effective and robust.

Stabilization of nonlinear time-delay systems: Flexible delayed impulsive control

This paper studies the stabilization of nonlinear time-delay systems under flexible delayed impulsive control. Some sufficient conditions are provided for establishing stability property in terms of exponential Lyapunov-Razumikhin functions. It is shown that the size of delay in continuous dynamics can be flexible. Specially, it can be smaller or larger than the impulsive intervals, and there is no magnitude relationship between the delay in continuous flow and impulsive delay. In most existing results, from the impulsive control point of view, the Lyapunov functions were based on the assumption that there was a common threshold at every impulse point. In this study, utilizing the proposed method of average impulsive estimation (AIE), the rate coefficients are flexible, and the impulsive delay can be integrated to guarantee the effect of stabilization of impulses. As an application, the theoretical results are applied to the synchronization of a chaotic neural network, and the impulsive control input is formalized in terms of linear matrix inequalities (LMIs). The efficiency of the derived results is illustrated by two numerical examples.

Reliable mixed [formula omitted] distributed estimation for periodic nonlinear systems with jumping topology

This paper investigates the problem of reliable mixed H2/H∞ distributed state estimation for periodic nonlinear systems. A sensor network with time-varying topology is used to measure the system, where the time-varying condition is described by a period index dependent Markov chain. A distributed state estimator is designed based on the local and neighbors’ innovation information of each node, while the non-fragile estimator is considered to improve the robustness of the estimator. An augmented estimation error system is derived, while the developed sufficient condition is carried out to ensure the stochastic stability and the mixed H2/H∞ performance. Then, the expected estimator gains are solved on the basis of the achieved sufficient condition. Finally, the effectiveness of the proposed state estimation method is emphasized by a numerical example and the comparative experiments.

Minimum f-Divergence Estimation With Applications to Degradation Data Analysis

Minimizing the divergence between two probability distributions offers an alternative parameter estimation method. The current literature mainly focuses on minimizing the Kullback-Leibler (K-L) divergence between the true and the proposed models in which the true model is assumed to be known or fixed. In this paper, we propose a parameter estimation method that minimizes the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> -divergence between two probability distributions. The method is suitable for different situations, no matter the true distribution is known or not. The statistical properties of the estimator, including consistency and asymptotic normality, are established. As an illustration, our method is employed to estimate the degradation model, which is a model frequently used to assess the lifetime of highly reliable products. A simulation study and a real degradation data analysis are presented to illustrate the effectiveness of the proposed estimation method.