Parameter Estimation Process Research Articles

A side-sensitive double sampling monitoring scheme with estimated process parameters

In statistical process monitoring (SPM), most of the monitoring schemes are designed assuming that the process parameters for the underlying distribution are known (i.e., Case K). In a variety of contexts, it has been shown that when the parameters used to design the control limits are unknown (i.e., Case U), this greatly affects the monitoring schemes properties. Hence, in this paper, we study the parameter estimation effect of the side-sensitive double sampling (SSDS) monitoring scheme for detecting changes in the process mean when distribution design parameters are estimated from an in-control retrospective sample. A thorough investigation is conducted using the unconditional run-length properties (i.e., average, standard deviation and percentiles), average sample size (ASS) and average number of observations to signal (ANOS) through exact integral formulas and simulations. In addition, the average extra quadratic loss (AEQL), average ratio of the average run-length (ARARL) and performance comparison index (PCI) are used to quantify the run-length of the SSDS scheme from an overall performance perspective. Comparisons with other established monitoring schemes when parameters are unknown indicate that the SSDS scheme has a better overall performance. An illustrative example is also given to facilitate the design and implementation of the new scheme. An additional section briefly discussing the synthetic version of the SSDS scheme is also provided.

DeepImaging: A Ground Moving Target Imaging Based on CNN for SAR-GMTI System

Imaging of ground multiple moving targets in a synthetic aperture radar (SAR) system is a challenging task due to the fact that targets are defocused owing to motions and contaminated by the strong background clutter. Motivated by recent advances in deep learning, a novel deep convolutional neural network (CNN)-based method, DeepImaging, is proposed for ground moving target imaging (GMTIm). Different from conventional imaging methods relying on the prior knowledge of imaging, the proposed DeepImaging is directly trained to learn an implicit imaging model of multiple moving targets. It is free of motion parameter estimation and iteration process. Then, the trained DeepImaging, as an imaging processor, can be applied to the SAR complex received data after clutter suppression to achieve the multiple moving target imaging and the residual clutter elimination simultaneously. Simulations and experiments on the Gotcha data show that the proposed method achieves significant improvements over existing state-of-the-art GMTIm methods in terms of imaging quality and efficiency.

MODELING AND METHODOLOGY OF EVALUATION OF ENGINEERING SITUATION IN RIVER BREAKDOWN OF SEREDNYODNIPROVSKA HES

The article deals with the process of parameters estimation of the flood zone at the dam break or its destruction, and a scale of possible destruction caused by possible emergencies. Modeling is carried out for the time of approach of the breakthrough wave to a given distance, the height of the breakthrough wave, the time of discharge of the reservoir, the duration of the passage of the wave at a predetermined distance for buildings and structures in the area of shock wave. The example is done for the dam of the Middle Dnieper hydroelectric power station. A technique for modeling a hazardous situation related to the consequences of a breakthrough of the dam at the Middle Dnieper Hydropower Plant has been developed. The prediction and simulation is based on the phenomenological parameters of the Kamyansky reservoir.The simulation of the assessment of the engineering environment for the dam of the Serednyodniprovska hydroelectric power station was carried out using the parameters of flooding of the terrain. Parameters used - the possible maximum depth of flooding, the width of flooding and the rate of water flow at the break of the dam, the time of arrival of the wave front, the crest and tail of the break wave, maximal flow dams, wave heights above water level above domestic flow. As a result, the maximum flooded area was estimated.Based on the use of dam and reservoir characteristics and the use of appropriate formulas for calculating emergency factors and using modeling using ArcGIS, the possible topographic consequences of the breakthrough of the dam in the city Kamianske and the locations of industrial enterprises in its vicinity were obtained. The latter enables rescue services to promptly assess the threat of an emergency in the event of an emergency, to have an idea of the consequences in order to take preemptive measures to prevent the occurrence of an emergency.

Trend and Pattern in Average Monthly Maximum Temperatures in Thailand from 1986 to 2015

This research aims to study the trends and patterns of average maximum temperature change data of monthly surface in Thailand during the 30 year-period from 1986 to 2015 that has already eliminated seasonal variations and autocorrelation. The data set obtained from Climatological center, Meteorological Development Division, Thailand Meteorological Department for 73 completed recording stations. The climatically regions of Thailand meteorology are divided into 5 regions: Central, Eastern, North, Northeast and South. After the process of parameter estimation using simple linear regression of average maximum temperature data that has already eliminated influences of variations, the study results showed that over the last 10 years, the northern, central, eastern, northeastern and southern regions of Thailand had trends of the average maximum surface temperature increasing by 0.164°C 0.137°C 0.127°C 0.194°C 0.068°C, respectively. For over the whole country, the average maximum surface temperature increasing by 0.141°C in the last 10 years at a significant level of 0.05.

Enhancing Accuracy and Numerical Stability for Repetitive Time-Varying System Identification: An Iterative Learning Approach

Time-varying system identification is an appealing but challenging research area. Existing identification algorithms are usually subject to either low estimation accuracy or bad numerical stability. These deficiencies motivate the development of an iterative learning identification algorithm in this paper. Three distinguished features of the proposed method result in the achievement of high estimation accuracy and high numerical stability: i) recursion along the iteration axis, ii) bias compensation, and iii) singular value decomposition (SVD). Firstly, an extra iteration axis associated with the original time axis is introduced in the parameter estimation process. A norm-optimal identification approach with the balance between convergence speed and noise robustness is then proposed along the iteration axis, followed by further analysis on the accuracy and the numerical stability. Secondly, in order to eliminate the estimation bias in the presence of noise and thus to improve the accuracy, a bias compensation algorithm along the iteration axis is proposed. Thirdly, a SVD-based update algorithm for the covariance matrix is developed to avoid the possible numerical instability during iterations. Numerical examples are finally provided to validate the algorithm and confirm its effectiveness.

Optimal Estimation and Feedforward Control of Strip-Longitudinal Hardness for Thickness Hunting Suppression of Tandem Cold Mill Process

Following the recent trend of weight reduction in car industry, producing high quality cold-rolled AHSS (advanced high-strength steel) strip becomes important. Thickness hunting (or fluctuation) problem can be more prominent for this cold-rolled AHSS strip making, which can stem from the non-uniformity of hot-rolled strip and can severely degrade product quality. In this paper, we propose a novel framework to estimate the strip-longitudinal hardness of the TCM (tandem cold mill) process and its feedforward control to substantially reduce the thickness hunting, while fully incorporating the interconnected nature and sensing sparsity of the TCM process. In particular, our estimator consists of the following two complementary loops: 1) fast real-time hardness estimation loop, which optimally fuses the process model and sensing information; and 2) slower constant process-parameter estimation loop via optimization utilizing the nonlinear process model and (stored/measured) sensor data. Efficacy of the proposed estimation and control frameworks are then validated with high-precision TCM process physics simulator.

Estimation of parameters from time traces originating from an Ornstein-Uhlenbeck process.

In this article, we develop a Bayesian approach to estimate parameters from time traces that originate from an overdamped Brownian particle in a harmonic potential, or Ornstein-Uhlenbeck process (OU). We show that least-square fitting the autocorrelation function, which is often the standard way of analyzing such data, is significantly underestimating the confidence intervals of the fitted parameters. Here, we develop a rigorous maximum likelihood theory that properly captures the underlying statistics. From the analytic solution, we found that there exists an optimal measurement spacing (Δt=0.7968τ) that maximizes the statistical accuracy of the estimate for the decay-time τ of the process for a fixed number of samples N, which plays a similar role than the Nyquist-Shannon theorem for the OU process. To support our claims, we simulated time series with subsequent application of least-square and our maximum likelihood method. Our results suggest that it is quite dangerous to apply least-squares to autocorrelation functions both in terms of systematic deviations from the true parameter values and an order-of-magnitude underestimation of confidence intervals. To see whether our findings apply to other methods where autocorrelation functions are typically fitted by least-squares, we explored the analysis of membrane fluctuations and fluorescence correlation spectroscopy. In both cases, least-square fits exhibit systematic deviations from the true parameter values and significantly underestimate their confidence intervals. This fact emphasizes the need for the development of proper maximum likelihood approaches for such methods. In summary, our results have strong implications for parameter estimation for processes that result in a single exponential decay in the autocorrelation function. Our analysis can directly be applied to single-component dynamic light scattering experiments or optical trap calibration experiments.

Ammonia Oxidizing Bacteria (AOB) kinetic parameters estimated using the actual maximum ammonia oxidation rate

The objective of this paper was to define a new and reliable approach to the Ammonia Oxidizing Bacteria kinetic parameters estimation process. The proposed approach is based on the use of the information provided by a continuous on-line differential titrimeter developed for the estimation of the Actual Maximum Ammonia Oxidation Rate. The instrument was tested in a 200000 p.e. WRRF (Water Resources Recovery Facility) treating urban and industrial (textile) sewage wastewater and truck-transported liquid wastes in the Tuscan textile district (Italy). Maximum Ammonia Oxidation Rate was used as an innovative data series for maximum specific growth rate (μmaxAOB) and decay coefficient (bAOB) estimation. Titrimetric tests were conducted under non-limiting condition for oxygen and substrate and half-saturation constant of ammonia (KNH) was calibrated referring to the effluent quality of the WRRF. The proposed procedure allows to define a new kinetic parameters data set for AOB biomass (μmaxAOB = 0.72 d−1, bAOB = 0.25 d−1, KNH = 0.4 mg N–NH4+ L−1). The new data set was used to produce a new model that allows to improve the accuracy of the description of the process efficiency of the WRRF when compared with a model using kinetic parameters estimated through conventional methods.

Resolution enhancement for inverse synthetic aperture radar images using a deep residual network

AbstractA framework for inverse synthetic aperture radar (ISAR) image resolution enhancement using a deep residual network is proposed. Our framework directly learns an end‐to‐end mapping in the form of a deep residual network (ResNet) between the input low‐resolution (LR) images and the output high‐resolution (HR) images with respect to the point spread function (PSF). In our network, residual blocks without batch normalization (BN) layers are applied to retain ISAR image contrast, and parametric rectified linear unit (PReLU) which can adaptively learn the negative coefficients is used as the activation function. The complex‐valued ISAR imagery is divided into two real‐valued channels to preserve phase information implicitly. Experimental results show that the proposed method can obtain higher quality HR images compared with traditional sparsity‐driven methods while freeing itself from a prior model for radar echoes and a complicated parameter estimation process.

Real-time inverse solution of the composites’ cure heat transfer problem under uncertainty

This paper addresses the development of an inversion scheme based on Markov Chain Monte Carlo integrating process modelling with monitoring data for the real-time probabilistic estimation of unknown stochastic input parameters such as heat transfer coefficient and resin thermal conductivity and process outcomes during the manufacture of fibrous composites materials. Kriging was utilized to build an efficient surrogate model of the composite curing process based on finite element modelling. The utilization of an inverse scheme with real-time temperature monitoring driving the estimation of process parameters during manufacture results in real-time probabilistic prediction of process outcomes.

The bounds of Fisher information induced by the superposed input states

Quantum Fisher information as an important quantity in quantum metrology determines the upper bound of the measurement accuracy. It is shown that the quantum feature such as entanglement can improve quantum Fisher information in some cases. Here we study how quantum coherence affects the quantum Fisher information from a general point of view. We find out that the bounds induced by the superposed states can well restrict the quantum Fisher information in a general parameter estimation scheme. As applications, we demonstrate these bounds by a parameter estimation process with the superposition input state undergoing different quantum channels.

Parameter estimation to study the immediate impact of aortic cross-clamping using reduced order models.

Aortic cross-clamping is a common strategy during vascular surgery, however, its instantaneous impact on hemodynamics is unknown. We, therefore, developed two numerical models to estimate the immediate impact of aortic clamping on the vascular properties. To assess the validity of the models, we recorded continuous invasive pressure signals during abdominal aneurysm repair surgery, immediately before and after clamping. The first model is a zero-dimensional (0D) three-element Windkessel model, which we coupled to a gradient-based parameter estimation algorithm to identify patient-specific parameters such as vascular resistance and compliance. We found a 10% increase in the total resistance and a 20% decrease in the total compliance after clamping. The second model is a nine-artery network corresponding to an average human body in which we solved the one-dimensional (1D) blood flow equations. With a similar parameter estimation method and using the results from the 0D model, we identified the resistance boundary conditions of the 1D network. Determining the patient-specific total resistance and the distribution of peripheral resistances through the parameter estimation process was sufficient for the 1D model to accurately reproduce the impact of clamping on the pressure waveform. Both models gave an accurate description of the pressure wave and had a high correlation (R2 > .95) with experimental blood pressure data.

Bayesian inference for parameter estimation in lactoferrin-mediated iron transport across blood-brain barrier

BackgroundIn neurodegenerative diseases such as Alzheimer's and Parkinson's, excessive irons as well as lactoferrin (Lf), but not transferrin (Tf), have been found in and around the affected regions of the brain. These evidences suggest that lactoferrin plays a critical role during neurodegenerative diseases, although Lf-mediated iron transport across blood-brain barrier (BBB) is negligible compared to that of transferrin in normal condition. However, the kinetics of lactoferrins and lactoferrin-mediated iron transport are still unknown. MethodTo determine the kinetic rate constants of lactoferrin-mediated iron transport through BBB, a mass-action based ordinary differential equation model has been presented. A Bayesian framework is developed to estimate the kinetic rate parameters from posterior probability density functions. The iron transport across BBB is studied by considering both Lf- and Tf-mediated pathways for both normal and pathologic conditions. ResultsUsing the point estimates of kinetic parameters, our model can effectively reproduce the experimental data of iron transport through BBB endothelial cells. The robustness of the model and parameter estimation process are further verified by perturbation of kinetic parameters. Our results show that surge in high-affinity receptor density increases lactoferrin as well as iron in the brain. ConclusionsDue to the lack of a feedback loop such as iron regulatory proteins (IRPs) for lactoferrin, iron can transport to the brain continuously, which might increase brain iron to pathological levels and can contribute to neurodegeneration. General significanceThis study provides an improved understanding of presence of lactoferrin and iron in the brain during neurodegenerative diseases.

A Simulation Framework for Passive Acoustic Thermometry of Homogenous Materials

Purpose: Passive Acoustic Thermometer (PAT) is a safe method for internal temperature estimation that works based on acoustic radiation of materials with a specific temperature. Several experimental studies have been carried out so far in the field of PAT. While, to the best of our knowledge, there is no simulation-based research reported yet. Materials and Methods: In this article (for the first time) we proposed a simulation framework for evaluating the PAT methodologies. This framework supports the generation of acoustic radiation, signal processing, parameter estimation, and temperature reconstruction processes. At the moment, the proposed framework estimates the temperature in the frequency domain and uses the frequency spectrum of the acquired ultrasound signals captured by a single transducer. Results: Using the proposed framework, we tried to implement previously practical experiments and the results of the simulation are consistent with those of the practical experiment. The mean error of temperature estimation was below 0.45 °C. The results show that it is possible to use this framework to evaluate the PAT in different scenarios. Conclusion: Therefore, this method enhances the possibility of examination of different conditions and algorithms. It also reduces the cost of practical experiment.

Application of the Fourier method to solve the problems of the theory of drying by electromagnetic radiation

On the basis of the idea of splitting the process into physical factors, using the Fourier method and the green function apparatus, an algorithm for constructing a new class of solutions to the initial boundary value problems of the theory of drying by electromagnetic radiation is developed. A subclass of asymptotic solutions, which are obtained by the limiting transition and give a description of the established drying processes, is distinguished. The dependences of the temperature and moisture content fields on time in such solutions are determined by the first eigenvalues of the Sturm – Liouville problem, and the distributions of these fields in space are the first eigenfunctions of this problem. The analysis of asymptotic solutions allows us to establish and visualize the basic laws of electromagnetic drying, which can be used for engineering estimates of process parameters and the calculation of temperature and moisture content fields in the first approximation.

Optimal Estimation of Wavelet Decomposition Level for a Matching Pursuit Algorithm

In this paper, we consider the application of the matching pursuit algorithm (MPA) for spectral analysis of non-stationary signals. First, we estimate the approximation error and the performance time for various MPA modifications and parameters using central processor unit and graphics processing unit (GPU) to identify possible ways to improve the algorithm. Next, we propose the modifications of discrete wavelet transform (DWT) and package wavelet decomposition (PWD) for further use in MPA. We explicitly show that the optimal decomposition level, defined as a level with minimum entropy, in DWT and PWD provides the minimum approximation error and the smallest execution time when applied in MPA as a rough estimate in the case of using wavelets as basis functions (atoms). We provide an example of entropy-based estimation for optimal decomposition level in spectral analysis of seismic signals. The proposed modification of the algorithm significantly reduces its computational costs. Results of spectral analysis obtained with MPA can be used for various signal processing applications, including denoising, clustering, classification, and parameter estimation.

Variable sampling interval exponentially weighted moving average median chart with estimated process parameters

AbstractThe variable sampling interval exponentially weighted moving average median chart with estimated process parameters is proposed. The charting statistic, optimal design, performance evaluation, and implementation of the proposed chart are discussed. The average of the average time to signal (AATS) criterion is adopted to evaluate the performance of the proposed chart. The estimated process parameter‐based VSI EWMA median (VSI EWMA median‐e) chart is compared with the estimated process parameter‐based Shewhart median (SH median‐e), EWMA median (EWMA median‐e), and variable sampling interval run sum median (VSI RS median‐e) charts, in terms of the AATS criterion, where the VSI EWMA median‐e chart is shown to be superior. When process parameters are estimated, the standard deviation of the average time to signal (SDATS) criterion is used to evaluate the AATS performance of the VSI EWMA median‐e chart. Based on the SDATS criterion, the minimum number of phase‐I samples required by the VSI EWMA median‐e chart so that its performance is close to the known process parameters VSI EWMA median chart is recommended.

A Model of Correction Mapping for Al-Quran Recitation Performance Evaluation Engine

The main objective of the research is to develop the correction-mapping model for Al-Quran recitation performance evaluation engine. Machine learning and Digital Signal Processing techniques are applied in representing and analyzing the recitation speech signal. Consequently, a form of recitation correction results is derived and formulated for the final performance evaluation. The proposed corrective mapping model demonstrated in this paper confronted, but not limited to, with the challenging issues of variability of speaker recitation, recitation representation, speaker adaptation, feature extraction, parameters estimation and threshold process classification. The experimental results concluded the Al-Quran automated correction system knowns as Intelligent Quran Recitation Assistant (nur-IQRA) will be able to fulfil the current and future trends of digital society.

Evaluation of Varied Model Order in GA-optimised Parameter Estimation of Toothbrush Rig System

Parameter estimation is a vital part in constructing the best model of a dynamic system. This paper analyzed the performance of toothbrush rig parameter estimation using different model orders. Parameter estimation process of the system is performed through system identification. The approximate mathematical model that resemble the real system is obtained when the output is measured after loading the input signal. The application of real-coded genetic algorithm (RCGA) is proposed as optimization method in estimating the parameters of dynamic system. The best model is obtained by optimizing the objective function of mean squared errors. The performance is analyzed to get the approximate model of the real system using three different model orders with 10 times analysis for each model. A few criteria have been considered which are the optimization result of objective function, time execution and validation process. Real- coded genetic algorithm indicates that parameter estimation with model order 3 is chosen as the best model or the dynamic system as it has the highest performance compared to others.

Estimation of Model Parameters for Earth Radiation Budget Instruments

Radiation budget instruments scan the earth, creating radiation maps that are interpreted as effective temperature. Then in a sense, these instruments continuously monitor the earth’s radiation budget. Since the measured signals are relatively weak, accurate mathematical modeling is critical to the design process. A mathematical model of the thermal response and the ensuing voltage signal response from a radiation budget instrument has been developed. An important ingredient of an accurate and robust model of the system response is a correspondingly accurate estimation of the model parameters. The main objective of this paper is to describe the procedure and results of the required parameter estimation process. First, the model equations are solved using a finite volume method. A sensitivity study is then performed to indicate the relative importance of the parameters. In order to estimate the best combination of system parameters, a least-squares technique is utilized to minimize the difference between the theoretical model and available experimental data. Fortunately, a statistically significant amount of data for the voltage output of the sensor in question were available. We were able to successfully estimate the model parameters and obtain an excellent fit between the mathematical model and the experimental data on which it was based.