Parameter Estimation Scheme Research Articles

Assessment of a Dynamic Model for the Optimization of Refinery Preheat Trains under Fouling

Fouling in heat transfer applications affects process efficiency, safety, emissions, and economics. Models of different predictive accuracy, data requirement, and computational feasibility are available to quantify its effects on individual exchangers and whole networks, and the benefits of mitigation techniques. A key question is how much model detail is required for which use. This article compares two dynamic thermo-hydraulic models for heat exchanger networks under fouling: a high-fidelity model (A) suitable for simulation and a simpler model (B) suitable for optimization. It identifies conditions where the two models are broadly equivalent and presents a parameter estimation scheme to match them, and a validation methodology. A detailed comparison for 37 exchangers in 8 networks shows that the simpler model (with parameters fitted as indicated) can approximate well the high-fidelity model for relatively long periods. The simpler Model B is then successfully used to simultaneously optimize cleaning schedule and flow distribution of an industrial preheat train. This solution is validated against Model A, with a difference in predicted operational cost of 1.4% over 1 year. Results indicate that the simpler model and fitting procedure can be confidently used in a closed loop nonlinear model predictive control strategy.

Observation of Dynamic State Parameters and Yaw Stability Control of Four-Wheel-Independent-Drive EV

Vehicle yaw stability control is an important part of the active safety of electric vehicles. In order to realize the yaw stability control of vehicles, this paper takes 4-WID electric vehicles as the research object, studies the nonlinear estimation of the state parameters of the lateral stability dynamic system and the yaw stability control strategy. The vehicle state parameter estimation strategy, based on the unscented Kalman filter (UKF) algorithm and the model predictive control algorithm, are designed to control the vehicle yaw stability, which realizes the safe and stable driving of the vehicle. Through CarSim–Simulink joint simulation and hardware-in-the-loop (HIL) experiments based on MicroAutoBox, the effectiveness and real-time performance of the designed control strategy are fully verified, which accelerate the development process of the vehicle controller, and realizes the safe and stable driving of the vehicle.

Full-Field Bridge Deflection Monitoring with Off-Axis Digital Image Correlation.

Video deflectometer based on using off-axis digital image correlation (DIC) has emerged as a robust non-contact optical tool for deflection measurements of bridges. In practice, a video deflectometer often needs to measure the deflections at multiple positions of the bridge. The existing 2D-DIC-based measurement methods usually use a laser rangefinder to measure the distance from each point to the camera to obtain the scale factor for the point. It is only suitable for the deflection measurements of a few points since manually measuring distances for a large number of points is time consuming and impractical. In this paper, a novel method for full-field bridge deflection measurement based on off-axis DIC is proposed. Because the bridge is usually a slender structure and the region of interest on the bridge is often a narrow band, the new approach can determine the scale factors of all the points of interest with a spatial straight-line fitting scheme. Moreover, the proposed technique employs reliability-guided processing and a fast initial parameter estimation strategy for real-time and accurate image-matching analysis. An indoor cantilever beam experiment verified the accuracy of the proposed approach, and a field test of a high-speed railway bridge demonstrated the robustness and practicability of the technique.

Towards high data-rate diffusive molecular communications: A review on performance enhancement strategies

Diffusive molecular communications (DiMC) have recently gained attention as a candidate for nano- to micro- and macro-scale communications due to its simplicity and energy efficiency. As signal propagation is solely enabled by Brownian motion mechanics, DiMC faces severe inter-symbol interference (ISI), which limits reliable and high data-rate communications. Herein, recent literature on DiMC performance enhancement strategies is surveyed; key research directions are identified. Signaling design and associated design constraints are presented. Studies on fundamental information theoretic limits of DiMC channel are reviewed. Classical and novel transceiver designs are discussed with an emphasis on methods for ISI mitigation and performance-complexity tradeoffs. Key parameter estimation strategies such as synchronization and channel estimation are considered in conjunction with asynchronous and timing error robust receiver methods. Finally, source and channel coding in the context of DiMC is presented.

Entanglement-Free Parameter Estimation of Generalized Pauli Channels

We propose a parameter estimation protocol for generalized Pauli channels acting on d-dimensional Hilbert space. The salient features of the proposed method include product probe states and measurements, the number of measurement configurations linear in d, minimal post-processing, and the scaling of the mean square error comparable to that of the entanglement-based parameter estimation scheme for generalized Pauli channels. We also show that while measuring generalized Pauli operators the errors caused by the Pauli noise can be modeled as measurement errors. This makes it possible to utilize the measurement error mitigation framework to mitigate the errors caused by the generalized Pauli channels. We use this result to mitigate noise on the probe states and recover the scaling of the noiseless probes, except with a noise strength-dependent constant factor. This method of modeling Pauli channel as measurement noise can also be of independent interest in other NISQ tasks, e.g., state tomography problems, variational quantum algorithms, and other channel estimation problems where Pauli measurements have the central role.

Nonintrusive Method for Induction Motor Equivalent Circuit Parameter Estimation using Chicken Swarm Optimization (CSO) Algorithm

This paper presents a nonintrusive method for estimating the parameters of an Induction Motor (IM) without the need for the conventional no-load and locked rotor tests. The method is based on a relatively new swarm-based algorithm called the Chicken Swarm Optimization (CSO). Two different equivalent circuits implementations have been considered for the parameter estimation scheme (one with parallel and the other with series magnetization circuit). The proposed parameter estimation method was validated experimentally on a standard 7.5 kW induction motor and the results were compared to those obtained using the IEEE Std. 112 reduced voltage impedance test method 3. The proposed CSO optimization method gave accurate estimates of the IM equivalent circuit parameters with maximum absolute errors of 5.4618% and 0.9285% for the parallel and series equivalent circuits representations respectively when compared to the IEEE Std. 112 results. However, standard deviation results in terms of the magnetization branch parameters, suggest that the series equivalent circuit model gives more repeatable results when compared to the parallel equivalent circuit.
 Keywords: Induction motor, Chicken Swarm Optimization, parameter estimation, equivalent circuit, objective function

Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms

Accurate solar cell modeling is essential for reliable performance evaluation and prediction, real-time control, and maximum power harvest of photovoltaic (PV) systems. Nevertheless, such a model cannot always achieve satisfactory performance based on conventional optimization strategies caused by its high-nonlinear characteristics. Moreover, inadequate measured output current-voltage (I-V) data make it difficult for conventional meta-heuristic algorithms to obtain a high-quality optimum for solar cell modeling without a reliable fitness function. To address these problems, a novel genetic neural network (GNN)-based parameter estimation strategy for solar cells is proposed. Based on measured I-V data, the GNN firstly accomplishes the training of the neural network via a genetic algorithm. Then it can predict more virtual I-V data, thus a reliable fitness function can be constructed using extended I-V data. Therefore, meta-heuristic algorithms can implement an efficient search based on the reliable fitness function. Finally, two different cell models, e.g., a single diode model (SDM) and double diode model (DDM) are employed to validate the feasibility of the GNN. Case studies verify that GNN-based meta-heuristic algorithms can efficiently improve modeling reliability and convergence rate compared against meta-heuristic algorithms using only original measured I-V data.

Asymmetric Univariate and Bivariate Laplace and Generalized Laplace Distributions

Alternative specifications of univariate asymmetric Laplace models are described and investigated. A more general mixture model is then introduced. Bivariate extensions of these models are discussed in some detail, with particular emphasis on associated parameter estimation strategies. Multivariate versions of the models are briefly introduced.

Nitsche’s method as a variational multiscale formulation and a resulting boundary layer fine-scale model

We show that in the variational multiscale framework, the weak enforcement of essential boundary conditions via Nitsche’s method corresponds directly to a particular choice of projection operator. The consistency, symmetry and penalty terms of Nitsche’s method all originate from the fine-scale closure dictated by the corresponding scale decomposition. As a result of this formalism, we are able to determine the exact fine-scale contributions in Nitsche-type formulations. In the context of the advection–diffusion equation, we develop a residual-based model that incorporates the non-vanishing fine scales at the Dirichlet boundaries. This results in an additional boundary term with a new model parameter. We then propose a parameter estimation strategy for all parameters involved that is also consistent for higher-order basis functions. We illustrate with numerical experiments that our new augmented model mitigates the overly diffusive behavior that the classical residual-based fine-scale model exhibits in boundary layers at boundaries with weakly enforced essential conditions.

Mounting Parameter Estimation From Velocity Vector Observations for Land Vehicle Navigation

A velocity model composing speed sensors and motion constraints is used as the direct and effective auxiliary information of global navigation satellite system (GNSS)/inertial navigation system integrated systems to improve the land vehicle navigation accuracy under interfered or blocked GNSS signal environments. The precise determination of the mounting parameter (the geometric relationship between sensors), including mounting angle and lever arm, is necessary to realize the full potential of velocity assistance. In this article, we propose an optimal mounting parameter estimation scheme based on the velocity vector observations. The quaternion-based optimal attitude determination method is used to estimate the mounting angle, and the weighted recursive least squares are applied to estimate the lever arm. A land vehicle test and an agricultural tractor test are carried out to verify the feasibility and correctness, and inertial measurement units (IMUs) of different grades, especially the micro electro mechanical system (MEMS) IMUs, are used. The results show that the attitude errors, especially the heading error, are the main factors influencing mounting angle estimation and that the statistical lateral velocity based on the estimated mounting parameters is better than 0.03 m/s, thus better satisfying vehicle motion constraints.

Joint Initial Uplink Synchronization and Interference Aware Resource Allocation for D2D Communication in the MU-OFDMA System

ABSTRACT Over recent epoch, initial uplink synchronization has become an essential part of the wireless communication systems owing to its amenities in the identification of new users to originate communication. Yet, allocating and sharing resources to users is a bottleneck due to the interference caused by multiple users. Besides, preceding works didn't concentrate on the usage of an effective mechanism to estimate the parameters of the received signal in the multiuser synchronization environment. These downsides affect the throughput and spectrum efficiency of the designed OFDMA system. To address these bottlenecks, this paper proposes the Joint Initial Uplink Synchronization and Interference Aware Resource Allocation (JIUS-IRA) in the Multi-User OFDMA system. JIUS-IRA comprises four processes: Parameter Estimation, Optimum Antenna Selection, Resource Allocation, and Resource Sharing. Initially, JIUS-IRA executes the Fast Block Least Mean Square-based Parameters Estimation Scheme (FBLMS-PES) to estimate the user parameters. To reduce the power consumption during transmission between 5G MIMO base station and users, JIUS-IRA selects the optimal antenna for transmission. The Sign Rank Oriented Resource Allocation (SO-RA) method is executed to allocate sub-channel resources to the cellular user. The resource requirement of D2D users present in the OFDMA system is resolved via the Harris Hawks Optimizer based Resource Sharing Scheme (H2ORSS). The numerical results acquired from the simulation are highly confidential with regard to metrics Root Mean Square Error (RMSE) of timing estimation (∼0.006), RMSE of power estimation (∼0.0079), RMSE of frequency estimation (∼ 0.008), Throughput (∼95%), Spectrum Efficiency (∼11.9 bps), and Complexity (0.045 × 104).

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

AbstractA state‐of‐the‐art review on various identification schemes proposed for the Hammerstein, Wiener, and Volterra systems is presented with respect to the special problems arising in the identification of unknown nonlinear systems. Past and recent developments in the field of nonlinear system identification, parameter estimation, and nonlinear control schemes along with the nonlinearity issues are also deeply investigated. A comprehensive analysis on various parameter estimation approaches and nonlinear control laws are made adding credit to the noteworthy contributions, constructive arguments, and remarkable breakthroughs of researchers in various research fields. The application of most popular nonlinear control strategies for many nonlinear systems in the existing literature are presented spotlighting their merits and major shortcomings. The challenges faced in the nonlinear control field and their emergences to future directions are projected.

Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt-Hydrogen Electrochemical Competition during a Cobalt Removal Process.

A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters to be estimated, a constrained guided parameter estimation scheme was derived based on model equations and experimental data. The proposed model and the parameter estimation scheme have two advantages: (i) The model reflected for the first time the mechanism of the electrochemical competition between cobalt and hydrogen ions in the process of cobalt removal in zinc hydrometallurgy; (ii) The proposed constrained parameter estimation scheme did not depend on the information of the possible value ranges of parameters to be estimated; (iii) the constraint conditions provided in that scheme directly linked the experimental phenomenon metrics to the model parameters thereby providing deeper insights into the model parameters for model users. Numerical experiments showed that the proposed constrained parameter estimation algorithm significantly improved the estimation efficiency. Meanwhile, the proposed cobalt–hydrogen electrochemical competition model allowed for accurate simulation of the impact of hydrogen ions on cobalt removal rate as well as simulation of the trend of hydrogen ion concentration, which would be helpful for the actual cobalt removal process in zinc hydrometallurgy.

Variational Bayesian based adaptive PDA filter in scenarios with unknown detection probability and heavy-tailed process noise

For target tracking systems, the probability of detecting a target is difficult to determine, and the process noise often has non-Gaussian heavy-tailed characteristics owing to interference from outliers. To address the issues associated with single target tracking within clutters in scenarios with an unknown detection probability and heavy-tailed process noise, this paper presents a variational Bayesian-based adaptive probabilistic data association filter (VB-APDAF). The beta distribution, Pearson type VII distribution and multinomial distribution are used to model the detection probability, the process noise, and the association events, respectively. To guarantee the conjugation, a novel parameter estimation strategy is employed. In this strategy, the previous state is introduced in the state update process to construct the joint probability density function of parameters to be estimated and data set. The VB framework is used to estimate the target state, detection probability, and associated events. An experiment was performed under simulated conditions to demonstrate the effectiveness of the proposed filter.

Computation‐Efficient Parameter Estimation for a High‐Resolution Global Tide and Surge Model

AbstractIn this study, a computation‐efficient parameter estimation scheme for high‐resolution global tide models is developed. The method is applied to Global Tide and Surge Model with an unstructured grid with a resolution of about 2.5 km in the coastal area and about 4.9 million cells. The estimation algorithm uses an iterative least squares method, known as DUD. We use time‐series derived from the FES2014 tidal database in deep water as observations to estimate corrections to the bathymetry. Although the model and estimation algorithm run in parallel, directly applying of DUD would not be affordable computationally. To reduce the computational demand, a coarse‐to‐fine strategy is proposed by using output from a coarser model to replace the fine model. There are two approaches; One is completely replacing the fine model with a coarser model during calibration (Coarse Calibration) and the second is Coarse Incremental Calibration, that replaces the output increments between the initial model and model with modified parameters by coarser grid model simulations. To further reduce the computation time, the parameter dimension is reduced from O(106) to O(102) based on sensitivity analysis, which greatly reduces the required number of model simulations and storage. In combination, these methods form an efficient optimization strategy. Experiments show that the accuracy of the tidal representation can be improved significantly at affordable cost. Validation for other time‐periods and using coastal tide‐gauges shows that the accuracy is improved significantly. However, the calibration period of two weeks is short and leads to some over‐fitting of the model.

SENSORLESS VECTOR CONTROLLED THREE-PHASE PWM INVERTER-FED INDUCTION MOTOR DRIVE SYSTEM WITH AUTO-TUNING ESTIMATION OF MACHINE PARAMETER APPROACH

This paper deal with a practical development technology on a highly accurate faster response adjustable speed drive implementation for the three-phase sinewave fed general purpose induction motor system which is based upon sensorless slip frequency type vector control scheme with an automatic auto-tuning machine parameter estimation strategy. The essential procedure and considerations to measure and estimate the exact stator and cage rotor circuit parameters of the induction motor treated here are discussed under its operating conditions. The speed regulation characteristics of the induction motor is illustrated and evaluated for the induction machine parameter variations under the actual operating conditions ranging from a low frequency to a high frequency for various specified load torque setting. The variable speed induction motor drive system employing sensorless slip frequency-based vector control scheme which incorporates the current controlled three-phase high frequency carrier PWM switching inverter with automatic auto-tuning estimation strategy on the temperature-dependent and -independent machine circuit parameters is practically implemented using DSP-based vector controller. The dynamic speed response performances under essentially changed load torque disturbances as well as steady state speed against torque characteristics of the proposed variable speed drive control and implementations are illustrated and discussed from an experimental point of view.

Use of Mobile EEG in Decoding Hand Movement Speed and Position

With the majority of brain–computer interface (BCI) research currently restricted to the controlled settings in labs, there is a growing interest to study the feasibility of BCI applications in the real world. This interest is largely driven by the availability of ergonomic electroencephalography (EEG) recording devices. In this article, we investigate the feasibility of applying a commercial EEG device in motor decoding and propose a signal processing strategy to classify and reconstruct hand movement speed and position. An experiment is designed to simultaneously record EEG and hand position, while the user executes movement toward the left or right direction at two different speeds. A visual interface is designed to guide the user to perform the task under each condition. Data are recorded from 21 subjects. The classification of direction-dependent and -independent speeds is implemented using spatial and spectral features. An optimized movement parameter estimation strategy is proposed to reconstruct the instantaneous position and speed of the hand from EEG. Average performances of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$73.36\ ({ \pm \! 11.95})\% $</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$69.46\ ({ \pm \! 13.39})\% $</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$68.98\ ({ \pm \! 14.79})\% $</tex-math></inline-formula> are obtained for direction independent, right and left direction fast versus slow classification, respectively. The average correlation between recorded and reconstructed hand position and speed along the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> -axis is in the range of [0.22, 0.57] and [0.26, 0.58], respectively. The reported results validate the use of commercial-grade EEG and low-frequency components of EEG relevant to motor decoding for real-world motor kinematics BCI applications.

A Model Parameter Estimation Scheme Based on Fast Search for CTU-Level Rate Control in HEVC

The estimation of a model parameter is very important for coding tree unit (CTU)-level rate control as it can significantly affect bit allocation and coding performance. However, CTU-level rate control models sometimes fail as the accuracy of the model parameter is not well considered in high efficiency video coding (HEVC). Based on this observation, a model parameter estimation scheme that is based on a fast search for CTU-level rate control is proposed. First, the estimation of a model parameter is formulated as an optimization problem. Second, we transform the optimal model parameter estimation into a process to search for the most similar CTU. To improve the searching efficiency and reduce the parameter estimation error, a new spiral search mechanism is proposed to find the most similar CTU from inside to outside. According to the experimental results, the proposed method can distinctly enhance the accuracy of the CTU-level rate control and consistently outperform HM16.18 and other state-of-the-art algorithms for all testing configurations. An average reduction of 7.8% and a maximum reduction of 10.0% of the BD-Rate were achieved compared with HM16.18. An average reduction of 3.4% and a maximum reduction of 4.6% of the BD-Rate were achieved compared with other state-of-the-art algorithms. A slight computational complexity overhead is observed.

Parameters Measurement of Multiple Exponentially Damped Sinusoids With Sub-Nyquist Sampling

Measurement of parameters of multiple exponentially damped sinusoids (MEDS) signals is significant for nuclear magnetic resonance imaging and speech analysis systems. Traditional technologies require a large number of samples and heavy processing burden to estimate the parameters. In this brief, we propose a sub-Nyquist sampling and parameters estimation scheme for MEDS signals to address this problem. The proposed system consists of two parallel sampling channels, where the sampling clock of one channel is staggered with the other. The first sampling channel is used to estimate complex amplitudes, while the other is used to solve frequency ambiguity problem and determine the damping factors and pole frequencies. The proposed scheme enables parameters measurement of K components MEDS signals from as few as 3K samples, and it is also applicable to the scenario with unknown K. We also propose a hardware prototype to implement the proposed system. Simulation and experimental results demonstrate the effectiveness and noise robustness of the proposed scheme.

Improving the flood forecasting capability of the Xinanjiang model for small- and medium-sized ungauged catchments in South China

Prediction in ungauged basins (PUB) is as crucial as it is challenging. Thus far, there have been abundant regionalization studies on PUB, whereas "regionalization" is the main research method. In order to estimate Xinanjiang model parameters in ungauged areas and improve the accuracy of flood simulation for small and medium-sized ungauged catchments, the Xinanjiang model was applied on 33 mountainous small- and medium-sized catchments in south China. This study investigated the relative benefits of traditional regionalization methods (physical similarity and parameter regression) and physically consistent parameter estimation method. The latter can directly estimate three sensitive parameters of the Xinanjiang model without the need of regionalization. In addition, the effect of the number of donor catchments was addressed. The results show that the prediction accuracy of the traditional regionalization methods did not obtain satisfactory prediction results. However, the integrated schemes, which combine the regionalization methods with physically consistent methods, performed considerably better than the traditional regionalization methods, indicating that directly performing a parameter estimation from underlying surface data of ungauged catchments can improve the transferability of the Xinanjiang model in these catchments. On the other hand, the best accuracy was obtained when the number of donor catchments was equal to five in the integrated schemes. The integrated parameter estimation schemes proposed in this study support more effective hourly flood event simulation for small- and medium-sized ungauged catchments in southern China.