Typical Estimation Methods Research Articles

Impedance Investigation of MIFM Ferroelectric Tunnel Junction Using a Comprehensive Small-Signal Model

The urge to develop efficient and ultra-low power architectures for modern and future technological needs lead to an increasing interest and investigation of neuromorphic and ultra-low power computing. In this respect, ferroelectric technology is found to be a perfect candidate to guide this technological transition. Elucidating the physical mechanisms occurring during ferroelectric-based devices operations is fundamental in order to improve the reliability of emerging architectures. In this work, we investigate metal/insulator/ferroelectric/metal (MIFM) ferroelectric tunnel junctions (FTJs) consisting of a ferroelectric hafnium zirconium oxide (HZO) layer and an alumina (Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) layer by means of C-f and G-f measurements performed at multiple voltages and temperatures. For a trustworthy interpretation of the measurements results, an innovative small signal model is introduced that goes beyond the state of the art by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula> ) separating the role played by the leakage in the two layers; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) including the impact of the series impedance (that depends on the samples layout); <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i> ) including the frequency dependence of the dielectric permittivity; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iv</i> ) accounting for the fact that not the whole HZO volume crystallizes in the orthorhombic ferroelectric phase. The model correctly reproduces measurements taken on different devices in different conditions. Results highlight that the typical estimation method for interface trap density may be misleading.

Toward a general framework for jointly processor-workload empirical modeling

The complexity of state-of-the-art processor architectures and their consequent vast design spaces have made it difficult and time-consuming to explore the best configuration for them. Design space exploration (DSE) refers to systematic analysis and pruning of unwanted design points based on parameters of interest. DSE requires analysis and estimation of performance criteria of design points. A more accurate estimation produces a more efficient target design. A typical estimation method is machine learning approaches based on statistical inference, also known as empirical modeling, which requires only a limited number of simulations. Undoubtedly, an empirical model finds the optima much faster than using cycle-accurate simulations and is much more accurate than employing analytical models. For that purpose, our paper proposes a general methodology and a framework to find an appropriate and most accurate empirical model to estimate the performance of general-purpose or embedded multiprocessors running multithreaded workloads. This framework consists of three main steps: (1) Workload characterization and clustering, (2) Finding optimal model, and (3) Estimating the performance of a new workload outside the training set. These optimal performance prediction models could be utilized in the process of exploring the architectural design space. An experimental case is also tested using this framework for feasibility purposes. Validation experiments show MAEs less than 10% for this case.

Electric submersible pump vibration analysis under several operational conditions for vibration fault differential diagnosis

An Electric Submersible Pump (ESP) vibration analysis was performed, considering different wear states and operational conditions. The pumps were tested with different fluid viscosities, operating points, and speeds to evaluate their vibration behavior, with the aim of providing characteristics for non-invasive differential vibration diagnosis. A specific frequency spectrum estimation method is described, focusing on the extraction of frequency component vibration amplitudes used in the petroleum industry-standard vibration analysis. A time-interval definition procedure was proposed to reduce signal amplitude losses due to variation of synchronous operating frequency. The results indicate that the frequency component peak amplitudes can be more accurately identified during synchronous frequency variation by the proposed method than by typical estimation methods. In this study, an ESP vibration differential diagnosis was achieved by analyzing relations between orders of the synchronous frequency, and by distinguishing that the synchronous component amplitude rose approximately proportionally to the square of the rotating speed; this differentiates unbalance fault from a bent shaft or a misalignment. A correlation matrix analysis is provided to demonstrate that variation in the fluid-temperature difference between the pump intake and discharge is related to the vibration amplitude variation in a pump with a vibration fault.

Method to estimate sag frequency in doubly fed induction generator integrated power system based on adaptive kernel density estimation

Voltage sag frequency estimation is necessary for understanding the voltage sag severity in power system and offering full information for the interested parties to mitigate voltage sag. The high penetration of wind power in the power system and the uncertainty of the fault distribution raise new challenges to accurate voltage sag frequency estimation. This study presents a systematic voltage sag frequency estimation method, considering the fault distribution density, fault ride-through (FRT) process of wind turbines during voltage sag and the interval characteristic of voltage sag frequency. First, this study proposes a fault distribution estimation model based on adaptive kernel density. Second, this study proposes a method for calculating the residual voltage and duration of voltage sag during FRT and combines the common distance protection action to analyse the effect on voltage sag by FRT process of wind turbines. Lastly, this study proposes an interval-valued voltage sag estimation method considering the interval characteristics of fault rate in the power system. IEEE 30-bus test system is used to verify the proposed method, the estimation results show better performance of the proposed method compared with the typical estimation methods.

Bias and covariance of the least squares estimate in a structured errors-in-variables problem

A structured errors-in-variables (EIV) problem arising in metrology is studied. The observations of a sensor response are subject to perturbation. The input estimation from the transient response leads to a structured EIV problem. Total least squares (TLS) is a typical estimation method to solve EIV problems. The TLS estimator of an EIV problem is consistent, and can be computed efficiently when the perturbations have zero mean, and are independently and identically distributed (i.i.d). If the perturbation is additionally Gaussian, the TLS solution coincides with maximum-likelihood (ML). However, the computational complexity of structured TLS and total ML prevents their real-time implementation. The least-squares (LS) estimator offers a suboptimal but simple recursive solution to structured EIV problems with correlation, but the statistical properties of the LS estimator are unknown. To know the LS estimate uncertainty in EIV problems, either structured or not, to provide confidence bounds for the estimation uncertainty, and to find the difference from the optimal solutions, the bias and variance of the LS estimates should be quantified. Expressions to predict the bias and variance of LS estimators applied to unstructured and structured EIV problems are derived. The predicted bias and variance quantify the statistical properties of the LS estimate and give an approximation of the uncertainty and the mean squared error for comparison to the Cramér–Rao lower bound of the structured EIV problem.

Comparative Study on the Battery State-of-Charge Estimation Method

Accurate SOC (State-of-Charge) estimation has been an important part for all applications including energy storage systems. The accurate SOC estimation protects a battery to be deeply discharged and over-charged. Many studies on SOC estimation methods have been developed for evaluating more accurate SOC value. The battery SOC can be influenced by the battery temperature, the type of battery and the external conditions. Because of these reasons, SOC estimation methods differ from battery applications such as energy storage system, hybrid electrical vehicle or electrical vehicle. This paper analyzes and compares the strengths and weaknesses of typical estimation methods which have been studied by researchers. By comparing these advantages and disadvantages of various methods, this paper presents proper estimation methods suitable for energy storage applications.

A torque estimator using online tuning grey fuzzy PID for applications to torque-sensorless control of DC motors

DC motors are used indispensably in industrial applications because they provide such advantages as small size, high speed, low construction cost, and safe operation. A major area of research in DC motors is to determine a better method to measure the torque of motor shaft. The traditional way to measure the mechanical torque of a rotating shaft is attachment a torque transducer in the transmission system between the driving motor and the load. However, this technique requires additional parts for the transmission system, which makes the design more complicated, time consuming, costly in construction, and in many cases impossible to achieve.The purpose of this paper is to present a new method for estimating the load torque of a DC motor shaft by using a novel modelling method based on an adaptive control technique, named as online tuning grey fuzzy PID (OTGFPID). A test rig using a DC motor is setup to investigate the torque behaviour as well as to evaluate the developed estimator. Firstly, mathematical model is developed for the motor. Secondly, the experimental speed-torque data and the optimized motor model is used to optimize the torque estimator. Then the optimized estimator is used to estimate accurately the load torque. Finally, the capability of the optimized torque estimator has been validated with the practical experiments in comparison with a typical estimation method.

A Method of Minimising Paper Requirements for Offset Printing

The conducted study desired to increase the accuracy of estimating paper quantity requirements for printing. Changes in press construction and auxiliary equipment help reduce the total waste during production; however, typical estimation methods do not take these changes into account. By specifying the number of important job parameters and using a dimensional analysis approach, it was possible to devise a model of waste sheet quantity estimation better suited for current production practices. Using this model, it is possible to reduce the quantity of paper required for a particular print run as well as better predict the total waste sheet quantity. As a result, less paper may be ordered, stocked, and utilised in production. Using this model, a printing house may develop unique technological allowance standards for their particular substrates and products. The method of waste quantity prediction presented in this paper is also suitable for establishing a quality control system.

An identification-based state estimation method for a class of non-linear systems

In this paper, an online state estimation method for unmeasured states appearing linearly in deterministic non-linear systems is developed. The method is based on a recursive least-squares identification algorithm. The proposed method possesses some superior properties, mainly its simplicity and better estimation performance, compared with other typical estimation methods. The effectiveness of the method is demonstrated on case studies with a comparison to the popular extended Kalman filter (EKF). Although the method is presented for state estimation, it can be easily modified for time-varying parameter estimation.