Linear Error Research Articles

A Novel Adaptive Model Predictive Control Strategy for DFIG Wind Turbine With Parameter Variations in Complex Power Systems

In this paper, a novel adaptive model predictive control (MPC) strategy is proposed for doubly fed induction generator (DFIG) wind turbine (WT), which is integrated into a complex power system, in order to improve the power output tracking precision and dynamic performance. Considering the existence of parameter variations in DFIG, an adaptive parameter estimation method is firstly designed. By analysis of stability, the convergence of the parameter estimation algorithm is rigorously proved. Furthermore, the parameter estimation algorithm is effectively integrated into MPC to realize real-time optimal control of DFIG with the adaptive model. To achieve the rotor side converter (RSC) design based on adaptive MPC, the DFIG model is linearized. In addition, a virtual output compensation (VOC) strategy is adopted to alleviate the impact of model linearization errors on the MPC, especially the variation of the model parameter. The newly proposed adaptive MPC-based RSC is capable of greatly improving the tracking performance, meanwhile taking into account the realistic constraints under various operation conditions. The simulation results demonstrate the effectiveness and superiority of the proposed control method.

Threshold Cointegration and Price Transmission in Commodity Marketsof India

The purpose of this research work is to examine the relationships and price dynamics between agricultural commodities in India, i.e.maize, wheat, barley and soybean. Our approach is to study the long-term relationship using the method of modelling the price transmission for both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. Results revealed that all the price series are well integrated, and threshold error correction models prove that all price series move to restore the long-run relationship, whereas commodity stock prices respond slightly faster than market prices in the short-run. Conclusions from this study show that understanding the price transmission flow and its impact on pricing might help in making better trading strategies. It also regulates the public policy implications of the active participation of farmers in national-level commodity exchanges.

Linearization errors in discrete goal-oriented error estimation

This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete, adjoint-based approach for error estimation is considered. The traditional method to derive an error estimate in this context requires linearizing both the nonlinear variational form and the nonlinear functional of interest which introduces linearization errors into the error estimate. In this paper, we investigate these linearization errors. In particular, we develop a novel discrete goal-oriented error estimate that accounts for traditionally neglected nonlinear terms at the expense of greater computational cost. We demonstrate how this error estimate can be used to drive mesh adaptivity. We show that accounting for linearization errors in the error estimate can improve its effectivity for several nonlinear model problems and quantities of interest. We also demonstrate that an adaptive strategy based on the newly proposed estimate can lead to more accurate approximations of the nonlinear functional with fewer degrees of freedom when compared to uniform refinement and traditional adjoint-based approaches.

Experimental study of geometric error of CNC turning machine tools based on ISO 13041-6

The product quality of machining results is greatly influenced by the accuracy and precision of CNC lathe machine tools. Regular inspection of the geometric inaccuracy of the machine tool is necessary to verify its operational viability. This research contribution focuses on conducting experimental studies to evaluate machine tool geometric error. The aim is to explore cost-effective measurement methods as alternatives to direct measurements, which often involve laser interferometers and ball bar tests. The objective of this study is to investigate the geometric inaccuracy of a CNC turning machine by conducting experimental cutting tests in accordance with ISO 13041-6:2009. The testing will utilize conventional workpiece forms and requirements, including circularity features, flatness, circular features, and maybe combination features. Several geometric errors that can be acquired with this method include circularity errors, linear positional errors, and squareness errors. The cutting test for each workpiece feature of the given shape and specification requires the use of 5 specimens. Consequently, the mean value of the geometric error may be computed. The geometric error value is derived by the analysis of measurement data collected from a Coordinate Measuring Machine (CMM) applied to a specimen of the machined workpiece. Moreover, the evaluation of the geometric error condition of machine tools is ascertained through the comparison of the average data for each category of geometric error against the permissible standard values given in ISO 10791-2, ISO 10791-4, and ISO 13041-4. The findings of the study indicate that the implementation of the object machine study is not viable for the production of machined workpieces of satisfactory quality. This is primarily due to the presence of geometric errors in CNC turning that exceed the acceptable tolerance levels. Specifically, these errors manifest as linear positional deviations along multiple coordinates along the X-axis and Z-axis, as well as squareness deviations between the X-axis and Z-axis. The maximum value of the linear positional error along the X-axis is 55.2 μm, while the maximum value of the linear positional error along the Z-axis is 25.6 μm. Additionally, the greatest observed squareness error is 37.3 μm. The X and Z machine axes exhibit deviations beyond acceptable limits in terms of unidirectional accuracy and unidirectional repeatability, as per the established norm

A nonlinear unbiased minimum-variance filter for structural identification with unknown external excitations

Excitations identification and structural identification are two pivotal factors in the field of structural health monitoring. The extended Kalman filter (EKF) is widely used for identifying structural dynamic systems. However, it has been demonstrated that an inevitable linearization error exists, which can make it difficult to deal with situations when external excitations are unknown. To address this issue, this paper proposes a novel derivative-free version of the nonlinear unbiased minimum variance filter for structural identification with unknown external excitations (NUMVF-UEE) based on the unscented Kalman filter (UKF). This method derives optimal estimations of structural parameters and unknown external excitations by minimizing the traces of the covariance matrices with respect to the augment vectors. To demonstrate the effectiveness of the proposed method, several numerical examples are introduced. The results show that NUMVF-UEE can simultaneously provide accurate estimations of both structural system parameters and unknown external excitations in real-time.

Low-Voltage Current-Mode Full-Wave Rectifier Employing FVF-Biased MOS Translinear Loop

In this communication, a low-voltage complementary metal-oxide semiconductor (CMOS) current-mode full-wave rectifier (FWR), implemented from a flipped voltage follower-biased MOS translinear loop (FVF-MTL) is presented. The use of flipped voltage follower (FVF) minimizes the overall supply voltage demand by reducing the voltage headroom while maintaining the operation of the MOSFETs in strong inversion mode. The functioning of the proposed FWR circuit has been demonstrated through simulations carried out on Cadence Virtuoso software at [Formula: see text] CMOS technology at a supply voltage of 0.85[Formula: see text]V. The proposed FWR works over an input range of [Formula: see text] with a maximum linearity error of 3% in its transfer characteristics. The -3 dB bandwidth of the circuit has been found to be 54.95[Formula: see text]MHz. The minimum power and maximum power consumed by the circuit are 22.6[Formula: see text]nW and [Formula: see text], respectively. Post-layout simulations, Monte Carlo (MC), corner and temperature analyses have been carried out to estimate the performance and robustness of the proposed circuit.

Bundle Enrichment Method for Nonsmooth Difference of Convex Programming Problems

The Bundle Enrichment Method (BEM-DC) is introduced for solving nonsmooth difference of convex (DC) programming problems. The novelty of the method consists of the dynamic management of the bundle. More specifically, a DC model, being the difference of two convex piecewise affine functions, is formulated. The (global) minimization of the model is tackled by solving a set of convex problems whose cardinality depends on the number of linearizations adopted to approximate the second DC component function. The new bundle management policy distributes the information coming from previous iterations to separately model the DC components of the objective function. Such a distribution is driven by the sign of linearization errors. If the displacement suggested by the model minimization provides no sufficient decrease of the objective function, then the temporary enrichment of the cutting plane approximation of just the first DC component function takes place until either the termination of the algorithm is certified or a sufficient decrease is achieved. The convergence of the BEM-DC method is studied, and computational results on a set of academic test problems with nonsmooth DC objective functions are provided.

Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19

ABSTRACT In this article, we define mixed predictor and stochastic restricted ridge predictor of partially linear mixed measurement error models by taking advantage of Kernel approximation. Under matrix mean square error criterion, we make the comparison of the superiorities the linear combinations of the new defined predictors. Then we investigate the asymptotic normality characteristics and the situation of the unknown covariance matrix of measurement errors. Finally, the study is ended with a Monte Carlo simulation study and COVID-19 data application.

Partially Functional Linear Models with Linear Process Errors

In this paper, we focus on the partial functional linear model with linear process errors deduced by not necessarily independent random variables. Based on Mercer’s theorem and Karhunen–Loève expansion, we give the estimators of the slope parameter and coefficient function in the model, establish the asymptotic normality of the estimator for the parameter and discuss the weak convergence with rates of the proposed estimators. Meanwhile, the penalized estimator of the parameter is defined by the SCAD penalty and its oracle property is investigated. Finite sample behavior of the proposed estimators is also analysed via simulations.

Testing symmetry of model errors for non linear multiplicative distortion measurement error models

To study the symmetry and asymmetry of the model error under multiplicative distortion measurement errors setting, we propose a correlation coefficient-based measure between the distribution function and the root of density function. The unknown distribution function and density function are estimated from four kinds of residuals: the conditional mean calibration-based residuals, the conditional absolute mean calibration-based residuals, the conditional variance calibration-based residuals, and the conditional absolute logarithmic calibration-based residuals. We study the asymptotic results of the estimators of correlation coefficient-based measure under four calibrations. Next, we consider statistical inference of the correlation coefficient-based measure by using the empirical likelihood method. The empirical likelihood statistics are shown to be an asymptotically standard chi-squared distribution. Simulation studies demonstrate the performance of the proposed estimators and test statistics. A real example is analyzed to illustrate its practical usage.

Optical freeform reflective imaging system design method with manufacturing constraints.

With the development of space optics, optical freeform surfaces have gradually been utilized in reflective optical imaging systems in recent years. Freeform surfaces not only bring many benefits to the optical imaging system, but also present many challenges to their manufacture. Regardless of the machining method used, machining errors during the fabrication of freeform surfaces will exist, which limits the accuracy of freeform surface machining. In this paper, the deviation root mean square (RMS) of a freeform surface from the reference aspheric surface is proposed to evaluate the manufacturability of the freeform surface by using single-point diamond turning. Then the deviation RMS of freeform surfaces is added to the design process of the optical system as a manufacturing constraint. Subsequently, an off-axis three-mirror system and an off-axis two-mirror system with and without manufacturing constraints are designed, respectively. Then the imaging quality of these optical systems and the linear interpolation error RMS of freeform mirror are analyzed. It can be concluded that, on the basis of reaching the imaging quality requirements, the machining difficulty of a freeform mirror can be reduced when adding manufacturing constraints to the design process.

A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement

The Laser interferometer plays an important role in the field of geometrics for large-size measuring applications. Its linear displacement error can be traced from indoor large-scale standard devices. However, the measuring range of the indoor large-scale standard device is too limited to meet the full range measurement of the laser interferometer. So, the lack of accuracy of the results is one of the key issues of laser interferometers. To solve this problem, the corner reflector is used to extend the range of indoor large-scale standard devices, which meet the indication errors in the effective measuring range of the laser interferometer. The range-extension method not only doubles the effective measuring range, but also provides a way to trace the source of other large-size measuring instruments. This work proposes a significant solution to the field of length measurement.

Minimum Squared Error Method based on Modal Analysis for Acoustic Emission Source Location using Microfiber Coupler Sensor

This study focuses on an improved modal analysis method to the source location using a microfiber coupler sensor (MFCS). This method uses the Minimum Squared Error(MSE) to reduce the dependence of locating results on frequency selection and automate the locating process of contour threshold modal analysis. First, the secondary enclosed package method of MFCS was studied to ensure that the signal detected by this sensor meets the requirements of the contour threshold modal analysis and can extend the MFCS working life as well. The MSE method was then presented detailed. Second, the directionality experiment of the MFCS was performed to determine the best layout method for sensors in planar localization. The planar source localization method with both two sensors and three sensors are then introduced. Last, both linear and planar location experiments were performed on an aluminium thin plate. The results indicate that the sensor packaged by cuboid glass tube is more suitable for practical ultrasonic testing; the location process is automated by valid contour extraction and initial frequency selection, and the linear error using this method is effectively reduced as well; the best arrangement of the sensor is to place the sensor at the corner of the planar, and the angle between the sensor and the horizontal line is 45°; compared with two sensors, the planar localization with three sensors provides a more accurate and high repeatability results. This study proved that the combination of the MFCS and the MSE method is quite feasible and can be applied to the planar localization in plate-like structures effectively.

Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study.

Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human's emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model's mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games.

A Comprehensive Methodology for Optimizing Read-Out Timing and Reference DAC Offset in High Frame Rate Image Sensing Systems.

This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor's operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e-rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e-·nJ. The sensor utilized a one-row read-out time of 6.9 µs, an amplifier bandwidth of 1.1 MHz, and a reference digital-to-analog converter (DAC) offset of 512 LSB. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications.

Diagnostics for partially linear measurement error models

Partially linear models are useful tools to analyze data from economic, genetic, and other fields. Similar to other data analyses, the identification of influential observations that may be potential outliers is an important step beyond estimation in such models. The objective of this article is to develop some diagnostic measures for identifying influential observations in partially linear models when some of the covariates are measured with errors. Deletion measures are developed based on case deletion, mean shift outlier models, and the corrected likelihood of Nakamura (1990). The performance of the methods is illustrated by an artificial example and a real example.

Decoding Quantum Tanner Codes

We introduce sequential and parallel decoders for quantum Tanner codes. When the Tanner code construction is applied to a sufficiently expanding square complex with robust local codes, we obtain a family of asymptotically good quantum low-density parity-check codes. In this case, our decoders provably correct arbitrary errors of weight linear in the code length, respectively in linear or logarithmic time. The same decoders are easily adapted to the expander lifted product codes of Panteleev and Kalachev. Along the way, we exploit recently established bounds on the robustness of random tensor codes to give a tighter bound on the minimum distance of quantum Tanner codes.

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time- and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum mean-squared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

A study on nuclear binding energy and beta decay energy using deep neural networks

The Artificial Neural Network (ANN) is one of the innovative methods for predicting the structural and dynamic properties of atomic nuclei. In this work, we employed machine learning based on the Deep Neural Network (DNN) technique to find the ground state binding energy and beta decay energy of various nuclei. 3560 experimental nuclear data sets have been used for training and validating the DNN model. Neutron number (N), proton number (Z), the pairing term (δ), a magic number greater than or equal to N and Z, asymmetry factor (a) and promiscuity factor (P) are used as input parameters. The Rectified Linear Unit (ReLU) function and Mean Absolute Error (MAE) are used as activation and Loss functions for the training process. We perform the predictions of ground state binding energy per nucleon and beta decay energies in Zinc and Promethium isotopes using the DNN model, which is in good agreement with experimental results.

Data-Aided CSI Estimation Using Affine-Precoded Superimposed Pilots in Orthogonal Time Frequency Space Modulated MIMO Systems

An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.