Significant Errors In Estimation Research Articles

Performance enhancement for a dual-chamber OWC conceived from side wall effects in narrow flumes

Performance enhancement is always the focus for the exploration in oscillating water column (OWC) wave energy converter devices. In the present study, the hydrodynamic characteristics of a dual-chamber OWC system composed of two 3D circular sub-units is examined under the framework of OpenFOAM. The model-scale simulations are firstly carried out in a wave flume with different widths under the excitation of regular waves. Then, conceived from the side wall effects in narrow flumes, a pair of harbor walls parallel to the wave propagation direction is introduced; by tuning its deployed location and geometric dimension, the enhancement of wave power extraction can be expected. The results indicate that significant estimation errors in the capture width ratio may occur when the incident wave length is nearly equal to the flume width or the resonant frequency of water column is close to the wave frequency in a narrow flume. In addition, the integrated system consisting of OWC and harbor walls shows that the distance between above two structures can be tuned to a threshold value to maximize the capture width ratio by strengthening the fluids and structures interaction. The medium submerged depth of harbor walls may be an alternative when the trade-off between performance enhancement and economic costs has to be considered during practical construction. Moreover, a well agreement between the model-scale and prototype-scale results proves that the performance enhancement can be scalable to large scales by integrating OWC device with harbor walls.

Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

Estimation of Wind by Estimating Derivatives of Longitudinal Equations of Motion of Aircraft

This paper considers the effect of wind on stability derivatives of longitudinal equations of motion of an aircraft and estimating steady wind by estimating the stability derivatives using an algorithm such as the least squares method (LSM). The wind defined in the inertial frame appears in the derivatives concerning the pitch angle in the linear form and is computed from the estimated derivatives. The numerical results computed for NASA's Generic Transport Model (GTM) verify the linear model in the presence of steady wind. The simulation results obtained for GTM using the LSM and an extended Kalman filter indicate that although the vertical wind has a significant estimation error, the horizontal wind is estimated well despite considerable estimation error of the stability derivatives. We also examined the reason analytically and numerically.

A note on orthogonal matching pursuit under restricted isometry property

The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal processing. In this work, by investigating the relationship between the iteration number for OMP and the signal estimation error based on the restricted isometry property, the authors obtained tighter bounds on the number of iterations required to approximately recover a sparse signal with noise and exact support recovering for the noiseless cases of OMP.

An active impulsive noise control algorithm with a post-adaptive filter and variable step size.

Active noise control algorithms aimed at reducing Gaussian noise have been studied. However, the performance of these algorithms can deteriorate if impulsive noise or other non-Gaussian noise such as piling noise exists. To solve this problem, an algorithm named the filtered-x affine projection sign algorithm with a post-adaptive filter is proposed to reduce impulsive noise. The proposed algorithm is based on the filtered-x affine projection sign algorithm and the post-adaptive filter structure. In addition, an algorithm with a variable step size is proposed to improve the performance of the filtered-x affine projection sign algorithm with a post-adaptive filter by using a convex combination strategy. The fixed step size in the convex combination is replaced with a linear function of the ratio between the estimated error signal and the estimated desired signal. The detailed derivation processes of the proposed algorithms are given. The computational complexities are analyzed. Numerical simulations are carried out to demonstrate the performance of the proposed algorithms. The results illustrate that the proposed algorithms can effectively reduce impulsive noise. At the same time, the algorithm with a variable step size has better convergence performance than the filtered-x affine projection sign algorithm with a post-adaptive filter.

Exposure assessment of PM2.5 using smart spatial interpolation on regulatory air quality stations with clustering of densely-deployed microsensors

Accurate mapping of air pollutants is essential for epidemiological studies and environmental risk assessments. Concentrations measured by air quality monitoring stations (AQMS) have primarily been used to assess the exposure of PM2.5. However, the low coverage and amount of monitoring stations affect the errors of spatial interpolation or geostatistical estimates. In contrast to other integrated approaches developed for improved air pollution estimates, this study utilizes data from low-cost microsensors densely deployed in Taiwan to improve the popular spatial interpolation approach called inverse distance weighting (IDW). A large dataset from thousands of low-cost sensors could improve spatial interpolation by describing the distribution of PM2.5 in detail. Therefore, this study presents a clustering-based method to assess the distribution of PM2.5. Then, a smarter IDW is performed based on correlated observations from the selected air quality stations. The publicly available data chosen for this investigation pertained to Taiwan, which has deployed 74 monitoring stations and more than 11,000 low-cost sensors since December 2020. The results of leave-one-out cross-validation indicate that there are fewer PM2.5 estimation errors in the developed approach than in estimations that use kriging across almost all of the months and sampled dates of 2019 and 2020, particularly those with higher PM2.5 spatial heterogeneities. Spatial heterogeneities could result in more significant estimation errors in mainstream approaches. The root mean square error of the monthly average estimate for PM2.5 ranged from 1.17 to 3.86 μg/m3. We also found that the clustering of one month characterizing the pattern of PM2.5 distribution could perform well in spatial interpolations based on historical data from monitoring stations. According to the information on the openaq platform, low-cost sensors are in demand in cities and areas. This trend might pave the way for the application of the proposed approach in other areas for superior exposure assessments.

A Universal Gains Selection Method for Speed Observers of Induction Machine

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.

How tissue T1-variability influences DCE-MRI perfusion parameters estimation of recurrent high-grade glioma after surgery followed by radiochemotherapy.

Quantification of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) kinetic parameters (KPs) requires a determination of native tissue T1. Two approaches are adopted: (i) tissue T1-maps are acquired; and (ii) an a priori T1 value (fT1) is fixed for all patients (fT1-approach). Although it is more attractive, the fT1-approach might bias the results of KP calculations due to tissue T1 variability. To quantify the tissue T1 variability of recurrent high-grade glioma (HGG) and the error in KP estimation when the fT1-approach is adopted. We reviewed the postoperative MRI scans of 28 patients with recurrent HGG after radiochemotherapy. MRI study included T1-maps from multiple-dynamic multiple-echo imaging, DCE-MRI, and contrast enhanced T1-weighted images. KPs were calculated using T1-map and fT1-approach. The tissue T1 variability of recurrent HGG was relevant. The absolute error in KP estimation, as a function of the deviation of fT1 from the true value, was 8% every 100 ms. The difference between the KPs obtained with fT1-approach from fT1 values of 1300, 1390, and 1500 ms and their reference values were mostly within the 95% confidence interval (± 1.96 standard deviation). Conversely, using fT1 values of 900, 1200, 1600, and 1900 ms causes a significant error in KP estimation (P<0.05). Recurrent HGG is characterized by a substantial T1 variability. Although the fT1-approach does not account for this variability, it results in a minor effect on the KP estimations provided the fT1 value is in the range of 1300-1500 ms.

Soil Moisture as Predictor of Plant Water Status

Soil water potential can be used as a proxy for plant available water in irrigation scheduling. This study investigated the relationship between soil water potential and plant water status of pines (Pinus sylvestris L.) planted into two different substrates. Predawn leaf water potential as a well-established measure of the plant water status and soil water potential correlated very well. However, estimating the plant water status from individual sensor readings is subject to significant estimation errors. Furthermore, it was shown that heterogeneous soil/root ball combinations can lead to critical effects on the soil water balance, and that sensors installed outside of the root balls cannot estimate the plant water status without site-specific calibration.

Scheduling and shortest path for trucks with working hours and parking availability constraints

The US Hours-of-Service (HOS) regulations require truck drivers to take regular breaks. However, drivers often have difficulty finding appropriate rest locations due to truck parking shortages. This may have a significant impact on drivers’ safety and productivity, cost to industry and environment. In this paper, we propose a model that takes into account parking availability when planning long-haul truck shipments in addition to HOS constraints. Given an ordered set of clients, the proposed method plans a minimum cost route and schedule such that the itinerary is regulation-compliant, and the driver is guaranteed to find parking at all scheduled stops. The problem is modeled as a shortest path problem with resource constraints, and a label correcting algorithm is used to generate optimal solutions. Computational experiments are used to compare the cost of solutions that use parking availability information with ones that do not. The cost of disregarding parking information was estimated by simulating drivers’ behavior when parking is unavailable and applying time and cost penalties. The effect of parking availability and alternative routes on trip costs is also studied, aiming to illustrate that disregarding these factors can lead to significant errors in cost estimates. Results show that, although imposing parking constraints can significantly increase costs, preventing accident-related costs can make it advantageous in the long-term.

A matched filter based approach for high-energy estimation in calorimetry

In high-energy calorimeters, a crucial task is the reconstruction of the energy deposited by particle interactions. Standard techniques used in modern calorimeters rely on the energy estimation to select the signal with relevant information. This work presents a new approach, which performs the signal detection against noise as a first step, followed by the energy estimation task. The method is fully based on the Matched Filter (MF) theory, which is known to produce the optimum detection efficiency with respect to the signal-to-noise ratio. Furthermore, the MF output can be calibrated to estimate the signal amplitude and, thus, the energy. The proposed method is compared to different optimum filtering algorithms, which are currently being used for energy reconstruction in modern calorimeter systems. The results from simulated data show that the proposed method achieves better performance in terms of both signal detection efficiency and estimation error.

Study of the influence of anisotropy parameters on reflection coefficients from a boundary between two azimuthally anisotropic media

The paper considers an algorithm for calculating reflection coefficients from boundary between two HTI media. Analysis of the presence of anisotropy above and below the target boundary, as well as variations in the parameters of HTI media, was done. Interpretation of reflection data from the boundary between two HTI media with neglect of anisotropy above or below potentially leads to significant errors in estimation of symmetry axes directions, and hence fracturing orientation. Overestimation/underestimation of an elastic parameter in the overlying HTI medium could lead to a corresponding overestimation/underestimation of similar parameter in the underlying target layer in the result of AVAZ inversion. Furthermore, among the anisotropy parameters Thomsen parameterγhas most significant influence on the reflection coefficients dependences. Thus, the parameterγcould be used foremost as a result of the AVAZ inversion.

High speed RLC equivalent RC delay model using normalized asymptotic function for global VLSI interconnects

This work proposes a mathematical delay models for global VLSI interconnects using normalized asymptotic value of characteristic impedance of RLC interconnected line. In the proposed Model (R0C0) the delay of RLC interconnects line is formulated by incorporating the line inductance in terms of effective impedance. The earlier simple RC interconnect models results in a significant error in delay estimation in long interconnects. Due to this analogy the non-ideal effect of inductive behaviour at high frequencies and scaled technologies such as ringing, spikes overshoot and undershoot can be suppressed. The dominance of inductance effect is optimized by Simulative Sweep Analysis Techniques (SSAT). Accuracy is verified by analytical and SPICE simulation results. Step response analysis of the proposed model is validated with the existing literature and found to be superior. Using this approach the results for both the signaling technique i.e. voltage and current mode signaling are found in close agreement between analytical and simulation for long interconnect lines.

Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.

Robust and efficient WLS-based dynamic state estimation considering transformer core saturation

For transformer asset management, the dynamic state estimation can track transient phenomena of transformers or extract an unmeasurable quantity such as magnetic flux linkage. Even the extended Kalman filter or unscented Kalman filter, which are widely-used dynamic state estimations, might result in significant estimation errors when dealing with highly nonlinear dynamics such as transformer core saturation. In this sense, this paper proposes a dynamic state estimation based on the weighted least squares, using real-time measurements and the dynamic model induced by numerically integrating differential equations. The proposed method can directly use implicit functions including differential equations while Kalman filters must rearrange the implicit functions into the explicit ones to obtain the prediction process. Further, because the weighted least squares approach is implemented via the Newton iterative method, the estimation accuracy and robustness to various conditions can be improved despite strong nonlinearity. For the numerical integration of dynamics, the computationally efficient trapezoidal rule or the numerically stable quadratic rule is used. This paper presents numerical simulation results, with a comparison with other dynamic state estimators.

A Simple and Non-destructive Method for Chlorophyll Quantification of Chlamydomonas Cultures Using Digital Image Analysis.

Growing interest in the use of microalgae as a sustainable feedstock to support a green, circular, bio-economy has led to intensive research and development initiatives aimed at increasing algal biomass production covering a wide range of scales. At the heart of this lies a common need for rapid and accurate methods to measure algal biomass concentrations. Surrogate analytical techniques based on chlorophyll content use solvent extraction methods for chlorophyll quantification, but these methods are destructive, time consuming and require careful disposal of the resultant solvent waste. Alternative non-destructive methods based on chlorophyll fluorescence require expensive equipment and are less suitable for multiple sampling of small cultures which need to be maintained under axenic growth conditions. A simple, inexpensive and non-destructive method to estimate chlorophyll concentration of microalgal cultures in situ from digital photographs using the RGB color model is presented. Green pixel intensity and chlorophyll a, b and total chlorophyll concentration, measured by conventional means, follow a strong linear relationship (R2 = 0.985–0.988). In addition, the resulting standard curve was robust enough to accurately estimate chlorophyll concentration despite changes in sample volume, pH and low concentrations of bacterial contamination. In contrast, use of the same standard curve during nitrogen deprivation (causing the accumulation of neutral lipids) or in the presence of high quantities of bacterial contamination led to significant errors in chlorophyll estimation. The low requirement for equipment (i.e., a simple digital camera, available on smartphones) and widely available standard software for measuring pixel intensity make this method suitable for both laboratory and field-based work, particularly in situations where sample, qualified personnel and/or equipment is limited. By following the methods described here it should be possible to produce a standard curve for chlorophyll analysis in a wide range of testing conditions including different microalga cultures, culture vessel and photographic set up in any particular laboratory.

State estimation and reconstruction of unknown inputs with arbitrary relative degree via a predefined-time algebraic solver

This paper proposes a sliding mode observer for the estimation of the state variables and the reconstruction of the unknown inputs of nonlinear dynamical systems regardless of whether these systems satisfy relative-degree-related conditions or not. This observer is based on a predefined-time sliding mode algebraic solver and does not require the explicit use of state transformations. The use of the proposed method is illustrated through its application to an abstract system and the model of a chemical process, achieving an accurate estimation of both the state vector and the unknown inputs in each case, while an extended Kalman filter applied to the chemical process for comparison maintains significant estimation error.

SkinSim: A Design and Simulation Tool for Robot Skin With Closed-Loop pHRI Controllers

The importance of tactile sensing for physical human–robot interaction (pHRI) and dexterous manipulation is well known. SkinSim is a novel simulation environment for tactile robot skins in which one can study design tradeoffs involved in deploying whole-body, dense sensor arrays. In this article, scalable modeling approaches are presented for simulating pressure-sensitive robot skin patches, with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The open-source simulation architecture of SkinSim is compatible with Gazebo and robot operating system (ROS) programming environments and supports both robot skin dynamic models, as well as tactile sensing element models. An experimentally validated force dispersion model was introduced for the simulation of sensors embedded in a mechanical damping layer. Simulation examples of robot skin with different tactile resolutions are presented using parameter values extracted from an experimental testbed. Thus, simulation results were experimentally validated and the skin sensor density impact on a simple pHRI controller performance was evaluated. Performance measures include center of pressure (COP) estimation error and control signal settling time, overshoot, and steady-state errors. Results suggest that while COP errors decrease in denser sensor skins, controller performance also deteriorates. Therefore, optimal robot skin designs will have to consider application-dependent tradeoffs. Similar results were confirmed in simulation with a larger skin patch containing approximately 4000 tactels and deployed on the end-effector of a collaborative mobile manipulator. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the expensive and time-consuming process of designing tactile skin for robots. When designing pressure-sensitive whole-body sensor arrays for physical human–robot interaction, there are tradeoffs related to sensor resolution, accuracy, and response time, among others. Instead of building prototypes and evaluating them experimentally, the SkinSim simulation environment allows automatic testing of skins with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The user can specify several configurations to test and thereby explore the best tradeoffs before actually prototyping any hardware.

Stabilization for Euler–Bernoulli Beam Equation with Boundary Moment Control and Disturbance via a New Disturbance Estimator

We address the output feedback stabilization for a Euler–Bernoulli beam equation with boundary moment control and disturbance. The stabilization of this system has been studied in Guo et al. (J Dyn Control Syst. 2014;20:539–58), where the controller is based on full state feedback. In order to derive the output feedback controller, we design a new disturbance estimator to estimate the total disturbance in the sense that the estimation error signal belongs $L^{2}(0,\infty )$ , and it decays exponentially if the initial state is smooth. Using the estimated total disturbance, we propose a control law to stabilize the system. Using admissibility theory, we show that the closed-loop system is exponentially stable and the signals in the disturbance estimator in the closed-loop are proved to be bounded.

Temporal Characteristics of P-Band Tomographic Radar Backscatter of a Boreal Forest

Temporal variations in synthetic aperture radar (SAR) backscatter over forests are of concern for any SAR mission with the goal of estimating forest parameters from SAR data. In this article, a densely sampled, two-year long time series of P-band (420 to 450 MHz) boreal forest backscatter, acquired by a tower-based radar, is analyzed. The experiment setup provides time series data at multiple polarizations. Tomographic capabilities allow the separation of backscatter at different heights within the forest. Temporal variations of these multipolarized, tomographic radar observations are characterized and quantified. The mechanisms studied are seasonal variations, effects of freezing conditions, diurnal variations, effects of wind, and the effects of rainfall on backscatter. An emphasis is placed on upper-canopy backscatter, which has been shown to be a robust proxy for forest biomass. The canopy backscatter was more stable than ground-level backscatter during nonfrozen conditions, supporting forest parameter retrieval approaches based on tomography or interferometric ground notching. Large backscatter variations during frozen conditions, which may be detected using cross-polarized backscatter observations, can result in large errors in forest parameter estimates. Diurnal backscatter variations observed during hot periods were likely connected to tree water transport and storage mechanisms. Backscatter changes were also observed during strong winds. These variations were small in comparison to the variations due to freeze-thaw and soil moisture changes and should not result in significant forest parameter estimation errors. The presented results are useful for designing physically based and semiempirical scattering models that account for temporal changes in scattering characteristics.