Estimation Performance Research Articles

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic and ex-vivo phantoms

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter

A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates the ’Data saturation’ problem experienced by least squares methods by dynamically adjusting weights of data. On the other hand, the EKF improves the robustness and adaptability of SOC estimation. Simulation results under Hybrid Pulse Power Characteristic (HPPC) conditions demonstrate that this new approach offers superior performance in SOC estimation in batteries for electric vehicles compared to existing methods, with better tracking of the true SOC curve, reduced estimation error, and improved convergence.

Optimal innovation-based attacks against remote state estimation with side information and historical data

This work investigates the attack strategy design problem of the false data injection attack against the cyber-physical system. Distinct from the relevant results which utilise the current intercepted data or additionally consider side information, a more universal attack model is proposed which combines historical data and side information with the current intercepted data to synergistically deteriorate the system estimation performance. In order to quantify the impact resulting from the proposed attack strategy, the optimisation objective is characterised by deriving the error covariance matrix under the attacks, which becomes more intricate since the proposed attack model introduces more decision variables and coupled terms. Take the stealthiness which is characterised by Kullback-Leibler divergence as the constraints, the problem investigated in this work is equivalently transformed into the constrained multi-variable non-convex optimisation problems, which are not able to be solved directly by the methods in the relevant results. By utilising the Lagrange multiplier method, the structural characteristic of the optimal mean and the optimal covariance which only related to the Lagrange multiplier are derived, such that the optimal distribution of the modified innovation is able to be obtained by a simple search procedure. Following that, the design of the optimal attack strategy is completed by using semi-definite programming to derive the optimal attack matrices. Finally, the simulation examples are given such that the validity of the results is verified.

Vehicle modeling and state estimation for autonomous driving in terrain

The automobile industry usually ignores the height of the path and uses planar vehicle models to implement automatic vehicle control. In addition, existing literature mostly concerns level terrain or homogeneous road surfaces for estimating vehicle dynamics. However, ground vehicles utilized in forestry, such as forwarders, operate on uneven terrain. The vehicle models built on level terrain assumptions are inadequate to capture the rolling or pitching dynamics of such machines as rollover of such vehicles is a potential risk. Therefore, knowledge about the height profile of the path is crucial for automating such off-road operations and avoiding rollover. We propose the use of a six-degrees-of-freedom (6-DOF) dynamic vehicle model to solve the autonomous forwarder problem. An adaptive linear tire model is used in the 6-DOF model assuming the vehicle operates in a primary handling regime. The force models are modified to include the three-dimensional (3D) map information. The calibration procedures, identifying actuator dynamics, and quantifying sensor delays are also represented.The proposed vehicle modeling contributed to realizing the continuous-discrete extended Kalman filter (CDEKF), which takes into account the 3D path during filtering and fixed-lag smoothing. Polaris (an all-terrain electric car) is used as a case study to experimentally validate the vehicle modeling and performance of the state estimator. Three types of grounds are selected — an asphalt track, a concrete track with a high elevation gradient, and a gravel track inside a forest. Stable state estimates are obtained using CDEKF and sparse 3D maps of terrains despite discontinuities in satellite navigation data inside the forest. The height estimation results are obtained with sufficient accuracy when compared to ground truth obtained by aerial 3D mapping. Finally, the proposed model’s applicability for predictive control is demonstrated by utilizing the state estimates to predict future states considering (3D) terrain.

State of charge estimation for lithium-ion batteries with pre-set convergence time based on a comprehensive unobservable model

State of charge (SOC) is a critical metric for assessing the remaining capacity of lithium-ion batteries, while convergence time is an essential indicator for evaluating the performance of SOC estimation. To reduce the convergence time of SOC estimation, a novel strategy has been proposed. Initially, an accurate and comprehensive model is employed for lithium-ion batteries, which includes the nonlinear behavior of the open circuit voltage (OCV) with respect to SOC. However, the linear component of this model is unobservable. Then, the nonlinear relationship between OCV and SOC is approximated using a first-order piecewise fitting function. This approach ensures accuracy while reducing computational complexity compared to non-linearization methods. Next, the unobservable model is decomposed into observable and unobservable subsystems, addressing the challenge of designing observers for unobservable models. Furthermore, a finite time observer is designed based on the observable subsystem, enabling SOC estimation to be achieved within any pre-set convergence time. Finally, theoretical analysis and extensive experimental results validate the feasibility and reliability of the proposed estimation strategy, demonstrating its superiority over traditional methods.

Discussion on the accuracy of methods for determining interface force between numerical and physical substructures in shaking table-based real-time hybrid simulation

Real-time hybrid simulation (RTHS) is a developing experimental technology that combines the advantages of numerical simulation and physical test. In RTHS, the interface force is a key physical quantity coupling the dynamic response of physical substructure and numerical substructure. An inaccurate force determination method can distort RTHS results and may cause incorrect estimations of the dynamic performance of structures. This article aims to systematically discuss the impact of possible error in determining the interface force during shaking table-based RTHS. The errors caused by two typical force determination methods, (1) using force sensors and (2) using strain gauges, are comprehensively investigated. These two force determination methods are typically used for (1) performance evaluation of nonlinear damping systems and (2) investigation of soil–structure interaction effects. Virtual hybrid simulation numerical models (in which both physical and numerical substructures are simulated numerically) representing the interface force determination processes are established. The impacts of error on the RTHS results are evaluate quantitatively. The reported findings can assist in developing a deeper understanding of the causes of errors during RTHS and enhance the experimental designs. This, in turn, leads to more accurate and reliable results in RTHS testing.

Operation optimization in large-scale heat pump systems: A scheduling framework integrating digital twin modelling, demand forecasting, and MILP

The integration of large-scale heat pumps and thermal energy storage can facilitate sector coupling, potentially lowering heating and cooling costs in industries and buildings. This cost reduction can be extended by optimizing the utilization of the available thermal energy storage capacity in accordance to fluctuating electricity prices. Although the literature offers methods for optimizing the operation of these integrated systems, they often overlook the impact of heat pump performance degradation over time, such as from fouling. This oversight can lead to suboptimal system performance and inaccurate operational cost estimates. The present study addresses this gap by introducing a novel operational scheduling framework that aimed to reduce the operational costs of a commercial large-scale heat pump system. The system comprised an open cooling tower, a thermal storage tank and two heat pumps affected by fouling. The framework incorporated a mixed-integer linear programming (MILP) model, thermal demand forecasting, and heat pump performance maps that account for varying fouling levels. These maps were obtained from online calibrated simulation models used as digital twins of the heat pumps. The results demonstrated that the proposed framework enhanced the thermal energy storage utilization in response to variable electricity prices and adjusted the heat pump operation based on the influence of fouling. This resulted in a reduction of operational costs of up to 5% compared to the conventional operation of the system. These savings were observed to vary depending on the forecasting accuracy and the prevailing fouling levels. Overall, this study demonstrates the potential of using the proposed framework for cost reduction in large-scale heat pump systems.

Design and implementation of an adaptive unscented Kalman filter with interval Type-3 fuzzy set for an attitude and heading reference system considering gyroscope bias

Low-cost microelectromechanical sensors used in attitude and heading reference systems (AHRS) suffer from unstable on–off biases, scaling errors, and nonlinearities that lead to an increase of navigation errors over time. To damp these errors and uncertainties from ultimate attitude and heading angles, the bias term of gyroscopes is augemented in under estimation state vector for high frequency feedback compensation. In this paper, an adaptive estimation algorithm to combine the strong tracking filter (STF) and interval type-3 fuzzy set (IT3FS) with an unscented Kalman filter (UKF) is proposed to deal with the large uncertainties in the microelectromechanical AHRS. The UKF, involving strong capability of dealing with nonlinearity, is limited by large instable bias uncertainty. By combining the STF to adaptively tuning of the UKF gain matrix and the IT3FS to determine the STF fading factor as well, the new IT3FS-STUKF algorithm is developed for high estimation performance against the significant uncertainties. To validate the proposed IT3FS-STUKF, real experiments are conducted with ground vehicles in urban areas, which show an improved accuracy compared to the extended Kalman filter (EKF) at reasonable computational burden.

An improved histogram algorithm for DOA estimation based on single vector acoustic system

The integration of a singular vector acoustic system onto an unmanned underwater platform can lead to achieving unambiguous direction finding across the entire space. To enhance the direction-finding capabilities of the single vector acoustic system for low noise target, a refined histogram algorithm utilizing coherent spectrum weighting is suggested. A comparative evaluation and analysis of the target azimuth estimation performance of the enhanced histogram algorithm, traditional frequency-weighted histogram algorithm, and energy-weighted histogram algorithm are carried out. Through computer simulations, it is observed that the three Direction of Arrival (DOA) algorithms exhibit comparable direction-finding capabilities for wideband signals, whereas for single-frequency signals, the improved histogram algorithm surpasses the two conventional algorithms in direction-finding accuracy. Specifically, at a signal-to-noise ratio (SNR) of −40 dB, the azimuth estimation root mean square error (RMSE) is approximately 2°. Findings from sea trials indicate that the improved histogram algorithm displays a narrower spectral peak width and robust resistance to noise interference, thereby substantiating the effectiveness of the enhanced histogram algorithm.

Generating realistic training images from synthetic data for excavator pose estimation

Computer vision-based 3D pose estimation for automated excavator operation monitoring requires numerous training images annotated with 3D pose labels. Owing to challenges in collecting such datasets in a field setting, using synthetic images from virtual environments has emerged recently. However, synthetic images lack the realism inherent in onsite images, potentially impacting pose estimation performance on real images. This paper thus proposes a generative model for generating realistic training excavator images with multiple backgrounds. The evaluation was conducted by comparing estimation models trained on synthetic images (Model #1), generated excavator images with single background (Model #2), and generated excavator images with multiple backgrounds (Model #3). Model #3 exhibited the lowest mean angular error of 5.96° on real data, implying its superiority in generalizing real patterns. The proposed model facilitates data acquisition for improving pose estimation without manual annotation, providing rich information on excavator movements for proactive safety and productivity management.

EQGraphNet: Advancing single-station earthquake magnitude estimation via deep graph networks with residual connections

Magnitude estimation is a critical task in seismology, and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution. In this context, we propose the Earthquake Graph Network (EQGraphNet) to enhance the performance of single-station magnitude estimation. The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules, where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals. Our work uses the STanford EArthquake Dataset for model training and performance testing. Compared with three existing deep learning models, EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales. To evaluate the robustness, we add natural background noise to the model input and find that EQGraphNet achieves the best results, particularly for signals with lower signal-to-noise ratios. Additionally, by replacing various network components and comparing their estimation performances, we illustrate the contribution of each part of EQGraphNet, validating the rationality of our approach. We also demonstrate the generalization capability of our model across different earthquakes occurring environments, achieving mean errors of ±0.1 units. Furthermore, by demonstrating the effectiveness of deeper architectures, this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.

DFT-spread OTFS-NOMA for downlink integrated positioning and communication

In this article, a non-orthogonal multiple-access (NOMA) transmission system that incorporates discrete Fourier transform (DFT) spread orthogonal time-frequency space (OTFS) modulation is proposed for the downlink integrated positioning and communication (IPAC). This system is designed to decrease the Peak-to-Average Power Ratio (PAPR) within the OTFS-NOMA system while simultaneously serving both communication user (CU) and positioning user (PU) within the same time-frequency resources. Firstly, we demonstrate the applicability of the proposed DFT-spread OTFS-NOMA (DFT-s-OTFS-NOMA) system for downlink transmission. It efficiently serves both the CU and PU concurrently within the same time and frequency resources, thereby increasing spectrum utilization. Subsequently, we develop a two-stage parameter estimation algorithm focusing on accurate positioning parameter estimation for PU and precise channel estimation for CU. Then, the position of the PU is estimated using the time of arrival (TOA) model and the linear least squares (LLS) approach. Simulation demonstrates a 9 dB PAPR reduction in the proposed system compared to the existing OTFS-NOMA system. Additionally, the proposed two-stage positioning method for the PU achieves centimeter-level accuracy in position estimation and near millimeter-per-second-level accuracy in velocity estimation. What is more, the performance of channel estimation and symbol detection, aided by the PU signal in the DFT-s-OTFS-NOMA system, demonstrates resilience against Doppler effects without the need for reliance on pilots. This ensures sustained an effective Bit Error Rate (BER) even in time-variant high mobility scenarios.

Estimating state of charge of cylindrical lithium-ion cells using multiple random convolutional kernel transform and low-frequency stress waves

Lithium-ion batteries (LIBs) represent an energy storage technology increasingly utilized across various fields. To maintain the safety and reliability of a LIB, monitoring the LIB state of charge (SoC) is essential. Currently, there are limited studies on SoC estimation of cylindrical LIBs using the low-frequency stress wave technique due to its limited sensitivity to internal changes of LIBs during the charging/discharging process. Here, we present the first demonstration of SoC estimation of cylindrical LIBs using low-frequency stress wave (5-50kHz). We adopt Pearson correlation coefficients (PCCs) to select sampling points relevant (>0.5) to the SoC from the response stress wave signals and then apply the multiple random convolutional kernel transform (Multi-Rocket) model to extract four types of features for SoC estimation. The SoC estimation performance of the developed method achieves similar accuracy in both original and down-sampled (low-resolution) signals, with estimation root mean square error of about 0.7%, 1.4%, 3.5%, and 5.3%, respectively in four scenarios. This new method based on low-frequency stress waves enables a drastic cost reduction compared to expensive high-frequency ultrasound methods, paving the way for low-cost, real-time SoC monitoring with excellent accuracy.

Weighted penalized m-estimators in robust ridge regression: an application to gasoline consumption data

ABSTRACT The OLS and ridge regression (RR) estimators are adversely affected, when the problem of multicollinearity and y-direction outliers occur together. The robust ridge regression with penalized parameters offers biased estimators with lower variance than OLS and RR. The optimal penalized parameter value is crucial for balancing variance and bias. This study proposes three new weighted robust penalized M-estimators to address both multicollinearity and y-direction outliers. Their performance is evaluated against OLS, RR and existing penalized M-estimators using Monte Carlo simulations based on mean squared error (MSE). The new estimators exhibit lower MSE in the presence of multicollinearity and y-direction outliers. A real data application on gasoline consumption demonstrates the superior performance of the new estimators.

Precise Error Performance of BPSK Modulated Coherent Terahertz Wireless LOS Links with Pointing Errors.

One of the key advantages of terahertz (THz) communication is its potential for energy efficiency, making it an attractive option for green communication systems. Coherent THz transmission technology has recently been explored in the literature. However, there exist few error performance results for such a wireless link employing coherent THz technology. In this paper, we explore a comprehensive terrestrial channel model designed for wireless line-of-sight communication using THz frequencies. The performance of coherent THz links is analyzed, and it is found to be notably affected by two significant factors, atmospheric turbulence and pointing errors. These could occur between the terahertz transmitter and receiver in terrestrial links. The exact and asymptotic solutions are derived for bit error rate and interrupt probability for binary phase-shift keying coherent THz systems, respectively, over log-normal and Gamma-Gamma turbulent channels. The asymptotic outage probability analysis is also performed. It is shown that the presented results offer a precise estimation of coherent THz transmission performance and its link budget.

High-resolution direction-finding algorithm using Qth−order cumulants tensor: Q−TMUSIC

To resolve more sources than sensors, the 2q−MUSIC algorithm has been developed recently by rearranging the 2qth−order circular cumulants tensor into a suitable size matrix. However, in 2q−MUSIC, the angular resolution and estimation accuracy have not attained their optimum values due to the loss of differential information by matricizing the cumulants tensor. This paper proposes a subspace-based MUSIC-like algorithm, namely Qth−order cumulants-based tensor MUSIC algorithm for the direction of arrival (DOA) estimation of statistically independent non-Gaussian sources by exploiting the Qth−order cumulants tensors, Q∈{3,4,5,⋯}, directly using multilinear algebra tools. The proposed algorithm offers an improved DOA estimation performance in terms of angular resolution and estimation accuracy compared to their equivalent matrix-based Q−MUSIC. Moreover, the computational complexity of the proposed Q-TMUSIC is lesser than that of their equivalent matrix-based Q−MUSIC for Q>4. However, the proposed Q−TMUSIC can identify atmost (M−1) sources using an array of M−sensors than that of (M⌊Q2⌋−1) with their equivalent matrix based Q−MUSIC.

A MODEL FOR FORECASTING DEW POINT PRESSURE IN NIGER DELTA CONDENSATE RESERVOIRS USING CONSTANT VOLUME DEPLETION TESTS DATA

The dew point pressure plays a critical role in developing gas condensate reservoirs. It is a crucial factor used in fluid characterisation, performance estimation of gas reservoirs, and design of production systems. However, the composition of gas condensate fluid varies from one location to another, and thus, empirical correlations have been developed to determine the dew point pressure without conducting routine tests. As a result, correlations that are solely useful in the region where they were developed and analysed are developed. This work developed an empirical correlation using data from gas condensate wells in the Niger Delta using multiple linear regression. The model was developed using the Analytical Tools and techniques in Microsoft Excel. To develop the model, we utilised 63 data sets from the Constant Volume Depletion (CVD) experiment, which involved gas condensates from resources in the Niger Delta. The model comprises an empirical correlation that estimates the dew point pressure for gas condensate reservoirs. It uses compositional information of the fluid and reservoir temperature as input parameters. The correlation is derived through multiple regression analyses. Comparing the prediction accuracy of formulas based on the developed model and other conventional methods indicated that this model is more accurate than other statistical methods in predicting DPP within the Niger Delta region. This model can produce more accurate results than other estimation methods when working under limited field information and time constraints. The correlation developed in this process has a coefficient of determination of R2=0.869 and an Average Absolute Relative Error (AARE) of -2.0767%, along with a root mean square of 195279, indicating a high level of reliability in the proposed correlation.

An optimized hybrid finite element analyses - Artificial neural networks technique for estimating in-plane orthotropic mechanical properties of printed circuit boards

Explicitly modeling Printed Circuit Boards (PCBs) in Finite Element Analysis (FEA) is not practical due to the complex multi-component structure of PCBs. To overcome this complication, a PCB can be simplified and modeled as an orthotropic plate with equivalent mechanical properties that result in the same natural frequencies as the actual PCB. This research introduces an optimized hybrid technique combining FEA and Artificial Neural Networks (ANNs) to accurately estimate the equivalent in-plane orthotropic mechanical properties of PCBs. This study presents a systematic approach where natural frequencies obtained from FEA are used to train ANNs for predicting the equivalent representative mechanical properties. The research employs a rigorous optimization procedure involving various ANN configurations (i.e., different learning algorithms, activation functions, and number of hidden neurons) and training dataset sizes. To further ensure the reliability of the results, 10 cross-validation are carried out for each ANN configuration by employing dynamic data-splitting strategy, and all measures used for assessing the accuracy of the ANN predictions are based on averaged results for the 10 cross-validations. The accuracy of the ANN predictions is tested against both simulated and experimental data. The Mean Absolute Percentage Error (MAPE) is found to be about 4 % based on material properties against the simulated data, and about 1.2 % based on natural frequencies against the experimental data point. The results demonstrate superior predictive accuracy and efficiency compared to existing models, highlighting the potential of the proposed approach in advancing the reliability and performance of PCB mechanical property estimations.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

Signaling confidence

We study gender differences in confidence in an experimental hiring market, focusing on how signaling confidence affects the likelihood of being hired. We document that moderate (versus low) levels of confidence enhance the probability of being hired, while excessive confidence, characterized by high performance estimates and high certainty, diminishes employment prospects. Men display greater levels of confidence than women, and this gender gap widens in forward-looking scenarios, where performance estimates are provided ex ante rather than ex post. These findings provide a cautionary reminder of the potential negative consequences of signaling excessive confidence. Hence, encouraging both men and women to signal performance predictions that are aligned with reality instead of simply advising women to be more confident may well be a safer way to foster equity in labor market outcomes.