Estimation Process Research Articles

PGD in thermal transient problems with a moving heat source: A sensitivity study on factors affecting accuracy and efficiency

AbstractThermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model's performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non‐separation yields the best approximation to the full order model.

Research on the Method of Light Intensity Detection based on Kalman Filtering

A light intensity detection algorithm using Kalman filtering is proposed for the usage of data fusion in multi-sensor scenarios, like industrial control, machine vision, and navigation systems. In traditional measurements, the result is often faced with noise interference, causing accuracy problems. This algorithm Kalman filtering can be a good solution to noise signal reduction and provide optimal estimation in various domains that need prediction. As Kalman filtering is suitable for light intensity measurements, This work experimented with the steps shown below. First, the light intensity data is reported by sensors, and the mean and standard deviation of the measurement data are calculated; Second, by introducing a Kalman filtering model into the detecting model, the precision of estimating is improved, and the reliability of the system is enhanced; Finally, the data and diagram are exported by Matlab, and the Kalman filtering algorithm is compared with classical algorithms like moving average, median filtering. The experimental data shows Kalman filtering can be better applied to state estimation and signal processing domains, and the average light intensity data is 949.744. This result supports that Kalman filtering provides a robust and efficient method and it bed-covers the future research in improving the precision in light intensity measurements.

A novel robust moving horizon estimator for discrete-time linear systems subject to measurement outliers

State estimation is a crucial problem in modern industries and has been widely applied across various fields. The performance of the estimator depends on the quality of the measurement data. Measurements being corrupted by outliers is becoming an unavoidable phenomenon that leads to degradation of estimator performance. It is critical to develop estimators with outlier suppression capabilities to mitigate the adverse impact of measurement outliers. In this paper, we propose an effective outlier suppression technique for discrete-time linear systems within the framework of moving horizon estimation (MHE). The proposed estimator solves the issues of poor estimation accuracy and low computational efficiency among the existing MHE-based outlier-robust estimators. Moreover, the proposed method allows us to not only achieve robust state estimation but also detect outliers. Specifically, we propose a set of least-squares cost functions and an outlier identification mechanism to implement the estimation process. Subsequently, the stability of the estimation error of the proposed estimator is demonstrated. The estimation error can achieve exponential convergence by choosing appropriate design parameters. Lastly, the proposed estimator is applied to target tracking simulations and compared with state-of-the-art outlier-robust estimation methods, confirming the effectiveness and superiority of the proposed estimator.

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets.

In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.

Interrelationships Among the Rice Export Prices of Major Exporting Countries: In Search of the Leader and Followers

AbstractBy examining the econometric interrelationships of rice export prices of India, Thailand, Vietnam, Pakistan, the U.S., Argentina, Brazil, and Uruguay, this study examines the most influential rice-exporting countries in the international rice export market. The sampled countries are the source of more than 80% of the global rice exports. In the process, this study relies on online data portals of the United Nations and country-level sources for aromatic and non-aromatic rice export price information from India, Pakistan, and Thailand. This study employed the Prais-Winsten and Autoregressive Distributive Lag (ARDL) estimation processes with the error correction procedure. Combining the findings from both models, this study concludes that in terms of interactions, the non-aromatic long-grain rice price of the U.S., and the aromatic rice price of Pakistan are the most correlated prices in the international rice market. Considering the number of countries influenced by the price of a given country, our results indicate that Vietnam is the price leader in the non-aromatic long-grain market, followed by Argentina, Brazil, and India. India, the largest exporter, is the fourth top price leader. In the case of aromatic rice, Thailand is the absolute leader. Many countries in Asia and Africa rely on rice imports to meet domestic demand, where the food security situation is already precarious. Based on the findings, this study suggests closely monitoring rice production and export trends of Vietnam, Argentina, Brazil, India and Thailand to provide early warning when necessary to better manage supply shocks in the international rice market.

Estimation of Multiresponse Multipredictor Nonparametric Regression Model Using Mixed Estimator

In data analysis using a nonparametric regression approach, we are often faced with the problem of analyzing a set of data that has mixed patterns, namely, some of the data have a certain pattern and the rest of the data have a different pattern. To handle this kind of datum, we propose the use of a mixed estimator. In this study, we theoretically discuss a developed estimation method for a nonparametric regression model with two or more response variables and predictor variables, and there is a correlation between the response variables using a mixed estimator. The model is called the multiresponse multipredictor nonparametric regression (MMNR) model. The mixed estimator used for estimating the MMNR model is a mixed estimator of smoothing spline and Fourier series that is suitable for analyzing data with patterns that partly change at certain subintervals, and some others that follow a recurring pattern in a certain trend. Since in the MMNR model there is a correlation between responses, a symmetric weight matrix is involved in the estimation process of the MMNR model. To estimate the MMNR model, we apply the reproducing kernel Hilbert space (RKHS) method to penalized weighted least square (PWLS) optimization for estimating the regression function of the MMNR model, which consists of a smoothing spline component and a Fourier series component. A simulation study to show the performance of proposed method is also given. The obtained results are estimations of the smoothing spline component, Fourier series component, MMNR model, weight matrix, and consistency of estimated regression function. In conclusion, the estimation of the MMNR model using the mixed estimator is a combination of smoothing spline component and Fourier series component estimators. It depends on smoothing and oscillation parameters, and it has linear in observation and consistent properties.

Instance-Aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning

Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction relies on reliable depth estimation for multi-camera images. However, existing approaches exhaustively predict depths for every pixel without prioritizing objects, which are precisely the entities requiring detection in the 3D space. To this end, we propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector. First, a category-specific structural priors mining approach is proposed for enhancing the efficacy of monocular depth generation. Besides, a self-boosting learning strategy is further proposed to encourage the model to place more emphasis on challenging objects in computation-expensive temporal stereo matching. Together they provide advanced depth estimation results for high-quality BEV features construction, benefiting the ultimate 3D detection. The proposed method achieves state-of-the-art performances on the challenging nuScenes benchmark, and extensive experimental results demonstrate the effectiveness of our designs.

Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging

Objective. The primary aim of our study is to advance our understanding and diagnosis of cardiac diseases. We focus on the reconstruction of myocardial transmembrane potential (TMP) from body surface potential mapping. Approach. We introduce a novel methodology for the reconstruction of the dynamic distribution of TMP. This is achieved through the integration of convolutional neural networks with conventional optimization algorithms. Specifically, we utilize the subject-specific transfer matrix to describe the dynamic changes in TMP distribution and ECG observations at the body surface. To estimate the TMP distribution, we employ LNFISTA-Net, a learnable non-local regularized iterative shrinkage-thresholding network. The coupled estimation processes are iteratively repeated until convergence. Main results. Our experiments demonstrate the capabilities and benefits of this strategy. The results highlight the effectiveness of our approach in accurately estimating the TMP distribution, thereby providing a reliable method for the diagnosis of cardiac diseases. Significance. Our approach demonstrates promising results, highlighting its potential utility for a range of applications in the medical field. By providing a more accurate and dynamic reconstruction of TMP, our methodology could significantly improve the diagnosis and treatment of cardiac diseases, thereby contributing to advancements in healthcare.

Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest

Fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) profoundly affects environmental systems, human health and economic structures. Multi-source data and advanced machine or deep-learning methods have provided a new chance for estimating the PM2.5 concentrations at a high spatiotemporal resolution. In this paper, the Random Forest (RF) algorithm was applied to estimate hourly PM2.5 of the North China area (Beijing–Tianjin–Hebei, BTH) based on the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) aerosol optical depth (AOD) products. To improve the estimation of PM2.5 concentration across large areas, we construct a method for co-weighting the environmental similarity and the geographical distances by using an attention mechanism so that it can efficiently characterize the influence of spatial–temporal information hidden in adjacent ground monitoring sites. In experiment results, the hourly PM2.5 estimates are well correlated with ground measurements in BTH, with a coefficient of determination (R2) of 0.887, a root-mean-square error (RMSE) of 18.31 μg/m3, and a mean absolute error (MAE) of 11.17 µg/m3, indicating good model performance. In addition, this paper makes a comprehensive analysis of the effectiveness of multi-source data in the estimation process, in this way, to simplify the model structure and improve the estimation efficiency of the model while ensuring its accuracy.

An inspired chaos-based estimation-theory optimization for low-density parity-check (LDPC) code decoding

Optimization techniques have emerged as powerful tools for solving complex engineering problems in real-time applications, surpassing traditional heuristic methods. This paper introduces a novel chaos optimization algorithm that integrates the bisection search method to enhance the convergence ability, solution accuracy, and search speed of conventional optimization problems. The proposed algorithm is applied to the design and analysis of a reduced complexity decoding scheme using Low-Density Parity-Check (LDPC) codes. By leveraging the Linear Minimum Mean Square Error (LMMSE) estimation criterion, the proposed method approximates the estimated parameters acquired during the message computation process. This combined approach effectively mitigates error propagation and premature convergence issues. The presented research offers a low-complexity decoding algorithm for LDPC codes, leveraging an optimized chaos bisection search method. The adoption of the LMMSE criterion overcomes data dependency and error propagation concerns during the estimation process. The modified chaos optimization technique, integrated with the bisection search, efficiently determines optimal values from perturbed observations, leading to improved convergence rates and data processing precision by reducing the observation dimension (search space). Simulation results validate the proposed approach, showcasing its ability to mitigate error propagation and premature convergence while maintaining coding gain accuracy. Experimental validation on an FPGA RF-SoC device (model XCZU27DR) demonstrates that the proposed method achieves a maximum throughput of 8.7 Gbps at a clock frequency of 386.72 MHz, with reduced hardware complexity. The experimental verification on an FPGA RF-SoC device highlights the algorithm's minimal hardware complexity, superior decoding throughput, and Bit Error Rate (BER) performance.

Hybrid approach for cost estimation of sustainable building projects using artificial neural networks

Abstract Inaccurate estimation in sustainable construction projects is a significant challenge for appraisers, particularly when data and knowledge about the projects are lacking. As a result, there is a need to use cutting-edge technology to solve the issue of estimation inaccuracy. Iraq’s productivity estimates are now made using outdated, ineffective methodologies and procedures. In addition, it is essential to implement cutting-edge, quick, precise, and adaptable technology for productivity estimation. This study’s major goal is to calculate the overall costs of sustainable buildings using the cutting-edge technique known as artificial neural networks (ANNs). For Iraq’s construction industry to handle projects successfully, ANNs must be used as a new technology, a methodology developed to estimate the overall costs of sustainable construction projects. In this study, the process of cost estimation was modeled using ANNs. Investigations of a number of examples involving the creation of ANNs have also been made, including network design and internal elements and how much they impact the effectiveness of models built using ANNs. Equations were developed to determine structural productivity. These networks were shown to have extremely strong predictive power for both accounting coefficients (R) (93.33%) and the overall costs of sustainable construction projects, with a prediction accuracy of 87.00 and 93.33%, respectively.

Energy‐aware power allocation using Ladybug Beetle Honey Badger Optimization in multiple‐input multiple‐output‐non‐orthogonal multiple access with multiple users

SummaryThe non‐orthogonal multiple access (NOMA) is highly capable of serving multiple users at similar times and frequencies. The power allocation (PA) is widely considered as a main factor in NOMA for efficient communication. Here, the application of multiple‐input multiple‐output (MIMO) is added to NOMA to fulfill demands of enriched spectral efficiencies and extra user data. In this research, the Ladybug Beetle Honey Badger Optimization (LBHBO) is proposed for efficient PA in MIMO‐NOMA. Initially, the received signals from the user are modulated for amplitude and frequency. Then, user grouping is conducted by fuzzy local information c‐means (FLICM) clustering followed by using PA done by proposed LBHBO. This power is then moved to the transmitter and then to the channel estimation process. Moreover, cyclic prefix (CP) removal is carried out that tends to discrete Fourier transform (DFT). Finally, quadrature amplitude modulation (QAM) demodulation is performed for reallocated output. Furthermore, LBHBO is formed by combining Ladybug Beetle Optimization (LBO) and Honey Badger Algorithm (HBA). The performance offered by the LBHBO‐PA with maximal values with energy efficiency (EE) of 25.38 Mbits/s, sum rate of 1.29 Mbits/s, and achievable rate of 100.47 Mbits/s.

Surrogate model‐aided global sensitivity analysis framework for seismic consequences estimation in buildings

AbstractSeismic consequences estimation for individual buildings is valuable for various stakeholders, including government entities, building owners, and insurers. The robustness of estimation results in the presence of incomplete input information can typically be investigated through sensitivity analysis. However, the estimation process's complexity and the sensitivity analysis's computational burden hinder its practical application, which requires a more efficient procedure to facilitate broader use. This paper proposes a novel framework for sensitivity analysis of seismic consequences estimation to improve the efficiency and reliability of such analysis. The proposed approach encompasses three key components: (1) stochastic ground motion modeling (SGMM)‐based seismic consequences estimation to evaluate the economic, environmental, and social consequences given specific buildings by considering different hazard levels, (2) the training of surrogate model (Gaussian process model) for structural analysis to reduce the computational cost of the evaluation process, and (3) variance‐based global sensitivity analysis to investigate the importance of parameters of concern in the estimation process. The entire procedure is implemented in Python, adhering to object‐oriented programming, and does not rely on external software. Then, the proposed methodology is applied to two distinct three‐story steel moment‐resistant frames (SMRFs) subjected to four different hazard levels to demonstrate its effectiveness. The SGMM method can generate specific ground motions for each hazard level, mitigating the potential for result bias from using ground motions with unrealistic characteristics. Furthermore, the SGMM method is particularly suitable for automated analysis processes reducing the laborious task of screening ground motions from the database. Comparative analysis with surrogate‐free estimation reveals that the surrogate‐based analysis delivers reliable results with significantly reduced computational cost. The results of analyzing different structures under varying hazard levels reflect the variability in sensitivity analysis of consequences estimation, highlighting the necessity of the proposed flexible and efficient framework. Furthermore, the proposed framework's advantages, limitations, and future research needs are discussed.

LoRa localisation using single mobile gateway

Effective use of GPS and mobile networks for localisation in rangeland areas is constrained by their high power consumption and high deployment costs. Long-range (LoRa), a low-power wide area network (LPWAN) technology, can be employed to mitigate these challenges. In contrast to prior research where the prevalent approaches entail multiple gateways. This work proposes a valuable methodology focused on a single mobile LoRa gateway for localisation. A particle filtering and machine learning-based pipeline is employed to map the distance between a target node and the gateway from the received signal strength indicator (RSSI). Particle filtering is used to reduce the impact of noise on the RSSI values. Then, several machine learning techniques, such as support vector machines, random forest, and k-nearest neighbour, are used on the RSSI values to estimate the distance. The estimated distance is then used for tracking using a centroid pseudo-trilateration method. The proposed method was tested in a real-world semi-line-of-sight setting, using three datasets generated by LoRaWAN-specified hardware components and a server. Two forms of experiments were performed: active searching and passive monitoring. We propose an iterative estimation process to address the dilution of precision caused by the initial positions of the gateway required for active searching applications. The results show that active searching typically requires 2 to 3 hops to reach a target node. The accuracy of passive monitoring depends on the proximity of the gateway, which varies from 20 m to 170 m. This proposed approach has the potential to open the way for localising, tracking, or monitoring target objects within sparsely populated rangeland areas, even when resources are severely constrained.

A Comparison Between Local Linear Regression and K- Nearest Neighbor Methods in Estimating the Nonparametric Regression Model with Simulation and Application

In this paper, the nonparametric regression model was estimated using two common estimation methods (Local Linear Regression Estimator (LLRE) and K-Nearest Neighbor Estimator (KNNE). The simulation process was conducted using the statistical programming language, R, for these methods, and they were compared using the Average Root Mean Squares Error (ARMSE) criterion, with three sample sizes (50, 100, 150), three levels of error variance (0.3, 0.7, 1) and two non-linear models. The results for the first model showed the superiority of the nearest neighbor estimator (KNNE) method in most cases, and for the second model the local linear regression estimator (LLRE) was superior at all sample sizes and for all levels of variance. On the applied side, the effect of hemoglobin (Hb) on the packed cell volume (PCV) of 150 patients with chronic kidney disease was studied. The estimation process was made using the two methods, and the comparison between them was made using the root mean square error (RMSE) criterion. It was shown through the results of the applied side that the preference was for the nearest neighbor estimator (KNNE) method.

Overlapping Batch Confidence Intervals on Statistical Functionals Constructed from Time Series: Application to Quantiles, Optimization, and Estimation

We propose a general purpose confidence interval procedure (CIP) for statistical functionals constructed using data from a stationary time series. The procedures we propose are based on derived distribution-free analogues of the χ 2 and Student’s t random variables for the statistical functional context, and hence apply in a wide variety of settings including quantile estimation, gradient estimation, M-estimation, CVAR-estimation, and arrival process rate estimation, apart from more traditional statistical settings. Like the method of subsampling, we use overlapping batches of time series data to estimate the underlying variance parameter; unlike subsampling and the bootstrap, however, we assume that the implied point estimator of the statistical functional obeys a central limit theorem (CLT) to help identify the weak asymptotics (called OB-x limits, x=I,II,III) of batched Studentized statistics. The OB-x limits, certain functionals of the Wiener process parameterized by the size of the batches and the extent of their overlap, form the essential machinery for characterizing dependence, and consequently the correctness of the proposed CIPs. The message from extensive numerical experimentation is that in settings where a functional CLT on the point estimator is in effect, using large overlapping batches alongside OB-x critical values yields confidence intervals that are often of significantly higher quality than those obtained from more generic methods like subsampling or the bootstrap. We illustrate using examples from CVaR estimation, ARMA parameter estimation, and NHPP rate estimation; R and MATLAB code for OB-x critical values is available at web.ics.purdue.edu/ ∼ pasupath .

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.

Classes of Gaussian states for squeezing estimation

This study explores a detailed examination of various classes of single- and two-mode Gaussian states as key elements for an estimation process, specifically targeting the evaluation of an unknown squeezing parameter encoded in one mode. To quantify the efficacy of each probe, we employ the concept of Average Quantum Fisher Information (AvQFI) as a robust metric to quantify the optimal performance associated with specific classes of Gaussian states as input. For single-mode probes, we identify pure squeezed single-mode states as the optimal choice and we explore the correlation between Coherence and AvQFI. Also, we show that pure two-mode squeezed states exhibit behavior resembling their single-mode counterparts for estimating the encoded squeezing parameter, and we studied the interplay between entanglement and AvQFI. This paper presents both analytical and numerical results that encompass all the studied classes, offering valuable insights for quantum estimation processes.

Car-following model considering jerk-constrained acceleration stochastic process for emission estimation

The continuity of acceleration changes is often overlooked by existing car-following models, leading to a limitation in capturing realistic driving dynamics for emission estimation, which are essential for the application in microscopic traffic evaluations. This paper investigated and modeled the jerk-constrained acceleration stochastic process using the Markov model. A new car-following model considering the acceleration stochastic process was proposed, which incorporated two modes of unconscious following and active acceleration approaching. Additionally, a bi-objective model calibration framework was introduced to balance the trade-off between traffic-related performance and emission estimation performance. Numerical simulation was conducted to compare the performance of the new model with the conventional Wiedemann model. Results demonstrated that the proposed model provides more realistic vehicle dynamics and accurate emission estimations. Specifically, compared to the Wiedemann model, the new model reduced the root-mean-square error (RMSE) of spacing headway by 0.73 m and the RMSE of vehicle-specific power (VSP) distribution by 11.57%.

Air-ground multi-agent system cooperative navigation based on factor graph optimization SLAM

In this study, a cooperative navigation algorithm centered on factor graph optimization—simultaneous localization and mapping (FGO-SLAM) is presented for an air-ground multi-agent system. The algorithm prioritizes the control of error statuses during the position and attitude estimation procedure throughout the entire back-end optimization process. In the conventional extended kalman filtering (EKF) algorithm, periodic cumulative errors may arise, introducing uncertainty to the estimation process. The application of the FGO algorithm not only mitigates deviation but also stabilizes errors, thereby eliminating the accumulation of periodic errors. In comparison to the practical EKF-SLAM, FGO-SLAM serves as a semi-offline optimization system that leverages key frames to minimize computational load. During multi-agent simulations, when two or more agents have overlapping field views, landmark data is merged, enhancing the optimization effectiveness. Through simulation experiments, the proposed algorithm demonstrates a 40% reduction in position error and a 41% reduction in attitude error, affirming the efficacy of FGO-SLAM for cooperative navigation.