Statistical Error Analysis Research Articles

Efficient network management and security in 5G enabled internet of things using deep learning algorithms

The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model.

Machine learning approaches for the treatment of textile wastewater using sugarcane bagasse (Saccharum officinarum) biochar.

Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar. In this study, we compare the efficacy of the response surface methodology (RSM), the artificial neural network (ANN), the k-nearest neighbour (kNN), and adaptive neuro-fuzzy inference system (ANFIS) in removing crystal violet (CV) from wastewater. The characterisation of biochar is carried out by scanning electron microscope (SEM) and Fourier transform infrared (FTIR). The impacts of the solution pH, adsorbent dosage, initial dye concentration, and temperature were investigated using a variety of models (RSM, ANN, kNN, and ANFIS). The statistical analysis of errors was conducted, resulting in a maximum removal effectiveness of 97.46% under optimised settings. These conditions included an adsorbent dose of 0.4mg, a pH of 5, a CV concentration of 40.1mg/L, and a temperature of 20°C. The ANN, RSM, kNN, and ANFIS models all achieved R2 0.9685, 0.9618, 0.9421, and 0.8823, respectively. Even though all models showed accuracy in predicting the removal of CV dye, it was observed that the ANN model exhibited greater accuracy compared to the other models.

A general framework of high-performance machine learning algorithms: application in structural mechanics

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been implemented over the past two decades in different fields of simulation-based engineering science. Most numerical procedures involve processing data sets developed from physical or numerical experiments to create closed-form formulae to predict the corresponding systems’ mechanical response. Efficient AI methodologies that will allow the development and use of accurate predictive models for solving computational intensive engineering problems remain an open issue. In this research work, high-performance machine learning (ML) algorithms are proposed for modeling structural mechanics-related problems, which are implemented in parallel and distributed computing environments to address extremely computationally demanding problems. Four machine learning algorithms are proposed in this work and their performance is investigated in three different structural engineering problems. According to the parametric investigation of the prediction accuracy, the extreme gradient boosting with extended hyper-parameter optimization (XGBoost-HYT-CV) was found to be more efficient regarding the generalization errors deriving a 4.54% residual error for all test cases considered. Furthermore, a comprehensive statistical analysis of the residual errors and a sensitivity analysis of the predictors concerning the target variable are reported. Overall, the proposed models were found to outperform the existing ML methods, where in one case the residual error was decreased by 3-fold. Furthermore, the proposed algorithms demonstrated the generic characteristic of the proposed ML framework for structural mechanics problems.

Application of Corpus Analysis Software and Error Analysis Theory on Non-English Majors’ Chinese-English Translation Features

This research focuses on non-English major students’ Chinese-English (C-E) translation features. It aims at annotating and summarizing the features and error types in students’ C-E translation by applying to corpus analysis software Readability Analyzer. This study first collects students’ C-E translation texts from a computer-based exam, then forms a self-constructed corpus and a reference corpus. The translation materials in the target corpus and the reference corpus are first analyzed and compared by Readability Analyzer to figure out the features of students C-E translation texts from the perspectives of sentence length, word length, percentage of difficult words, Flesch Reading Ease, Dale-Chall Score, Fry Readability Grade Level. After that, different types of errors in students C-E translation are labeled and analyzed based on statistical analysis and Error Analysis (EA) theory. According to Readability Analyzer, students C-E translation texts have the following features. From passage statistics, it shows that translation texts that get lower scores tend to use shorter sentences and easier words. In addition, from readability scores, Flesch scores, Dale-Chall Scores shows and Fry Readability Grade Level are taken into consideration. Flesch scores shows that compared with the reference corpus, students’ translation which get lower scores are harder to understand since there are more language errors than higher-score translation. Dale-Chall Scores indicate that translation materials which score 8-9 are very close in the difficulty of word-using with the reference corpus. Fry Readability Grade Level shows that texts of lower scores are simpler and more accurate than texts of higher scores. According to Error Analysis (EA) Theory, students’ errors are further classified and discussed on lexical level, sentence level and textual level. The conclusions of this research are expected to make contributions to the improvement of C-E translation teaching and help college students, especially non-English majors, to enhance their C-E translation competence.

Forecasting the strength of nanocomposite concrete containing carbon nanotubes by interpretable machine learning approaches with graphical user interface

The sustainable development of the construction industry necessitates the utilization of multipurpose Cement Composites (CC). Therefore, the integration of nanomaterials has the potential to provide CC that exhibits superior performance and possesses several functionalities. Hence, the use of Carbon Nanotubes (CNTs) inside the concrete cementitious sector holds significant potential for implementing effective solutions toward creating a sustainable ecosystem characterized by versatile attributes. Nevertheless, the prediction of the characteristics of these composites is a significant challenge owing to their complex composite structure and non-linear response. Furthermore, the process of designing and executing experimental trials on diverse samples and across various age groups is arduous, time-consuming, and financially burdensome. There is currently a dearth of a predictive model capable of estimating the compressive strength of concrete including nanoparticles. The utilization of such models is of significant importance in the project and study of Reinforced Concrete (RC) structures including nanoparticles. Three machine learning algorithms, including Gene Expression Programming (GEP), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized in this study to forecast the Compressive Strength (CS) of nanocomposites that incorporate CNTs. The evaluation of the models' reliability was conducted by the utilization of cross-validation with K-folding and subsequent statistical error analysis. According to the results of the coefficient of determination (R2), the XGB model achieved the highest R2 value (0.95), while the GB model and GEP model both earned R2 values of 0.94. Furthermore, the validation method for the models included the implementation of statistical analysis and k-fold cross-validation. Therefore, the XGB model exhibited much lower values for statistical metrics compared to the GEP and GB models. In addition, a GEP empirical equation and a Graphical User Interface (GUI) have been created for practical applications in predicting the strength of concrete. This streamlines the procedure and provides a valuable instrument for harnessing the model's potential in the field of civil engineering. Furthermore, the use of Shapley analysis is conducted to assess the predominant factors in concrete prediction. The findings of this research indicate that the curing time, type of cement, and water-to-cement ratio significantly influence the properties of CNT-based concrete composites.

Quantifying uncertainty in trans-membrane stresses and moments in simulation.

The lateral stress profile of a lipid bilayer constitutes a valuable link between molecular simulation and mesoscopic elastic theory. Even though it is frequently calculated in simulations, its statistical precision (or that of observables derived from it) is often left unspecified. This omission can be problematic, as uncertainties are prerequisite to assessing statistical significance. In this chapter, we provide a comprehensive yet accessible overview of the statistical error analysis for the lateral stress profile. We detail two relatively simple but powerful techniques for generating error bars: block-averaging and bootstrapping. Combining these methods allows us to reliably estimate uncertainties, even in the presence of both temporal and spatial correlations, which are ubiquitous in simulation data. We illustrate these techniques with simple examples like stress moments, but also more complex observables such as the location of stress profile extrema and the monolayer neutral surface.

Modelling the effects of flexible pavement distresses in the long-term pavement performance database on performance

Evaluating flexible pavement performance is mandatory for managing transport infrastructure. This study focuses on modeling the relationships between international roughness index (IRI) and a total of 10 types of pavement distress, including alligator, block, wheel path length, wheel path longitudinal, non-wheel path longitudinal, transverse crackings, patches, bleeding, raveling areas, and pumping. The data recorded under the Long-Term Pavement Performance was used to develop the models. Data sets covering General Pavement Studies from seven states of the United States were used in modeling. The study used modeling approaches, including nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks (ANN), in which IRI was the dependent variable and pavement distress was the independent variable. In the developed models, 0.516, 0.623, and 0.684 regression coefficients values were obtained for nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks approaches, respectively. The analysis results have determined that the artificial neural networks technique performs more successfully than the other techniques. The statistical error analyses of the root mean square error, Nash-Sutcliffe coefficient of efficiency, mean absolute error, and normalized root mean square error also showed that the same modeling approach performs more successfully. With these data generated from a universally used database, it has been determined once again that ANN is the most efficient mathematical approach in modeling the relationships between surface distresses and IRI.

Thermal performance estimation of a v-corrugated solar air heater using ann techniques

A solar air heater (SAH) is a unique form of solar thermal collector that utilizes solar energy emitted from the sun to produce heated air. Various experimental and theoretical investigations have been undertaken to improve the poor thermal performance of SAHs. The difficulties related to these studies drew attention toward a reliable soft computing technique exemplified by the Artificial Neural Network (ANN) technique. The current work applied actual meteorological data from Miskolc City, Hungary, to an ANN model with the structure of a Multi-layer Perceptron (MLP) to forecast the energy performance of a V-corrugated solar-powered air heater. Seven input parameters and one output parameter make up the ANN structure, with a single hidden layer. For the purpose of selecting the most effective network for predicting output parameters, ten neurons have been assessed. The suggested ANN model was trained with 336 data sets using the Levenberg-Marquardt (LM) learning technique. The comparison of anticipated and real thermal performance values shows a very good agreement. The statistical error analysis showed that the optimal ANN model structure of 7-8-1 can reliably and accurately predict SAH’s thermal performance and thus it can save both time and cost.

Toward empirical correlations for estimating the specific heat capacity of nanofluids utilizing GRG, GP, GEP, and GMDH

When nanoparticles are dispersed and stabilized in a base-fluid, the resulting nanofluid undergoes considerable changes in its thermophysical properties, which can have a substantial influence on the performance of nanofluid-flow systems. With such necessity and importance, developing a set of mathematical correlations to identify these properties in various conditions can greatly eliminate costly and time-consuming experimental tests. Hence, the current study aims to develop innovative correlations for estimating the specific heat capacity of mono-nanofluids. The accurate estimation of this crucial property can result in the development of more efficient and effective thermal systems, such as heat exchangers, solar collectors, microchannel cooling systems, etc. In this regard, four powerful soft-computing techniques were considered, including Generalized Reduced Gradient (GRG), Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH). These techniques were implemented on 2084 experimental data-points, corresponding to ten different kinds of nanoparticles and six different kinds of base-fluids, collected from previous research sources. Eventually, four distinct correlations with high accuracy were provided, and their outputs were compared to three correlations that had previously been published by other researchers. These novel correlations are applicable to various oxide-based mono-nanofluids for a broad range of independent variable values. The superiority of newly developed correlations was proven through various statistical and graphical error analyses. The GMDH-based correlation revealed the best performance with an Average Absolute Percent Relative Error (AAPRE) of 2.4163% and a Coefficient of Determination (R2) of 0.9743. At last, a leverage statistical approach was employed to identify the GMDH technique’s application domain and outlier data, and also, a sensitivity analysis was carried out to clarify the degree of dependence between input and output variables.

The statistical properties analysis of reconstruction error in greedy pursuit algorithms: Taking OMP as an example

AbstractGreedy pursuit algorithms are widely used in sparse signal processing for their computational efficiency. However, research on the reconstruction error properties is far from comprehensive. This paper derives the statistical properties of reconstruction error in greedy pursuit algorithms, including probability density function (PDF), expectation, and covariance. The reconstruction error follows a mixture distribution, which is composed of multiple multivariate random variables and weighted by the probability of support sets. The multivariate random variable obeys a truncated distribution that results from restricting the noise domain. The validity of the derivations is verified by using the orthogonal matching pursuit (OMP) algorithm as an example.

The Cr(III) Exchange Mechanism of Macroporous Resins: The Effect of Functionality and Chemical Matrix, and the Statistical Verification of Ion Exchange Data

This study focuses on investigating and comparing the influence of the surface functional groups and chemical matrices of macroporous resin on the Cr(III) exchange mechanism. The results discussed herein indicate that sulfonic resin removed Cr(III) ions with faster kinetics than carboxylic resin. Equilibrium was established within 15 and 7 min for the carboxylic and sulfonic resins, respectively, with a 99.5% removal efficiency at 333 K. The Langmuir exchange capacity was observed to be higher for the sulfonic resin (1.5 mmol∙g−1) than the carboxylic resin (0.80 mmol∙g−1) at 333 K. The adsorption isotherms obtained for the carboxylic and sulfonic resins were H and S types, respectively, representing a higher affinity of the carboxylic resin for Cr(III) removal at a low metal ion concentration. Additionally, it was noted that the carboxylic resin preferentially co-sorbed H+ and Cr(OH)2+ ions below Cr(III) concentrations of 6–8 mmol.L−1. The H+ ions co-sorption was almost negligible, whereas the Cr(III) exchange was 87 and 34.5% for the carboxylic acid resin and sulfonic acid resins, respectively. The data of the concentration studies were evaluated using non-linear forms of Freundlich, Langmuir, and Dubinin–Radushkevich adsorption isotherm models, and the kinetic data were analyzed using pseudo-Ist- and pseudo-IIst-order kinetic models. The activation energy Ea for Amberlite IRC-50 (Na+) was greater (22.4 kJ∙mol−1) than that of Amberlyst-15 (Na+) 17 kJ∙mol−1, indicating a higher energy barrier for the ion exchange reaction on carboxylic resin. As per the findings of a statistical error analysis (RMSE and SSE) and absolute average relative distribution (AARD) statistical model, a close agreement between the experimental and theoretical values suggested that the Langmuir isotherm was well-fitted to the current adsorptive investigations. The interaction of the COO− and SO3− functional sites of the resins for the exchange of Cr(III) ions was validated through an FT-IR analysis. The macroporous resins used in the current study for Cr(III) exchange showed promising performances compared to other resins. The current investigations revealed valuable insights for choosing macroporous resins as adsorbents in water filtration systems.

Improving Linear Interpolation of Missing Hydrological Data by Applying Integrated Autoregressive Models

The application of linear interpolation for handling missing hydrological data is unequivocal. On one hand, such an approach offers good reconstruction in the vicinity of last observation before a no-data gap and first measurement after the gap. On the other hand, it omits irregular variability of hydrological data. Such an irregularity can be described by time series models, such as for instance the autoregressive integrated moving average (ARIMA) model. Herein, we propose a method which combines linear interpolation with autoregressive integrated model (ARI, i.e. ARIMA without a moving average part), named LinAR (available at GitHub), as a tool for inputing hydrological data. Linear interpolation is combined with the ARI model through linear scaling the ARI-based prediction issued for the no-data gap. Such an approach contributes to the current state of art in gap-filling methods since it removes artificial jumps between last stochastic prediction and first known observation after the gap, also introducing some irregular variability in the first part of the no-data gap. The LinAR method is applied and evaluated on hourly water level data collected between 2016 and 2021 (52,608 hourly steps) from 28 gauges strategically located within the Odra/Oder River basin in southwestern and western Poland. The data was sourced from Institute of Meteorology and Water Management (Poland). Evaluating the performance with over 100 million assessments in the validation experiment, the study demonstrates that the LinAR approach outperforms the purely linear method, especially for short no-data gaps (up to 12 hourly steps) and for rivers of considerable size. Based on rigorous statistical analysis of root mean square error (RMSE) – expressed (1) absolutely, (2) as percentages and (3) using RMSE error bars – the percentage improvement, understood as percentage difference between RMSE of linear and LinAR interpolations, was found to reach up to 10%.

Four-dimensional particle tracking velocimetry measurements of unsteady three-dimensional vortex onset and progression for 5415 straight ahead, static drift, and pure sway

Large-scale towed system four-dimensional particle tracking velocimetry (4DPTV) implementation and measurements are presented for towing tank tests for a naval combatant 5415 ship model for straight ahead, static drift β = 10°, and pure sway βmax = 10° conditions. The results, for the first time, provide instantaneous volumetric flow field data around the ship model including near the hull surface, and a complete description of the sonar dome 3D unsteady vortex separation onset and progression due to the 4DPTV larger measurement volume and higher data rates in comparison with previous results using tomographic particle image velocimetry (TPIV); thereby, providing the essential data required for the assessment of current hybrid Reynolds-averaged Navier–Stokes/large eddy simulation turbulence modeling capabilities and guidance for the necessary modeling improvements. The 4DPTV system is summarized and compared with the previous TPIV, including the camera calibration procedures, trigger systems, and synchronization with the sway motion. Analysis procedures, including data reduction procedures and the 3D vortex core detection and voxel labeling techniques, are described. The identification of the complex vortex separations and breakdown is aided using complementary detached eddy simulations. The vortex–vortex interaction process is visualized from instantaneous volumetric datasets. The pros and cons of the 4DPTV vs TPIV, the comparison between the second- and third-generation vortex visualization technique, and the statistical convergence error analysis of sonar dome port vortex for β = 10° are discussed. Plans for increased spatial resolution of the 4DPTV system and additional data reduction techniques for detailed turbulence analysis are also discussed.

Forecasting the strength of micro/nano silica in cementitious matrix by machine learning approaches

This work provides an intelligent machine learning-based technique for anticipating cement paste compressive strength incorporating nanocomposite. In this approach, artificial neural network (ANN), gene expression programming (GEP), random forest (RF), and ensemble approaches are used to combine multiple weak learners capable of identifying the mapping between input and output data into a robust learner. The accuracy of the strong learner as a whole will be increased by incorporating the weak learner with the least prediction error with enhanced correlation. The components of the cement paste mixture (such as water-to-cement ratio, nano-silica (%), micro-silica (%), curing time (days), and the compressive strength value are used as input data and output data. Thus, 205 datasets of cement compressive strength tests are collected to train and assess the learners. The reliability of the models was evaluated using cross-validation with K-folding and statistical error analysis (i.e., MAE, MAE, and RMSE). Therefore, interpretable methods using AdaBoost provide a significant correlation with a lower error rate of R2 = 0.96. The two that perform the best are ANN with ensemble Adaboost and RF with an R-square of 0.97. Additionally, the cross-validation findings demonstrate that the answer was correct and had minimal mistakes. The ensemble model, therefore, displays a strong connection. In addition, new expressions for the predictions of the 28-day compressive strength of nanocomposite containing silica content are developed based on the database. Sensitivity analysis reveals that curing time and silica content has a major influence on prediction. In addition, a model capable of predicting the values of the desired outputs when the necessary input parameters are provided is trained to generate the graphical user interface (GUI). It simplifies the procedure and provides a useful instrument for employing the model's civil engineering abilities.

Application of RSAPSO Hybrid Optimizer for Parameter Extraction of Solar PV Cell Models under Temperature Variations

Accurate modeling of photovoltaic (PV) modules/cells is crucial for evaluating the efficiency of solar PV systems. However, the lack of specific parameters of solar cells, which are not included in the manufacturer’s datasheet, often results in flawed cell modeling. Quick and convenient parameter extraction techniques are required to overcome this challenge and create a robust solar PV cell model. These models are useful in optimization, simulation, and enhanced energy harvesting from PV-based renewable energy systems. This paper uses a recent optimizer called Rat Search Algorithm Particle Swarm Optimization (RSAPSO), for extracting the parameters of single diode and double diode models of PV cells. RSAPSO combines the exploration and exploitation advantages of both algorithms. The RSAPSO's parameter optimization results were compared against five other techniques, and the superiority of the suggested algorithm was confirmed through ranking tests, statistical error analyses, and temperature variation sensitivity analyses.

Accuracy of Different Electronic Basis Set Families for Anharmonic Molecular Vibrations: A Comprehensive Benchmark Study.

In this work, the accuracy and convergence of different electronic basis set families for the computation of anharmonic molecular vibrational spectroscopic calculations are benchmarked. A series of 39 different basis sets from different families following their hierarchy are assessed on VSCF and VSCF-PT2 algorithms with commonly used MP2 and DFT based B3LYP-D potentials for a set of molecular systems. Such an effort has been validated in a previous work ( J. Phys. Chem. A 2020, 124, 9203-9221) with split-valence basis sets for fundamentals and intensities. Here, fundamental transitions, vibrationally excited states, and intensities are compared with the experimental data to estimate the accuracy for a series of Jensen, Dunning, Calendar, Karlsruhe, and Sapporo basis set families. The convergence of basis sets are also compared with the large ANO basis set. Comprehensive statistical error analysis in terms of accuracy and precision was carried out to assess the performance of each basis set. It is observed that the improvement for the calculated harmonic and anharmonic values from the smaller basis sets to the medium (i.e., triple-ξ) is considerable. Beyond this, from medium to large basis sets, the convergence is slow and mostly posits nearly converged values. Basis sets with and without diffuse functions offer characteristically different accuracies and convergence patterns. Finally, recommendations are given on the choice of basis set chosen as black-box which can balance between accuracy and computational time, estimation of the errors, and their selections especially for large molecules.

Interviewer Effects on the Measurement of Physical Performance in a Cross-National Biosocial Survey.

Biosocial surveys increasingly use interviewers to collect objective physical health measures (or "biomeasures") in respondents' homes. While interviewers play an important role, their high involvement can lead to unintended interviewer effects on the collected measurements. Such interviewer effects add uncertainty to population estimates and have the potential to lead to erroneous inferences. This study examines interviewer effects on the measurement of physical performance in a cross-national and longitudinal setting using data from the Survey of Health, Ageing and Retirement in Europe. The analyzed biomeasures exhibited moderate-to-large interviewer effects on the measurements, which varied across biomeasure types and across countries. Our findings demonstrate the necessity to better understand the origin of interviewer-related measurement errors in biomeasure collection and account for these errors in statistical analyses of biomeasure data.

Strength predictive models of cementitious matrix by hybrid intrusion of nano and micro silica: Hyper-tuning with ensemble approaches

The incorporation of nanomaterials (NMs) in concrete will produce the utmost properties. Nevertheless, investigational work takes a great deal of time with efforts to measure the strength of concrete. Thus, the process of estimating the properties of cement paste can be accelerated by employing soft machine learning (SML) techniques. Individual machine learning approaches (MLAs) like random forest (RF), decision tree (DT), and support vector machine (SVM) were utilized. These individual MLAs models then ensemble with two approaches namely bagging and boosting. The outcome of the model depicts a robust performance by showing significant correlations (R2) as compared to individual MLAs. Data points with 205 having contribution factors (e.g., curing time (days), water to binder (w/b), micro-silica (%), and nano-silica (%) were used as input parameters, and compressive strength as output for MLAs modeling. To ensure that each model is as accurate as possible, cross-validation with K-folding and statistical error analysis (i.e., MAE, MAE, and RMSE) were utilized. Thus, interpretable approaches with AdaBoost give a strong correlation of R2 ranging from 0.85 to 0.90 respectively with fewer errors. Out of which DT with ensemble Adaboost and RF give a robust performance. Moreover, the results of the cross-validation show that the response was accurate, with fewer errors. Therefore, the ensemble model represents a robust correlation. Therefore, its usage with individual ML would enhance the model's performance. In addition, the graphical user interface (GUI) is created by training a model that can predict the values of the desired outputs when the necessary input parameters are provided. It simplifies the procedure and provides a handy tool for employing the model's capabilities in the civil engineering domain.

A decision tree model for accurate prediction of sand erosion in elbow geometry

Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0%, 6.27%, 6.26%, and 5.5%, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industry's cost and time.

Low-Complexity Wideband Interference Mitigation for UWB ToA Estimation.

Reliable time of arrival (ToA) estimation in dense multipath (DM) environments is a difficult task, especially when strong interference is present. The increasing number of multiple services in a shared spectrum comes with the demand for interference mitigation techniques. Multiple receiver elements, even in low-energy devices, allow for interference mitigation by processing coherent signals, but computational complexity has to be kept at a minimum. We propose a low-complexity, linearly constrained minimum variance (LCMV) interference mitigation approach in combination with a detection-based ToA estimator. The performance of the method within a realistic multipath and interference environment is evaluated based on measurements and simulations. A statistical analysis of the ToA estimation error is provided in terms of the mean absolute error (MAE), and the results are compared to those of a band-stop filter-based interference blocking approach. While the focus is on receivers with only two elements, an extension to multiple elements is discussed as well. Results show that the influence of strong interference can be drastically reduced, even when the interference bandwidth exceeds 60% of the signal bandwidth. Moreover, the algorithm is robust to uncertainties in the angle of arrival (AoA) of the desired signal. Based on these results, the proposed mitigation method is well suited when the interference bandwidth is large and when computational power is a critical resource.