Efficient Estimation Research Articles

Efficient estimation of the density and distribution functions of Weibull-Burr XII distribution

This study takes into account the efficient estimation of the probability density function (PDF) of the Weibull-Burr XII distribution and the cumulative distribution function (CDF), which allows for greater flexibility than many other generalized distributions in use today. Nine traditional estimators are used to produce the analytical formulations for the bias and the mean squared error (MSE). A simulated investigation is conducted to evaluate the finite sample performance of the suggested estimators in terms of MSE values. In addition to that a real data application is also demonstrated for illustration.

On-Board Diagnostics for Li-Ion Batteries Using Electrochemical Impedance Spectroscopy (EIS)-Based Health Estimation Models

There exists a growing need for standardized On-Board Diagnostics (OBD) for electric vehicles [1] to provide accurate health metrics and guarantees to both consumers and manufacturers.Previous work has shown the powerful LIB capacity-based State-of-Health (SoH) estimation capability of Electrochemical Impedance Spectroscopy (EIS) measurements and data-driven models [2] [3]. Since EIS measurements are dependent not only on SoH but also State-of-Charge (SoC) and temperature [4], it is important that the measurements are conducted after the cell reaches an equilibrium, and that these other variables are also tracked. Although EIS measurements are somewhat quicker than some traditional capacity-determination experimental methods, the time taken for such measurements is not insignificant. Therefore, building a pipeline to determin the EIS frequency measurements most important for SoH estimation is an important task in developing a suitable EIS-based OBD. By exploring a frequency range between 0.1 and 200 Hz, we study EIS measurements related to diffusion and charge transfer processes in LIB operation [5], with each process being partially distinguishable due to their varying timescales.In this work, using EIS data collected from 5Ah LIBs with an NMC-111 cathode and a graphite anode at various SoCs (0, 25, 50, 75 and 100%) and cell lifetime (0, 10, 20, 40 and 90 days) as input features, we develop sequential, data-driven health estimation models for LIBs. The 22 cells used for this analysis have been aged over a period of 90 days in two different ways: either through active cycling at different C-rates (0.2 and 1C) and temperatures (0, 25 and 40⁰C), or passive “calendar aging” where the cells are left without use at a specific temperature and SOC. Using feature attribution techniques (Shapley values, feature occlusion, etc.), we find the most influential of the frequency ranges in the EIS measurements that relate strongly with cell performance degradation. To develop a streamlined and efficient SoH estimation framework, we formulate an optimization problem to find the EIS experimental design in terms of frequency ranges that delivers maximum accuracy in SoH estimation at different points in the lifetime of the cell. These streamlined and optimized EIS experimental designs and SoH estimation models can be used directly in the development of rapid and efficient on-board diagnostic tools.

Iron Production By Molten Sulfide Electrolysis

With urgency and incentives to cut CO2 emissions, alongside increasing demand for steel, there is a need for technologies that use solely electricity for iron ore reduction, eliminating the role of carbon as a reductant. Herein, we propose the electrolytic production of liquid cast iron using a novel sulfide route, molten sulfide electrolysis (MSE). The process operates using sulfide chemistry and an inert anode. Sulfides are well known from non-ferrous metallurgy1 and the exclusion of oxygen supports a virtual elimination of green-house gases(GHG) emissions from the reduction step. The electrolytic decomposition of iron sulfide into iron and elemental sulfur gas operates in a multi-component molten sulfide electrolyte. The underlying thermodynamics suggests the absence of trivalent iron species (Fe3+) in such conditions, supporting the reduction of only divalent iron (Fe2+) and reducing the energy need proportionally, as compared to other oxide-based routes. Iron sulfide deposits or tailings, as well as conventional iron oxide ores after sulfidation2 are some of the suitable feedstocks for MSE.Both thermal only, and galvanostatic electrochemical experiments were carried out on electrolyte droplets (~200mg) in a thermal imaging furnace, to confirm the electrolytic deposition of Fe and the evolution of sulfur in a 2-electrode set-up. Faradaic efficiency estimates based on mass-loss measurements – i.e. with respect to gaseous sulfur anodic evolution – are of the order of 90%. Key electrochemical attributes of MSE to be reported include impedance measurement at various fixed DC potentials, measurements at various current densities up to 2A/cm2, and the role of the total charge passed, to highlight the potential limitations observed in such an experimental set up. Characterization of the electrochemical deposits are also presented.

Bank cost efficiency and credit market structure under a volatile exchange rate

We study the impact of exchange rate volatility on cost efficiency and market structure in a cross-section of banks that have non-trivial exposures to foreign currency (FX) operations. We use unique data on quarterly revaluations of FX assets and liabilities (Revals) that Russian banks were reporting between 2004 Q1 and 2020 Q2. First, we document that Revals constitute the largest part of the banks’ total costs, 26.5% on average, with considerable variation across banks. Second, we find that stochastic estimates of cost efficiency are both severely downward biased – by 30% on average – and generally not rank preserving when Revals are ignored, except for the tails, as our nonparametric copulas reveal. To ensure generalizability to other emerging market economies, we suggest a two-stage approach that does not rely on Revals but is able to shrink the downward bias in cost efficiency estimates by two-thirds. Third, we show that Revals are triggered by the mismatch in the banks’ FX operations, which, in turn, is driven by household FX deposits and the instability of Ruble’s exchange rate. Fourth, we find that the failure to account for Revals leads to the erroneous conclusion that the credit market is inefficient, which is driven by the upper quartile of the banks’ distribution by total assets. Revals have considerable negative implications for financial stability which can be attenuated by the cross-border diversification of bank assets.

Simulation-Based Study on Extreme Ranked Set Sampling from Rician Distribution

The RSS approach is a useful method of sampling that reduces the cost and improves the representativeness of the population. It provides more efficient estimators than the competitors based on SRS. However, using RSS could be a difficult task to observe all the ranks. Thus, using only the extreme ranks eases the task and reduces the error in ranking. Samawi et al. (1996) proposed the method of Extreme Ranked Set Sampling (ERSS) to reduce errors in ranking and showed that the method gives an unbiased estimate of the population mean in the case of symmetric populations and it provides a more efficient estimator than SRS. However, the estimator of this method is biased when the distribution is skewed. Many researchers have considered ERSS, investigated several estimators, and studied their properties. In this paper, we adopt the ERSS technique when the samples are drawn from the Rician distribution. Several estimators have been studied, including arithmetic mean, geometric mean, harmonic mean, quadratic mean, median, variance, mean deviation, skewness, and kurtosis. Computer simulations were used to check the properties of these estimators and compared with the corresponding estimators using SRS. Some estimators based on ERSS are more efficient than the corresponding estimators from SRS, but some others are not.

Harmonic Oscillator Staging Coordinates for Efficient Path Integral Simulations of Quantum Oscillators and Crystals.

Imaginary-time path integral (PI) is a rigorous quantum mechanical tool to compute static properties at finite temperatures. However, the stiff nature of the internal PI modes poses a sampling challenge. This is commonly tackled using staging coordinates, in which the free particle (FP) contribution of the PI action is diagonalized. We introduce novel and simple staging coordinates that diagonalize the entire action of the harmonic oscillator (HO) model, rendering it efficiently applicable to (exclusively) systems with harmonic character, such as quantum oscillators and crystals. The method is not applicable to fluids or systems with imaginary modes. Unlike FP staging, the HO staging provides a unique treatment of the centroid mode. We provide implementation schemes for PIMC and PIMD simulations in NVT ensemble. Sampling efficiency is assessed in terms of the precision and accuracy of estimating the energy and heat capacity of a one-dimensional HO and an asymmetric anharmonic oscillator (AO). In PIMC, the HO coordinates propose collective moves that perfectly sample the HO contribution, then (for AO) the residual anharmonic term is sampled using standard Metropolis method. This results in a high acceptance rate and, hence, high precision, in comparison to the FP staging. In PIMD, the HO coordinates naturally prescribe definitions for the fictitious masses, yielding equal frequencies of all modes when applied to the HO model. This allows for a substantially larger time step sizes relative to standard staging, without affecting accuracy or integrator stability. For completeness, we also present results using normal mode (NM) coordinates, based on both HO and FP models. While staging and NM coordinates show similar performance (for FP or HO), staging is computationally preferable due to its cheaper scaling with the number of beads. The simplicity and the enhanced sampling gained by the HO coordinates open avenues for efficient estimation of nuclear quantum effects in more complex systems with harmonic character, such as real molecular bonds and quantum crystals.

Real-Time Estimation of Origin–Destination Matrices Using a Deep Neural Network for Digital Twins

The digital twin, a real-time replica of physical systems, has garnered attention as a promising tool to strategize and evaluate solutions for complex real-world issues. However, developing digital twins in the field of transportation faces significant challenges related to the real-time estimation of dynamic origin–destination (OD) matrices constrained by computation time. To bridge this gap, microscopic traffic simulations with real-time synchronization are being researched. Nonetheless, the computational issue persists, emphasizing the need for more efficient OD estimation methods. In this regard, our objective is to reduce computation time in simulation-based methods by developing a data-driven metamodel using a deep neural network. The proposed model serves to map the correlation between the OD matrix and detector data. This model simplifies the computational process using hidden layers, rather than calculating complex interactions between vehicles in the traffic simulation. Compared to conventional methods, we evaluate the capability to estimate a reasonable OD matrix within a restricted time and validate our approach using detector data from Daejeon City, South Korea. The findings indicate that by combining our data-driven metamodel with the simultaneous perturbation stochastic approximation, it becomes feasible to estimate a reasonable OD matrix within a stipulated time frame, compared to the conventional method. Given a 1-min time frame, the proposed method outperforms the conventional simulation-based method by improving the calibration performance of traffic flow by 44.5 percentage points. This paper proposes a practical and versatile approach for real-time OD estimation, laying the foundation for extending microscopic traffic simulation into the digital twin.

Improving estimation efficiency of case-cohort studies with interval-censored failure time data.

The case-cohort design is a commonly used cost-effective sampling strategy for large cohort studies, where some covariates are expensive to measure or obtain. In this paper, we consider regression analysis under a case-cohort study with interval-censored failure time data, where the failure time is only known to fall within an interval instead of being exactly observed. A common approach to analyzing data from a case-cohort study is the inverse probability weighting approach, where only subjects in the case-cohort sample are used in estimation, and the subjects are weighted based on the probability of inclusion into the case-cohort sample. This approach, though consistent, is generally inefficient as it does not incorporate information outside the case-cohort sample. To improve efficiency, we first develop a sieve maximum weighted likelihood estimator under the Cox model based on the case-cohort sample and then propose a procedure to update this estimator by using information in the full cohort. We show that the update estimator is consistent, asymptotically normal, and at least as efficient asthe original estimator. The proposed method can flexibly incorporate auxiliary variables to improve estimation efficiency. A weighted bootstrap procedure is employed for variance estimation. Simulation results indicate that the proposed method works well in practical situations. An application to a Phase 3 HIV vaccine efficacy trial is provided for illustration.

Efficient quantum state estimation with low-rank matrix completion

This paper introduces a novel and efficient technique for quantum state estimation, coined as low-rank matrix-completion quantum state tomography for characterizing pure quantum states, as it requires only non-entangling bases and 2n+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$2n + 1$\\end{document} local Pauli operators. This significantly reduces the complexity of the process and increases the accuracy of the state estimation, as it eliminates the need for the entangling bases, which are experimentally difficult to implement on quantum devices. The required minimal post-processing, improved accuracy and efficacy of this matrix-completion-based method make it an ideal benchmarking tool for investigating the properties of quantum systems, enabling researchers to verify the accuracy of quantum devices, characterize their performance, and explore the underlying physics of quantum phenomena. Our numerical results demonstrate that this method outperforms contemporary techniques in its ability to accurately reconstruct multi-qubit quantum states on real quantum devices, making it an invaluable contribution to the field of quantum state characterization and an essential step toward the reliable deployment of intermediate- and large-scale quantum devices.

Behavioral Efficiency and Residential Electricity Consumption: A Microdata Study

Sustainability requires that policy makers be able to use market signals to implement energy and environmental policy and that energy consumers respond rationally to these signals. Therefore, it is essential to understand how consumers’ responses to market signals are formed. We propose a new model to measure behavioral efficiency in residential electricity consumption derived from the individual householder indirect utility function. This leads to a pair of simultaneous stochastic demand frontiers for electricity consumption (kWh) and power demand (kVA). Each is a function of power demand (standing) charges and energy demand (running) charges together with the net income after demand charges, the stock of appliances and household characteristics. We estimate the model using two samples of household responses, each of which represents around one percent of the total national population available, and we also pool these samples using pseudo-panel data procedures. We demonstrate how the resulting elasticity and efficiency estimates are related to the theory of behavioral agents from recent developments in behavioral economics. These developments also use the individual indirect utility function to derive propositions based on internality and hyperbolic discounting. The econometric estimates permit the calibration of the individual welfare effects of policy initiatives using carbon tax and price incentives with behavioral agents.

Efficient Estimation of ascorbic acid in vitamin C tablets enabled by phase-transfer strategy

In this paper, we introduced a novel phase-transfer strategy tailored for the efficient batch detection of ascorbic acid in vitamin C tablets. This method entailed the reaction between ascorbic acid and an excess of potassium permanganate. Subsequent reaction of the residual potassium permanganate with sodium oxalate in an acidic medium led to the generation of carbon dioxide. The quantification of the produced carbon dioxide was achieved using headspace GC, enabling the indirect measurement of ascorbic acid. The obtained findings revealed that the headspace method exhibited satisfied precision with a relative standard deviation of less than 2.11 % and high sensitivity with a limit of quantitation of 0.27 μmol. These results firmly establish the reliability of this innovative approach for determining ascorbic acid. In addition, the highly automated feature of headspace method significantly enhances the efficiency of batch sample detection and reduces the errors caused by human operation. Thus, the adoption of the transformed phase strategy has demonstrated its effectiveness in assessing ascorbic acid, especially for large-scale sample analysis in industrial applications, owing to its efficiency, precision, and sensitivity.

Active mutual conjoint estimation of multiple contrast sensitivity functions.

Recent advances in nonparametric contrast sensitivity function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine learning CSF estimation with Gaussian processes allows for design optimization in the kernel, acquisition function, and underlying task representation, to name a few. This article describes a novel kernel for CSF estimation that is more flexible than a kernel based on strictly functional forms. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities, or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.

Physics-Based Efficient Full Projector Compensation Using Only Natural Images.

Achieving practical full projector compensation requires the projection display to adapt quickly to textured projection surfaces and unexpected movements without interrupting the display procedure. A possible solution to achieve this involves using a projector and an RGB camera and correcting both color and geometry by directly capturing and analyzing the projected natural image content, without the need for additional patterns. In this study, we approach full projector compensation as a numerical optimization problem and present a physics-based framework that can handle both geometric calibration and radiometric compensation for a Projector-camera system (Procams), using only a few sampling natural images. Within the framework, we decouple and estimate the Procams' factors, such as the response function of the projector, the correspondence between the projector and camera, and the reflectance of projection surfaces. This approach provides an interpretable and flexible solution to adapt to the changes in geometry and reflectance caused by movements. Benefitting from the physics-based scheme, our method guarantees both accurate color calculation and efficient movement and reflectance estimation. Our experimental results demonstrate that our method surpasses other state-of-the-art end-to-end full projector compensation methods, with superior image quality, reduced computational time, lower memory consumption, greater geometric accuracy, and a more compact network architecture.

Transforming the Tradition of Discrete Milk Yield Correction Factors: A Continuous One-step DeLorenzo-Wiggans Method

Over the past decades, various methods have been proposed to estimate daily milk yields from partial yields. Many of these methods divide milking interval time into varied classes, assuming that the yield correction factors are constant within classes but vary between classes. The DeLorenzo and Wiggans (D-W) method has been widely used in the United States, typically following a 2-step process. It calculates discrete yield factors for segmented milking interval classes and then refines them through a follow-up smoothing step. Such a 2-step approach is computationally inefficient, and discrete yield correction factors introduce biases. This study explored strategies to integrate continuous yield factors into established methods, exemplified by the D-W method. The renovated method, also called the polynomial-interaction regression (PIR) model, postulates multiplicative yield correction factors as a linear or quadratic function of milking interval time, operating on interactions with partial yields. It utilizes all available data in a single step, exhibiting greater computability efficiency and higher estimation accuracy. A reparameterization leads to a linear model, making estimating the model parameters convenient. We evaluated the performance of the revised methods using a previous data set of milking records from Holstein cows compared with some existing methods. The results showed that the refurbished model gave more accurate estimates of daily milk yields.

Experimental Demonstration of Joint Sensing and Communications Using Spectrally Efficient High-Accuracy Range Estimation

Experimental Demonstration of Joint Sensing and Communications Using Spectrally Efficient High-Accuracy Range Estimation

Pore‐Morphology‐Based Estimation of the Freezing Characteristic Curve of Water‐Saturated Porous Media

AbstractAssessment of freezing effects on soil requires estimating the soil freezing characteristic curve (SFCC)—the variation of unfrozen water content with temperature. The existing methods for obtaining SFCCs often involve either costly experiments or heuristic inference from water retention data. Here, we propose a pore‐morphology‐based method for simple and efficient estimation of the freezing characteristic curve of water‐saturated porous media, whereby the pore‐scale configurations of water and ice phases are simulated in a digital image of porous microstructure. Idealizing the pore space as a system of overlapping spherical pores, the method simulates the freezing process with the Gibbs‐Thomson equation that can consider freezing‐point depression—a decrease in the freezing point due to spatial confinement—based on thermodynamics. For validation, we apply the proposed method to estimate the SFCC of a field soil for which the experimental freezing characteristics data are available. Results show that even with a digital pore image extracted from a surrogate discrete‐element packing of the soil, the proposed method provides an SFCC very close to the experimental data.

Estimates of speech efficiency in monolingual and bilingual speakers of English.

Introduction The Speech Efficiency Score (SES) serves as an acoustic metric for assessing fluency in conversational speech within the temporal domain. This study leverages SES to investigate conversational speech efficiency among native speakers of American English (AE) compared to speakers of Mandarin-accented English (MAE). Methods SES, speaking rate, articulation rate, and vocabulary diversity were measured and compared between two groups: native AE speakers and MAE speakers. The study utilized conversational speech samples collected from both groups to analyze these metrics. Results Findings indicate a disparity in speaking rate and articulation rate between the AE and MAE groups, with the AE group exhibiting significantly faster speech. However, no significant differences were observed in SES and vocabulary diversity between the two groups. Conclusion The results are discussed in the context of the interplay between speaking rate, speech fluency, and vocabulary diversity. These findings shed light on the maintenance of speech efficiency among bilingual speakers, suggesting that despite differences in speaking rate and articulation rate, SES and vocabulary diversity remain comparable between native AE speakers and MAE speakers. .

A Novel Hybridized Optimization Mechanism for Efficient Channel Estimation Using Adaptive and Attention-Based Convolutional Autoencoder in MIMO-NOMA for mm Wave Systems

A Novel Hybridized Optimization Mechanism for Efficient Channel Estimation Using Adaptive and Attention-Based Convolutional Autoencoder in MIMO-NOMA for mm Wave Systems

An efficient model for extracting respiratory and blood oxygen saturation data from photoplethysmogram signals by removing motion artifacts using heuristic-aided ensemble learning model

Patients with surgical, pulmonary, and cardiac problems, continual monitoring of Oxygen Saturation of a Person (SpO2) and Respiratory Rate (RR) is essential. Similarly, the persons with cardiopulmonary health issues, RR estimation is crucial. The performance of the ventilator assistance and lung medicines are evaluated using SpO2 and RR. For the persons, those who are living alone with respiratory illnesses need a compulsory estimation of RR. In case of serious illness, the RR might face abrupt changes. The immobility of the disturbance and RR makes the RR evaluation from the PhotoPlethysmoGraphic (PPG) signals is a difficult challenge. So, an efficient RR and SpO2 estimation framework from the PPG signal using the deep learning method is developed in this paper. At first, the PPG signal is collected from standard data sources. The collected PPG signals undergo signal pre-processing. The pre-processing procedures include Motion Artifacts (MA) removal and filtering techniques. The pre-processed signals are split into distinct windows. From the split windows of the signals, the spectral features, RR, and Respiratory Peak Variance (RPV) features are extracted. The retrieved features are selected optimally with the help of Advanced Golden Tortoise Beetle Optimizer (AGTBO). The weights are chosen optimally with the same AGTBO. The optimally selected features are fused with the optimal features to get the weighted optimal features. These weighted optimal features are fed into the Ensemble Learning-based RR and SpO2 Estimation Network (ELRR-SpO2EN). The ensemble learning model is developed by combining Multilayer Perceptron (MLP), AdaBoost, and Attention-based Long Short Term Memory (A-LSTM). The performance of the developed RR and SpO2 estimation model is compared with other existing techniques. The experimental analysis results revealed that the proposed AGTBO-ELRR-SpO2EN model attained 96 % accuracy for the second dataset, which is higher than the conventional models such as MLP (90 %), Adaboost (92 %), A-LSTM (92 %), and MLP-ADA-ALSTM (94 %). Thus, it has been confirmed that the designed RR and SpO2 estimation framework from PPG signals is more efficient than the other conventional models.

An Intelligent Vehicle Price Estimation Approach Using a Deep Neural Network Model

In recent years, the market for used-vehicle trade in the Kingdom of Saudi Arabia has grown significantly. This is due to the high cost of new vehicles that are not affordable by most buyers and lifting the ban on women drivers. Recently, several online websites for selling vehicles are available with different functions. However, estimating the vehicle price is based on traditional calculation methods, and this is inaccurate in several selling situations, as there are many factors that may affect the vehicle price, and these factors must be taken into consideration when estimating the vehicle’s price. Therefore, there is high demand to develop an automated vehicle price estimation system through adopting artificial intelligence (AI) technologies. Hence, this paper proposes an efficient vehicle price estimation system through developing an efficient deep neural network (DNN) model. The developed DNN model has been trained using a recent collected dataset for used-vehicle prices in the Kingdom of Saudi Arabia. The developed system has been validated using a recent vehicle price dataset, and the obtained results are compared with seven different machine learning models and showed a promising regression accuracy. In addition, we developed a reliable graphical user interface (GUI) for the purpose of allowing the user to estimate the price of any vehicle using the pre-trained DNN model.