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The goal for the next generation of terrestrial reference frames (TRF) is to achieve a 1-mm- and 0.1-mm/yr-accurate frame realization through the combination of reference station solutions by multi-technique geodetic observatories. A potentially significant source of error in TRF realizations is the inter-system ties between the instruments at multi-technique stations, usually independently determined through ground-based local surveying. The quality of local tie surveys is varied and inconsistent, largely due to differences in measurement techniques, surveying instruments, site conditions/geometries, and processing methods. The Global Geodetic Observing System (GGOS) has tried to address these problems by issuing guidelines for the construction and layout of future multi-technique observatories, promoting uniformity and quality while minimizing existing problems with local surveying that are exacerbated over longer baseline distances. However, not every observatory is going to be able to completely satisfy these guidelines, and in this work, a successful endeavor to satisfy the accuracy goals while exceeding the GGOS baseline guideline is detailed for the McDonald Geodetic Observatory (MGO) in the Davis Mountains of Texas, USA. MGO consists of a VLBI Geodetic Observing System (VGOS), infrastructure in place for a Space Geodesy Satellite Laser Ranging (SGSLR) telescope, and several Global Navigation Satellite Systems (GNSS) stations spanning a 900 m baseline and a 120 m elevation change. The results of the local ties between the GNSS stations across the near-kilometer baseline, as measured from their antenna reference points, show sub-mm precision and 1 mm accuracy validated through repeatability across several surveys conducted in 2021as well as 1 mm consistency with the monthly averaged daily solutions of the GNSS-based positioning. In this paper, we report these results as well as the framework of the surveys with sufficient detail and rigor in order to give confidence to the quality claims and to present the novel design and techniques employed in the procedure, processing, and error-budget analysis, which were determined through iterative research methods across repeated survey campaigns. [ABSTRACT FROM AUTHOR]

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise. [ABSTRACT FROM AUTHOR]

In this paper, interval observers are designed for linear dynamic systems described by continuous-time models with exogenous disturbances, measurement noises, and parametric uncertainties. Jordan canonical form-based relations are presented for an interval observer that estimates the set of admissible values of a given linear function of the system state vector. The theoretical results are illustrated by a practical example. [ABSTRACT FROM AUTHOR]

The estimation of agricultural output is an important issue in the digital development of modern agriculture. Using embedded technology, this paper designs a multimedia platform for digital estimation of agricultural output. The terminal design of the multimedia platform selects the Intel Xscale series PXA255 processor as the core processor, uses multiple sensors to collect agricultural output-related environmental parameters and selects the digital media processing chip TMS320DM642 for multimedia processing. The software used selects the grey relational Support Vector Machine (SVM) algorithm to estimate agricultural output. First, the collected agricultural sown area, labor force, mechanical power, irrigated area, agricultural production environment and other parameters are collected as input to grey relational SVM. Second, the estimated results are outputted. Finally, the results of agricultural output estimation are digitally displayed by a display interface on the embedded multimedia terminal. The experimental results show that the prediction error is less than 1%, and the prediction accuracy is higher than 99%, which validates that the method can accurately predict agricultural output with an abundance of applications. [ABSTRACT FROM AUTHOR]

This paper proposes an online identification and compensation scheme for a distorted waveform's harmonic and interharmonic content in electrical circuits. The proposed novel identification scheme allows the simultaneous estimation of the harmonic components' frequency, amplitude, and phase. One of the main characteristics of the proposed online identification scheme is how the harmonics and interharmonics are estimated using a fast-convergent state estimator based on the state-space representation of the harmonic content. The proposed estimator has a decentralized approach that relies on efficient implementation, a relevant result for real-time feedback control applications, such as power conditioners control. The decentralized structure allows, if needed, selective harmonic suppression. The paper exposes a case study of identifying and suppressing the harmonic and interharmonic content produced by a three-phase load connected to the electrical power grid to demonstrate the proposed estimator's efficiency. Hardware-in-the-loop and simulation results validate the identifier effectiveness to determine in real time the harmonic content and how it can be used to reduce the total harmonic distortion through a nonlinear optimal tracking control scheme. [ABSTRACT FROM AUTHOR]

This paper develops a Kalman filter-based method to estimate the magnitude and phases of currents and voltages of a single-phase transmission line. Unlike common-place practices, in which phasors are estimated by using Fourier-based or least squares methods, the standard Kalman filter algorithm is used. We represent the transmission line by using a steady-state model in which the phasors of voltages and currents are considered to be in the complex d - q domain. We then show the estimation performance of the filter-based estimator by means of a numerical simulation of a medium-length transmission line and compare the proposed method to standard methods for phasor estimation. The results presented show that the filter is highly efficient and encourages future research on the estimation of the electric parameters of transmission lines by using the proposed method. We conclude the paper by reasoning that the proposed estimation method could be used in phasor measurement devices for improved monitoring and control capabilities of electrical systems. [ABSTRACT FROM AUTHOR]

This paper introduces a position sensorless operation of a brushless DC (BLDC) motor based on an efficient state estimation algorithm. In the proposed method, the speed and rotor position estimation is performed only by measuring the phase currents and line voltages. Estimated values are used to determine the commutation logic and speed controller. The line voltage difference is employed, which eliminates the need for neutral. The states estimation is based on the particle filter that doesn't have the limitations and drawbacks of the Extended Kalman Filter. First, there is no assumption for the probability density functions of the measurement and process noise, it works for Gaussian and non-Gaussian noises. Second, it is not based on any linearization and can efficiently estimate the states of nonlinear systems. Moreover, a suitable method is adopted for resampling step, which improves the estimation performance and does not increase the complexity. The simulation and experimental results demonstrate the effectiveness of the proposed sensorless method. The proposed method is more robust than other common techniques like EKF at different sampling times. The experimental results show the possibility of the BLDC motor states estimation with appropriate accuracy in steady-state and dynamic operation. [ABSTRACT FROM AUTHOR]

The choice of an estimation method has received considerable attention in the Operations Research literature. In this paper we depart from the standard use of linex and double-linex loss functions which are widely used in parameter estimation and forecasting problems and we propose a non-standard use for them. Specifically, we propose to use the corresponding linex and double-linex error densities as models for the errors of a regression problem when more emphasis should be placed on over-estimation or under-estimation of errors. The new techniques are applied to synthetic as well real data concerning the role of management in production as well as to an application of forecasting volatility in intradaily data. [ABSTRACT FROM AUTHOR]

Pooled (or group) testing has been widely used for the surveillance of infectious diseases of low prevalence. The potential benefits of pooled testing include savings in testing time and costs, reducing false positive tests, and estimating models or making predictions from limited observed data information (e.g., only initial pooled responses). However, realizing these benefits often critically depends on the pool size used. Statistical methods introduced in the literature for optimal pool size determination have been developed mainly to accommodate simpler pooling protocols or perfect diagnostic assays. In this article, we study these issues with the goal of presenting a general optimization technique. We evaluate the efficiency of the estimators of disease prevalence (i.e., the proportion of diseased individuals in a population) while accounting for testing costs. Then, we determine the optimal pool size by minimizing the measures of optimality, such as screening efficiency and estimation efficiency. Our findings are illustrated using data from an ongoing screening application at the Louisiana Department of Health. We show that when a pooling application is properly designed, substantial advantages can be realized. We provide an R package and a software application to facilitate the implementation of our optimization techniques. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Since COVID-19 was declared as a pandemic by World Health Organization in March 2020, 169,682,828 cases have been reported worldwide, with 151,416,570 recovered, and 3,526,647 deaths by May 28, 2021. Oxygen gas cylinders demand is booming globally due to its need for COVID-19's for intensive care. Thus, it is critical for hospitals to know exactly the time of receiving oxygen gas cylinders since this will help in minimizing the fatality rate. In this regards, this paper proposes a Multilayer Perceptron Neural Network-based model to predict the delivery time of oxygen gas cylinders for a real-life logistics data from a company that delivers oxygen gas cylinders to all cities around Saudi Arabia. Besides, Multilayer Perceptron Neural Network is benchmarked to supported vector machine and multiple linear regression. Although all the considered models have the ability to provide accurate prediction results, the findings indicate that the proposed supported vector machine and Multilayer Perceptron Neural Network model provide better prediction results. The analysis was achieved through a methodology to identify factors with the highest impact and build a neural network model. The model was further optimized to identify the best order and select the best subset of input variables. The analysis showed that the neural network model can be used effectively to estimate the delivery time of oxygen gas cylinders. The model illustrated high accuracy of prediction by comparing the predicted values to the actual values. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of functional observer design for systems with non-commensurate time varying delays. The main procedure of three types of functional observers are presented in this article, more precisely, observers with delays dependent, observers without internal delays and delay-free functional observers. A necessary condition for the existence of these observers is given as rank equality. The design conditions for functional observers are presented in terms of linear matrix inequalities (LMIs). The solution of these LMIs is used to determine the observers parameters. Finally, some numerical examples are given at the end of the paper to show the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

During heavy rainfalls, the surface soil on a slope may be eroded and the erosion is much dependent on the tensile strength of soil. In addition, the tensile strength of soil is also one of the governing factors which affect the cracking of soil. It is noted that the water content of soil has a significant effect on the tensile strength of soil. The experimental works for measurement of the tensile strength of soil in laboratory (especially for the unsaturated soil) are challenging, and the indirect estimation techniques draw a lot of attentions among practical engineers. In this paper, the tensile strength of soil is estimated from the soil–water characteristic curve (SWCC) by adopting the concept of pore-size distribution function. The proposed equations were verified with the experimental data from published literature. The comparison results show that the estimated tensile strengths agree well with the experimental results of the selected soils from published literatures. In addition, the variations of SWCC due to different initial densities and fine contents are also considered and the effects of the variation of SWCC on the estimated tensile strength are also investigated and discussed in this paper. It is observed that the modified SWCCs incorporating the effects of different initial densities and fine contents are able to provide a more accurate estimation of the tensile strength of unsaturated soil. [ABSTRACT FROM AUTHOR]

To minimize power consumption while maximizing performance, today's multicore processors rely on fine-grained run-time dynamic power information—both in the time domain, e.g. μ s to ms, and space domain, e.g. core-level. The state-of-the-art for deriving such power information is mainly based on predetermined power models which use linear modeling techniques to determine the core-performance/core-power relationship. However, with multicore processors becoming ever more complex, linear modeling techniques cannot capture all possible core-performance related power states anymore. Although artificial neural networks (ANN) have been proposed for coarse-grained power modeling of servers with time resolutions in the range of seconds, few works have yet investigated fine-grained ANN-based power modeling. In this paper, we explore feed-forward neural networks (FFNNs) for core-level power modeling with estimation rates in the range of 10 kHz. To achieve a high estimation accuracy while minimizing run-time overhead, we propose a multi-objective-optimization of the neural architecture using NSGA-II with the FFNNs being trained on performance counter and power data from a complex-out-of-order processor architecture. We show that relative power estimation error for the highest accuracy FFNN decreases on average by 7.5% compared to a state-of-the-art linear power modeling approach and decreases by 5.5% compared to a multivariate polynomial regression model. For the FFNNs optimized for both accuracy and overhead, the average error decreases between 4.1% and 6.7% compared to linear modeling while offering significantly lower overhead compared to the highest accuracy FFNN. Furthermore, we propose a micro-controller-based and an accelerator-based implementation for run-time inference of the power modeling FFNN and show that the area overhead is negligible. [ABSTRACT FROM AUTHOR]

This paper presents brain emotional controller (BEC)-based adaptive mechanism in Model Reference Adaptive System (MRAS) to control sensorless permanent magnet synchronous motor (PMSM) drive. BEC is a bio-inspired intelligent controller developed using neurobiological connections of mammalian brain by inspiring limbic system. The developed controller is applied as speed controller and parameter estimation (i.e. rotor speed and rotor position) of PMSM drive. The proposed BEC stability is proved to guarantee convergence characteristics. The performance of BEC-based adaptive mechanism is evaluated in simulation environment, and obtained results are compared in real time in hardware-in-loop (HIL) environment. In order to hold the effective performance of the proposed method compared with fuzzy logic controller with similar tests, further integral performances are evaluated for BEC and fuzzy logic controllers. Results demonstrated in HIL and in simulation show applicability, robust and sensitiveness of the proposed BEC method. [ABSTRACT FROM AUTHOR]

The paper investigates some stochastic models with discrete and continuous time to solve important problems of predicting the spread of epidemiological diseases in the population. Various factors of epidemic spread and the main parameters influencing the forecast assessment are taken into account. Some test calculations based on the proposed methods have been performed. [ABSTRACT FROM AUTHOR]

In this paper we consider mixture generalized autoregressive conditional heteroskedastic models, and propose a new iteration algorithm of type EM for the estimation of model parameters. The maximum likelihood estimates are shown to be consistent, and their asymptotic properties are investigated. More precisely, we derive simple expressions in closed form for the asymptotic covariance matrix and the expected Fisher information matrix of the ML estimator. Finally, we study the model selection and propose testing procedures. A simulation study and an application to financial real-series illustrate the results. [ABSTRACT FROM AUTHOR]

Purpose: Intraoperative estimated blood loss (EBL) is often reported in nearly all surgical papers; however, there is no consensus regarding its measurement. The aim of this study was to determine whether EBL (ml) is as reliable and reproducible in predicting complications as a simple binary grading of EBL. Methods: All consecutive patients undergoing colectomies between January 2015 and December 2018 were included. EBL was assessed prospectively by the surgeon and anaesthesiologist in ml and with a binary scale: bleeding "as usual" versus "more than usual" by the surgeon. Differences between pre- and post-operative haemoglobin levels (ΔHb g/dl) were correlated to EBL. Blood loss impact on 30-day postoperative morbidity was analysed. Results: A total of 270 patients were included, with a mean age of 65 years (SD 17). Mean EBL documented by surgeons correlated to EBL by anaesthesiologists (79.5 ml, SD 99 vs. 84.5 ml, SD 118, ϱ = 0.926, p < 0.001). Surgeons and anaesthesiologists' EBL correlated also with ΔHb (ϱ = − 0.273, p = 0.01 and ϱ = − 0.344, p = 0.01, respectively). Patient with surgeon EBL ≥ 250 ml or graded as "more than usual" bleeding had significantly more severe complications (8% vs. 20%, p = 0.02 and 8% vs. 27%, p = 0.001, respectively). Conclusion: Anaesthesiologist and surgeon's EBL correlated with ΔHb. Simple grading of blood loss as "usual" and "more than usual" predicted severe complications and higher mortality rates. This simple binary grading of blood loss in colon surgery could be an alternative to the estimation of blood loss in ml as it is easy to apply but needs to be validated externally. [ABSTRACT FROM AUTHOR]

The accurate models of photovoltaic systems are the core of solar energy studies that are describing the system performance and behavior under different operating conditions. The improving of photovoltaic models based on optimizing method is recently the main simulation tool to construct I-V and P–V characteristic curves with the aid of system parameters. These parameters are extracted by using powerful optimal techniques that are building-up from datasheet of manufacturers or experimental data. This paper presents a novel proposed photovoltaic model based on Marine Predators Algorithm to estimate the optimal model parameters of solar cells or modules. Also, it can extract parameters of single diode, double diode and three diode models. Moreover, the Route Mean Square Error value between each model computed parameters and measured results of photovoltaic components are considered as the objective function. R.T.C. France Solar Cell, Photowatt-PWP201 and thin-film photovoltaic modules have been implemented to extract parameters for all three previously mentioned models at different irradiance intensities or temperature degrees. The proposed algorithm results of such cells and modules for each diode model are compared with other research works and manufacturer's results. Moreover, the evaluation of proposed algorithm has been presented considering the complexity analysis and statistical tests. The compared results show that, the accuracy of algorithm results is the best and their I-V and P–V characteristic curves are highly coinciding with manufacturer's curves. Therefore, the results of the proposed algorithm are satisfied with high superiority and better reliability to optimize parameters under different operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic regressor extension and mixed estimator is proposed with finite-time convergence and freedom to choose its time-varying adaptation gain and its derivation order. This freedom is exploited to enhance the transient and robustness performance of the estimation by analytically establishing the effects of both variables. The proposed estimator is used to design adaptive controllers and observers for nonlinear systems, which exhibit exponential order of convergence at an arbitrary rate of decay with robust and improved transient properties. These results are illustrated in a tracking control of nonlinear systems with parametric uncertainty. [ABSTRACT FROM AUTHOR]

The authors consider solution methods for overdetermined SLAE whose main matrix is known exactly and the right-hand side contains an error. The component-wise error is assumed to be a random variable that belongs to a small bounded interval. Given exact values of the right-hand side, the system has a unique solution. The approach being developed in the paper is based on guarantee estimation of membership intervals of the exact solution, which may be used for quality assessment of the approximate solutions. These guarantee estimates are applied for comparison and quality assessment of the solution methods. The results of the numerical simulation allow making a comparative analysis of the methods and formulating the recommendations on their practical application. [ABSTRACT FROM AUTHOR]

Despite the high complexity of the real world, linear regression still plays an important role in estimating parameters to model a physical relationship between at least two variables. The precision of the estimated parameters, which can usually be considered as an indicator of the solution quality, is conventionally obtained from the inverse of the normal equations matrix for which intensive computation is required when the number of observations is large. In addition, the impacts of the distribution of the observations on parameter precision are rarely reported in the literature. In this paper, we propose a new methodology to model the distribution of observations for linear regression in order to predict the parameter precision prior to actual data collection and performing the regression. The precision analysis can be readily performed given a hypothesized data distribution. The methodology has been verified with several simulated and real datasets. The results show that the empirical and model-predicted precisions match very well, with discrepancies of up to 6% and 3.4% for simulated and real datasets, respectively. Simulations demonstrate that these differences are simply due to finite sample size. In addition, simulation also demonstrates the relative insensitivity of the method to noise in the independent regression variables that causes deviations from the data distribution function. The proposed methodology allows straightforward prediction of the parameter precision based on the distribution of the observations related to their numerical limits and geometry, which greatly simplify design procedures for various experimental setups commonly involved in geodetic surveying such as LiDAR data collection. [ABSTRACT FROM AUTHOR]

RSSI is a reference magnitude of radio signal strength received by a wireless terminal. Traditionally, it has been used to implement wireless services like scanning in handoff. But its high long term variability ( volatility) makes its high precision estimation be impossible, complicating its applicability. Moreover, every wireless terminal has its own way to provide RSSI. Recently a high impact paper has formally shown that RSSI of laboratory radio signal in WiFi always reverts to its mean. That is, its long term stability can be estimated. We present a new RSSI estimation model that improves other recently published methods; and shows that it can be used, in contrast with those other techniques, to a wide range of current wireless terminals and real world scenarios. We also show its applicability to indoor localization service, by observing how it can be used in a recent high impact paper and improving another paper also published in a high impact journal. Our method can be used in SDN. [ABSTRACT FROM AUTHOR]

Currently, in Scrum, there are different methods to estimate user stories in terms of effort or complexity. Most of the existing techniques consider factors in a fine grain level; these techniques are not always accurate. Although Planning Poker is the most used method in Scrum to estimate user stories, it is primarily effective in experienced teams since the estimation mostly depends on the observation of experts, but it is difficult when is used by inexperienced teams. In this paper, we present a proposal for complexity decomposition in a coarse grain level, in order to consider important factors for complexity estimation. We use a Bayesian network to represent those factors and their relations. The edges of the network are weighted with the judge of professional practitioners about the importance of the factors. The nodes of the network represent the factors. During the user estimation phase, the Scrum team members introduce the values for each factor; in this way, the network generates a value for the complexity of a User story, which is transformed in a Planning Poker card number, which represents the story points. The purpose of this research is to provide to development teams without experience or without historical data, a method to estimate the complexity of user stories through a model focused on the human aspects of developers. [ABSTRACT FROM AUTHOR]

This paper presents practical design procedure of the electric measuring circuit and evaluation/communication unit of the multi-cell series–parallel connection of traction lead-acid batteries. The target use is online SOC monitoring during the operation of automated guided vehicle (AGV), which is being used for in-house automated industrial processes. Based on the state-of-the-art review, proper methodology for SOC determination was selected for the given lead-acid battery technology. For that purpose, the measuring procedure for identification of the battery equivalent circuit was initially realized and is being described within the main text. The results from the equivalent circuit identification were used for design of the precise measuring circuit which is responsible for the determination of SOC levels based on selected methodologies. These methodologies were implemented within evaluating unit (MCU), while functionality of the proposed system for the SOC identification was tested and evaluated within standard operational conditions of AGV robot. [ABSTRACT FROM AUTHOR]

Road construction projects on the territory of the Republic of Croatia are characterized by the overrun of planned costs. The experience of the contractor on previous road projects is an important element that can help to prevent errors and increase the chances of success in similar future projects. Data on construction costs collected from past projects can be used to estimate costs at different stages of the project life cycle through artificial neural networks. In this paper, artificial neural networks (MLP, GRNN, RBFNN) for estimating road construction costs are modeled. During the modeling, the database of roads constructed on the territory of the Republic of Croatia was used. Comparison of performance of neural networks has shown that the GRNN has obtained the best accuracy with MAPE of 13% and coefficient of determination of 0.9595. The neural network has proven to be a promising approach to use in the initial design phase when there is usually a limited or incomplete set of data for cost analysis, and this method could yield much more accurate results and the estimation error could be reduced. [ABSTRACT FROM AUTHOR]

Atmospheric Ar:H2 plasma is an eco-friendly option for the reduction of metal oxides. For better reduction performance and safety concern, the hydrogen gas injected into the reactor should be monitored. A hydrogen flow rate estimation system is presented in this paper by using an artificial neural network (ANN) model fed with features of optical emission spectra of the plasma. ANN models are studied with two different sets of input, i.e. for the first case the inputs to the model are the three features of Hα line such as the peak intensity count, full-width half maximum and area under Hα line, while for the second case, the peak intensity count of a group of emission lines like Hα, Ar I, O I, K I, Na D lines are considered as the inputs. ANN model is developed for estimating four different sets of hydrogen flow rates 5, 8, 10 and 12 litres per minute (lpm) when the argon flow rate is constant at 10 lpm. For both the input features, the model performances are compared, and it is shown that improved estimation accuracy is observed from the second case, i.e. from peak intensity count of a group of emission lines instead of only hydrogen emission line. [ABSTRACT FROM AUTHOR]

Basic insulation and heating system retrofits of existing homes could achieve annual energy savings of up to $4–5 billion in the USA. However, current US utility energy efficiency (EE) programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and on-line advertising that lack specificity about particular home retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments that can be inconvenient to homeowners, expensive, and of variable accuracy. Our paper discusses using communicating thermostat data to significantly increase the customer uptake of energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. We extended our previously developed gray-box building model to identify physical building parameters corresponding to the target retrofit opportunities, i.e., whole-home R value, air leakage characteristics, and heating system efficiency. The estimated R values and air leakage characteristics compare favorably with the ground truth. The validated algorithms can calculate home-specific energy savings estimate for each ECM, and the algorithm outputs can be used by utility EE programs to formulate home-specific retrofit offers. [ABSTRACT FROM AUTHOR]

Mediation analysis is a methodology used to understand how and why behavioral phenomena occur. New mediation methods based on the potential outcomes framework are a seminal advancement for mediation analysis because they focus on the causal basis of mediation. Despite the importance of the potential outcomes framework in other fields, the methods are not well known in prevention and other disciplines. The interaction of a treatment (X) and a mediator (M) on an outcome variable (Y) is central to the potential outcomes framework for causal mediation analysis and provides a way to link traditional and modern causal mediation methods. As described in the paper, for a continuous mediator and outcome, if the XM interaction is zero, then potential outcomes estimators of the mediated effect are equal to the traditional model estimators. If the XM interaction is nonzero, the potential outcomes estimators correspond to simple direct and simple mediated contrasts for the treatment and the control groups in traditional mediation analysis. Links between traditional and causal mediation estimators clarify the meaning of potential outcomes framework mediation quantities. A simulation study demonstrates that testing for a XM interaction that is zero in the population can reduce power to detect mediated effects, and ignoring a nonzero XM interaction in the population can also reduce power to detect mediated effects in some situations. We recommend that prevention scientists incorporate evaluation of the XM interaction in their research. [ABSTRACT FROM AUTHOR]

In this paper we analyse some bootstrap techniques to make inference in INAR(p) models. First of all, via Monte Carlo experiments we compare the performances of these methods when estimating the thinning parameters in INAR(p) models; we state the superiority of model-based INAR bootstrap approaches on block bootstrap in terms of low bias and Mean Square Error. Then we adopt the model-based bootstrap methods to obtain coherent predictions and confidence intervals in order to avoid difficulty in deriving the distributional properties. Finally, we present an empirical application. [ABSTRACT FROM AUTHOR]

Due to their notable share of the total energy consumption of industries, pumps hold particular significance in industrial energy audits. In industrial facilities, pumps exist in large numbers, which leads to only the pumps most critical to the process being monitored. Meanwhile, many old or poorly sized pumps may operate inefficiently without attention. To determine the energy efficiency of a pump, information on the pump's flow rate is required. To this end, flow meters are often used to determine the flow rate produced by a pump, which can be used in determining the pump's specific energy consumption. However, flow meters tend to be costly and impractical for some applications and are laborious for energy auditing, if no preexisting metering can be found in the audited process setup. Thus, soft sensor approaches to estimate flow rate are suggested by several authors. However, the use of model-based flow rate estimators is still uncommon, because they require detailed information on the pump characteristic curves and manual configuration of the device employing the flow rate estimator. This paper introduces a novel flow rate estimator tuned with only four variables: the nominal flow rate, head, shaft power and rotational speed of a centrifugal pump. The estimator operation is based on the expected characteristic performance curves of the centrifugal pump according to its specific speed. Due to a small number of required input parameters, and no need for manufacturer's pump curves, the proposed estimator is easier to commission than existing solutions available both in fixed speed systems and commercial variable-speed drives. The performance and accuracy of the presented estimator are studied by comparing it with the basic QP and QH-curve-based estimation methods through simulations with four different centrifugal pumps and through measurements with two different centrifugal pumps in a test laboratory. In the experimental evaluation, the suggested estimator estimated the flow rate produced by centrifugal pump with an error of 7–15% of the nominal flow rate through a flow rate range of 60–100% of the nominal flow rate. Thus, the presented method is accurate enough to be used in continuous large-scale energy auditing, for example to highlight inefficiently running pumps from large pump populations. [ABSTRACT FROM AUTHOR]

Aiming at the disadvantages of greedy algorithms in sparse solution, a modified adaptive orthogonal matching pursuit algorithm (MAOMP) is proposed in this paper. It is obviously improved to introduce sparsity and variable step size for the MAOMP. The algorithm estimates the initial value of sparsity by matching test, and will decrease the number of subsequent iterations. Finally, the step size is adjusted to select atoms and approximate the true sparsity at different stages. The simulation results show that the algorithm which has proposed improves the recognition accuracy and efficiency comparing with other greedy algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present and numerically study an adaptive coding procedure for data transfer among quadrotors moving in a formation. The quadrotor's parameters are identified using experimental data from a digital communication channel with limited bandwidth. We compare the obtained results with theoretical expectations and illustrate the efficiency of the adaptive coding procedure. [ABSTRACT FROM AUTHOR]

The major objectives in the design of thermal systems are obtaining the information about thermophysical, transport and boundary properties. The main purpose of this paper is to estimate the unknown heat flux at the surface of a solid body. A constant area mild steel fin is considered and the base is subjected to constant heat flux. During heating, natural convection heat transfer occurs from the fin to ambient. The direct solution, which is the forward problem, is developed as a conjugate heat transfer problem from the fin and the steady state temperature distribution is recorded for any assumed heat flux. In order to model the natural convection heat transfer from the fin, an extended domain is created near the fin geometry and air is specified as a fluid medium and Navier Stokes equation is solved by incorporating the Boussinesq approximation. The computational time involved in executing the forward model is then reduced by developing a neural network (NN) between heat flux values and temperatures based on back propagation algorithm. The conjugate heat transfer NN model is now coupled with Genetic algorithm (GA) for the solution of the inverse problem. Initially, GA is applied to the pure surrogate data, the results are then used as input to the Levenberg- Marquardt method and such hybridization is proven to result in accurate estimation of the unknown heat flux. The hybrid method is then applied for the experimental temperature to estimate the unknown heat flux. A satisfactory agreement between the estimated and actual heat flux is achieved by incorporating the hybrid method. [ABSTRACT FROM AUTHOR]

A lot of progress regarding estimation of nested Archimedean copulas has been booked since their introduction by Joe (Multivariate models and dependence concepts. Chapman and Hall, London, 1997). The currently published procedures can be seen as particular cases of two different, more general, approaches. In the first approach, the tree structure of the target nested Archimedean copulas is estimated using hierarchical clustering to get a binary tree, and then parts of this binary tree are collapsed according to some strategy. This two-step estimation of the tree structure paves the way for estimation of the generators according to the sufficient nesting condition afterwards, this sufficient nesting condition on the generators ensuring the resulting estimated nested Archimedean copula is a proper copula. In contrast to the first approach, the second approach estimates the tree structure free of any concern for the generators. While this is the main strength of this second approach, it is also its main weakness: estimation of the generators afterwards so that the resulting nested Archimedean copula is a proper copula still lacks a solution. In this paper, both approaches are formally explored, detailed explanations and examples are given, as well as results from a performance study where a new way of comparing tree structure estimators is offered. A nested Archimedean copula is also estimated based on exams results from 482 students, and a naive attempt to check the fit is made using principal component analysis. [ABSTRACT FROM AUTHOR]

In this paper we compare two estimation methods to deal with samples of different support: (1) the indirect approach using accumulation and (2) kriging with samples of different support. These two methods were tested in a simple example. The estimates of the two methods were compared against a benchmark scenario. The benchmark consisted of kriging using a complete set of samples on the same support. The effects of the nugget effect, variogram range and type on the weight of long samples, the estimate, and the error variance were assessed. Kriging with samples of different support led to lower error variance and to estimates closer to the estimates of the benchmark scenario. Furthermore, in the case of spatially continuous attributes (low nugget effect), the indirect approach assigns greater weight to long samples than kriging with samples of different support. A cross validation study comparing the two methods with a database from a bauxite deposit was performed. The results of the cross validation study showed that kriging with samples of different support resulted in more precise estimates. [ABSTRACT FROM AUTHOR]

The analysis of quantitative trait loci (QTLs) aims at mapping and estimating the positions and effects of the genes that may affect the quantitative trait, and evaluating the relationship between the gene variation and the phenotype. In existing studies, most methods mainly focus on the association/linkage between multiple gene loci and one trait, in which some useful joint information of multiple traits may be ignored. In this paper, we proposed a method of simultaneously estimating all QTL parameters in the framework of multiple-trait multiple-interval mapping. Simulation results show that in accuracy aspect, the proposed method outperforms an existing method for mapping multiple traits. A real example is also provided to validate the performance of the new method. [ABSTRACT FROM AUTHOR]

Vehicle sideslip angle is one of the important indicators to determine whether vehicles are stable and is also a significant parameter for vehicle stability control. However, it is almost impossible to be measured directly without complex and expensive sensors or devices. Soft measurements are therefore generally adopted to estimate vehicle sideslip angle on the basis of easily observed physical quantities. This paper proposes an estimation method of sideslip angle based on steering torque instead of steering angle, for the fact that steering torque signal has more rapid and direct response compared with that obtained from steering angle. The frequency is analyzed between steering torque-sideslip angle and steering angle-sideslip angle transfer functions, and the extended Kalman filter (EKF) for vehicle sideslip angle based on steering torque is proposed. Both simulation results and vehicle tests prove the validity of our estimating method, indicating that the proposed method based on steering torque exhibits better accuracy and more rapid response in extreme region than the one based on steering angle. [ABSTRACT FROM AUTHOR]

Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits. To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis (PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60 km northeast of the Anarak city and 270 km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold, arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation. [ABSTRACT FROM AUTHOR]

Many real-life count data are frequently characterized by overdispersion, excess zeros and autocorrelation. Zero-inflated count time series models can provide a powerful procedure to model this type of data. In this paper, we introduce a new stationary first-order integer-valued autoregressive process with random coefficient and zero-inflated geometric marginal distribution, named ZIGINAR(1) process, which contains some sub-models as special cases. Several properties of the process are established. Estimators of the model parameters are obtained and their performance is checked by a small Monte Carlo simulation. Also, the behavior of the inflation parameter of the model is justified. We investigate an application of the process using a real count climate data set with excessive zeros for the number of tornados deaths and illustrate the best performance of the proposed process as compared with a set of competitive INAR(1) models via some goodness-of-fit statistics. Consequently, forecasting for the data is discussed with estimation of the transition probability and expected run length at state zero. Moreover, for the considered data, a test of the random coefficient for the proposed process is investigated. [ABSTRACT FROM AUTHOR]

This paper examines the effectiveness of small star trackers for orbital estimation. Autonomous optical navigation has been used for some time to provide local estimates of orbital parameters during close approach to celestial bodies. These techniques have been used extensively on spacecraft dating back to the Voyager missions, but often rely on long exposures and large instrument apertures. Using a hyperbolic Mars approach as a reference mission, we present an EKF-based navigation filter suitable for nanosatellite missions. Observations of Mars and its moons allow the estimator to correct initial errors in both position and velocity. Our results show that nanosatellite-class star trackers can produce good quality navigation solutions with low position ( $$<300\,\text {m}$$ ) and velocity ( $$<0.15\,\text {m/s}$$ ) errors as the spacecraft approaches periapse. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. Advances in micro lenses and image sensors have enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair using triangulation, this concept remains ambiguous when applied in the case of plenoptic cameras. We present a geometrical light field model allowing the triangulation to be applied to a plenoptic camera in order to predict object distances or specify baselines as desired. It is shown that distance estimates from our novel method match those of real objects placed in front of the camera. Additional benchmark tests with an optical design software further validate the model's accuracy with deviations of less than ±0.33% for several main lens types and focus settings. A variety of applications in the automotive and robotics field can benefit from this estimation model. [ABSTRACT FROM AUTHOR]

For cellular operators, estimating the end-user experience from network measurements is a challenging task. For video-streaming service, several analytical models have been proposed to estimate user opinion from buffering metrics. However, there remains the problem of estimating these buffering metrics from the limited set of measurements available on a per-connection basis for encrypted video services. In this paper, a system testbed is presented for automatically constructing a simple, albeit accurate, Quality-of-Experience (QoE) model for encrypted video-streaming services in a wireless network. The testbed consists of a terminal agent, a network-level emulator, and Probe software, which are used to compare end-user and network-level measurements. For illustration purposes, the testbed is used to derive the formulas to compute video performance metrics from TCP/IP metrics for encrypted YouTube traffic in a Wi-Fi network. The resulting formulas, which would be the core of a video-streaming QoE model, are also applicable to cellular networks, as the test campaign fully covers typical mobile network conditions and the formulas are partly validated in a real LTE network. [ABSTRACT FROM AUTHOR]

The Electro-Mechanical Brake (EMB) is anticipated by research communities and car manufacturers to be the future adopted brake system due to its advantages. However, to be competitively priced, the high cost load cell of EMB, which measures the clamping force to a disk, should be replaced by a clamping force estimation algorithm. For this purpose, a new clamping force estimator, compatible with readily available sensors, is proposed in this paper. This estimator determines the kissing point where the brake pads start to come into contact with the disk, and generates the characteristic curve of the polynomial function between the clamping force and the motor angle. Periodically updating the characteristic curve can enhance robustness to the pad's changing thickness. Also, the model includes a description of the hysteresis of the clamping force in the overall algorithm, which prevents the estimation performance from decreasing in the transient state. The performance of the propose algorithm was validated by comparison with measured values on a developed EMB test bench. [ABSTRACT FROM AUTHOR]

With the rapid growth of link speed, obtaining detailed traffic statistics becomes much more difficult. In order to reduce the resource consumption of measurement systems, more and more passive traffic measurement employs sampling at the packet level. Packet sampling has become an attractive and scalable method to measure flow data on high-speed links. However, knowing the length distributions of traffic flows passing through a network link is very useful for network operation and management. In this paper, we consider the problem of estimating flow length distributions based on sampled flow data. This paper introduces two algorithms for estimating flow length distributions, fitting estimation and factoring estimation. The fitting estimation uses piecewise Pareto distribution to fit the original traffic for a small sampling period. The factoring estimation is used for a large sampling period. It first factorizes the large sampling period into a product of some smaller integer factors, then iteratively invokes fitting estimation or other algorithms such as $${\textit{EM}}$$ in the arranged order of the factors. Evaluations of the proposed algorithms on large Internet traces obtained from several sources demonstrate that they have high measurement accuracy with low computation overhead. The algorithms allow us to recover the complete flow length distributions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce additional statistical tools for estimating the maximum regional earthquake magnitude, $$ m_{\max} $$ , as complement to those already introduced by Kijko and Singh (Acta Geophys 59(4):674-700, 2011). Four new methods are introduced and investigated, with regard to their applicability and performance. We present an example of application and a comparison that includes the methods introduced earlier by the previous authors. A condition for the existence of the Tate-Pisarenko estimate and a proof of the asymptotic equivalence of the Tate-Pisarenko and Kijko-Sellevoll estimates are presented in the two appendices. [ABSTRACT FROM AUTHOR]

A new model-based on-line control of building energy consumption with no drawbacks related to the thermal model or the unplanned changes (occupation, exterior temperature, ...) is presented in this paper. The main theoretical tools of this method are the flatness-based control and the recent new setting of numerical differentiation for fast and on-line parameter estimation. Indeed the estimation techniques form an added value in terms of the reliability of the thermal model. Furthermore, the main advantage of using the differential flatness control scheme is that the system behavior can be described by the trajectory of a so-called flat output and a number of its successive time derivatives. This leads to a simple design of the control strategy without the integration of any differential equations. Numerical simulations as well as comparative studies with a classical PID (proportional-integral-derivative) controller are provided to demonstrate the relevance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the accurate estimation of the torque transferred through the engine clutch installed between the engine and the drive motor in parallel-type hybrid electric vehicles. The estimation of the engine clutch torque primarily relies on the forward-direction observer which uses the nominal engine net torque information. To overcome the limitation of using the nominal engine torque information that it may not be accurate during the transient states or due to the influence of external disturbance such as the road condition and wind, the forward-direction observer is supplemented by the use of reverse-direction observer which uses the driveline model and wheel speed measurements. In addition, the drive motor torque information is used to calibrate the nominal engine torque during the idle charging state, so that the driveline characteristic unique to parallel-type hybrid electric vehicle can be utilized to increase the estimation accuracy. Finally, the estimation performance of the designed observer is tested via simulation and experiments based on a real vehicle. [ABSTRACT FROM AUTHOR]

In the era of big data, the vast majority of the data are not from the surface Web, the Web that is interconnected by hyperlinks and indexed by most general purpose search engines. Instead, the trove of valuable data often reside in the deep Web, the Web that is hidden behind query interfaces. Since numerous applications, like data integration and vertical portals, require deep Web data, various crawling methods were developed for exhaustively harvesting a deep Web data source with the minimal (or near-minimal) cost. Most existing crawling methods assume that all the documents matched by queries are returned. In practice, data sources often return the top k matches. This makes exhaustive data harvesting difficult: highly ranked documents will be returned multiple times, while documents ranked low have small chance being returned. In this paper, we decompose this problem into two orthogonal sub-problems, i.e., query and ranking bias problems, and propose a document frequency based crawling method to overcome the ranking bias problem. The rational of our method is to use the queries whose document frequencies are within the specified range to avoid the effect of search ranking plus return limit and significantly reduce the difficulty of crawling ranked data source. The method is extensively tested on a variety of datasets and compared with two existing methods. The experimental result demonstrates that our method outperforms the two algorithms by 58 % and 90 % on average respectively. [ABSTRACT FROM AUTHOR]

Error analysis in length measurements is an important problem in geographic information system and cartographic operations. The distance between two random points-i.e., the length of a random line segment-may be viewed as a nonlinear mapping of the coordinates of the two points. In real-world applications, an unbiased length statistic may be expected in high-precision contexts, but the variance of the unbiased statistic is of concern in assessing the quality. This paper suggesting the use of a k-order bias correction formula and a nonlinear error propagation approach to the distance equation provides a useful way to describe the length of a line. The study shows that the bias is determined by the relative precision of the random line segment, and that the use of the higher-order bias correction is only needed for short-distance applications. [ABSTRACT FROM AUTHOR]

We continue the investigation of some problems in learning theory in the setting formulated by F. Cucker and S. Smale. The goal is to find an estimator $f_{\bf z}$ on the base of given data $\mbox{\footnotesize\bf z}:=((x_1,y_1),\dots,(x_m,y_m))$ that approximates well the regression function $f_\rho$ of an unknown Borel probability measure $\rho$ defined on $Z=X\times Y.$ We assume that $f_\rho$ belongs to a function class $\Theta.$ It is known from previous works that the behavior of the entropy numbers $\epsilon_n(\Theta,{\cal C})$ of $\Theta$ in the uniform norm ${\cal C}$ plays an important role in the above problem. The standard way of measuring the error between a target function $f_\rho$ and an estimator $f_{\bf z}$ is to use the $L_2(\rho_X)$ norm ( $\rho_X$ is the marginal probability measure on X generated by $\rho$ ). This method has been used in previous papers. We continue to use this method in this paper. The use of the $L_2(\rho_X)$ norm in measuring the error has motivated us to study the case when we make an assumption on the entropy numbers $\epsilon_n(\Theta,L_2(\rho_X))$ of $\Theta$ in the $L_2(\rho_X)$ norm. This is the main new ingredient of thispaper. We construct good estimators in different settings: (1) we know both $\Theta$ and $\rho_X$ ; (2) we know $\Theta$ but we do not know $\rho_X;$ and (3) we only know that $\Theta$ is from a known collection of classes but we do not know $\rho_X.$ An estimator from the third setting is called a universal estimator. [ABSTRACT FROM AUTHOR]