High-precision Estimation Research Articles

Daylight factor estimation at an early design stage to reduce buildings' energy consumption due to artificial lighting: A numerical approach based on Doehlert and Box–Behnken designs

In buildings of residential and tertiary sectors, daylight issues primarily respond to increasing needs regarding the occupants' well being and visual confort as well as to the supply of natural light for working, by decreasing energy consumption due to necessary artificial lightning for the internal human activities. Daylight optimization at an early design stage can be referred to a way of designing architectural forms that take advantage of the prevailing urban context, such as existing shadow masks, to achieve a comfortable interior environment while minimizing energy use and reliance on artificial lighting systems. Our past study concluded in the development of EcCoGen, which is a kind of software that belongs to the family of tools based on interactive generative genetic algorithm optimization. The software generates solutions evaluated according to certain criteria, including mainly energy performance issues. Since the energy evaluation is included in the software features, we aimed to enhance software's applicability including energy reduction due to lighting issues via visual comfort optimization. Based on this objective, in this paper we describe a simple methodology to optimize the daylight potential of an architectural form at an early design stage. A numerical approach conducted employing the DIALux 4.5© is presented while a variety of simulation scenarios has been investigated on the basis of Doehlert and Box–Behnken DOE (design of experiments) methods. Our purpose is to develop accurate enough regression models for daylight factor prediction at an early design stage, when the problem's data are not precisely determined (dimension of glazing area, materials, opacities). To validate this statistical forecast system, many simulation scenarios were carried out and the statistical results are in compliance with the numerical simulations. The regression models' results show that the error caused by simplification is acceptable in most conditions, and a lot of coupling calculation is saved. Finally, a formal error analysis for the resulted regression models has been conducted to validate its forecast capacity. As a conclusion the statistical reduction of complex numerical modeling to simple regression models in the form of polynomial equations aims to assist architects and engineers to directly obtain a high precision estimation of their architectural form's daylight potential at an early design stage.

Cross-coupled Controller Based on High-precision Contouring Error Estimation

Cross-coupled Controller Based on High-precision Contouring Error Estimation

Estimation of single hydrophone target motion parameter based on cepstrum analysis

Based on the multipath structure of shallow water ray acoustics, a new robust high-precision motion parameter estimation method using single hydrophone was proposed. Aiming at uniform linear motion target, colligating multipath delay difference and kinematic geometric relation, we have constructed a tridimensional multipath delay model. Then nonlinear time map can be obtained from motion parameters to delay difference, and the cepstrum expression of typical underwater acoustic channel can be studied. The delay difference is extracted from it, and the extraction strategy is put forward. Differential evolution algorithm is used to estimate motion parameters, and the robustness can be improved. Theoretical and simulation results show that the time resolution of cepstrum is not restricted by the bandwidth of signals, which depends mainly on the type of signals and the signal to noise ratio. The cepstrum of CW is not sensitive to Doppler effect. The precision of parameter estimation depends mainly on the accuracy of time delay estimation and the amount of information attached to the differential evolution algorithm. When the information includes recent data points, parameter estimation can be better. Pool test results further verify the correctness and validity of this method.

SAGE-Based Algorithm for Direction-of-Arrival Estimation and Array Calibration

Most existing array processing algorithms are very sensitive to model uncertainties caused by the mutual coupling and sensor location error. To mitigate this problem, a novel method for direction-of-arrival (DOA) estimation and array calibration in the case of deterministic signals with unknown waveforms is presented in this paper. The analysis begins with a comprehensive perturbed array output model, and it is effective for various kinds of perturbations, such as mutual coupling and sensor location error. Based on this model, the Space Alternating Generalized Expectation-Maximization (SAGE) algorithm is applied to jointly estimate the DOA and array perturbation parameters, which simplifies the multidimensional search procedure required for finding maximum likelihood (ML) estimates. The proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. At the same time, it forms a unified framework for DOA and array perturbation parameters estimation in the presence of mutual coupling and sensor location error. The simulation results demonstrate the effectiveness of the algorithm.

Glacier Thickness Change Mapping Using InSAR Methodology

This letter presents a novel method for high-precision estimation of mountain glacier thickness change from the conventional interferometric synthetic aperture radar (InSAR) interferograms and multiaperture InSAR measurements. The method exploits the two components of displacement along the line of sight of radar beam and along track for deriving the glacier thickness change. To demonstrate the method, we estimate the glacier thickness change for the Dongkemadi Glacier in Tibet Plateau, China. The performance of this method is validated by field survey data. The results obtained with three InSAR pairs covering the Dongkemadi Glacier in the same seasons of three years show considerable spatial variability. The results in this study are, to our knowledge, the first ones with such a method, and they demonstrate the feasibility of the approach to obtain and analyze the mountain glacier thickness change.

Tire Radii Estimation Using a Marginalized Particle Filter

In this paper, the measurements of individual wheel speeds and the absolute position from a global positioning system are used for high-precision estimation of vehicle tire radii. The radii deviation from its nominal value is modeled as a Gaussian random variable and included as noise components in a simple vehicle motion model. The novelty lies in a Bayesian approach to estimate online both the state vector and the parameters representing the process noise statistics using a marginalized particle filter (MPF). Field tests show that the absolute radius can be estimated with submillimeter accuracy. The approach is tested in accordance with regulation 64 of the United Nations Economic Commission for Europe on a large data set (22 tests, using two vehicles and 12 different tire sets), where tire deflations are successfully detected, with high robustness, i.e., no false alarms. The proposed MPF approach outperforms common Kalman-filter-based methods used for joint state and parameter estimation when compared with respect to accuracy and robustness.

Nonlinear estimation of the navigation parameters of an object based on the integration of satellite and tracker measurements

The problem of the high-precision estimation of the navigation parameters of a mobile object by eliminating the most significant measurement noise in the process of integration of satellite and tracker navigation measurements and the application of nonlinear filtering methods is solved.

High-Precision Bayesian Modeling of Samples Susceptible to Inbuilt Age

Radiocarbon dates on samples susceptible to inbuilt age are common in the chronological record of many archaeological and environmental sites. Indeed, fragments of charcoal and wood are sometimes the only materials sufficiently well preserved for dating. However, where high-precision estimates arc required the extra uncertainty associated with such measurements often renders them unusable. This article tests three Bayesian modeling approaches that are designed to tackle this problem. The findings of our study suggest that successful corrections can be made for the inherent age offsets. The most effective and versatile approach was based on a version of outlier analysis. It is hoped that this method will become more widely employed and enable samples susceptible to inbuilt age to be included in high-precision chronologies.

Estimating the drift time for processes subject to linear trend disturbance using fuzzy statistical clustering

Although control charts can notify the state of out-of-control in a process by generating a signal, the indication is usually followed by a considerable amount of delay. Identifying the real time of the change in a process would provide a starting point for further investigation of an assignable cause. This paper addresses the problem of detecting the change point in different processes when the quality characteristics drift steadily away from an in-control state. For this purpose, a fuzzy statistical clustering (FSC) method is used to estimate the drift time in different processes. Since the application of an FSC method requires both in- and out-of-control values of the process parameter, a linear regression model is utilised to estimate the trend rate and then calculate the out-of-control process parameter. Through extensive simulations, the performance of the proposed change point estimation method is analysed and compared with the most recent estimators for several control charts. The results demonstrate that the proposed method is more effective in detecting the drift time through a wide range of trend rates. Furthermore, it is shown that the proposed method offers a higher estimation precision compared to conventional statistical methods.

Short Communication: Defining Optimality of a Test for Recent Infection for HIV Incidence Surveillance

The estimation of HIV incidence from cross-sectional surveys using tests for recent infection has attracted much interest. It is increasingly recognized that the lack of high performance recent infection tests is hindering the implementation of this surveillance approach. With growing funding opportunities, test developers are currently trying to fill this gap. However, there is a lack of consensus and clear guidance for developers on the evaluation and optimization of candidate tests. A fundamental shift from conventional thinking about test performance is needed: away from metrics relevant in typical public health settings where the detection of a condition in individuals is of primary interest (sensitivity, specificity, and predictive values) and toward metrics that are appropriate when estimating a population-level parameter such as incidence (accuracy and precision). The inappropriate use of individual-level diagnostics performance measures could lead to spurious assessments and suboptimal designs of tests for incidence estimation. In some contexts, such as population-level application to HIV incidence, bias of estimates is essentially negligible, and all that remains is the maximization of precision. The maximization of the precision of incidence estimates provides a completely general criterion for test developers to assess and optimize test designs. Summarizing the test dynamics into the properties relevant for incidence estimation, high precision estimates are obtained when (1) the mean duration of recent infection is large, and (2) the false-recent rate is small. The optimal trade-off between these two test properties will produce the highest precision, and therefore the most epidemiologically useful incidence estimates.

Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates

Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection.

Variable Step Pulse Deinterleaving Method

This paper presents a variable step pulse deinterleaving method. The method can not only realize signal sorting and at the same time frequency high precision estimation of the impulse, and avoids the problem of how to divide the range of pulse repetition interval of interest. Furthermore, the sorting method is not sensitive to the frequency jitter size, can be used for high frequency jitter radar pulse string sorting. Because the search step size increases with the pulse repetition interval, the sorting method also helps to reduce the signal sorting computation.

Data-Aided Parameters Estimation of Satellite-Based AIS Signals

Aiming at the problem of poor accuracy and small range of frequency estimation in the area of satellite-based AIS signals, this paper presents a novel data-aided estimation method based on the autocorrelation and DFT. The algorithm determines the principal value and the expansion of the frequency and then acquires high-precision estimation by data fitting. Based on that, other parameters can be estimated. Computer simulations were used to illustrate better estimation accuracy and a large estimation range.

Guaranteed high-precision estimation for P 0 interpolation constants on triangular finite elements

We consider an explicit estimation for error constants from two basic constant interpolations on triangular finite elements. The problem of estimating the interpolation constants is related to the eigenvalue problems of the Laplacian with certain boundary conditions. By adopting the Lehmann–Goerisch theorem and finite element spaces with a variable mesh size and polynomial degree, we succeed in bounding the leading eigenvalues of the Laplacian and the error constants with high precision. An online demo for the constant estimation is also available at http://www.xfliu.org/onlinelab/.

Following Single Molecules to a Better Understanding of Self-Assembled One-Dimensional Nanostructures

Single-molecule tracking (SMT) methods are now being employed to probe the morphologies and mass-transport characteristics of self-assembled one-dimensional (1D) nanostructures. Such nanostructures are found in surfactant-templated mesoporous metal oxides, phase-separated block copolymers, and lyotropic liquid-crystal mesophases. This Perspective begins with a review of investigations in which SMT methods have been employed for in situ visualization of 1D nanostructures and their ability to guide and support 1D diffusion of fluorescent probe molecules. New orthogonal regression methods for the quantitative characterization of 1D nanostructure alignment and order are subsequently discussed. Recent investigations in which the confined orientational motions of single molecules are probed by single-molecule emission polarization measurements are highlighted. These data are used to access high-precision estimates of the effective lateral nanostructure dimensions. The results reveal the important role played by...

Research on the Calibration Algorithm for Circular Array in the Presence of Amplitude and Phase Error

The calibration algorithm for circular array in the presence of amplitude and phase error is researched deep exploiting the assistant sensors. In order to calibrate the amplitude and phase error for the circular array, in this paper, two setting methods of assitant elements are presented, i.e., assitant elements are setted on the coordinate axis and on circle. Simulation experiments results show that the two proposed setting methods are effective in the presence of amplitude and phase error for circular array, and has low computational burden and high estimation precision of angle and amplitude and phase error.

Debiased Converted Measurements Kalman Filter Algorithm for Motion Parameter Estimation

The directional warhead of ATBM missile requires accurate initiation delay time and the initiating direction to obey effective damage for TBM, which needs to estimate the relative motion parameters to improve the accuracy. A debiased converted measurement Kalman Filter is presented and used for the estimation of TBMs position in body coordinate system of ATBM missile and the relative velocity. Results of simulation shows that this algorithm has high estimation precision for the parameters and satisfies the need of building initiating control algorithm for The directional warhead of ATBM missile.

Efficient Estimation of Non-Linear Finite Population Parameters by Using Non-Parametrics

SummaryCurrently, high precision estimation of non-linear parameters such as Gini indices, low income proportions or other measures of inequality is particularly crucial. We propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a non-parametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any non-linear parameter that is associated with any study variable of the survey, using a plug-in principle. Based on a rigorous functional approach and a linearization principle, the asymptotic variance of the estimators proposed is derived, and variance estimators are shown to be consistent under mild assumptions. The theory is fully detailed for penalized B-spline estimators together with suggestions for practical implementation and guidelines for choosing the smoothing parameters. The validity of the method is demonstrated on data extracted from the French Labour Force Survey. Point and confidence interval estimation for the Gini index and the low income proportion are derived. Theoretical and empirical results highlight our interest in using a non-parametric versus a parametric approach when estimating non-linear parameters in the presence of auxiliary information.

Analysis of Watson's Strategies for Playing Jeopardy!

Major advances in Question Answering technology were needed for IBM Watson to play Jeopardy! at championship level -- the show requires rapid-fire answers to challenging natural language questions, broad general knowledge, high precision, and accurate confidence estimates. In addition, Jeopardy! features four types of decision making carrying great strategic importance: (1) Daily Double wagering; (2) Final Jeopardy wagering; (3) selecting the next square when in control of the board; (4) deciding whether to attempt to answer, i.e., "buzz in." Using sophisticated strategies for these decisions, that properly account for the game state and future event probabilities, can significantly boost a player's overall chances to win, when compared with simple "rule of thumb" strategies. This article presents our approach to developing Watson's game-playing strategies, comprising development of a faithful simulation model, and then using learning and Monte-Carlo methods within the simulator to optimize Watson's strategic decision-making. After giving a detailed description of each of our game-strategy algorithms, we then focus in particular on validating the accuracy of the simulator's predictions, and documenting performance improvements using our methods. Quantitative performance benefits are shown with respect to both simple heuristic strategies, and actual human contestant performance in historical episodes. We further extend our analysis of human play to derive a number of valuable and counterintuitive examples illustrating how human contestants may improve their performance on the show.

Quasi-adiabatic quantum Monte Carlo algorithm for quantum evolution in imaginary time

We propose a quantum Monte Carlo (QMC) algorithm for non-equilibrium dynamics in a system with a parameter varying as a function of time. The method is based on successive applications of an evolving Hamiltonian to an initial state and delivers results for a whole range of the tuning parameter in a single run, allowing for access to both static and dynamic properties of the system. This approach reduces to the standard Schr\"odinger dynamics in imaginary time for quasi-adiabatic evolutions, i.e., including the leading non-adiabatic correction to the adiabatic limit. We here demonstrate this quasi-adiabatic QMC (QAQMC) method for linear ramps of the transverse-field Ising model across its quantum-critical point in one and two dimensions. The critical behavior can be described by generalized dynamic scaling. For the two-dimensional square-lattice system we use the method to obtain a high-precision estimate of the quantum-critical point $(h/J)_c=3.04463(12)$, where $h$ is the transverse magnetic field and $J$ the nearest-neighbor Ising coupling. The QAQMC method can also be used to extract the Berry curvature and the metric tensor.