Simple Averaging Technique Research Articles

Underwater localization with binary measurements: From compressed sensing to deep unfolding

1-bit compressive sensing (1-bit CS) refers to reconstructing a sparse signal from the sign of its measurements. Unlike compressive sensing measurements, the binary measurements in 1-bit CS can be realized using a simple comparator, thereby reducing the hardware complexity and the cost of the receiving array. Many recovery algorithms have been proposed for 1-bit CS, including recently methods based on deep learning. In this paper, we design a new method for underwater source localization by combining the matched field processing method (MFP) with 1-bit CS. We use the Fixed Point Continuation (FPC) method and a deep neural network designed by unfolding its iterations to solve the 1-bit recovery problem and evaluate their performance in the associated source localization problem. Furthermore, in order to improve the robustness of the signal recovery to noise added in the binary measurements, we propose to preprocess the received data with a simple average technique; this formulates the proposed Average FPC-ℓ1 (AVG-FPC-ℓ1) method. Our experiments show that an underwater target can be successfully located by using the sign of the measurements. Moreover, with the aid of the noise immunity method and the deep neural network (DNN) structure, the accuracy of the location estimates is greatly improved.

Effect of aggregation and disaggregation of land surface temperature imagery on evapotranspiration estimation

Understanding the spatial distribution of evapotranspiration (ET) and other surface energy fluxes in tree crops is vital for optimizing precision irrigation management practices. The land surface temperature (LST) is the primary input to estimate ET at field scale, but currently, available satellite data have a coarse resolution, which is not suitable for estimating ET at the required spatiotemporal scale at the field level. The development of unmanned aerial vehicles (UAVs) or airborne platforms during the last two decades has enabled the acquisition of LST and subsequent derivation of ET at the finer spatial resolution of up to a few centimeters due to the advances in low-weight thermal cameras onboard these platforms. In this study, the disaggregation methodology was applied to Landsat imagery based on aerial imagery collected at 0.5 m resolution over almond orchards in the Sacramento Valley of California, USA, to estimate ET at the orchard scale. The TsHARP algorithm was applied to sharpen the Landsat LST images (30 m spatial resolution) to target resolutions of 0.5, 10, 20 m. Also, the simple average aggregation technique was used to aggregate the aerial imagery (0.5 resolution) to 10, 20, and 30 m scales. Two Source Energy Balance (TSEB) model was used to estimate spatially distributed ET fluxes by combining the ground-based meteorological data for aggregated and disaggregated LST images. The estimated ET fluxes were compared to the measured eddy covariance fluxes, and then the aggregation and disaggregation processes were evaluated at 0.5, 10, 20, and 30 m spatial scales. At four spatial scales (0.5, 10, 20, and 30 m) aggregation and disaggregation derived ET values were highly correlated with the measured eddy covariance fluxes within the source footprint area. Furthermore, both aggregation and disaggregation approaches demonstrated similar ET spatial patterns within the study area, with slight variations observed under the Landsat 30 m resolution. The results indicated that TsHARP downscaled high-resolution imagery provides optimal spatial resolution for mapping and analyzing ET at the plant to orchard scale, with a 95% confidence interval and an RMSE of 0.43 mm/day. Overall, the results suggest that high spatial resolution imagery disaggregated from Landsat was the best alternative for estimating ET fluxes from the plant to the orchard scale, which is beneficial for precision irrigation management in orchards, but the framework can also be applied to other precision agriculture applications.

Techniques for mitigating the checkerboard formation: application in bone remodeling simulations

The checkerboard phenomenon is a numerical problem that can occur in bone remodeling simulations. This phenomenon degrades the quality of the results and emphasizes a strong mesh dependency. The node-based approach is a well-used method for solving this problem. However, this method does not provide a stable solution, and long-time simulations do not reach steady-state. This paper aims to discuss techniques for mitigating the checkerboard formation and obtaining bone characterization. For this, we apply the simple nodal average technique on stress, density, or both simultaneously. Then, we compare the element-based and node-based approaches with and without smoothing. Also, we introduced a quantification parameter called standard density variation. This parameter measures the intensity of the checkerboard formation and analyzes the evolution of the bone tissue behavior. The application of the smoothing techniques mitigates the checkerboard formation. Also, the application allows obtaining a stable behavior for long-time simulations. The standard density variation allowed visualizing this stable behavior. In particular, the stress field smoothing technique provides the best results. Besides the stabilization and checkerboard control, the technique allows obtaining a physiological density distribution.

Generalized Prager–Synge identity and robust equilibrated error estimators for discontinuous elements

The well-known Prager–Synge identity is valid in H1(Ω) and serves as a foundation for developing equilibrated a posteriori error estimators for continuous elements. In this paper, we introduce a new identity, that may be regarded as a generalization of the Prager–Synge identity, to be valid for piecewiseH1(Ω) functions for diffusion problems.For nonconforming finite element approximation of arbitrary odd order, we improve the current methods by proposing a fully explicit approach that recovers an equilibrated flux in H(div;Ø) through a local element-wise scheme. The local efficiency for the recovered flux is robust with respect to the diffusion coefficient jump regardless of its distribution.For discontinuous elements, we note that the typical approach of recovering a H1 function for the nonconforming error can be proved robust only under some restrictive assumptions. To promote the unconditional robustness of the error estimator with respect to the diffusion coefficient jump, we propose to recover a gradient in H(curl;Ω) space through a simple explicit averaging technique over facets. Our resulting error estimator is proved to be globally reliable and locally efficient regardless of the coefficient distribution. Nevertheless, the reliability constant is no longer to be 1.

Analysis of Rural Roads in Developing Countries Using Principal Component Analysis and Simple Average Technique in the Development of a Road Safety Performance Index

Analysis of Rural Roads in Developing Countries Using Principal Component Analysis and Simple Average Technique in the Development of a Road Safety Performance Index

Exploratory Study on Approaches for Traffic count Prediction; Using Toll-Way Traffic Count

The process of predicting or simulating traffic conditions, based on current and past traffic observations is an important component of any of the Intelligent Transportation System (ITS) applications. There are several methods to predict traffic count. However, each method in different literatures may use different datasets, different time intervals of input traffic flow/count. One of the aims of this study is to provide a review and performance analysis of parametric and non-parametric approaches on traffic prediction. Second, to explore the possibilities in the implementation of Advanced Traffic Management Systems (ATMS), one of the functional areas of Intelligent Transportation Systems (ITS), by predicting traffic count on toll plazas for optimizing of toll-plaza operations. An RFID (Radio-Frequency Identification) based Electronic Toll Collection (ETC) system gives timely varying traffic counts observed at toll plazas, which has been utilized to develop prediction models based on historic data. An empirical differentiation of four methods, namely Seasonal Autoregressive Integrated Moving Average (SARIMA) model based on time series analysis, Monte Carlo Simulation (MCS), Random Forest (RF) based on tree ensemble learning technique and KNN non-parametric regression-based machine learning technique, are proposed. Performance analysis at varying time intervals (5, 10, and 15 minutes) of input traffic count, for all the aforesaid models were compared with Simple Average Technique (SAT) using the historic data collected from two different toll-plazas in India. It was observed that K Nearest Neighbors (KNN) non-parametric regression performed better than other methods in most of the cases.

Simulation of conjugate heat transfer problems by lattice Boltzmann flux solver

In this work, a lattice Boltzmann flux solver (LBFS) is presented for simulation of conjugate heat transfer problems. Specifically, the mass and momentum equations used in the flow domain and the energy equation applied in both the flow and solid domains are discretized by finite volume method (FVM) and the numerical fluxes at the cell interface are reconstructed by the local solution of lattice Boltzmann equation (LBE) truncated to the Navier-Stokes level. To calculate the numerical fluxes, the Chapman-Enskog analysis is carried out first to reveal the connection between the macroscopic fluxes and the solution of LBE. With this relationship, the macroscopic fluxes can then be calculated locally and independently at each cell interface by the solution of LBE, which makes the developed scheme be very easy for application on non-uniform mesh and curved boundary as compared with lattice Boltzmann method (LBM). Overall, the present method well combines the advantages of simplicity and kinetic nature of LBM and geometric flexibility of Navier-Stokes solver discretized by FVM. In addition, in the calculation of numerical flux of energy equation, the concept of thermal resistance is innovatively introduced to evaluate the thermal conductivity at the cell interface. As compared with simple average technique, the introduced strategy can provide a more accurate prediction for temperature field. Numerical results showed that the solid-solid and fluid-solid conjugate heat transfer problems can be well simulated by the developed scheme and the second-order accuracy is achieved in space.

Multi-frame speckle reduction in OCT using supercontinuum pumped by noise-like pulses

In most coherent imaging modality, speckle noise is a major cause that blurs the boundary of tissues and degrades the image contrast. By utilizing the unique properties of supercontinuum (SC) generated by noise-like pulses (NLPs) and a simple multi-frame averaging technique, we achieved significant speckle reduction in spectral domain optical coherence tomography (SD-OCT). We quantitatively compared the speckle of our proposed method with those of conventional swept source OCT (SS-OCT) and SD-OCT based on commercial light sources. The experimental results show that SC pumped by NLPs combined with noncoherent averaging method achieves better denoising performance in terms of contrast to noise ratio (CNR).

Modelling groundwater level variations by learning from multiple models using fuzzy logic

ABSTRACTModelling time series of groundwater levels is investigated by three fuzzy logic (FL) models, Sugeno (SFL), Mamdani (MFL) and Larsen (LFL), using data from observation wells. One novelty in the study is the re-use of these three models as multiple models through the following strategies: (a) simple averaging, (b) weighted averaging and (c) committee machine techniques; these are implemented using artificial neural networks (ANN). These strategies provide some evidence that (i) multiple models improve on the performance of individual models and those using committee machines perform better than the other two options; and (ii) committee machine models produce defensible modelling results to develop management scenarios. The study investigates water table declines through management scenarios and shows that in this aquifer water use has higher impacts on water table variations than climatic variations. This provides evidence of the need for planned management in the study area.

Evaluation and optimization of the design schemes of reconfigurable machine tools based on multiple-attribute decision-making

A reconfigurable manufacturing system is one designed for rapid change in its structure, to quickly adjust its production capacity and functionality within a part family. Aiming at the diversity, cost and complexity of reconfiguration design, a method for evaluating the design scheme of a reconfigurable machine tool based on VIKOR is proposed. The module similarity between a reconfigurable machine tool and a prototype machine tool is defined, and on this basis, three quantitative evaluation indicators are established as follows: the module chain similarity determines the difficulty, effort, time and efficiency of machine tool reconstruction; the module interface complexity determines the feasibility and complexity of the disassembly and assembly of the reconfiguration module; and the reconfiguration cost determines the economic advantages of reconfigurable machines relative to fixed-structure equipment. According to three indicators, a multi-attribute decision-making method based on VIKOR is used to evaluate, calculate and sort the design scheme set of the reconfigurable machine tool, and the optimal feasible solution is obtained. An example of the reconfiguration design of a machine tool is analysed to verify the validity and feasibility of the proposed method compared with the methods of simple average weighting and technique for order preference by similarity to an ideal solution.

An Algorithm for Approximating the Second Moment of the Normalizing Constant Estimate from a Particle Filter

We propose a new algorithm for approximating the non-asymptotic second moment of the marginal likelihood estimate, or normalizing constant, provided by a particle filter. The computational cost of the new method is O(M) per time step, independently of the number of particles N in the particle filter, where M is a parameter controlling the quality of the approximation. This is in contrast to O(MN) for a simple averaging technique using M i.i.d. replicates of a particle filter with N particles. We establish that the approximation delivered by the new algorithm is unbiased, strongly consistent and, under standard regularity conditions, increasing M linearly with time is sufficient to prevent growth of the relative variance of the approximation, whereas for the simple averaging technique it can be necessary to increase M exponentially with time in order to achieve the same effect. This makes the new algorithm useful as part of strategies for estimating Monte Carlo variance. Numerical examples illustrate performance in the context of a stochastic Lotka–Volterra system and a simple AR(1) model.

Unified Reliability Index Development for Utility Performance Assessment

The major purpose of the continuous development of reliability indices is to capture a comprehensive view of systems performance. The lack of consensus among utilities and regulators on which indices should be used complicates the problem more. Regulators inevitably make comparisons between utilities’ routinely reported reliability indices. Thus, an adequate and fair process needs to be implemented. Utilities are reporting indices in a chaotic way. One utility might report SAIFI and SAIDI, while others report SAIFI and CAIDI. In this work, a development of a unified reliability index, which can yield proper performance assessment, fair comparisons, and reflection of all the knowledge embedded within all current indices, will be formed. The initiated unified index may provide several benefits, among which is suitable standards design, improved tools for planning and design optimization, and less technical burden on operators. Also, the development of a unified reliability index requires the initiation of a standard normalization methodology based on a maximum that takes into account the variation between systems under study. After normalization, combining indices is made possible through a proposed methodology. The developed methodology is validated by comparing it to both simple averaging technique and a fuzzy inference based engine.

Monte Carlo Integration Using Spatial Structure of Markov Random Field

Monte Carlo integration (MCI) techniques are important in various fields. In this study, a new MCI technique for Markov random fields (MRFs) is proposed. MCI consists of two successive parts: the first involves sampling using a technique such as the Markov chain Monte Carlo method, and the second involves an averaging operation using the obtained sample points. In the averaging operation, a simple sample averaging technique is often employed. The method proposed in this paper improves the averaging operation by addressing the spatial structure of the MRF and is mathematically guaranteed to statistically outperform standard MCI using the simple sample averaging operation. Moreover, the proposed method can be improved in a systematic manner and is numerically verified by numerical simulations using planar Ising models. In the latter part of this paper, the proposed method is applied to the inverse Ising problem and we observe that it outperforms the maximum pseudo-likelihood estimation.

Simple Moving Voltage Average Incremental Conductance MPPT Technique with Direct Control Method under Nonuniform Solar Irradiance Conditions

A new simple moving voltage average (SMVA) technique with fixed step direct control incremental conductance method is introduced to reduce solar photovoltaic voltage (VPV) oscillation under nonuniform solar irradiation conditions. To evaluate and validate the performance of the proposed SMVA method in comparison with the conventional fixed step direct control incremental conductance method under extreme conditions, different scenarios were simulated. Simulation results show that in most cases SMVA gives better results with more stability as compared to traditional fixed step direct control INC with faster tracking system along with reduction in sustained oscillations and possesses fast steady state response and robustness. The steady state oscillations are almost eliminated because of extremely smalldP/dVaround maximum power (MP), which verify that the proposed method is suitable for standalone PV system under extreme weather conditions not only in terms of bus voltage stability but also in overall system efficiency.

Prediction of Compressional and Shear Slowness from Conventional Well Log Data: Using Intelligent Systems

Compressional slowness (DTCO) and shear slowness (DTSM) are two fundamental parameters that have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using a dipole sonic imaging tool, but unfortunately this tool is run in just a few wells of a field. Therefore, it is important to predict compressional and shear slowness indirectly from the other conventional well logs that have strong correlation with these two parameters when the dipole sonic log is missing in a given well. The overriding impetus of this work is to construct intelligent systems, including artificial neural network, fuzzy logic, and adaptive neuro fuzzy inference system, for prediction of compressional and shear slowness. Finally, the results are combined with a committee machine algorithm using a simple averaging technique. A total number of 2,715 data points from Asmari formation, which has compressional and shear slowness, are used. These data are divided into two groups: 2,172 data points for construction of intelligent systems and 543 data points used for model testing. The results showed that, despite a difference in concept, all of the intelligent techniques were successful for estimation of compressional and shear slowness.

Convergence and accuracy of displacement based finite element formulations over arbitrary polygons: Laplace interpolants, strain smoothing and scaled boundary polygon formulation

Three different displacement based finite element formulations over arbitrary polygons are studied in this paper. The formulations considered are the conventional polygonal finite element method (FEM) with Laplace interpolants, the cell-based smoothed polygonal FEM with simple averaging technique and the scaled boundary polygon formulation. For the purpose of numerical integration, we employ the sub-triangulation for polygonal FEM and classical Gaussian quadrature for the smoothed FEM and the scaled boundary polygon formulation. The accuracy and the convergence properties of these formulations are studied with a few benchmark problems in the context of linear elasticity and the linear elastic fracture mechanics. The extension of scaled boundary polygon to higher order polygons is also discussed.

Scorpion image segmentation system

Death as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two non-overlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection.

Effective acoustic response and spectral representation

The problem of finding an effective or average acoustic properties of inhomogeneous materials has been treated extensively in connexion with problems of porous media, rock physics and composite materials. Recently, there has been a renewed interest due to the potential applications of acoustic metamaterials. There are different techniques for finding effective acoustic properties. These range from simple averaging techniques, to variational methods, to coherent phase approximations. There is not a unique method for finding effective properties. In the case of dielectric materials, there are at least thirteen procedures reported. In this work, we present the acoustic version of the spectral representation of effective media, first developed to find dielectric effective properties. This method has the advantage that it separates the geometric contribution from the physical property to be calculated, in our case the acoustic impedance. The method is based on a Green's function solution of the acoustic wave equation and finding the effective properties is done by calculating the poles of the so called spectral function. Furthermore, we show that any effective medium model that can be described in the spectral representation satisfies the Kramers-Kronig relations. Numerical examples and comparisons between the spectral representation and other existing procedures will be discussed.

A Spatiotemporal Correlation Technique to Improve Satellite Rainfall Accumulation

AbstractA spatiotemporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of limited and infrequent measurements while accounting for the measurement accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. The technique is developed using synthetic radar data. Nine months of Operational Program for the Exchange of Weather Radar (OPERA) data are used on grid sizes of 100, 248, and 500 km with pixel resolutions of 8, 12, and 24 km to simulate satellite fields of view and are then applied to the real satellite data over the Southwest to calculate 3-h rainfall accumulations. The results are compared with the simple averaging technique, which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. Using synthetic data, depending on the time separation of the measurements and their accuracy, a spatiotemporal correlation technique has shown the potential to yield improvements of up to 40% in absolute error and up to 25% in root-mean-square error when compared with the simple averaging technique. When applied to the real satellite data over the Southeast, the technique showed much less skill (general improvement of only 2%–6%).

Averaging for superconvergence: Verification and application of 2D edge elements to Maxwell’s equations in metamaterials

In this paper, a simple averaging technique for edge elements is justified to improve the standard convergence rate (hence achieving superconvergence) when they are used to solve the Maxwell’s equations. For simplicity, here we focus on the lowest-order triangular edge element, which is widely used in practice. Though there exists no natural superconvergence points for the numerical solution obtained on such an edge element, superconvergence can be obtained after a simple average of solutions over the neighboring elements. A comprehensive analysis for the lowest-order triangular edge element is carried out, and one-order higher convergence rate than the standard interpolation error estimate is proved for the averaged solution at midpoints of those interior edges of parallelograms (formed by two triangles), i.e., superconvergence happens at the parallelogram centers. We also provide detailed analysis to explain why several cases do not have superconvergence. Extensive numerical results consistent with our analysis are presented.