Proposed Estimation Technique Research Articles

Landslide Analysis with Incomplete Data: A Framework for Critical Parameter Estimation

Landslides are one of the most common geohazards, posing significant risks to infrastructure, recreation, and human life. Slope stability analyses rely on detailed data, accurate materials testing, and careful model parameter selection. These factors are not always readily available, and estimations must be made, introducing uncertainty and error to the final slope stability analysis results. The most critical slope stability parameters that are often missing or incompletely constrained include slope topography, depth to water table, depth to failure plane, and material property parameters. Though estimation of these values is common practice, there is limited guidance or best practice instruction for this important step in the analysis. Guidance is provided for the estimation of: original and/or post-failure slope topography via traditional methods as well as the use of open-source digital elevation models, water table depth across variable hydrologic settings, and the iterative estimation of depth to failure plane and slope material properties. Workflows are proposed for the systematic estimation of critical parameters based primarily on slide type and scale. The efficacy of the proposed estimation techniques, uncertainty quantification, and final parameter estimation protocol for data-sparse landslide analysis is demonstrated via application at a landslide in Colorado, USA.

Development of a reliable adaptive estimation approach for a low‐cost attitude and heading reference system

AbstractA main challenge within inertial navigation systems involves attitude determination, which refers to the process of estimating the angles that define orientations. A new algorithm is presented to estimate a reliable and proper model for a low‐cost attitude and heading reference system (AHRS), while also, including long‐term global navigation satellite system (GNSS) outages with various dynamical manoeuvres. In the proposed approach, the initial AHRS model is continuously refined at each time step through the incorporation of complementary terms using a new prediction method. These complementary terms are determined using attitude information from the accelerometers output and the GNSS as reference systems. The proposed estimation technique undergoes mathematical analysis to ascertain stochastic stability and is further assessed through real‐world aerial experimental tests conducted with hardware‐in‐the‐loop mechanisation. The obtained results demonstrate a significant enhancement in the reliability and accuracy of the AHRS through the proposed estimation algorithm, even under GNSS blockages. The comparative results highlight the higher performance of the suggested data fusion scheme in providing a reliable and accurate model for the AHRS in normal conditions and assisting a powerful neural network‐based algorithm to provide reliable heading information even during long‐term GNSS outages under dynamical manoeuvres.

Fractional stochastic volatility model

This article introduces a discrete‐time fractional stochastic volatility model (FSV) based on fractional Gaussian noise. The new model includes the standard stochastic volatility model as a special case and has the same limit as the fractional integrated stochastic volatility (FISV) model, which is the continuous‐time fractional Ornstein–Uhlenbeck process. A simulated maximum likelihood method, which maximizes the time‐domain log‐likelihood function calculated by the importance sampling technique, and a frequency‐domain quasi maximum likelihood method (or quasi Whittle) are employed to estimate the model parameters. Simulation studies suggest that, while both estimation methods can accurately estimate the model, the simulated maximum likelihood method outperforms the quasi Whittle method. As an illustration, we fit the FSV and FISV models with the proposed estimation techniques to the S&P 500 composite index over a sample period spanning 45 years.

A novel wall parameter estimation approach based on transmission line method from through-wall radar measurements

A novel technique for estimation of the wall’s relative permittivity, thickness and conductivity from signals measured from the through-wall radar is presented in this paper. The retrieval process is built around an electromagnetic circuit model that represents the wall structure as a transmission line segment. The propagation of the radar signal through the wall is equivalently described as a pair of voltage and current waves that propagate along the transmission line, using analogies between the electrical parameters of the transmission line segment and the constituent parameters of the materials of the wall structure. The synthetic time domain signal obtained analytically and the real through wall imaging time domain signal were correlated to estimate the wall parameters of the layers that comprise the wall structure. The effectiveness of the proposed estimation technique has been demonstrated through simulation and experimentation on two actual building walls. A comparison with conventional methods revealed consistent results between the estimated wall parameters using the proposed technique and those obtained through traditional approaches. These findings highlight the potential of the proposed method in enhancing the accuracy of real-time target localization and tracking in through-wall imaging applications.

Validation of FEM-Based Parameter Estimation for Variable Phase-Pole Induction Machines

In multiphase electric machines, high-performance control strategies require suitable electrical parameter estimation. This aspect is particularly true for the class of multiphase machines known as variable phase-pole induction machines. This work analyzes, calculates, and experimentally validates different parameter estimation methods for a wound independently-controlled stator coil induction machine, which can dynamically change the phase-pole configuration during real-time operation. The parameter estimation methods are a conventional finite-element analysis-based stator impedance calculation and an implementation of the harmonic plane decomposition theory. Experimental verification illustrates the proposed estimation technique in different machine configurations. Comparing the two analyzed methods, the estimation of all machine parameters based on the harmonic plane decomposition theory well agrees with the practical results. The results indicate that using the proposed method as the initial parameter estimate in a commissioning process for a variable phase-pole drive is possible and beneficial for the reduced computing time.

Empirical study of periodic autoregressive models with additive noise – estimation and testing

Periodic autoregressive (PAR) time series with finite variance is considered as one of the most common models of second-order cyclostationary processes. However, in the real applications, the signals with periodic characteristics may be disturbed by additional noise related to measurement device disturbances or to other external sources. Thus, the known estimation techniques dedicated for PAR models may be inefficient for such cases. When the variance of the additive noise is relatively small, it can be ignored and the classical estimation techniques can be applied. However, for extreme cases, the additive noise can have a significant influence on the estimation results. In this paper, we propose four estimation techniques for the noise-corrupted PAR models with finite-variance distributions. The methodology is based on Yule-Walker equations utilizing the autocovariance function. It can be used for any type of the finite-variance additive noise. The presented simulation study clearly indicates the efficiency of the proposed techniques, also for extreme case, when the additive noise is a sum of the Gaussian additive noise and additive outliers. The proposed estimation techniques are also applied for testing if the data correspond to noise-corrupted PAR model. This issue is strongly related to the identification of informative component in the data in case when the model is disturbed by additive non-informative noise. The power of the test is studied for simulated data. Finally, the testing procedure is applied for two real time series describing particulate matter concentration in the air.

GARTFIMA process and its empirical spectral density based estimation

ABSTRACT In this article, we introduce a Gegenbauer autoregressive tempered fractionally integrated moving average process. We work on the spectral density and autocovariance function for the introduced process. The parameter estimation is done using the empirical spectral density with the help of the nonlinear least square technique and the Whittle likelihood estimation technique. The performance of the proposed estimation techniques is assessed on simulated data. Further, the introduced process is shown to better model the real-world data in comparison to other time series models.

Electrical capacitance tomography-based estimation of slug flow parameters in horizontally aligned pneumatic conveyors

Pneumatic conveying is a widely applied technique for the transportation of powders. Dense phase conveying such as slug flow is the preferable operating condition of such transport systems. Advantages are the reduced energy and conveying gas consumption and reduced material wear. The design and the operation of dense phase conveyors is a challenging task due to complex flow dynamics and the lack of reliable flow meters, which are crucial for process control. In this work the capabilities of electrical capacitance tomography (ECT) for slug flow parameter estimation are analysed. The estimation of flow parameters is addressed and demonstrated by means of laboratory experiments. Amongst other parameters it is shown that mass concentration distributions as well as particle velocity fields can be obtained from tomographic image reconstruction results, which are substantial parameters for potential mass flow metering. Additionally, the proposed estimation techniques are applied to signals provided by an ECT system, which is installed within a large-scale pneumatic conveyor of an industrial process plant. It is observed that also Geldart group A powders with Archimedes numbers in the scale of Ar≈1e0 are conveyable in slug-like structures which can be separated by stationary or non-stationary layers. Furthermore, various slug flow properties of powders with Ar>1e2 reported in literature are confirmed but also deviating properties are observed, leading to novel insights into horizontal dense phase flow processes of powders with Ar≈1e0. The results demonstrated in this work are pointing out the capabilities of ECT for flow analyses, which are enabling further insights into flow mechanisms and future developments of flow meters for dense phase flow processes.

Destructive cure models with proportional hazards lifetimes and associated likelihood inference

In survival analysis, cure models have gained much importance due to rapid advancements in medical sciences. More recently, a subset of cure models, called destructive cure models, have been studied extensively under competing risks scenario wherein initial competing risks undergo a destructive process. In this article, we study destructive cure models by assuming a flexible weighted Poisson distribution (exponentially weighted Poisson, length biased Poisson and negative binomial distributions) for the initial number of competing causes and lifetimes of the susceptible individuals being defined by proportional hazards. The expectation-maximization (EM) algorithm and profile likelihood approach are made use of to estimate the model parameters. An extensive simulation study is carried out under various parameter settings to examine the properties of the models, and accuracy and the robustness of the proposed estimation technique. Effects of model mis-specification on the parameter estimates are also discussed in detail. For further illustration of the proposed methodology, a real-life cutaneous melanoma data set is analyzed.

Online Short-Circuit Current Estimation of Permanent Magnet Motors

In this article, online short-circuit estimation of permanent magnet synchronous motors (PMSMs) are presented, which is essential for postfault ac drive operations. For this purpose, initially, the PMSM model with interturn short-circuit fault (ITSC) is extended to cover motors with saliency. By utilizing ITSC model voltage equations and voltage reference equations, the amplitude and phase relationship between short-circuit current and the phase reference voltage is established. In the next step, second harmonics in the reference voltage and feedback current are used to extract the short-circuit current information. In order to efficiently use ac drive microcontroller bandwidth, the proposed short-circuit current estimation method is executed after the ITSC fault is detected, not continuously. Instead of running the proposed short-circuit current estimation continuously, it is reasonable to run it after an ITSC fault detection to efficiently use ac drive microcontroller bandwidth. Therefore, two intermediate steps, a simple ITSC fault detection and severity estimation algorithms are proposed as well. The proposed technique can estimate the fault current and severity without the knowledge of fault location and valid for all PM motors. The findings are experimentally tested using two PMSMs to demonstrate the effectiveness and practicability of the proposed estimation technique.

Prediction based cost estimation technique in agile development

Agile has been invented to improve and overcome the deficiencies of efficient software development. At present, the agile model is used in software development vastly due to its support to both developers and clients resourcefully. Agile methodology increases the interaction between the developer and client to make the software product defect-free. The agile model is getting to be a well-known life cycle model because of its particular features and most owing is to allow changes at any level of the project from the product owner. However, on other hand, this novel feature is a disadvantage of the agile model due to frequent change requests from the client has increased the cost and time. To overcome cost and time estimation issues different cost estimation techniques are being used in agile development but no one is pertinent for accurate estimation. Therefore, this study has proposed a cost estimation technique. The proposed estimation technique is predictions-based and has different categorizations of projects based on user stories complexities and the developer's expertise. We have applied the suggested technique to ongoing projects to find the results and effectiveness. We have used two projects with different sizes and user stories. Both projects have different modules and developers with different expertise. We have used the proposed estimation technique on projects and done a survey session with the teams. This survey session's main objective is to reveal the statistical findings of the proposed solution. We have designed the 12 hypotheses for statistical analysis.

Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs

This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target’s location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, more-costly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data received from neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target’s location via these measurements, move along with the target. Note to Practitioners—Inspired by recent development in industrial UAVs along with emerging progress in low-cost processing units, fog computing systems, and wireless communications, this paper considers mobile tracking of a moving target via a group of wireless-connected autonomous drones. In the classical tracking methods, which are prone to single-point-of-failure, all the sensors need to send their information to a ground central station over a long-range and costly data-transmission channel. In contrast, by collaborative tracking the processing and decision-making are distributed among a swarm of drones equipped with onboard miniaturized electronic parts such as sensors, microcontrollers, microprocessors, and communication units. This article provides an efficient algorithm to enable such drones to autonomously track the moving target in real-time. Note that the cost and tracking ability of the UAV swarm are directly determined by the computational efficiency and communication burden of the estimation algorithm. In this regard, most available estimation algorithms are over budget and even infeasible due to the need for fast data-transmission channels, fast CPUs, and high network traffic. Our proposed estimation technique outperforms similar algorithms in terms of required communication bandwidth, data-transmission rate, and computational resources, which considerably reduces the hardware cost and improves tracking efficiency in real-time large-scale applications. We show the feasibility and efficiency of our distributed tracking method by simulation.

Composite Imagery-Based Non-Uniform Illumination Sensing for System Health Monitoring of Solar Power Plants

Abstract Incidence of non-uniform illumination (NUI) degrades the affected solar photovoltaic modules temporarily. However, in worst case, it may also lead to permanent degradation. Thus, non-uniform illumination sensing in terms of encompassed area over solar photovoltaic module becomes important for sustainable power generation. An integrated framework by combining optical and thermal images for non-uniform illumination sensing and classification (static and dynamic) is proposed. The proposed technique detects hotspots along with identification of the nature of shading on the solar photovoltaic modules. Additionally, Hungarian Kalman filter is implemented for estimating the coverage of the non-uniform illumination-affected region including its abrupt shape. The proposed estimation technique also calculates the total loss in the output energy of the solar photovoltaic system due to non-uniform illumination. Overall, the proposed methodology develops a hybrid imagery-dependent advanced early warning system for large-scale solar power plants which are cost-effective and bypass multi-sensor data fusion to attain real-time application.

A software-based cost estimation technique in scrum using a developer's expertise

BackgroundScrum methodology is the most useful and adopted part of the agile methodology. Many organizations are adopting Scrum for the last decade to meet their software requirements. It is facilitating the software industries to develop software applications according to the user's requirements. ObjectiveScrum helps to fulfill the customer's requirements by interacting with the client and developer. Instead of this, scrum still has some challenges that influence the cost of a scrum. These challenging factors include coordination, team size, change request, complexity, and issues in daily meeting sessions. Due to these factors mostly the cost and time exceed scrum-based projects. To overcome these issues from the accurate and precise estimation in the scrum project we have developed a web-based software system to estimate the effort of a user story as well as the budget. MethodologyThere are some sophisticated cost estimation techniques for scrum-based projects. But all estimation techniques have some limitations that make them less useful for estimation and overcoming the effects of these factors in the scrum. We have surveyed the multiple software industries to get the static findings of the survey. We have designed the 30 hypotheses that main aim to reveal how much the proposed estimation technique is valuable for the accuracy of estimation. For the inclusion of participants, we have set the age criteria to get the opinions of the most experienced programmers. ResultsThe main aim of this paper is to provide knowledge about key challenging factors in Scrum that cause exceed in cost and time, and also briefly explain why existing scrum cost estimation techniques are not pertinent for projects. In the last, we propose a framework as a solution to manage these factors and control cost and time from exceeding. We also adopted this estimation technique in different case studies in software industries for findings and results. The technique calculates the efforts autonomously. We design a software-based estimation technique that collects inputs from team/s and estimates the cost and time of a project. ConclusionIn outcomes, we found that our cost estimation approach reduced the issues from scrum software project development and make it more proficient for estimation of projects. Our proposed estimation technique introduced the new concept of estimation that facilitate the client, software industry, and developers as well to meet the requirements of the client, fulfill the changes during the sprints/s development and deliver the project within the time and cost.

Statistical Inference and Optimal Design of Accelerated Life Testing for the Chen Distribution under Progressive Type-II Censoring

This paper discusses statistical inference and optimal design of constant-stress accelerated life testing for the Chen distribution under progressive Type-II censoring. The scale parameter of the life distribution is assumed to be a logarithmic linear function of the stress level. The maximum likelihood estimates of the parameters are obtained. Then, the observed Fisher information matrix is derived and utilized to construct asymptotic confidence intervals. Meanwhile, the parametric bootstrap methods are provided for the interval estimation. In addition, the Bayes estimates under the squared error loss function are obtained by applying the Tierney and Kadane technique and Lindley’s approximation. As for the optimal design, D- and A-optimality criteria are considered to determine the optimal transformed stress level. Finally, the simulation is carried out to demonstrate the proposed estimation techniques and the optimal criteria, and a real data set is discussed.

Time-Domain Channel Estimation Scheme for OFDM over Fast Fading Channels

In high-mobility scenarios, the time variation of mobile radio channels leads to a loss of orthogonality among subcarriers in orthogonal frequency division multiplexing (OFDM) systems, resulting in intercarrier interference (ICI) and performance deterioration. Conventional channel estimation schemes are usually based on pilot tones, which are distributed in each OFDM symbol to estimate the channel variation. Hence, the channel estimator itself suffers from ICI. In this study, a new estimation scheme, which does not suffer from ICI, is proposed to estimate the channel variation within OFDM symbols. The main idea is to zero-pad (ZP) the OFDM symbol in the time domain. Then, in the middle of the ZP interval, an impulse signal is inserted as a pilot sample, which is used to estimate the channel at the pilot signal in the OFDM symbol. Finally, a linear model is used to estimate the channel variation over an OFDM symbol. Additionally, we derive the mean squared error (MSE) of the proposed estimation technique under the constraint that the channel varies linearly within OFDM symbols. Simulation results show that our scheme can achieve a substantial improvement in the bit error rate (BER) performance of OFDM, in spite of the OFDM symbol length being increased. Moreover, in many cases, the new scheme can achieve the same BER performance as the perfect knowledge of channel state information (CSI). Theoretical analysis and numerical simulations show that our scheme achieves excellent performance with much lower computational complexity.

Estimation of parameters of the logistic exponential distribution under progressive type-I hybrid censored sample

ABSTRACT The present paper addresses the problem of estimation of model parameters of the logistic exponential distribution based on progressive type-I hybrid censored sample. The maximum likelihood estimates are obtained and computed numerically using Newton–Raphson algorithm. Further, the Bayes estimates are derived under the squared error, LINEX and the generalized entropy loss functions. Two types (independent and bivariate) of prior distributions are considered for the purpose of Bayesian estimation. It is seen that the Bayes estimates are not of explicit forms. Thus, Lindley’s approximation technique is employed to get approximate Bayes estimates. Interval estimates of the parameters based on the normal approximation of the maximum likelihood estimates and the log-transformed maximum likelihood estimates are constructed. The highest posterior density credible intervals are obtained by using the importance sampling method. Numerical simulation is performed to see the performance of the proposed estimation techniques. A real-life data set is considered and analysed for the purpose of illustrations.

Sequential Fusion Based Approach for Estimating Range Gate Pull-Off Parameter in a Networked Radar System: An ECCM Algorithm

Networked radar is an emerging and effective alternative to traditional radar systems to provide improved performance by fusing information from multiple radars. Further, networked radar systems (NRS) have found numerous deployments in military and civilian infrastructures in recent years. Electronic countermeasures (ECM) like jamming, Range Gate Pull-Off (RGPO), and Velocity Gate Pull-Off (VGPO) generally pose a high risk to the radar systems by injecting intentional interference. This paper proposes networked radar to detect the RGPO ECM attack and estimate the range gate deception parameter of the deceived local track in an NRS. Each radar comprises a local tracker to provide the local estimates (updated state and updated covariance), and these estimates are then sent to the fusion node. Thereafter, a track-to-track association (T2TA) is formulated at the fusion node to detect the deceived tracks using all the available local tracks. For the deceived track, the pseudo-measurements are created using the inverse Kalman filter-based tracklets. All the local tracks except deceived track are compensated and sequentially fused to create a reference measurement. After that, the deception parameter of the deceived track is estimated by using pseudo-measurement and the reference measurement by employing the recursive least square estimator (RLSE). In addition, the proposed algorithm is analyzed for single and multiple RGPO based ECM scenarios. Further, the Cramer Rao Lower Bound (CRLB) for the proposed methodology is derived. The results are quantified with a Position Root Mean Square Error (PRMSE), CRLB, innovation test, normalized estimation error squared (NEES) test, and confidence interval. The simulation results demonstrate that the proposed estimation technique provides good performance in the presence of all the local tracks are being attacked by RGPO ECM. Besides, it is evident from the results that estimator efficiency is falling below the 5% tail probability of the chi-square distribution.

Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles

This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery SOC in real-time, with 2% accuracy during real-time hardware testing.

Efficient Ventricular Parameter Estimation Using AI-Surrogate Models.

The onset and progression of pathological heart conditions, such as cardiomyopathy or heart failure, affect its mechanical behaviour due to the remodelling of the myocardial tissues to preserve its functional response. Identification of the constitutive properties of heart tissues could provide useful biomarkers to diagnose and assess the progression of disease. We have previously demonstrated the utility of efficient AI-surrogate models to simulate passive cardiac mechanics. Here, we propose the use of this surrogate model for the identification of myocardial mechanical properties and intra-ventricular pressure by solving an inverse problem with two novel AI-based approaches. Our analysis concluded that: (i) both approaches were robust toward Gaussian noise when the ventricle data for multiple loading conditions were combined; and (ii) estimates of one and two parameters could be obtained in less than 9 and 18 s, respectively. The proposed technique yields a viable option for the translation of cardiac mechanics simulations and biophysical parameter identification methods into the clinic to improve the diagnosis and treatment of heart pathologies. In addition, the proposed estimation techniques are general and can be straightforwardly translated to other applications involving different anatomical structures.