Instantaneous Estimation Research Articles

In-ear and on-body measurements of impulse-noise exposure

Accurate quantification of noise exposure in military environments is challenging due to movement of listeners and noise sources, spectral and temporal noise characteristics, and varied use of hearing protection. This study evaluates a wearable recording device designed to measure on-body and in-ear noise exposure, specifically in an environment with significant impulse noise resulting from firearms. A commercial audio recorder was augmented to obtain simultaneous measurements inside the ear canal behind an integrated hearing protector, and near the outer ear. Validation measurements, conducted with an acoustic test fixture and shock tube, indicated high impulse peak insertion loss with a proper fit of the integrated hearing protector. The recording devices were worn by five subjects during a live-fire data collection at Marine Corps Base Quantico where Marines fired semi-automatic rifles. The field test demonstrated the successful measurement of high-level impulse waveforms with the on-body and in-ear recording system. Dual channels allowed for instantaneous fit estimates for the hearing protection component, and the device worked as intended in terms of hearing protection and noise dosimetry. Accurate measurements of noise exposure and hearing protector fit should improve the ability to model and assess the risks of noise-induced hearing loss.

MMSE Receiver Design and SINR Calculation in MU-MIMO Systems With Imperfect CSI

The performance of the uplink of multiuser multiple input multiple output systems depends critically on the receiver architecture and on the quality of the acquired channel state information. A popular approach is to design linear receivers that minimize the mean squared error (MSE) of the received data symbols. Unfortunately, most of the literature does not take into account the presence of channel state information errors in the MSE minimization. In this letter we develop a linear minimum MSE (MMSE) receiver that employs the noisy instantaneous channel estimates to minimize the MSE, and highlight the dependence of the receiver performance on the pilot-to-data power ratio. By invoking the theory of random matrices, we calculate the users' signal-to-interference-plus-noise ratio as a function of the number of antennas and the pilot-to-data power ratio of all users. Numerical results indicate that this new linear receiver outperforms the classical mismatched MMSE receiver.

Adaptive Artificial Neural Network-Based Models for Instantaneous Power Estimation Enhancement in Electric Vehicles’ Li-Ion Batteries

This paper investigates the role of artificial neural networks in enhancing the accuracy of instantaneous power estimation of electric vehicles’ batteries. In electric vehicles, a battery is used as a main or complementary bidirectional power source. To optimize the energy management of the vehicle, the power sourced or sinked by the battery must be estimated in real time under any condition. The power of the battery is a function of many variables including the current, the state of charge, the ambient temperature, and the state of health. This paper evaluates some existing equivalent circuit models for estimating the instantaneous power of electric vehicles' batteries and proposes new artificial neural network-based models to enhance the power estimation accuracy. The experimental data obtained by performing standardized electric vehicle tests using a 3.6-V/16.5-Ah lithium-ion battery cell and a 12.8-V/150-Ah lithium-ion battery pack are presented and used for models' evaluation.

Adaptive Estimation of Instantaneous Angular Speed for Wind Turbine Planetary Gearbox Fault Detection

Planetary gearbox faults are the leading causes of downtime in wind turbines (WTs). In recent years, numerous and various vibration-based approaches have been put forward for WT gearbox fault detection. In the vibration-based techniques, order tracking-based methods, which by identifying fault orders in gearbox drivetrains, are regarded as very promising and powerful techniques. In the currently available order tracking methods, auxiliary devices are required to accurately obtain the instantaneous angular speed (IAS) of drivetrain. To tackle this problem, instantaneous angular speed estimation from vibration signals has been studied and some tacho-less order tracking (TLOT) approaches have been developed. However, many vital parameters for IAS estimation in the currently available TLOT algorithms need to be manually selected, which raise the question of user-friendliness, even result in a false diagnosis. As mentioned earlier, aiming at the shortcomings, a novel TLOT method based on adaptive IAS estimation is proposed for WT planetary gearbox fault diagnosis. In the proposed method, the nonlinear mode decomposition (NMD) method is improved, and its computational burden is reduced. And, the tachometer information of the drivetrain is adaptively extracted by the improved NMD method from generator vibration signal for gearbox vibration signal resampling. A field test is conducted, and the vibration signal of WT planetary gearbox with the compound fault is used for further investigation. The experimental validation results demonstrate that the planetary gearbox compound fault can be successfully detected, and the proposed method outperforms the traditional method based on generalized demodulation.

Novel WkNN-based technique to improve instantaneous rainfall estimation over the north of Algeria using the multispectral MSG SEVIRI imagery

For the estimation of rainfall in northern Algeria, a new method is proposed in this study. It based on the k nearest weighted neighbours (WkNN) classification algorithm using the dataset from the radiometer-imager SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) boarded on the satellite MSG (Meteosat Second Generation) to determine the pixel rainfall rate among the 16 predefined intensity levels observed in the Sétif meteorological radar.The implementation of the classification consists in using the spectral characteristics of a new sample (pixel) as input variables of the WkNN classifier to predict his class of membership based on the weighted distances separating him from the samples of the set of learning. The results thus obtained are validated with respect to the rainfall intensity classes observed and co-located by ground radar. The results showed a significant improvement for instantaneous estimate of precipitation during the day, with a correlation coefficient r=0.87 and statistical parameters RMSE=2.29mm/h,Bias=0.45mm/h,MAE=0.35mm/h, and we have obtained the results similar overnight.

Discrete Tangent Stiffness Estimation Method for Pseudo Dynamic Test

In pseudo dynamic (PSD) test, researchers have long recognized the importance and potential benefits of utilizing the tangent stiffness of experimental specimen to correct the restoring force and analyze the energy error. However, improving accuracy and efficiency of the instantaneous stiffness estimation still presents a challenge. Based on the theory of discrete curve parameter recognition and the geometrical analysis approach, this paper proposes a discrete tangent stiffness estimation (DTSE) method to estimate the instantaneous tangent stiffness of a single degree of freedom (SDOF) experimental specimen. For different magnitudes of measurement noise, the proposed method can adaptively select and retain a series of latest valid data and ignore outdated information, of which the advantage is highly improving the accuracy and promptness of instantaneous stiffness estimation. The numerical study shows that the DTSE method has better accuracy and promptness of tangent stiffness estimation when compared with other existing methods. In a PSD test involving a sliding isolator, the DTSE method is utilized to analyze the cumulative energy error, the result of which shows the cumulative energy error is negative and decreases gradually. The analysis of experimental results demonstrates that the undershooting error of actuator added extra energy into the PSD testing system. Thus, the proposed method provides a desirable solution to instantaneous stiffness estimation.

Estimating net biomass production and loss from repeated measurements of trees in forests and woodlands: Formulae, biases and recommendations

There is widespread interest in ensuring that assessment and knowledge of changes in forest biomass, and associated carbon gains or losses, are accurate and unbiased. Repeated measurements of individually-marked trees in permanent plots permit the estimation of rates of biomass production by tree growth and recruitment and of loss from mortality. But there are challenges, for example, simple estimates of production rate (i.e., the sum of biomass gain by growth of surviving trees and new recruits divided by census duration) decline as the census interval increases due to unrecorded growth. Even if we allow for these unobserved changes, additional biases may arise due to the non-independence of growth and mortality and to the heterogeneity and compositional changes within the forest. Here we examine these issues and demonstrate how problems can be minimized. We provide and compare alternative approaches to estimate net biomass production and loss from tree growth and mortality. Under the assumption that specific rates of biomass production and loss, i.e., turnover, are constant over time, we derive estimates of absolute biomass turnover rates that are independent of census duration. We show census-interval dependence of simple turnover rates grows with increasing specific turnover rates. While the time-dependent bias in simple estimates has previously been suggested to increase in proportion to the square of production, we show this relationship is approximately linear. Correlations between stem growth and mortality do not influence our estimates. We account for biomass gain by recruited stems without discounting their initial biomass in production estimates. We can reduce additional biases by accounting for differences in turnover among subpopulations (such as species, sites) and changes in their abundances. We provide worked examples from four forests covering a range of conditions (in Indonesia and Japan) and show the effects of accounting for these biases. For example, over five years in an Indonesian rain forest, simple estimates and instantaneous estimates neglecting species heterogeneity underestimated production by 4.9% and 1.6%, respectively when compared to comprehensive (instantaneous species-structured) estimates.

Performance Analysis of SWIPT Relay Networks With Noncoherent Modulation

In this paper, we investigate the performance of noncoherent modulation in simultaneous wireless information and power transfer (SWIPT) relay networks. Noncoherent modulation schemes eliminate the need for instantaneous channel state information estimation, and therefore, minimize the overall energy consumption of the network. In particular, we adopt a moments-based approach to develop a comprehensive novel analytical framework for the analysis of the outage probability, achievable throughput, and average symbol error rate (ASER) of a dual-hop SWIPT relay system considering the time switching (TS) and power splitting (PS) receiver architectures. In addition, through the derivation of new asymptotic analytical results for the outage probability and ASER, we analytically demonstrate that the diversity order of the considered system is non-integer less than 1 in the high signal-to-noise (SNR) regime. Our results show that there is a unique value for the PS ratio that minimizes the outage probability of the system, while this is not the case for the TS protocol. We also demonstrate that, in terms of system throughput, the TS relaying scheme is superior to the PS relaying scheme at lower SNR values. An extensive Monte Carlo simulation study is presented to corroborate the proposed analytical model.

Target Speaker Localization Based on the Complex Watson Mixture Model and Time-Frequency Selection Neural Network

Common sound source localization algorithms focus on localizing all the active sources in the environment. While the source identities are generally unknown, retrieving the location of a speaker of interest requires extra effort. This paper addresses the problem of localizing a speaker of interest from a novel perspective by first performing time-frequency selection before localization. The speaker of interest, namely the target speaker, is assumed to be sparsely active in the signal spectra. The target speaker-dominant time-frequency regions are separated by a speaker-aware Long Short-Term Memory (LSTM) neural network, and they are sufficient to determine the Direction of Arrival (DoA) of the target speaker. Speaker-awareness is achieved by utilizing a short target utterance to adapt the hidden layer outputs of the neural network. The instantaneous DoA estimator is based on the probabilistic complex Watson Mixture Model (cWMM), and a weighted maximum likelihood estimation of the model parameters is accordingly derived. Simulative experiments show that the proposed algorithm works well in various noisy conditions and remains robust when the signal-to-noise ratio is low and when a competing speaker exists.

Motion-Aware Correlation Filters for Online Visual Tracking

The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.

Prediction of Surface Wear of Involute Gears Based on a Modified Fractal Method

A new wear prediction method of tooth surfaces of involute gears based on a real tooth surface model and a modified fractal method is developed. The real tooth surface model of an involute gear pair is introduced, and microgeometry feature detection of tooth surfaces is achieved by monitoring variations of normal vectors of each discrete data point of the real tooth surface model. To predict wear progression of tooth surfaces of a gear pair, an abrasive wear analysis model and the modified fractal method are used to analyze contact performance and its changes with accumulation of surface wear. The abrasive wear analysis model can analyze wear depths of gear tooth surfaces with sliding distances, local contact pressure, and directions of wear progression based on Archard's model. The modified fractal method is proposed to calculate instantaneous contact stiffness and estimate elastic and plastic deformation regions based on an asperity contact model. Microgeometry features of tooth surface asperities can be described as the basis of an asperity contact model and allow tooth contact analysis of real tooth surface models with their local microgeometry feature changes due to plastic deformations. Feasibility and effectiveness of this wear prediction method were verified by comparing predicted results of gear surface wear progression with gear wear test results.

Quantifying the contribution of the Megha‐Tropiques mission to the estimation of daily accumulated rainfall in the Tropics

Satellite estimation of accumulated precipitation is an important facet of the study of the tropical water cycle. An advanced data merging approach using infrared geostationary imagery and microwave constellation based instantaneous rain rate estimates has been implemented in the framework of the Megha‐Tropiques and Global Precipitation Measurements missions. The Tropical Amount of Rainfall with Estimation of ERors (TAPEER) algorithm has been tailored to account for the loss of the MADRAS conical scanning radiometer by using the SAPHIR sounder rainfall detection capability, thanks to a novel two‐constellation implementation of the algorithm. A new bias correction module based on the TRMM PR observations is also presented. The performances of this new version of the product are reviewed with emphasis on West Africa. In particular, using data‐denial experiments, the contribution of SAPHIR data to the rainfall daily accumulation is quantified for various configurations of the microwave constellation and various algorithmic parameter selections. The results show that the daily accumulation statistics are well improved when SAPHIR is taken into the constellation. The improvements can be quantified using bulk statistics but are more evident following a frequency analysis. The pattern of the impact is a complex convolution of rainfall occurrence and of the Megha‐Tropiques mission original sampling. Over the 20°N–20°S belt, in zonal mean, the inclusion of SAPHIR data alters the daily accumulation substantially (more than 50% of the daily accumulation) more than 10% of the time and more than 20% when conditioned upon rainfall. Under both metrics, the improvement is majored in the 12°–17° latitude band where the Megha‐Tropiques mission sampling is at its maximum.

An Extended Kalman Filter Approach for Accurate Instantaneous Dynamic Phasor Estimation

This paper proposes the application of a non-linear Extended Kalman Filter (EKF) for accurate instantaneous dynamic phasor estimation. An EKF-based algorithm is proposed to better adapt to the dynamic measurement requirements and to provide real-time tracking of the fundamental harmonic components and power system frequencies. This method is evaluated using dynamic compliance tests defined in the IEEE C37.118.1-2011 synchrophasor measurement standard, providing promising results in phasor and frequency estimation, compliant with the accuracy required in the case of off-nominal frequency, amplitude and phase angle modulations, frequency ramps, and step changes in magnitude and phase angle. An important additional feature of the method is its capability for real-time detection of transient disturbances in voltage or current waveforms using the residual of the filter, which enables flagging of the estimation for suitable processing.

Restoring force correction based on online discrete tangent stiffness estimation method for real-time hybrid simulation

In real-time hybrid simulation (RTHS), it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation (online DTSE) to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that: (1) the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and (2) the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.

Using National Performance Management Research Data Set for Corridor Performance Measures: A US-281N Corridor Case Study

The National Performance Management Research Data Set (NPMRDS), made available by Federal Highway Administration in 2013, provides fine-resolution travel-time data, which have been used in numerous network performance management and operations applications. This article discusses corridor-level performance measures computed using the NPMRDS. Three measures are analyzed on a 20.2-mile long corridor in San Antonio, Texas, including corridor travel time, corridor travel-time reliability, and day-to-day variation in travel time. The primary contributions of this article are the analysis of the impact of using two different approaches for travel-time aggregation across segments—instantaneous and time-dependent approaches—and defining a mean absolute error-based method to identify days when travel times significantly deviate from typical traffic conditions. The findings suggest that the temporal patterns of corridor travel times obtained using instantaneous and time-dependent aggregation approaches are similar; however, instantaneous travel-time estimates lead to inaccuracies that become more apparent during peak hours and for longer segments. In addition, it is found that a [Formula: see text]-means clustering analysis performed on daily travel-time profiles provides a useful statistic for corridor performance analysis. Using this methodology, 9.23% of weekdays in the 20-month study period are classified as atypical for the corridor. The numerical results reinforce the value of the NPMRDS in estimating corridor performance measures and highlight potential limitations of traditional techniques for evaluating corridor performance measures when applied in practice to support enhanced traffic planning and operations.

A Review of Demand Models for Water Systems in Buildings including a Bayesian Approach

Instantaneous flow rate estimation is essential for sizing pipes and other components of water systems in buildings. Although various demand models have been developed in line with design and technology trends, most water supply system designs are routinely and substantially over-sized to keep failure risks to a minimum. Three major types of demand models from the literature are reviewed in this paper: (1) deterministic approach; (2) probabilistic approach; and (3) demand time-series approach. As findings show some widely used model estimates are much larger than the field measurements, this paper proposes a Bayesian approach to bridge the gap between model-based and field-measured values for the probable maximum simultaneous water demand. The proposed approach is flexible to adopt estimates as its prior values from a wide range of existing water demand models for determining the Bayesian coefficients for reference models, codes, and design standards with relevant measurement data. The approach provides a useful method not only for evaluating the corresponding demand values from various design references, but also for responding to the call for sustainable building design.

Direct Instantaneous Frequency Rate Estimation to Improve the Carrier Estimation Performance in Mars Entry, Descent, and Landing Flight

This paper focuses on the carrier estimation performance improvement in Mars entry, descent, and landing (EDL) flights. Carrier reconstruction could be used for trajectory derivation and Martian atmosphere profile inversion, and is the critical information for mission operations, as it helps determine the flight status of the spacecraft, demodulate the downlink information. The current approach is maximum likelihood estimation based on a two-dimensional (2D) maximum energy search algorithm, which computes the grid energy over all the combinations of frequency cells and frequency rate cells among the search space. Although it has good performance on robust estimation, the frequency estimation accuracy is limited due to the short coherent integration. An instantaneous frequency rate tracking approach based on the cubic phase function (CPF) is proposed that directly estimates the instantaneous frequency rate over the frequency rate cells, followed by the frequency estimation among the frequency cells. A sequential estimation method is introduced to propose the sequential CPF statistics, which uses the a priori Doppler phase information to suppress the noise squaring loss inherent in the standard CPF statistics. Simulations have been made on the released Mars Science Laboratory EDL trajectory for the two approaches, which show that considerable estimation improvement has been achieved for aerobraking flight by the new algorithm.

Use of cardiorespiratory coherence to separate spectral bands of the heart rate variability

Abstract Regulation of heart rate variability (HRV) is dependent on the autonomic nervous system (ANS). The regulation is achieved by the two ANS activity branches distribution, namely the parasympathetic and the sympathetic activities. Their fluctuations can be observed in the HRV signal spectral domain through respective dissociated frequency domains. Power spectrum parameters in low frequency (LF) and high frequency (HF) domains are classically estimated using predetermined fixed frequency ranges which are [0.04–0.15 Hz] band for LF domain and [0.15–0.4 Hz] band for HF domain. However, the 0.15 Hz frequency threshold is controversial in dynamic situations or positions changing. Several studies advanced that the observed cardiac and respiratory oscillation interactions could reflect the functionality of the ANS. These interactions can be estimated by the cardiorespiratory spectral coherence tool which quantifies the existence and strength of linearity between the two modes. We propose in this paper to jointly use the heart rate variability (HRV) and respiration modes by exploiting the cardiorespiratory spectral coherence tool for the time-varying frequency threshold definition between LF and HF bands. The instantaneous center frequency computed on the time-frequency coherence estimator is the proposed variable threshold (VT). The VT method was implemented on both simulated data and real data acquired from the experimental orthostatic test protocol with 10 healthy subjects. The classical 0.15 Hz fixed threshold (FT) and the individual time-dependent spectral boundaries method (AT) using the lower limit of the respiratory HF band as a time-varying threshold are used for comparison. The proposed VT strategy makes it possible to obtain: (i) a strengthened data-dependent definition of boundaries for the HF domain; (ii) instantaneous estimations of the LF and HF band energies; (iii) a better discrimination between the two positions SUPINE and UPRIGHT of the classical orthostatic test.

Unsaturated-Inductance-Based Instantaneous Torque Online Estimation of Switched Reluctance Machine with Locally Linearized Energy Conversion Loop

This paper proposes a novel dynamic torque estimation method, which can quickly and accurately obtain the instantaneous torque with an online calculation based on the local linearization of the energy conversion loop on the flux linkage-current plane of the switched reluctance machine. According to the real-time information of the points on the loop, six situations are illustrated and analyzed. For each situation, the output mechanical energy is obtained first by calculating the area of the corresponding segmented regions, and then, the instantaneous torque is obtained by the difference algorithm concerning the output mechanical energy and rotor position. For the entire process, the unsaturated phase inductance of the machine, which can be conveniently measured, is the only information required in advance. To verify the effectiveness of the proposed method, detailed simulations and experiments are performed under different operating conditions.

Rapid Evaluation of Development Ideas has Engineers Thinking: What If?

What started as a project to automate the tedious task of creating and updating well production forecasts at Range Resources led to a system that challenges engineers there to question the status quo. The program has evolved from predicting future well output to showing engineers the many ways their ideas could affect asset performance, including a feature often missing from engineering software, instant estimates of the likely cost and return of each option considered. “As Range was developing the Marcellus, they knew their teams of engineers and geologists were being creative and effective, but as the volume of data and number of databases grew, so did the painstaking job of merging and manipulating data. One of the things that struck me about a year ago was that engineers were coming to me with ideas, with more thoughts and creative solutions than a year or two before,” said Ken Brown, Vice President of Reservoir Engineering for Range’s Appalachian Division. The independent operator is primarily focused on the Marcellus, where production is mostly natural gas and NGLs. Automation has given engineers more time to think. “Because of the sheer volume of data, we were spending an enormous amount of time on a routine task, thus reducing the time available for critical thinking,” he said. The new tools allow the team to quickly determine if ideas, that might otherwise be viewed as long shots, are actually worth pursuing. Each idea is evaluated based on a variety of measures, including widely used financial performance metrics. The Range system relies on what computers do well, keep track of more variables and how they interact than a human can in real time. And it can offer alternatives. As a result, Brown said, “it might provide you with options you have not thought of before.” For example, the analysis may point out that rather than drilling and completing a five-well pad, it would be better to drill four wells and use the proppant that would have been used in the fifth well to more intensely fracture the other four wells. The program can quickly predict how production would be affected by various versions of that approach, as well as how that would affect the costs and returns on the investment. If increasing proppant by a significant amount improves recoveries and returns, what if we add even more? Setting up that simulation would take less than 5 minutes, said Matt Ockree, Senior Reservoir Engineer for Range who is the liaison to Novi, the company developing the software. Convincing analysis generated quickly greatly enhances the consideration of ideas that require changes in the operation, and people’s habits.