Erroneous Measurements Research Articles

Implications of face mask use on body composition assessment by air-displacement plethysmography.

The BOD POD (COSMED USA Inc., Concord, CA) is a common instrument used to assess body composition by employing air displacement plethysmography and whole-body densitometry to determine body volume. This instrument requires isothermal conditions during testing; therefore, the introduction of outside isothermal air can impact testing results. With the COVID-19 pandemic introducing face mask mandates, it is unknown whether the use of a face mask during BOD POD testing may lead to erroneous measurement by introducing isothermal air. Thus, the purpose of this cross-sectional study was to investigate the impact of wearing a surgical face mask compared to not wearing a surgical face mask on body composition assessment among adults. During testing, female subjects were required to wear a swimsuit or form-fitting lycra shorts and a sports bra and male subjects were required to wear form-fitting lycra shorts. American Society for Testing and Materials (ASTM) level one surgical face masks (bacterial and particulate filtration efficiency of 95%) and standard swim caps were provided by researchers. Variables of interest included percent body fat, body fat, percent lean body mass, and lean body mass. Participants (n=33) completed one test wearing a mask and one test without a mask back-to-back with conditions held constant. Dependent-sample sign tests, Bland-Altman Plots, and Passing-Bablok regression analyses were used to test mask-on versus mask-off differences and agreement between variables of interest. There were no significant median differences in any body composition results between face mask use and non-face mask use using dependent-sample sign tests. Bland-Altman Plots demonstrated acceptable agreement between mask usage and non-mask usage. No significant differences were seen in the slopes of the variables using Passing-Bablok regression. Results suggest that wearing a face mask does not appreciably impact body composition results. Therefore, ASTM level 1 disposable surgical face mask does not introduce a significant amount of isothermal air during BOD POD testing.

A weighted range‐free localization algorithm for irregular multihop networks

SummaryRange‐free localization is a widely concerned technique that supports many essential network applications like coverage and clustering. Most existing localization technologies assume that nodes are uniformly distributed in a regular network with no obstacles and that propagation paths between pairwise nodes are close to straight lines. However, in real‐world scenarios, network topology is usually irregular, resulting in estimated distances that differ significantly from the corresponding physical distances and poor anchor deployment. These will lead to erroneous distance measurement and inaccurate localization results. This paper proposes a new algorithm, called the weighted range‐free localization algorithm (WRL for short), to improve the localization performance in irregular networks. Specifically, we first present a feasible scheme that can fulfill the information exchange and relative distance estimation for each node simultaneously. We then employ an optimal weight function to suppress the effect of cumulative errors and introduce the weighted bounding‐box algorithm to mitigate outlier location estimation induced by probably collinear anchor nodes. Simulation results show that, compared to state‐of‐the‐art methods, WRL improves localization accuracy while incurring lower computation and communication overheads in various irregular network topologies.

Using Skeleton Correction to Improve Flash Lidar-based Gait Recognition

ABSTRACT This paper presents GlidarPoly, an efficacious pipeline of 3D gait recognition for flash lidar data based on pose estimation and robust correction of erroneous and missing joint measurements. A flash lidar can provide new opportunities for gait recognition through a fast acquisition of depth and intensity data over an extended range of distance. However, the flash lidar data are plagued by artifacts, outliers, noise, and sometimes missing measurements, which negatively affects the performance of existing analytics solutions. We present a filtering mechanism that corrects noisy and missing skeleton joint measurements to improve gait recognition. Furthermore, robust statistics are integrated with conventional feature moments to encode the dynamics of the motion. As a comparison, length-based and vector-based features extracted from the noisy skeletons are investigated for outlier removal. Experimental results illustrate the superiority of the proposed methodology in improving gait recognition given noisy, low-resolution flash lidar data.

Employing a statistical framework to constrain orientation measurement inaccuracies from digital outcrop models: Insights from sandstone intrusions

In this study, we examine the errors and uncertainties associated with orientation measurements collected from digital outcrop models using the geometrical property, collinearity. Collinearity is expressed as the characteristics of a set of points lying on a single straight line, and is beneficial because a trace far from collinear is required for obtaining accurate orientation measurements of planar geological bodies from digital outcrop models. We, thus, demonstrate this relationship, as well as assess the impact any associated errors have on orientation distribution forms using orientation measurement traces collected from sandstone intrusions with a digital outcrop model of the Panoche Giant Injection Complex acquired through light detection and ranging and photogrammetry techniques.Our experiments highlight how in addition to sampling bias and sample size, unreliable orientation estimates can negatively affect interpretation. We show that the distribution of orientation for a network of geological structures (e.g., fractures, sandstone intrusions) in a particular region can be altered with the inclusion of erroneous measurements. From our case study, it was noted that the high proportion of unreliable orientation measurements obtained when using a digital outcrop model of the study area resulted in inconsistencies in the structural analysis that could have been attributed to other factors. Thus, without putting into consideration the reliability of the samples, a sampling issue will still be faced, regardless of using the appropriate sample size and technique.

A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

Target tracking process consists of tracking and measuring a quantity over time, which is a difficult estimation problem. The latter gives erroneous measurements that are affected by the unknown number of targets to be tracked, as well as, measurements that are corrupted by noise. Thus, overcoming the target tracking problem includes basically solving the filtering problem which leads to a data association problem. Tracking problems that force lightly nonlinear models are solved appropriately with the Extended Kalman Filter. Although, problems that use nonlinear models, applying analytical expressions of the Jacobian of dynamic systems, or measurement vector functions are potentially solved by the Unscented Kalman Filter. The first technique is well built, but not precise, while the second technique yields very accurate results, but in terms of complexity, it is not suitable for real-time applications. Another filter called Particle Filter used a Monte Carlo method based on probability distribution over the state is proposed. Subsequently, we used a Gaussian Mixture Model which is based on the Expectation Maximization algorithm for fitting mixture-of-Gaussian models is considered to guarantee the estimation of optimal parameters set for each target. In order to achieve better trade between optimality and robustness of the tracking effect, the Smooth Variable Structure Filter is proposed as an alternative. This latter filter does not make any assumptions about noise characteristics and provides accurate estimation results. Furthermore, in this article, we propose a new target tracking algorithm based on the second form of Smooth Variable Structure Filter, which is implemented in a simulation using different scenarios. Experimental scenarios, under realistic conditions, are presented and the obtained results confirm the effectiveness of the Smooth Variable Structure Filter approach compared to other filters.

Use of Low-Activity Nuclear Density Gauge in Iron Oxide-Rich Soils

Nuclear density gauges (NDG) have been widely used for field compaction control. However, their shortcomings include the burden of licensing with nuclear regulatory agencies, gauge maintenance, storage and transportation restrictions, requirement to wear dosimeters, and radiation safety training. A recently developed low-activity NDG (LANDG) alternative utilizes a low-strength gamma ray source to measure soil density that is Nuclear Regulatory Commission exempt in the U.S.A. and its territories. An accompanying non-nuclear moisture probe yields the moisture content by measuring the dielectric constant of the soil. This study aims to compare the LANDG results with those of the NDG in two common soil types on the island of Oahu, Hawaii: volcanic soils and coralline soils. Coralline soils are partially broken down remains of coral comprised primarily of calcium carbonate. Volcanic soils are basalt derivatives rich in iron oxide and the question arises as to whether the iron oxide will affect the dielectric value resulting in erroneous moisture content measurements. This research shows that the LANDG and NDG measurements of total unit weight are in good agreement for both soils. The moisture content measurements from the LANDG’s accompanying moisture probe are in good agreement with those of the NDG in coralline soils but are less satisfactory in volcanic soils. A plausible explanation is offered. Moisture offsets may be applied to the moisture probe’s measurements to reduce scatter and increase accuracy. Instances of when this is required are suggested.

Sample Preparation of Cosmetic Products for the Determination of Heavy Metals

The sample preparation of a cosmetic specimen in Cosmetic Science for the purpose of determining the analytical composition of heavy and toxic metals such as lead (Pb), mercury (Hg), cadmium (Cd), and arsenic (As) is of particular importance due to the difficulty of handling the sample. There are two main methods of sample preparation. The first method is the wet digestion of the sample with strong acids such as H2SO4, HNO3, HF, and HNO3/HCl (1:3) and the combination of a strong acid with H2O2. Liquid digestion of the sample under the influence of strong acids damages the organic material of the sample and converts the carbon into carbon dioxide. The contained metals are oxidized to the highest oxidizing step and converted to soluble salts. A problem with this method is the loss of metals during digestion because it occurs at high temperatures as well as the decrease in the concentration of the residual acid. The second method of preparation is the wet liquid digestion of the sample with strong acids in a microwave oven in a closed vessel. The acids that are used are mainly HNO3 or mixtures of acids such as HNO3–HCl and HNO3–H2SO4. When the sample in the acid’s solvent is exposed to microwave energy, it can reach temperatures substantially above the boiling temperature of the acid solution. The result is the decomposition of the organic material, the oxidation of the metals, and their conversion to soluble nitrates. The advantages of using microwaves are the ability to control the temperature, pressure, and loss of metals and, thus, avoid erroneous measurement results. Simultaneously with the above, extraction methods have been, for almost a decade, very effective complementary processes that we can use to enrich a sample of a cosmetic product. Liquid–liquid dispersion micro-extraction (DLLME) and solid phase extraction (SPE) are the two main methods used in sample preparation and are usually applied after the digestion process.

The Brettenham storm of 25 July 2021

The <scp>Brettenham</scp> storm of 25 <scp>July</scp> 2021

A novel approach for detecting error measurements in a network of automatic weather stations

ABSTRACT In the present work, a novel methodology for error detection in automatic weather stations has been implemented. Time series acquired from two highly correlated stations with a station under analysis are utilised to obtain a 24-h air temperature forecast that allows to know if a station register erroneous measurements. Four models to obtain a reliable forecast have been analysed, auto-regressive integrated moving average, Long Short-Term Memory (LSTM), LSTM stacked and a convolutional LSTM model with uncertainty error reduction. The analysis carried out exhibits a significant success with the methodology for three stations reaching error values between 0.98C and 1.50C and correlation coefficients between 0.72 and 0.81.

Resolving Pseudohyponatremia: Validation of Plasma Sodium on Radiometer ABL800 Blood Gas Analyzers for Immediate Reflex Testing.

To perform validation of plasma sodium on blood gas analyzers to reflexively correct erroneous measurements by ion-selective electrodes (ISEs). We compared remnant specimens of whole blood and plasma collected by lithium heparin vacutainer with normal protein concentrations and no lipemia. Whole-blood specimens were tested for sodium concentration on the ABL800 Flex blood gas analyzer, followed by centrifugation for plasma separation, and repeat sodium determination on an aliquot of the plasma only. Also, plasma specimens were analyzed by indirect ISE on the Cobas 8000 series and by direct ISE on the ABL800 Flex for instrument comparison. Plasma aliquots yielded comparable results to the parent whole-blood specimen, with an average change of -1.33 mmol/L (R2 = 0.9727). Comparison of indirect ISE to direct ISE similarly yielded comparable results, with an average change of + 0.8 mmol/L (R2 = 0.9016). Plasma is a valid specimen matrix for use on blood gas analyzers for sodium determination, eliminating the need for re-collection of whole-blood specimens from patients with pseudohyponatremia.

Online estimation of thévenin equivalent using discrete fourier transform

This paper presents a method for the determination of the Thévenin equivalent (TE) parameters of a power system using local phasor measurement unit (PMU) measurements. The proposed method is based on an innovative use of Discrete Fourier Transform (DFT), where the DFT coefficients are calculated for the measured voltage and current phasors, which are used to determine the TE parameters. The method presented exploits the characteristics of DFT to develop a new simple and efficient method for TE determination. The calculated DFT is also utilized to detect the presence of measurement errors and to get the best estimate of TE parameters in such conditions and also enable identification of the erroneous measurement. The proposed method is illustrated using a simple system and tested with the data of standard test systems, which all verified the accuracy of the proposed method.

Sensor Fault-Resilient Control of Electronically Coupled Distributed Energy Resources in Islanded Microgrids

In this paper a sensor fault resilient control approach for electronically interfaced distributed energy resources (DERs) in islanded microgrids is proposed. The proposed control strategy consists of a sliding mode observer (SMO) and a H infinity output feedback controller that ensures resiliency of the DER units in an islanded microgrid against erroneous measurements due to sensor malfunctioning. Any error in the measurements due to malfunctioning of the associated sensors are modelled as sensor faults in this paper. The SMO in the proposed approach estimates the sensor faults, and the estimated faults are used to devise fault tolerant control strategy so that the H infinity output feedback control remains unperturbed by the erroneous measurements. In order to demonstrate the efficacy of the proposed control strategy, rigorous simulation studies are carried out on a test islanded microgrid system with single DER and multiple DERs. The efficacy of the proposed approach has been further validated through an experimental study carried out on a prototype islanded microgrid system

In-situ sensor correction method for data center cooling systems using Bayesian Inference coupling with autoencoder

• In-situ sensor correction method based on Bayesian inference and autoencoder. • Impacts of the system model accuracy on the correction performance. • A series of case studies were designed to fully demonstrate the performance. • Result shows that deviation rates of sensor measurements are decreased to 7.67%. The quality of measurements from working sensors has a considerable influence on the system operation and energy usage in data center cooling systems. However, due to the malfunction, aging, and installation positions, it is very common phenomenon that the measurements are far away from their true values. Virtual sensor correction is a promising solution to correct erroneous measurements. This study proposed a novel sensor correction method for data center cooling systems using the Bayesian Inference coupling with autoencoder (AE) to eliminate the sensor errors. The correction performance of the proposed method was comprehensively investigated under a series of single/multiple sensor error scenarios in a typical computer room air handler (CRAH) unit. Furthermore, the impact of the system model accuracy on the correction performance was also quantified. The results show the excellent correction accuracies of the proposed method in the data center cooling systems: following correction, the sensor deviation rates are decreased to 7.67% and 3.00% for the single and multiple simultaneous sensor error scenarios, respectively. Additionally, the sensor correction error increases to 18% when the deviation rate of the heat transfer coefficient is set to 10%.

A Nonparametric, Data-Driven Approach to Despiking Ocean Surface Wave Time Series

Abstract We propose a methodology for despiking ocean surface wave time series based on a Bayesian approach to data-driven learning known as Gaussian process (GP) regression. We show that GP regression can be used for both robust detection of erroneous measurements and interpolation over missing values, while also obtaining a measure of the uncertainty associated with these operations. In comparison with a recent dynamical phase space–based despiking method, our data-driven approach is here shown to lead to improved wave signal correlation and spectral tail consistency, although at a significant increase in computational cost. Our results suggest that GP regression is thus especially suited for offline quality control requiring robust noise detection and replacement, where the subsequent analysis of the despiked data is sensitive to the accidental removal of extreme or rare events such as abnormal or rogue waves. We assess our methodology on measurements from an array of four collocated 5-Hz laser altimeters during a much-studied storm event in the North Sea covering a wide range of sea states.

Resolving challenges of monitoring PV systems: a case study for three 1.5 kW generators in Lima, Peru

This paper addresses challenges from monitoring three grid-connected PV systems with different module technologies in Lima, Peru, following the International Electrotechnical Commission (IEC) standards. Missing and erroneous data due to sensor malfunction or partial shadowing can negatively impact the measured data quality. We developed a program to automatically detect erroneous measurements, enhance the data quality, and calculate PV performance parameters following the IEC-61724 standard to resolve these challenges. The program includes mathematical methods to replace faulty or missing measurements. Finally, we present the impact of the algorithms for erroneous data detection, deletion, and correction on performance calculations.

The State-of-the-Art Estimation of Groundwater Recharge and Water Balance with a Special Emphasis on India: A Critical Review

Groundwater recharge estimation is essential for sustainable water management and water supply schemes. In this paper, we review groundwater recharge estimation techniques and identify the appropriate methods by considering India’s hydrological and climatic conditions. Significant components of recharge, factors affecting groundwater recharge, aquifer systems of India, and historical groundwater recharge estimation practices are reviewed. Currently used recharge estimation methods are assessed based on case studies. The most popular estimation methods are studied and compared based on their application in various regions. It is observed that the accuracy of the recharge estimates is largely influenced by false assumptions, the possibility of erroneous measurements, a potential lack of reliable data, and a variety of problems associated with parameter estimation. The suitability of different methods for a region is found to depend on time and space considerations, the objective of the study, hydrogeological condition, and availability of data. In Indian conditions, it is suggested to use water table fluctuation and water balance methods for the recharge estimation, provided that accurate water level measurements are assured.

Erroneous measurement of hematocrit from arterial cannula on failed fistula limb site in renal transplant recipient: A cautious path to tread!

Erroneous measurement of hematocrit from arterial cannula on failed fistula limb site in renal transplant recipient: A cautious path to tread!

Rescue of groundwater level time series: How to visually identify and treat errors

Groundwater level time series are of great value for a variety of groundwater studies, particularly for those dealing with the impacts of anthropogenic and climate change. Quality control of groundwater level observations is an essential step prior to any further application, e.g., trend analysis. Often the quality control of data is limited to the removal of outliers or elimination of entire time series from a dataset, while such approaches drastically reduce the spatial coverage of initially huge datasets. Frequently studies tend to present already quality-controlled data, but neglect to demonstrate how the data were selected, judged, and modified. We present a data rescue approach developed for correcting the Latvian national groundwater level database, containing 1.68 million groundwater level observations since 1959, including 0.69 million manual measurements. A web-based R-Shiny interface was developed and used for visual identification and manual correction of erroneous measurements in groundwater level time series. All data manipulations were performed programmatically. Reproducibility and traceability were ensured by deploying separate data tables for raw observations, data repair actions and the final dataset. As a result of applied actions, 34.3% of all automatic measurements were either deleted or corrected, while only 6.5% of manual measurements were edited. Commonly found errors in groundwater level time series were grouped into: errors in measurement and data recording; technical problems at the observation site; local anthropogenic impact and other unclassified problems. The improvement from the rescue approach was assessed by comparing the Akaike information criterion derived from fitted ARMA and ARIMA models to both original and repaired time series. The results showed that models fitted using repaired time series were better than those fitted on the original time series for the same time series sections. The presented rescue approach and results can be of great value for all studies using groundwater level time series as an input.

A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation.

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.

Following Molecular Mobility during Chemical Reactions: No Evidence for Active Propulsion.

The reported changes in self-diffusion of small molecules during reactions have been attributed to "boosted mobility". We demonstrate the critical role of changing concentrations of paramagnetic ions on nuclear magnetic resonance (NMR) signal intensities, which led to erroneous measurements of diffusion coefficients. We present simple methods to overcome this problem. The use of shuffled gradient amplitudes allows accurate diffusion NMR measurements, even with time-dependent relaxation rates caused by changing concentrations of paramagnetic ions. The addition of a paramagnetic relaxation agent allows accurate determination of both diffusion coefficients and reaction kinetics during a single experiment. We analyze a copper-catalyzed azide-alkyne cycloaddition "click" reaction, for which boosted mobility has been claimed. With our methods, we accurately measure the diffusive behavior of the solvent, starting materials, and product and find no global increase in diffusion coefficients during the reaction. We overcome NMR signal overlap using an alternative reducing agent to improve the accuracy of the diffusion measurements. The alkyne reactant diffuses slower as the reaction proceeds due to binding to the copper catalyst during the catalytic cycle. The formation of this intermediate was confirmed by complementary NMR techniques and density functional theory calculations. Our work calls into question recent claims that molecules actively propel or swim during reactions and establishes that time-resolved diffusion NMR measurements can provide valuable insight into reaction mechanisms.