Required Level Of Accuracy Research Articles

Process parameters optimization and performance analysis of micro-complex geometry machining on Ti6Al4V

The aeronautical industry relies on high-performance equipment that demands materials with exceptional engineering attributes, such as the Ti6Al4V alloy. However, the complexity of the parts used in these applications presents challenges related to stack-up tolerances during assembly. Therefore, conventional machining processes are not economically viable due to their two-step nature solution (machining and surface treatment) and their inability to achieve the required accuracy level, particularly given the alloy’s hard-to-cut nature. In this matter, wire electric discharge machining (WEDM) emerges as an attractive alternative for fabricating complex geometries. This research focuses on evaluating the potential of the WEDM process for machining micro-complex profiles. A comprehensive range of process parameters, including servo voltage (Vs), pulse on time (Ton), pulse off time (Toff), and wire speed (Ws), are studied using a Taguchi-based design of experiments. The results are analyzed through parametric significance analysis, parametric control analysis, surface morphological analysis using scanning electron microscopy, and modified layer analysis. Additionally, both mono-objective and multi-objective process optimization techniques are employed to achieve superior accuracy and speed. The findings indicate that Ton and Ws have the most significant influence on both cutting speed and spark gap, whereas Vs and Toff play a crucial role in determining the accuracy index. In addition, adequate flushing, reduced wire speed (economically viable), and stability of the spark are recommended to attain a lower spark gap and higher accuracy. The confirmatory experiments show that the optimal parametric conditions of Vs = 60 V, Toff = 30 μs, Ton = 8 μs, and Ws = 6 mm/s provide the highest speed of 3.4 mm/min, minimum spark gap of 0.344 mm, and accuracy index of 98.72%. The findings will contribute to enhancing manufacturing efficiency, precision, and cost-effectiveness in the aeronautical industry, meeting the demand for high-quality components with tight tolerances.

Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

Abstract. Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing.

Adaptation of metrology-grade ac current source in velocity mode of Planck-Balance 2: direct referencing induced voltages with ac quantum voltage standard

The adaptation of developed metrology-grade ac current source (MCS) to the velocity mode of measurements of the Planck-Balance 2 as a means for generating ac mechanical oscillations is presented. The universality in operating with the MCS unit especially practical for the Planck-Balance setup for frequencies of 0.1 Hz–20 Hz (including but not limited to the negligence of a broader range of 0.01 Hz up to several hundred Hz) and for amplitudes of up to 10 mA with 16 (offset with 14)-bit effective resolution is demonstrated. MCS allows generating complex ac waveform signals as waveform synthesizers by adding to the original signal an extra five independent harmonic components, each of which with an adjustable resolution of 10 ns for phase and 16-bit for amplitude. Additionally, the MCS is supported by an external clock at 10 MHz frequency which serves also as a common reference time base for the comparison between the direct output signal of MCS, or of the induced voltage in the coil of the Planck-Balance resulting due to the applied current by MCS, with the ac quantum voltage standard at the required accuracy levels.

Specialist hybrid models with asymmetric training for malaria prevalence prediction.

Malaria is a common and serious disease that primarily affects developing countries and its spread is influenced by a variety of environmental and human behavioral factors; therefore, accurate prevalence prediction has been identified as a critical component of the Global Technical Strategy for Malaria from 2016 to 2030. While traditional differential equation models can perform basic forecasting, supervised machine learning algorithms provide more accurate predictions, as demonstrated by a recent study using an elastic net model (REMPS). Nevertheless, current short-term prediction systems do not achieve the required accuracy levels for routine clinical practice. To improve in this direction, stacked hybrid models have been proposed, in which the outputs of several machine learning models are aggregated by using a meta-learner predictive model. In this paper, we propose an alternative specialist hybrid approach that combines a linear predictive model that specializes in the linear component of the malaria prevalence signal and a recurrent neural network predictive model that specializes in the non-linear residuals of the linear prediction, trained with a novel asymmetric loss. Our findings show that the specialist hybrid approach outperforms the current state-of-the-art stacked models on an open-source dataset containing 22 years of malaria prevalence data from the city of Ibadan in southwest Nigeria. The specialist hybrid approach is a promising alternative to current prediction methods, as well as a tool to improve decision-making and resource allocation for malaria control in high-risk countries.

Optimization of a Microwave Polarimeter for Astronomy with Optical Correlation and Detection

Cosmic Microwave Background (CMB) B-modes detection is the main focus of future CMB experiments because of the valuable information it contains, particularly to probe the physics of the very early universe. For this reason, we have developed an optimized polarimeter demonstrator sensitive to the 10-20 GHz band in which the signal received by each antenna is modulated into a Near Infrared (NIR) laser by a Mach-Zehnder modulator. Then, these modulated signals are optically correlated and detected using photonic back-end modules consisting of voltage-controlled phase shifters, a 90-degree optical hybrid, a pair of lenses, and an NIR camera. During laboratory tests, a 1/f-like noise signal related to the low phase stability of the demonstrator has been found experimentally. To solve this issue, we have developed a calibration method that allows us to remove this noise in an actual experiment, until obtaining the required accuracy level in the measurement of polarization.

Postprocessing subtraction of tilt-to-length noise in LISA

The coupling of an angular jitter into the interferometric phase readout is summarized under the term tilt-to-length (TTL) coupling. This noise is expected to be a major noise source in the intersatellite interferometry for the Laser Interferometer Space Antenna (LISA) space mission. Despite efforts to reduce it by satellite construction, some remaining TTL noise will need to be removed in postprocessing on Earth. Therefore, such a procedure needs to be developed and validated to ensure the success of the LISA mission. This paper shows a method to calibrate and subtract TTL noise that has no impact on LISA science operations. This solution relies on noise minimization and uses the differential wavefront sensing (DWS) measurements to estimate the TTL contribution. Our technique is applied after the laser frequency noise is suppressed via the time-delay interferometry (TDI) postprocessing algorithm. We use a simulation to show as a proof-of-principle that we can estimate the TTL coefficients to the required accuracy level based on the current design configuration of LISA. We then use these estimates to subtract the TTL noise, ensuring that any remaining TTL noise is below the current estimate of the other noise sources. We validate the procedure on simulated data for different operating scenarios. Our work shows that it is indeed possible to estimate the effect of TTL coupling and subtract it a posteriori from the TDI data streams.

Band-Averaged Response Sensitivity Study of an Imaging Spectrometer for the CLARREO Pathfinder Mission

Prelaunch absolute, SI-traceable radiometric calibration of satellite-based sensors is key to ensuring the utility of imaging spectrometer-based data products. The development of detector-based calibration techniques leads to the feasibility of meeting the 0.3% uncertainty level needed to provide climate quality data sets. Detector-based calibration is a method in which a well-understood and stable transfer radiometer is calibrated in a standards laboratory to SI-traceable standards, and transported to a facility calibrating a sensor of interest. The transfer radiometer provides the calibration of the source used in the radiometric calibration. A detector-based calibration approach is part of the prelaunch calibration of the CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF) sensor with the Goddard Laser for Absolute Measurement of Radiance (GLAMR) system. The SI-traceability of GLAMR is through the electric watt as part of the absolute radiometric calibration of the detectors at the National Institute of Standards and Technology using the Primary Optical Watt Radiometer. The current work uses GLAMR data collected with a visible and near-infrared imaging spectrometer calibration demonstration system to develop a source/sensor modeled calibration data set as part of a sensitivity study to evaluate uncertainties from the spectral sampling and processing methods that accompany the GLAMR calibration process. The spectral “supersets” include realistic instrumental features as well as effects from the GLAMR source. The methods needed to ensure that spurious sensor and GLAMR data are excluded are described. Results are given from the sensitivity study related to GLAMR spectral sampling and signal-to-noise ratio (SNR) effects, sensor integration time, and frame averaging of the imaging spectrometer data. The study shows that the 6 nm bandwidth sensor simulation requires a 1 nm spectral sampling of the GLAMR source with a radiance level that provides an in-band peak SNR > 200 to ensure that climate quality accuracies can be achieved. The results are also used to refine the test plan for the independent calibration for the CLARREO Pathfinder sensor calibration to optimize test time while meeting the required accuracy levels.

A novel wind farm equivalent model for high voltage ride through analysis based on multi-view incremental transfer clustering

The voltage swell (VS) in power system may require high voltage ride through (HVRT) of wind farms (WFs), and the detailed WF simulation model would need huge computational time, and thus is not be suitable for the analysis of HVRT dynamic behaviour of large-scale WFs. In this paper, a novel WF equivalent model with the required accuracy level for HVRT analysis is proposed. Firstly, multiscale entropies (MSEs) of the operational parameters in wind turbines (WTs) are calculated to represent their distinguishability in different HVRT processes, and the time series of several parameters which have obvious distinguishability are selected as the multi-view clustering indicators (CIs). To handle the multi-view CIs, a new clustering algorithm namely multi-view incremental transfer fuzzy C means (MVIT-FCM) is proposed. This algorithm integrates the transfer learning technique to increase the stability and accuracy of the WTs clustering. Also, the high-dimensionality of the time series based CIs and the consequent computational burden in clustering are considered, and an incremental technique is applied in MVIT-FCM to handle the large-scale WF modelling. A real WF system is used for case study. The results indicate that the multi-view CIs are very effective for increasing the equivalent accuracies. In addition, with the aid of incremental and transfer learning techniques, MVIT-FCM can acquire stable clustering results and handle large-scale WF accurately and efficiently.

Web-Based Smartphone Algorithm for Calculating Blood Pressure From Photoplethysmography Remotely in a General Adult Population: Validation Study.

BackgroundOutside of a clinical setting, oscillometric devices make remote monitoring of blood pressure and virtual care more convenient and feasible. HeartBeat Technologies Ltd developed a novel approach to measuring blood pressure remotely after an initial blood pressure reading by a nurse using the conventional measurement method. Using a finger pulse oximeter, a photoplethysmogram wave is transmitted by Bluetooth to a smartphone or tablet. A smartphone app (MediBeat) transmits the photoplethysmogram to a server for analysis by a proprietary algorithm—the person’s current blood pressure is sent back to the smartphone and to the individual’s health care provider.ObjectiveThis study sought to determine whether the HeartBeat algorithm calculates blood pressure as accurately as required by the European Society of Hypertension International Protocol revision 2010 (ESH-IP2) for validation of blood pressure measuring devices.MethodsESH-IP2 requirements, modified to conform to a more recent international consensus statement, were followed. The ESH-IP2 establishes strict guidelines for the conduct and reporting of any validation of any device to measure blood pressure, including using the standard manual blood pressure instrument as a comparator and specific required accuracy levels for low, medium, and high ranges of blood pressure readings. The consensus statement requires a greater number of study participants for each of the blood pressure ranges. The validation of the accuracy of the algorithm was conducted with a Contec CMS50EW pulse oximeter and a Samsung Galaxy XCover 4 smartphone.ResultsThe differences between the HeartBeat-calculated and the manually measured blood pressures of 62 study participants did not meet the ESH-IP2 standards for accuracy for either systolic or diastolic blood pressure measurements. There was no discernible pattern in the inaccuracies of the HeartBeat-calculated measurements.ConclusionsThe October 4, 2019 version of the HeartBeat algorithm, implemented in combination with the MediBeat app, a pulse oximeter, and an Android smartphone, was not sufficiently accurate for use in a general adult population.Trial RegistrationClinicalTrials.gov NCT04082819; http://clinicaltrials.gov/ct2/show/NCT04082819

Thermal Vacuum Cold Target for the Metop SG MicroWave Imager

The assessment of the Metop-SG microwave imager radiometric performance is based on a complete set of thermal vacuum tests. Such an extensive experimental campaign requires the development of large calibration targets (black bodies) with controlled systematics and low residual uncertainties. The significant size of such targets, which are based on pyramidal emitting surfaces, does not allow for a complete minimization of the thermal gradients below the required accuracy level. This article presents the modeling approach that has been developed to deal with both such thermal gradients and the proximity of the baffle. Results on the frequency dependence of the cold target brightness temperature (ECCOSORB CR-117) are reported between 18.7 and 183 GHz. The complete uncertainty budget provides 1-sigma errors levels lower than 0.22 K. The systematic effect of thermal coupling with the earth target has been found as relevant as the coupling to the chamber. This interdependence requires a look-up-table approach to maintain the low uncertainty levels mentioned above.

A Comparative Study on Inverse Vibration Based Damage Assessment Techniques in Beam Structure Using Ant Colony Optimization and Particle Swarm Optimization

Use of swarm intelligence has proliferated over previous couple of years for damage assessment in large and complex structures using vibration data. Available literatures shows ‘ant colony optimization’ (ACO) and ‘particle swarm optimization’ (PSO) are predominantly used for solving complex engineering problems including damage identification and quantification problems. The time requirement and accuracy of the vibration based damage identification algorithms depends on early exploration and late exploitation capabilities of soft computing techniques. However, there are not any literature available comparing algorithms on these bases. In the current study, an inverse problem is constructed using the natural frequency changes which is then solved using ACO and PSO algorithms. The algorithm is run for identification of single and multiple damages in simple support and cantilever beam structures. It's found that, both ACO and PSO based algorithms are capable of detecting and quantifying the damage accurately within the limited number of iterations. However, ACO based algorithm by virtue of its good exploration capability is able to identify near optimal region faster than PSO based algorithm, whereas PSO algorithm has good exploitation capability and hence able to provide better damage quantification than ACO algorithm at latter stages of iteration. Further, PSO based algorithm takes less time to reach at required accuracy level. It is also observed that, the time required for these algorithms are independent of numbers of damage and support conditions.

Assessing the Accuracy and Feasibility of Using Close-Range Photogrammetry to Measure Channelized Erosion with a Consumer-Grade Camera

Water-induced channel is one of the main forms of soil erosion in cultivated fields. Channelized erosion is often measured by the volume of the channels. Traditionally, the measurements were conducted with rulers or measuring tapes. However, these traditional methods are generally time- and labor-consuming and can cause soil surface disturbance. Close-range photogrammetry with a Consumer-Grade Camera (CGC-CRP) provides an alternative way of measuring channel volume and can overcome limitations of traditional methods and provides much higher spatial resolution. However, quantitative information on the accuracy of this technique is rare. In this study, the accuracy of the CGC-CRP method under different settings were examined with an in-house experiment and validated with a field experiment. In the in-house experiment, a wood board surface with Artificial Channels (AC) of different shapes, orientations, and sizes were built. These ACs were surveyed using the CGC-CRP method with a series of settings of shooting angles and image overlapping rates. Selected cross-sectional areas were extracted to compare against manual measurements to assess the absolute and relative errors of the CGC-CRP method. The applicability of the CGC-CRP method with different settings was evaluated by comparing time consumption and the size of detection areas. The results indicated that in order to maintain an acceptable accuracy level, the image overlapping rate should be ≥70%, and the shooting angle should be in the range of 60° to 80°. For the channel shape, the accuracy for V-channel was ~15% higher than that for U-channel. For the U-channel, the impact of the channel orientation on the accuracy was not significant when the shooting angle was relatively high, whereas for the V-channel, the vertically oriented channel had higher accuracy than horizontal or angle channels. Last, channel size did not strongly affect accuracy when the channel was vertically orientated, and the shooting angle and image overlapping rate were set in the optimum ranges. However, when the shooting angle or image overlapping rate was low, or when the channel was angled or horizontally orientated, the accuracy was lower with larger channel size. In the field experiment, under the optimum camera setting, the error for the ten cross-sectional areas was about 1.6%. This result suggests that the CGC-CRP method is promising in volumetric assessment of rill and gully erosion. The quantitative information on the accuracy provided in this study can help researchers to select the setting of CGC-CRP methods to achieve their required accuracy level.

H2-He collisions:Ab initiotheory meets cavity-enhanced spectra

Fully quantum ab initio calculations of the collision-induced shapes of two rovibrational ${\mathrm{H}}_{2}$ lines perturbed by He provide an unprecedented subpercent agreement with ultra-accurate cavity-enhanced measurements. This level of consistency between theory and experiment hinges on a highly accurate potential energy surface and a realistic treatment of the velocity changing and dephasing collisions. In addition to the fundamental importance, these results show that ab initio calculations can provide reference data for spectroscopic studies of planet atmospheres at the required accuracy level and can be used to populate spectroscopic line-by-line databases.

Measurement of temperature and wavelength-dependent emissivity distributions using multi-wavelength radiation thermometry

A multi-wavelength radiation thermometry method used to determine the temperature and wavelength-dependent emissivity according to spectroscopic radiation intensities is presented. Over a predetermined temperature range, with a set step size, the method traverses the temperature range, and for each temperature, inversely fits a spectral line according to the intensity in the middle of the wavelength region, based on the Rayleigh approximation. The temperature with the spectral line closest to the measured spectral line is considered to be the optimum temperature at that accuracy level. Then, near that optimum temperature, the temperature range and step size are narrowed, to determine the optimum temperature at higher levels of accuracy. Iterating on this method until reaching the required accuracy level, the temperature can be achieved, and the wavelength-dependent emissivity can also be determined. Simulation verification was conducted showing good anti-noise performance and rapid processing capacity. Temperature and emissivity distributions for six heights of an axisymmetric laminar diffusion flame with 194 mL/min ethylene and 240 L/min air were given. Results show that along the line of sight, the radiation temperatures exhibit an average effect of the local temperatures, while the emissivity exhibits a cumulative effect of the absorption coefficients.

Asymptotic Analysis and Numerical Methods for Oscillatory Infinite Generalized Bessel Transforms with an Irregular Oscillator

In this work, we perform a complete asymptotic analysis and the construction of affordable quadrature rules for a class of oscillatory infinite Bessel transform with a general oscillator. Especially in the presence of critical points, e.g., endpoints, zeros and stationary points, we first derive a series of useful asymptotic expansions in inverse powers of the frequency parameter $$\omega $$. The resulting asymptotic expansions clarify the large $$\omega $$ behavior of the transform and provide powerful tools for designing quadrature rules and conducting error analysis. As a consequence, efficient and affordable new modified Filon-type methods for computing the transform numerically are proposed. Particularly, we carry out the rigorous error analysis and obtain asymptotic error estimates in inverse powers of $$\omega $$. Numerical examples can confirm our analysis. The accuracy can be improved greatly by either adding more derivatives interpolation at endpoints or adding more interior nodes. Moreover, only using a small number of nodes and multiplicities, we can obtain the required accuracy level. For fixed number of nodes and multiplicities, the higher accuracy can be achieved with the larger values of $$\omega $$.

Electromagnetic Modeling Techniques for Switched Reluctance Machines: State-of-the-Art Review

Switched Reluctance Machines (SRMs) are gaining more attention due to their simple, and rugged construction, low manufacturing cost, and high-speed operation capability. An electromagnetic model of the machine is needed in the design, and analysis processes. The required accuracy level of the model depends mainly on the application. A high-fidelity model is required to achieve a good design, and predict the performance accurately. However, it requires high computational cost, and longer simulation time. Other fast, and less-comprehensive models with less computational burden could be utilized in the design, and analysis of the motor drives. This paper extensively analyzes various electromagnetic modeling techniques of SRMs. Analytical, numerical, and hybrid models are considered. The paper investigates analytical models that are based on Maxwell's equations in addition to interpolation, and curve fitting techniques. Numerical techniques such as Finite Element Method (FEM), and Boundary Element Method (BEM) are presented. Moreover, Magnetic Equivalent Circuit (MEC) method is discussed. Finally, potential research areas are proposed for the electromagnetic modeling of SRMs.

A multi-view and multi-scale transfer learning based wind farm equivalent method

With the increasing capacity of wind farm (WF), detailed WF model is not appropriate for power system studies, and the equivalence of WF with the required accuracy level poses a complicated technical challenge. In this paper, a multi-view and multi-scale transfer learning based WF equivalent method is proposed. Three steps are taken in this method. (a) Extract feature from active and reactive power of wind turbine (WT) using refined composite multi-scale entropy (RCMSE), on this basis, construct clustering indicator considering view and scale two aspects. (b) Aiming at the feature of the clustering indicator, a multi-view and multi-scale fuzzy C-means (VS-FCM) clustering algorithm is proposed, and transfer learning is used in it for better WTs cluster performance. (c) transfer Q-learning is adopted to optimize the parameters of collector network for each equivalent WT, so as to improve parameter optimization efficiency. To verify the effectiveness of the proposed method, an actual system in East Inner Mongolia of China is utilized for case study. Simulation results shows that the dynamic characteristics and the robustness of the proposed model perform a good behavior in different wind scenarios and voltage sag levels, besides, the method has an advantage in the efficiency of simulation time and parameter optimization.

SARIMA Modelling for Forecasting the Electricity Consumption of a Health Care Building

Healthcare buildings have an immense demand for electricity, because of which they exhibit distinctive ability for forecasting of electricity consumption. In this work, ability for forecasting electricity consumption of a large healthcare building was researched. Both the techniques change non-stationary data into stationary data to make an effective and simple data representation and removing of noise subspaces. The comparison of experimental results is done among the SARIMA and ARIMA models. Analysis of the results concludes that performance of SARIMA is better when compared to ARIMA model. The analysis of data from 11 years in the hospital demonstrates that these dynamic models are sufficiently adaptable to forecast the electricity consumption at required accuracy levels.

Ranking and quantifying the effects of load model parameters on power system stability

Power system loads have been proven to have a significant influence on power system stability, and their accurate modelling is essential for reliable assessment of dynamic system behaviour. This study first applies an efficient sensitivity analysis method, to rank power system load model parameters based on their influence on system voltage, frequency, and angular stability considering a range of load models, operating conditions, and parameter correlations. Following this, the required accuracy levels of identified critical parameters for different types of stability studies are determined. Finally, this study demonstrates the importance of modelling correlation between load model parameters on establishing the parameter accuracy requirements for different types of stability studies. The practical significance of the approach is in enabling power system operators to identify and focus on accurate (with specified accuracy) modelling of only a subset of loads while being confident that the accuracy of system stability studies will not be compromised.

Shockproof Precision Strapdown Inertial Navigation System Development for Terrestrial Applications

The paper discusses the structure, algorithms and testing results for a hybrid precision strapdown inertial navigation system (SINS) which is resistant to fast extreme impacts. Impact resistance of the precision SINS is provided by incorporating an auxiliary strapdown inertial attitude control system (SIACS) based on rough sensors that can endure fast extreme shocks without losing the attitude accuracy; and by post-processing of inertial sensors data performed in the onboard computer simultaneously with the main navigation task. The post-processing procedure is designed to identify the moment of shock occurrence and to repeat the attitude problem solving in the onboard computer based on the auxiliary SIACS data within the time interval of exposure to the extreme shock. In the absence of shocks, the auxiliary SIACS is calibrated in accordance with the data of the main precision SINS. The main precision SINS is based on precision fiber-optic gyros (FOG) and the auxiliary SIACS—on micromechanical gyros (MMG). Thus, a hybrid-type precision SINS has been constructed, which has demonstrated required accuracy level during on-ground vehicle tests, including shock tests.