Estimation Of Width Research Articles

Bridging multifluid and drift-diffusion models for bounded plasmas

Fluid models represent a valid alternative to kinetic approaches in simulating low-temperature discharges: a well-designed strategy must be able to combine the ability to predict a smooth transition from the quasineutral bulk to the sheath, where a space charge is built at a reasonable computational cost. These approaches belong to two families: multifluid models, where momenta of each species are modeled separately, and drift-diffusion models, where the dynamics of particles is dependent only on the gradient of particle concentration and on the electric force. It is shown that an equivalence between the two models exists and that it corresponds to a threshold Knudsen number, in the order of the square root of the electron-to-ion mass ratio; for an argon isothermal discharge, this value is given by a neutral background pressure Pn≳1000 Pa. This equivalence allows us to derive two analytical formulas for a priori estimation of the sheath width: the first one does not need any additional hypothesis but relies only on the natural transition from the quasineutral bulk to the sheath; the second approach improves the prediction by imposing a threshold value for the charge separation. The new analytical expressions provide better estimations of the floating sheath dimension in collisions-dominated regimes when tested against two models from the literature.

Semiclassical Estimates of Pressure-induced Line Widths for Infrared Absorption in Hot (Exo)planetary Atmospheres

Semiclassical Estimates of Pressure-induced Line Widths for Infrared Absorption in Hot (Exo)planetary Atmospheres

Study on Slipform Paving of Concrete Containing Alkali-Free Accelerators on Roadway Floor.

Aiming at the problems of collapse, deformation, and displacement in the concrete paving of roadway floors, this paper adopts the way of adding alkali-free accelerators to the concrete on both sides, through mechanical analysis, single factor experiment, orthogonal experiment, and polynomial fitting method, and determines the relevant parameters of concrete and accelerators in the sliding form paving of roadway floor from two aspects of paving material and size. The results show that the FSA-AF alkali-free liquid accelerator is more suitable for roadway floor paving than the J85 powder accelerator. When the FSA-AF accelerator dosage reaches 8%, the decreasing trend of initial setting time curve tends to be flat. The deformation resistance of concrete containing accelerator is positively correlated with the dosage of the accelerator. Concrete side pressure is positively correlated with pavement paving height. The FSA-AF accelerator can reduce the compressive strength of concrete; the compressive strength and retention rate of concrete at all ages are the highest when the dosage of FSA-AF is 7%. A water-cement ratio of 0.4 and a 9% dosage of accelerator are the optimal combination to meet the four evaluation indexes. According to the width estimation formula, the width of the side concrete should be set to 14 cm.

Properties of 3D H i filaments in the smith high velocity cloud

ABSTRACT We present findings of 3D filamentary structures in the Smith Cloud, a high-velocity cloud (HVC) located at $l=38^{\circ }$, $b=-13^{\circ }$. These data represent the first detection of velocity-resolved 3D H i filaments within an HVC. We use data from the Galactic Arecibo L-Band Feed Array H i (GALFA-H i) along with our new filament detection algorithm, fil3d, to characterize these structures. In this paper, we also discuss how different input parameters affect the output of fil3d. We study filaments in the local interstellar medium (ISM) and compare them to those found in the Smith Cloud. Based on thermal line width estimations we find supporting evidence that the Smith Cloud filaments are part of its warm neutral medium. We also find a relationship between thermal line width and the $v_{\mathrm{LSR}}$ of the filaments. We study the plane-of-sky magnetic field as traced by Planck 353 GHz polarized dust emission along the line of sight and find none of our filament populations are aligned with this tracer of the magnetic field. This is likely related to their location close to dynamic processes in the Galactic Plane and/or the low column density of the filaments relative to emission in the Plane. The results show that 3D H i filaments are found in a wide range of Galactic environments and form through multiple processes.

Automatic detection of rice disease in images of various leaf sizes

AbstractTo help farmers manage limited resources, rice disease diagnosis must be accurate, timely, and affordable. This study addresses challenges in rice field images, such as environmental variability and differences in rice leaf sizes. The proposed technique combines convolutional neural network object detection with image tiling, using estimated rice leaf width as a reference for image division. An 18‐layer ResNet model was trained using ground truth leaf width values for regression in leaf width estimation. Experiments used a dataset of 4960 images representing 8 rice diseases. The leaf width prediction model achieved a mean absolute percentage error of 11.18% and was used to generate a tiled dataset for training advanced rice disease detection models. The tiling technique was evaluated using YOLOv4, YOLOv8n, YOLOv8l, DINO‐5scale Swin‐L, and Co‐DINO‐5scale Swin‐L models by comparing detection performance on original and tiled datasets. Mean average precision improved significantly: YOLOv4 increased from 87.56% to91.14%, YOLOv8n from 89.80% to 91.70%, and YOLOv8l from 89.80% to 93.20%. More advanced models, such as DINO5scale Swin‐L and Co‐DINO‐5scale Swin‐L, achieved even higher precision, at 93.40% and 94.20%, respectively. In conclusion, the tiling technique improved detection efficiency and addressed object size variability, enhancing rice disease detection accuracy inreal‐world scenarios.

Statistical estimation of mean Lorentzian line width in spectra by Gaussian processes

Statistical estimation of mean Lorentzian line width in spectra by Gaussian processes

Deep Learning-Enabled De-Noising of Fiber Bragg Grating-Based Glucose Sensor: Improving Sensing Accuracy of Experimental Data

This paper outlines the successful utilization of deep learning (DL) techniques to elevate data quality for assessing Au-TFBG (tilted fiber Bragg grating) sensor performance. Our approach involves a well-structured DL-assisted framework integrating a hierarchical composite attention mechanism. In order to mitigate high variability in experimental data, we initially employ seasonal decomposition using moving averages (SDMA) statistical models to filter out redundant data points. Subsequently, sequential DL models extrapolate the normalized transmittance (Tn) vs. wavelength spectra, which showcases promising results through our SpecExLSTM model. Furthermore, we introduce the AttentiveSpecExLSTM model, integrating a composite attention mechanism to improve Tn sequence prediction accuracy. Evaluation metrics demonstrate its superior performance, including a root mean square error of 1.73 ± 0.05, a mean absolute error of 1.20 ± 0.04, and a symmetric mean absolute percentage error of 2.22 ± 0.05, among others. Additionally, our novel minima difference (Min. Dif.) metric achieves a value of 1.08 ± 0.46, quantifying wavelength for the global minima within the Tn sequence. The composite attention mechanism in the AttentiveSpecExLSTM adeptly captures both high-level and low-level dependencies, refining the model’s comprehension and guiding informed decisions. Hierarchical dot and additive attention within this model enable nuanced attention refinement across model layers; dot attention focuses on high-level dependencies, while additive attention fine-tunes its focus on low-level dependencies within the sequence. This innovative strategy enables accurate estimation of the spectral width (full-width half maxima) of the Tn curve, surpassing raw data’s capabilities. These findings significantly contribute to data quality enhancement and sensor performance analysis. Insights from this study hold promise for future sensor applications, enhancing sensitivity and accuracy by improving experimental data quality and sensor performance assessment.

Getting over wide obstacles by the multi-legged robot

An upper estimate of the maximum width of the forbidden zone for foot fulcrums, which a walking robot with many legs can overcome in static stability mode, is presented. Using the mathematical models of six-legged and four-legged robots, it is shown that the obtained estimate can't be improved. For this purpose, the sequences of the robot's foot placement have been formed, ensuring the achievement of the estimation meaning. The dependence of the maximum width of the zone on the length of the body was found for the six-legged robot model.

Evaluation of Norm-Constrained Capon Method on Improving Doppler Peak Localization of Antenna-Arrayed High-Frequency Coastal Radar

Abstract Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method. Significance Statement We have recently implemented a high-frequency coastal radar equipped with an antenna array to monitor metocean variables within the offshore waters adjacent to Taichung Harbor in Taiwan. Our study focuses on enhancing the Doppler spectral resolution of radar sea echoes by employing an adaptive beamforming technique known as the norm-constrained Capon method. In comparison with the traditional linear beamforming approach, the norm-constrained Capon method demonstrates superior noise resilience. We anticipate that the norm-constrained Capon method holds promise as a dependable approach for refining the accuracy of metocean parameters obtained from antenna-arrayed high-frequency coastal radar systems.

Cross-sectional analysis of timber boards using convolutional long short-term memory neural networks

This paper proposes a one-dimensional convolutional long short-term memory (1D-CNN-LSTM) model for estimating the pith position and average ring width in Norway spruce timber boards. The model predicts these cross-sectional parameters by processing sequences of light-intensity signals derived from optical scans of the board’s four surfaces. The dataset used for training the model consists of synthetic boards sawn from simulated 3D logs. The model was evaluated on a dataset consisting of 552 end cross-sections from actual Norway spruce boards. Comparisons between the automatic and manual pith and ring width estimations demonstrated a very good accuracy. The computational speed of the model was more than twice as fast as the quickest method available in the literature. A large set of boards was then used to determine the advantages of incorporating the automatically determined average ring width in formulating indicating properties for machine strength grading. This evaluation revealed that the average ring width could, in certain situations, compensate for unknown variables such as density or resonance frequency in predicting the tensile and bending strength of Norway spruce boards.

Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

Binaural audio recordings become increasingly popular in multimedia repositories, posing new challenges in indexing, searching, and retrieval of such excerpts in terms of their spatial audio scene characteristics. This paper presents a new method for the automatic estimation of one of the most important spatial attributes of binaural recordings of music, namely “ensemble width.” The method has been developed using a repository of 23,040 binaural excerpts synthesized by convolving 192 multi-track music recordings with 30 sets of head-related transfer functions (HRTF). The synthesized excerpts represented various spatial distributions of music sound sources along a frontal semicircle in the horizontal plane. A binaural auditory model was exploited to derive the standard binaural cues from the synthesized excerpts, yielding a dataset representing interaural level and time differences, complemented by interaural cross-correlation coefficients. Subsequently, a regression method, based on gradient-boosted decision trees, was applied to the formerly calculated dataset to estimate ensemble width values. According to the obtained results, the mean absolute error of the ensemble width estimation averaged across experimental conditions amounts to 6.63° (SD 0.12°). The accuracy of the method is the highest for the recordings with ensembles narrower than 30°, yielding the mean absolute error ranging between 0.8° and 10.2°. The performance of the proposed algorithm is relatively uniform regardless of the horizontal position of an ensemble. However, its accuracy deteriorates for wider ensembles, with the error reaching 25.2° for the music ensembles spanning 90°. The developed method exhibits satisfactory generalization properties when evaluated both under music-independent and HRTF-independent conditions. The proposed method outperforms the technique based on “spatiograms” recently introduced in the literature.

The Method for Obtaining Shot Cloud Time Span and Range Dependence

When studying the behavior of a shot cloud in space, a variety of information and measurement systems can be applied, for example, based on light screens (photoelectronic blocking devices of a small ballistic measuring installation consisting of a linear emitter and an optical sensor based on a photodiode). To study the process of shot cloud movement in space and to assess its parameters (for example, length at a given range from the muzzle) correctly, it is advisable to have a simulation model of the optical sensor signal when the light screen is crossed by a shot cloud. Taking into account previous studies, it is necessary to know the magnitude of the scale parameter in the Rayleigh distribution describing the shot cloud in the direction of firing in time in order to obtain a similar model at some distance from the muzzle. A method that allows obtaining function of dependence of the mentioned scale parameter and the distance from the muzzle and based on real data from several optical sensors of light screens installed on the shooting track is proposed. The example of application the proposed method to obtain similar functions based on the results of processing optical sensor signals obtained in real experiments for cartridges loaded with three different shot types is given. The obtained dependences were approximated by polynomials of the first and second degree using the least squares method, and the error was estimated for various types of cartridges. Based on the obtained results, specific recommendations for the practical application of the developed method are proposed. The estimation of the confidence interval width for the expectation function of the shot cloud time span is performed. The ways of creating advanced signal simulation models from optical sensors of light screens when shooting with shot are outlined.

Conversion of a piston–cylinder dimensional dataset to the effective area of a mechanical pressure generator

Recent developments in diameter metrology at NIST have improved the dimensional characterization of piston-cylinder assemblies (PCAs) to unprecedented precision. For the newest generation of PCAs, the standard uncertainty in the measurement of the outer diameter is 12 nm, while uncertainty in the measurement of the inner diameter is 14 nm. With a high-accuracy dimensional dataset in hand, the task of determining the pressure generated by a specific PCA is reduced to converting the diameter (and straightness and roundness) to an effective area (and distortion coefficient). The details on how this was performed for the artifact PCA2062 are described. PCA2062 was dimensioned in 2017 and 2020; the area repeated within . The calculation produced estimates of fall rate and rotation decay that agreed with experimental observations within 12 %. The fall rate is proportional to the square of the gap width; therefore, the agreement between calculation and measurement validates the dimensional estimate of the gap width within (36 ± 42) nm, where the 42 nm standard uncertainty is governed by the present state of flow theory. The piston gage model is buttressed by three comparison tests against a laser barometer, which support a view that PCA2062 is linear and reproducible within 0.2 μPa Pa-1. Finally, an estimate of uncertainty in the effective area of a dimensioned artifact is provided: as expected, the diameter measurement is the main culprit, but there are open questions regarding the flow model that preclude an accurate evaluation of the distortion coefficient. For the 530 kPa operating range of PCA2062, distortion is not a significant problem, but the effect would be dominant in assemblies operating at 1 MPa and above.

Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions

Multiple benefits and advantages that offer laser cutting technology are difficult to achieve if the laser cutting parameter conditions are not adequately set. Determining laser cutting conditions is not trivial task considering large number of controllable inputs, existence of multiple process performances which are often mutually opposed as well as effects of noise factors. Different scientific methods and engineering approaches represent more sophisticated approaches which aid assessment and determination of the most favourable (optimized) cutting conditions. This study is focused assessment and ranking of laser cutting conditions in CO2 laser cutting of AISI 316L stainless steel. Based on kerf width and surface roughness experimental data and estimation of material removal rate (MRR) and total variable costs, four criteria in the multi-criteria decision making (MCDM) model were considered. For solving the developed model, six well-known MCDM methods were applied, and in addition, in order to overcome ranking inconsistency, robust decision-making rule was developed upon which final ranking was obtained.

Automated quantification of crack length and width in asphalt pavements

AbstractRapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1‐fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices.

How Many Glucan Chains Form Plant Cellulose Microfibrils? A Mini Review.

Assessing the number of glucan chains in cellulose microfibrils (CMFs) is crucial for understanding their structure-property relationships and interactions within plant cell walls. This Review examines the conclusions and limitations of the major experimental techniques that have provided insights into this question. Small-angle X-ray and neutron scattering data predominantly support an 18-chain model, although analysis is complicated by factors such as fibril coalescence and matrix polysaccharide associations. Solid-state nuclear magnetic resonance (NMR) spectroscopy allows the estimation of the CMF width from the ratio of interior to surface glucose residues. However, there is uncertainty in the assignment of NMR spectral peaks to surface or interior chains. Freeze-fracture transmission electron microscopy images show cellulose synthase complexes to be "rosettes" of six lobes each consistent with a trimer of cellulose synthase enzymes, consistent with the synthesis of 18 parallel glucan chains in the CMF. Nevertheless, the number of chains in CMFs remains to be conclusively demonstrated.

MSDQ: Multi-Scheduling Dual-Queues coflow scheduling without prior knowledge

Coflow scheduling is crucial for enhancing application-level communication performance in data-parallel clusters. While schemes like Varys can potentially achieve optimal performance, their dependence on a prior information about coflows poses practical challenges. Existing non-clairvoyant solutions, such as Aalo, approximate the classical online Shortest-Job-First (SJF) scheduling but fail to identify bottleneck flows in coflows. Consequently, they often allocate excessive bandwidth to non-bottleneck flows, leading to bandwidth wastage and reduced overall performance. In this paper, we introduce MSDQ, a coflow scheduling mechanism that operates without prior knowledge, utilizing multi-scheduling dual-priority queues, and using width estimates. This method adjusts coflow queue priorities and scheduling sequences based on the coflow’s width and the volume of data transmitted. By reallocating unused network bandwidth at multiple points during the scheduling process, MSDQ maximizes the bandwidth usage and significantly reduces the average coflow completion time. Our evaluation, using a publicly available production cluster trace from Facebook, demonstrates that MSDQ reduces the average coflow completion time by 1.42× compared to Aalo.

Probabilistic Deep Learning Approach for Fatigue Crack Width Estimation and Prognosis in Lap Joint Using Acoustic Waves

Abstract Accurate fatigue crack width estimation is crucial for aircraft safety, however, limited research exists on (i) the direct relationship between fatigue crack width and Lamb wave signatures and (ii) probabilistic artificial intelligence approach for automated analysis using acoustic emission waveforms. This paper presents a probabilistic deep learning approach for fatigue crack width estimation, employing an automated wavelet feature extractor and probabilistic Bayesian neural network. A dataset constituting the fatigue experiment on aluminum lap joint specimens is considered, in which Lamb wave signals were recorded at several time instants for each specimen. Signals acquired from the piezo actuator–receiver sensor pairs are related to the optically measured surface crack length. The sensitive features are automatically extracted from the signals using decomposition techniques called maximal overlap discrete wavelet transform (MODWT). The extracted features are then mapped through the deep learning model, which incorporates Bayesian inference to account for both aleatoric as well as epistemic uncertainty, that provides outcomes in the form of providing probabilistic estimates of crack width with uncertainty quantification. Thus, employing an automated wavelet feature extractor (MODWT) on a dataset of fatigue experiments, the framework learns the relationship between Lamb wave signals and crack width. Validation on unseen in situ data demonstrates the efficacy of the approach for practical implementation, paving the way for more reliable fatigue life prognosis.

Impact of the use of small-area models on estimation of attributable mortality at a regional level.

The objective of this study is to assess the impact of applying prevalences derived from a small-area model at a regional level on smoking-attributable mortality (SAM). A prevalence-dependent method was used to estimate SAM. Prevalences of tobacco use were derived from a small-area model. SAM and population attributable fraction (PAF) estimates were compared against those calculated by pooling data from three national health surveys conducted in Spain (2011-2014-2017). We calculated the relative changes between the two estimates and assessed the width of the 95% CI of the PAF. Applying surveys-based prevalences, tobacco use was estimated to cause 53 825 (95% CI: 53 182-54 342) deaths in Spain in 2017, a figure 3.8% lower obtained with the small-area model prevalences. The lowest relative change was observed in the Castile-La Mancha region (1.1%) and the highest in Navarre (14.1%). The median relative change between regions was higher for women (26.1%), population aged ≥65 years (6.6%), and cardiometabolic diseases (9.0%). The differences between PAF by cause of death were never greater than 2%. Overall, the differences between estimates of SAM, PAF, and confidence interval width are small when using prevalences from both sources. Having these data available by region will allow decision-makers to implement smoking control measures based on more accurate data.

On the long neck principle and width estimates for initial data sets

On the long neck principle and width estimates for initial data sets