Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications.

Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error(ARE),relative standard deviation(RSD) and root mean squared error of prediction(RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%,RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.

A rapid on-site calibration method for measuring dynamic envelope curve of high-speed trains

Dynamic envelope curve is a significant parameter to assess the running safety of high-speed trains.Up to now the method based on binocular stereo vision is the only way available to measure the dynamic envelope curve of a train,the speed of which is over200km/h.Nevertheless the method has two limitations,one is large field-〇f-view(FO V),the other is calibration time.Hence portable calibration equipment,easy-t〇-build target and rapid calibration algorithm are required to complete the calibration.In this paper,a new rapid on-site calibration method with large FOV based on binocular stereo vision is proposed.To address these issues,a light target has been designed,the rail coordinate system(RCS)is represented by40fixed retroreflective points on the target,and they are utilized to calibrate the parameters of two cameras.In addition,two cameras merely capture a single image of the target simultaneously,and the intrinsic and extrinsic parameters of the cameras can be calculated rapidly.To testify the proposed method,the experiments have been conducted and the results reveal that the accuracy can reach+1mm,which can meet the measurement requirement.

Excitation curve calibration for the SSRF booster magnets and applications in the commissioning

The Shanghai Synchrotron Radiation Facility (SSRF) is a low emittance third-generation synchrotron radiation light source under commissioning. The excitation curve calibration for the booster magnets is important to provide the magnet current configurations as reference. Calibration studies give the polynomial coefficients of each type of magnets and provide the magnet current configurations under different beam energies as beam is ramped at speed of 2 Hz. The applications of calibration in booster commissioning which show the accuracy of the magnetic excitation curve calibration are also discussed.

Calibration of the Infiltration Rate Curve from the LN Trend Line at Eight Sites of Interest, Based on Infiltration Tests Carried out

The purpose of this research is to demonstrate that a calibration curve can be obtained that can be used for any infiltration test, with the double ring method, as well as an equation that helps speed up data processing. The experimentation was carried out in eight points in Nicaragua, of which five were distributed in Managua and three in Rivas-Nandaime. These results can be used for purposes of other studies of interest. As a result, a calibration curve is obtained, and an expression equal to is deduced, which will be the equation to determine the average infiltration of a field test occupying the double ring, for a total of 7 hours. And it is from the result that the texture of the soil can be determined by means of the indicator table. The basic methodology allowed analyzing the data since they are obtained, processed and analyzed, resulting in the calibration curve for infiltration tests. Finally, an equation was determined from the averages of the processed data, resulting in a correlation of 0.9976, above 0.5, which means it is very high and reliable.

Fitting Nonlinear Calibration Curves: No Models Perfect

The study of the calibration of a series of compounds of environmental concern (six perfluoroalkyl compounds (perfluorooctane sulfonic acid and five perfluoroalkyl carboxylic acids), three preservatives (methyl-, ethyl- and propylparabens) and the brominated flame retardant hexabromocyclododecane) by LC-MS/MS has been carries out, with a view to their simultaneous determination in samples of environmental interest. In some cases nonlinear calibration curves are obtained, but restricting the concentration range a linear model may be used to fit the data. Residual analysis has been performed in order to verify which models fit the data better, opting for a compromise decision given the apparent complexity of residuals plots. As Box states there are no perfect models (but models that work better than others).

Inverse design of mechanical metamaterial achieving a prescribedconstitutive curve

Besides exhibiting excellent capabilities such as energy absorption,phase-transforming metamaterials offer a vast design space for achieving nonlinear constitutive relations.This is facilitated by switching between different patterns under deformation.However,the related inverse design problem is quite challenging,due to the lack of appropriate mathematical formulation and the convergence issue in the post-buckling analysis of intermediate designs.In this work,periodic unit cells are explicitly described by the moving morphable voids method and effectively analyzed by eliminating the degrees of freedom in void regions.Furthermore,by exploring the Pareto frontiers between error and cost,an inverse design formulation is proposed for unit cells.This formulation aims to achieve a prescribed constitutive curve and is validated through numerical examples and experimental results.The design approach presented here can be extended to the inverse design of other types of mechanical metamaterials with prescribed nonlinear effective properties.

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

A dynamic soil freezing characteristic curve model for frozen soil

The soil freezing characteristic curve(SFCC)plays a fundamental role in comprehending thermohydraulic behavior and numerical simulation of frozen soil.This study proposes a dynamic model to uniformly express SFCCs amidst varying total water contents throughout the freezing-thawing process.Firstly,a general model is proposed,wherein the unfrozen water content at arbitrary temperature is determined as the lesser of the current total water content and the reference value derived from saturated SFCC.The dynamic performance of this model is verified through test data.Subsequently,in accordance with electric double layer(EDL)theory,the theoretical residual and minimum temperatures in SFCC are calculated to be-14.5℃to-20℃for clay particles and-260℃,respectively.To ensure that the SFCC curve ends at minimum temperature,a correction function is introduced into the general model.Furthermore,a simplified dynamic model is proposed and investigated,necessitating only three parameters inherited from the general model.Additionally,both general and simplified models are evaluated based on a test database and proven to fit the test data exactly across the entire temperature range.Typical recommended parameter values for various types of soils are summarized.Overall,this study provides not only a theoretical basis for most empirical equations but also proposes a new and more general equation to describe the SFCC.

An optimal on-site calibration method in measurement system of dynamic envelope curve of high-speed vehicle

High-speed vehicle dynamic envelope curve is defined as the maximum limit outline affected by a variety of adverse factors while the train is running. Considering the difficulties in the current measurement system such as complicated calibration process,cumbersome aided-instruments,strict limitation of working distance, this paper carries out an optical method in which two high-speed cameras with variable-zoom lenses are adopted as binocular stereo sensors of measurement system and a high-ac-curacy 3D target with fast reconstruction is designed. The intrinsic parameters of the sensors and the relative positions between coordinate systems are solved by the method of colinearity constrained optimization algorithm. The calibration process is easy to operate and the device is also of portability. Most importantly, the severe working distance limitation between sensors and measured body is solved, enhancing the adaptability of measurement system to environment. Experimental results show that when the sensors are in the range of 8 -16 m away from the measured body, system accuracy can reach up to ±0. 5 mm, which meets the requirements to measure the dynamic envelope curve of high-speed vehicle.

Curve25519点乘算法的高效FPGA实现

为了提高X25519密钥交换算法的运算效率,文章基于现场可编程门阵列(field programmable gate array,FPGA)提出一种高效的曲线Curve25519的点乘设计方案。首先在底层的有限域计算上,针对模约减计算次数多的问题,提出一种基于冗余数的模运算单元,减少了约减次数;同时,所提出的结构可以减少点乘中常系数乘法的运算周期,从而优化了点乘运算通路,提高了并行度,最终减少了运算时间。该文在Xilinx XC7Z020 FPGA上实现了该点乘设计方案,完成一次点乘需要125μs。研究结果表明,与现有的设计相比,所提出的方案具有较低的面积时间积,达到了面积和性能的平衡。

A real-time calibration method based on time-to-digital converter for accelerator timing system

The high-intensity heavy-ion accelerator facility(HIAF)is a scientific research facility complex composed of multiple cas-cade accelerators of different types,which pose a scheduling problem for devices distributed over a certain range of 2 km,involving over a hundred devices.The white rabbit,a technology-enhancing Gigabit Ethernet,has shown the capability of scheduling distributed timing devices but still faces the challenge of obtaining real-time synchronization calibration param-eters with high precision.This study presents a calibration system based on a time-to-digital converter implemented on an ARM-based System-on-Chip(SoC).The system consists of four multi-sample delay lines,a bubble-proof encoder,an edge controller for managing data from different channels,and a highly effective calibration module that benefits from the SoC architecture.The performance was evaluated with an average RMS precision of 5.51 ps by measuring the time intervals from 0 to 24,000 ps with 120,000 data for every test.The design presented in this study refines the calibration precision of the HIAF timing system.This eliminates the errors caused by manual calibration without efficiency loss and provides data support for fault diagnosis.It can also be easily tailored or ported to other devices for specific applications and provides more space for developing timing systems for particle accelerators,such as white rabbits on HIAF.

Wavelength calibration and spectral analysis of vacuum ultraviolet spectroscopy in EAST

A vacuum ultraviolet(VUV)spectroscopy with a focal length of 1 m has been engineered specifically for observing edge impurity emissions in Experimental Advanced Superconducting Tokamak(EAST).In this study,wavelength calibration for the VUV spectroscopy is achieved utilizing a zinc lamp.The grating angle and charge-coupled device(CCD)position are carefully calibrated for different wavelength positions.The wavelength calibration of the VUV spectroscopy is crucial for improving the accuracy of impurity spectral data,and is required to identify more impurity spectral lines for impurity transport research.Impurity spectra of EAST plasmas have also been obtained in the wavelength range of 50–300 nm with relatively high spectral resolution.It is found that the impurity emissions in the edge region are still dominated by low-Z impurities,such as carbon,oxygen,and nitrogen,albeit with the application of fulltungsten divertors on the EAST tokamak.

Method for determining the position of landslide slip-surface with a typical inclinometric curves

In the actual monitoring of deep hole displacement,the identification of slip surfaces is primarily based on abrupt changes observed in the inclinometric curve.In conventional identification methods,inclinometric curves exhibiting indications of sliding can be categorized into three types:B-type,D-type,and r-type.The position of the slip surface is typically determined by identifying the depth corresponding to the point of maximum displacement mutation.However,this method is sensitive to the interval of measurement points and the observation scale of the coordinate axes and suffers from unclear sliding surfaces and uncertain values.Based on the variation characteristics of these diagonal curves,we classified the landslide into three components:the sliding body,the sliding interval,and the immobile body.Moreover,three different generalization models were established to analyze the relationships between the curve form and the slip surface location based on different physical indicators such as displacement rate,relative displacement,and acceleration.The results show that the displacement rate curves of an r-type slope exhibit a clustering feature in the sliding interval,and by solving for the depth of discrete points within the step phase,it is possible to determine the location of the slip surface.On the other hand,D-type slopes have inflection points in the relative displacement curve located at the slip surface.The acceleration curves of B-type slopes exhibit clustering characteristics during the sliding interval,while the scattered acceleration data demonstrate wandering characteristics.Consequently,the slip surface location can be revealed by solving the depth corresponding to the maximum acceleration with cubic spline interpolation.The approach proposed in this paper was applied to the monitoring data of a landslide in Yunnan Province,China.The results indicate that our approach can accurately identify the slip surface location and enable computability of its position,thereby enhancing applicability and reliability of the deep-hole displacement monitoring data.

A new calibration method for radon detector in seismic systems

Radon observation is an important measurement item of seismic precursor network observation.The radon detector calibration is a key technical link for ensuring radon observation accuracy.At present,the radon detector calibration in seismic systems in China is faced with a series of bottleneck problems,such as aging and scrap,acquisition difficulties,high supervision costs,and transportation limitations of radon sources.As a result,a large number of radon detectors cannot be accurately calibrated regularly,seriously affecting the accuracy and reliability of radon observation data in China.To solve this problem,a new calibration method for radon detectors was established.The advantage of this method is that the dangerous radioactive substance,i.e.,the radon source,can be avoided,but only“standard instruments”and water samples with certain dissolved radon concentrations can be used to realize radon detector calibration.This method avoids the risk of radioactive leakage and solves the current widespread difficulties and bottleneck of radon detector calibration in seismic systems in China.The comparison experiment with the traditional calibration method shows that the error of the calibration coefficient obtained by the new method is less than 5%compared with that by the traditional method,which meets the requirements of seismic observation systems,confirming the reliability of the new method.This new method can completely replace the traditional calibration method of using a radon source in seismic systems.

Research into Applicability of Wöhler Curve Method for Low-Cycle Fatigue of Metallic Materials

Recently,a description on a practicability of the Wöhler Curve Method for low-cycle fatigue of metals was given by the author.By the description and the low cycle fatigue test data of 16 MnR steel,it is important to show that,for low cycle fatigue of metals,such a way that a stress-based intensity parameter calculated by the linear-elastic analysis is taken to be a stress intensity parameter,S,to establish a relationship between the stress intensity parameter,S,and the fatigue life,N,is practicable.In this paper,many metallic materials from the literature are given to show that the Wöhler Curve Method is well suitable for low-cycle fatigue analysis of metals.

Compression Properties and Energy Absorption of A Novel Double Curved Beam Negative Stiffness Honeycomb Structures

This paper presents the design of a novel honeycomb structure with a double curved beam.The purpose of this design is to achieve vibration isolation for the main engine of an offshore platform and reduce impact loads.An analytical formula for the force-displacement relationship of the honeycomb single-cell structure is presented based on the modal superposition method.This formula provides a theoretical basis for predicting the compression performance of honeycomb structures.The effects of structural geometric parameters,series and parallel connection methods on the mechanical and energy absorption properties are investigated through mathematical modeling and experimental methods.Furthermore,the study focuses on the vibration isolation and impact resistance performance of honeycomb panels.The results show that the designed honeycomb structure has good mechanical and energy absorption performance,and its energy absorption effect is related to the geometric parameters and series and parallel connection methods of the structure.The isolation efficiency of the honeycomb with 4 rows and 3 columns reaches 38%.The initial isolation frequency of the isolator is 11.7 Hz.

Deep structure of the Southeast Asian curved subduction system and its dynamic process

The deep structure,material circulation,and dynamic processes in the Southeast Asia have long been an elusive scientific puzzle due to the lack of systematic scientific observations and recognized theoretical models.Based on the deep seismic tomography using long-period natural earthquake data,in this study,the deep structure and material circulation of the curved subduction system in Southeast Asia was studied,and the dynamic processes since 100 million years ago was reconstructed.It is pointed out that challenges still exist in the precise reconstruction of deep mantle structures of the study area,the influence of multi-stage subduction on deep material exchange and shallow magma activity,as well as the spatiotemporal evolution and coupling mechanism of multi-plate convergence.Future work should focus on high-resolution land-sea joint 3-D seismic tomography imaging of the curved subduction system in the Southeast Asia,combined with geochemical analysis and geodynamic modelling works.