Numerical analysis of high‑speed railway slab tracks using calibrated and validated 3D time‑domain modelling

Concrete slabs are widely used in modern railways to increase the inherent resilient quality of the tracks,provide safe and smooth rides,and reduce the maintenance frequency.In this paper,the elastic performance of a novel slab trackform for high-speed railways is investigated using three-dimensional finite element modelling in Abaqus.It is then compared to the performance of a ballasted track.First,slab and ballasted track models are developed to replicate the full-scale testing of track sections.Once the models are calibrated with the experimental results,the novel slab model is developed and compared against the calibrated slab track results.The slab and ballasted track models are then extended to create linear dynamic models,considering the track geodynamics,and simulating train passages at various speeds,for which the Ledsgard documented case was used to validate the models.Trains travelling at low and high speeds are analysed to investigate the track deflections and the wave propagation in the soil,considering the issues associated with critical speeds.Various train loading methods are discussed,and the most practical approach is retained and described.Moreover,correlations are made between the geotechnical parameters of modern high-speed rail and conventional standards.It is found that considering the same ground condition,the slab track deflections are considerably smaller than those of the ballasted track at high speeds,while they show similar behaviour at low speeds.

Thrombin Generation Increasing with Age and Decreasing with Use of Heparin Indicated by Calibrated Automated Thrombogram Conducted in Chinese

Objective Calibrated Automated Thrombogram（CAT） is a test to monitor the generation of thrombin. It can be described by four parameters： lag time, peak thrombin, endogenous thrombin potential （ETP） and time to peak （ttPeak）. This study aims to determine the normal ranges of CAT parameters in Chinese, and evaluate whether thrombin generation is correlated with the concentration of heparin/Iow molecular weight heparin. Methods Plasma from 120 healthy subjects were collected to determine the normal rangea of CAT parameters in Chinese. Normal plasma pool （NPP, n=25） spiked with different concentrations of heparin or enoxaparin were used to detecte CAT parameters. The overall and age specific normal ranges of CAT parameters were calculated using descriptive statistics method with mean＋-2SD. The correlation between CAT parameters and age or concentrations of heparin, enoxaparin were analyzed with linear regression model. Results The normal ranges for lag time, peak thrombin, ETP, ttPeak in the subjects were 3.648＋2.465 min, 367.39＋151.93 nmol/L, 2277＋_1030 nmol/L.min and 6.372＋_4.280 min respectively. Age was linearly correlated with lag time （r=-0.6583, P〈0.0002）, peak thrombin （r=0.4863, P〈0.0002）, ETP （r=0.3608, P〈0.0014） and ttPeak （r=-0.6323, P〈0.0002）. The values of ETP/peak ratio were linearly correlated with concentrations of heparin. Conclusion The normal ranges of four CAT parameters for Chinese were determined. CAT parameters are associated with age. ETP/peak ratio could be used to monitor the process of anticoagulation therapy.

A Log-Penalty-Based Method for Multi-Parameters Estimation with Partly Calibrated COLD Array

In this paper,we focus on the problem of joint estimation of DOA,power and polarization angle from sparse reconstruction perspective with array gain-phase errors,where a partly calibrated cocentered orthogonal loop and dipole(COLD)array is utilized.In detailed implementations,we first combine the output of loop and dipole in second-order statistics domain to receive the source signals completely,and then we use continuous multiplication operator to achieve gain-phase errors calibration.After compensating the gain-phase errors,we construct a log-penalty-based optimization problem to approximate`0 norm and further exploit difference of convex(DC)functions decomposition to achieve DOA.With the aid of the estimated DOAs,the power and polarization angle estimation are obtained by the least squares(LS)method.By conducting numerical simulations,we show the effectiveness and superiorities of the proposed method.

TWO NEW TYPES OF MICROWAVE RADIOMETER: A COMPUTER GAIN COMPENSATIVE DESIGN AND A REAL-TIME CALIBRATED DESIGN

Long term stability is the main factor that influences the minimum detectable signal of microwave radiometers. Two new types of microwave radiometer were studied: a computer gain compensative microwave radiometer and a real-time calibrated microwave radiometer. The long term stability of both designs was optimal because they were insensitive to system gain fluctuations. The continuous calibrated microwave radiometer was also insensitive to system noise fluctuations. The minimum detectable signals were 0.13 and 0.19K respectively under an integration time of 0.6s.

Development of River Rating Curves for Simple to Complex Hydraulic Structure Based on Calibrated HEC-RAS Hydraulic Model, in Kwale, Coastal Kenya

Accurate and reliable river flow information is critical to planning and management for sustainable water resources utilization. Most of engineering activities related to hydrologic designs, flood, drought, reservoirs and their operations are heavily dependent on the river flow information derived from river rating curve. The rating curve for a given river section is normally developed from a set of direct stage-discharge measurements for different periods. This involves considerable labour, risk and resources, and presupposes a complex and extensive measuring survey. Extrapolating the rating curve beyond the measured range, as common in many cases, is fraught with errors and uncertainties, due to the complex hydraulic behaviour of the surface water profile in transition from section, channel, downstream and flood plain controls which are often poorly understood with direct measurements. Hydraulic modeling has recently emerged as one of the more promising methods to efficiently develop accurate rating curves for a river section with simple or complex hydraulic structures and conditions. This paper explores the use of a Hydraulic Engineering Center-River Analysis System (HEC-RAS) model to review and develop river rating curves for three hydrometric stations on two rivers in Kwale, coastal Kenya. The HEC-RAS models were set up based on topographical (cross section and longitudinal) survey data for the reaches and engineering drawings for the hydraulic structures commonly used as section controls for flow measurement. The model was calibrated under unsteady state conditions against measured stage-discharge data which were captured using a Velocity Current Meter (Valeport) and an Acoustic Doppler Current Profiler (ADCP) for both low and high flow. The rating curves were extracted from model results and the uncertainty associated with each rating curve analyzed. The results obtained by the HEC-RAS model were satisfactory and deemed acceptable for predicting discharge across the stage range at each river section.

Digitally calibrated broadband dual-comb gases absorption spectral measurements

From the perspective of error compensation in the sampling process, a digital calibration algorithm was studied for the processing of spectral data in dual-comb spectroscopy. In this algorithm, dynamic adaptation to phase fluctuations maintained constant measurement results of spectral line positions and intensities. A mode-resolved broadband absorption spectrum was obtained over the full-spectral range of the comb with a Hertz linewidth of radio frequency comb mode.The measured spectrum spanned over 10 THz, which covered the multiplexed absorption regions of mixed gases, such as CO2 and N2 O. The calibrated interferograms were also capable of direct coherent averaging in the time domain. The transmittance obtained deviated from the theoretical calculation by no more than 2% in the whole spectral span.

L-Moments Based Calibrated Variance Estimators Using Double Stratified Sampling

Variance is one of the most vital measures of dispersion widely employed in practical aspects.A commonly used approach for variance estimation is the traditional method of moments that is strongly influenced by the presence of extreme values,and thus its results cannot be relied on.Finding momentum from Koyuncu’s recent work,the present paper focuses first on proposing two classes of variance estimators based on linear moments(L-moments),and then employing them with auxiliary data under double stratified sampling to introduce a new class of calibration variance estimators using important properties of L-moments(L-location,L-cv,L-variance).Three populations are taken into account to assess the efficiency of the new estimators.The first and second populations are concerned with artificial data,and the third populations is concerned with real data.The percentage relative efficiency of the proposed estimators over existing ones is evaluated.In the presence of extreme values,our findings depict the superiority and high efficiency of the proposed classes over traditional classes.Hence,when auxiliary data is available along with extreme values,the proposed classes of estimators may be implemented in an extensive variety of sampling surveys.

Application of Spatially Distributed Calibrated Hydrological Model in Evapotranspiration Simulation of Three Gorges Reservoir Area of China:A Case Study in the Madu River Basin

Evapotranspiration(ET)is the key to the water cycle process and an important factor for studying near-surface water and heat balance.Accurately estimating ET is significant for hydrology,meteorology,ecology,agriculture,etc..This paper simulates ET in the Madu River Basin of Three Gorges Reservoir Area of China during 2009-2018 based on the Soil and Water Assessment Tool(SWAT)model,which was calibrated and validated using the MODIS(Moderate-resolution Imaging Spectroradiometer)/Terra Net ET 8-Day L4 Global 500 m SIN Grid(MOD16A2)dataset and measured ET.Two calibration strategies(lumped calibration(LC)and spatially distributed calibration(SDC))were used.The basin was divided into 34 sub-basins,and the coefficient of determination(R^(2))and NashSutcliffe efficiency coefficient(NSE)of each sub-basin were greater than 0.6 in both the calibration and validation periods.The R2 and NSE were higher in the validation period than those in the calibration period.Compared with the measured ET,the accuracy of the model on the daily scale is:R^(2)=0.704 and NSE=0.759(SDC results).The model simulation accuracy of LC and SDC for the sub-basin scale was R^(2)=0.857,R^(2)=0.862(monthly)and R^(2)=0.227,R^(2)=0.404(annually),respectively;for the whole basin scale was R^(2)=0.902,R^(2)=0.900(monthly)and R^(2)=0.507 and R^(2)=0.519(annually),respectively.The model performed acceptably,and SDC performed the best,indicating that remote sensing data can be used for SWAT model calibration.During 2009-2018,ET generally increased in the Madu River Basin(SDC results,7.21 mm/yr),with a multiyear average value of 734.37 mm/yr.The annual ET change rate for the sub-basin was relatively low upstream and downstream.The linear correlation analysis between ET and meteorological factors shows that on the monthly scale,precipitation,solar radiation and daily maximum and minimum temperature were significantly correlated with ET;annually,solar radiation and wind speed had a moderate correlation with ET.The correlation between maximum temperature and ET is best on the monthly scale(Pearson correlation coefficient R=0.945),which may means that the increasing ET originating from increasing temperature(global warming).However,the sub-basins near Shennongjia Nature Reserve that are in upstream have a negative ET change rate,which means that ET decreases in these sub-basins,indicating that the’Evaporation Paradox’exists in these sub-basins.This study explored the potential of remote-sensing-based ET data for hydrological model calibration and provides a decision-making reference for water resource management in the Madu River Basin.

An improved calibrated normalized resolution product(r*') and the application in HPLC fingerprints of soy isoflavone extract

In this paper, we proposed a novel resolution criterion（improved calibrated normalized resolution product, r＊＇） to evaluate separation quality of fingerprints. By comparing with the calibrated normalized resolution product（r＊） and the hierarchical chromatographic response function（HCRF）, the validity of this criterion was demonstrated by experimental chromatograms. The soy isoflavone extract was selected as the analytical object. The initial and end percentages of methanol and elution time affecting gradient elution were tested by orthogonal design. The final optimized conditions were as follows. It was detected by UV absorbance at 254 nm, column temperature was maintained at 36 oC, solvent A was 0.1%（v/v） acetic acid, solvent B was methanol, gradient elution was from 34% to 65% B in a linear gradient in 25 min, and the flow-rate was set at 1.0 m L/min. In addition, the main ingredients of the soy isoflavone extract were confirmed by LC-ESI/MS.

从中国首台紫外-可见光高光谱卫星仪器反演得到的高空间分辨率臭氧廓线

Understanding the vertical distribution of ozone is crucial when assessing both its horizontal and vertical transport,as well as when analyzing the physical and chemical properties of the atmosphere.One of the most effective ways to obtain high spatial resolution ozone profiles is through satellite observations.The Environmental Trace Gases Monitoring Instrument(EMI)deployed on the Gaofen-5 satellite is the first Chinese ultraviolet-visible hyperspectral spectrometer.However,retrieving ozone profiles using backscattered radiance values measured by the EMI is challenging due to unavailable measurement errors and a low signal-to-noise ratio.The algorithm developed for the Tropospheric Monitoring Instrument did not allow us to retrieve 87%of the EMI pixels.Therefore,we developed an algorithm specific to the characteristics of the EMI.The fitting residuals are smaller than 0.3%in most regions.The retrieved ozone profiles were in good agreement with ozonesonde data,with maximum mean biases of 20%at five latitude bands.By applying EMI averaging kernels to the ozonesonde profiles,the integrated stratospheric column ozone and tropospheric column ozone also showed excellent agreement with ozonesonde data,The lower layers(0-7.5 km)of the EMI ozone profiles reflected the seasonal variation in surface ozone derived from the China National Environmental Monitoring Center(CNEMC).However,the upper layers(9.7-16.7 km)of the ozone profiles show different trends,with the ozone peak occurring at an altitude of 9.7-16.7 km in March,2019.A stratospheric intrusion event in central China from August 11 to 15,2019,is captured using the EMI ozone profiles,potential vorticity data,and relative humidity data.The increase in the CNEMC ozone co ncentration showed that downward transport enhanced surface ozone pollution.

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.

Self-calibrated global rainbow refractometry:a dual-wavelength approach

Calibration of the relationship between the scattering angle and the CCD pixel is a key part of achieving accurate measurements of rainbow refractometry.A novel self-calibrated global rainbow refractometry system based on illumination by two lasers of different wavelengths is proposed.The angular calibration and refractive index measurement of two wavelengths can be completed simultaneously without extra measurement devices.The numerical and experimental results show the feasibility and high precision of the self-calibration method,which enables the rainbow refractometry to be implemented in a more powerful and convenient way.The self-calibrated rainbow system is successfully applied to measure the refractive indices of ethanol-water solutions with volume concentrations of 10% to 60%.

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.

Pressure generation under deformation in a large-volume press

Deformation can change the transition pathway of materials under high pressure,thus significantly affects physical and chemical properties of matters.However,accurate pressure calibration under deformation is challenging and thereby causes relatively large pressure uncertainties in deformation experiments,resulting in the synthesis of complex multiphase materials.Here,pressure generations of three types of deformation assemblies were well calibrated in a Walker-type largevolume press(LVP)by electrical resistance measurements combined with finite element simulations(FESs).Hard Al_(2)O_(3) or diamond pistons in shear and uniaxial deformation assemblies significantly increase the efficiency of pressure generation compared with the conventional quasi-hydrostatic assembly.The uniaxial deformation assembly using flat diamond pistons possesses the highest efficiency in these deformation assemblies.This finding is further confirmed by stress distribution analysis based on FESs.With this deformation assembly,we found shear can effectively promote the transformation of C60 into diamond under high pressure and realized the synthesis of phase-pure diamond at relatively moderate pressure and temperature conditions.The present developed techniques will help improve pressure efficiencies in LVP and explore the new physical and chemical properties of materials under deformation in both science and technology.

Construction of the particle simulation model for ginger-soil system using discrete element method

In order to systematically obtain the excavation characteristic parameters for ginger harvesting, experimental analysis was conducted on the discrete elemental parameters in a particle simulation model of the ginger-soil system. Through stacking tests, the surface energy of soil-ginger tuber JKR was determined to be 3.7 J/m2, the coefficient of static friction of soil-steel (65 Mn) was 0.56, the coefficient of rolling friction was 0.03, and the coefficient of restitution of collision was 0.40. Utilizing normal and lateral compression tests conducted on the soil body, the soil base parameters required for the Bonding model were determined. Subsequently, a three-dimensional model of ginger root and stem was constructed using these parameters. With the aid of 3D scanning technology, a discrete element parameter model was established for the ginger field during the harvesting period. On the basis of the measured parameters, a three-dimensional model of ginger rhizome was established and finally a discrete parameter model of ginger field was constructed in the harvesting period. The calibration parameters are highly reliable after the model’s tightness and field harvesting test, which provides reliable data support for the soil flow and the force of the soil-touching parts during the later simulation of ginger harvesting and digging operation.

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

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++.

Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

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.