Research on Consistency Judgement of Indication Error for Calibration Result of Humidity Sensor in Meteorology

In order to solve the lack of relevant evaluation research on the accuracy of HMP155A humidity sensor calibration results in the past, this paper designs the corresponding experimental scheme, and obtains the corresponding calibration results according to the experimental scheme;Then the measurement uncertainty of the indication error in the calibration results is evaluated by GUM, and the corresponding extended uncertainty U95 is obtained. Finally, according to the requirements of JJF1094-2016 characteristic evaluation of measuring instruments, combined with the calibration results and the actual situation of U95, the conformity of the indication error of calibration is determined. The result is that each calibration point of the sensor meets the requirements of conformity determination and is within the qualified range. This research effectively makes up for the blank of the previous research on the conformity determination of the indication error of the calibration results and has strong theoretical and practical significance.

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++.

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.

Relationship of Ambient Humidity with Cardiovascular Diseases:A Prospective Study of 24,510 Adults in a General Population

Objective This study aimed to explore the association between humidity exposure and the risk of cardiovascular disease(CVD),utilizing follow-up data and relative humidity(RH)metric assessments.Methods We extracted the baseline data from the China Hypertension Survey(CHS)of 24,510 enrolled participants aged≥35 years without a history of CVD between 2012 and 2015 and followed them up from 2018 to 2019.The National Meteorological Information Center(NMIC)of the China Meteorological Administration(CMA)provided the quality-controlled relative humidity(RH)datasets.Cox proportional hazards models were used to estimate hazard ratios(HRs)for CVD in relation to RH.Results During the follow-up period(2018-2019),973 patients with CVD were identified.The HR of CVD risk was 1.17(95%CI:1.04-1.31)per 10%increase in summer mean RH.Compared with participants in the 3rd quintile group,those in the 1st and 5th quintiles of RH had a higher risk of CVD.For summer mean RH,the HRs(95%CIs)for the 1st and 5th quintiles were 1.34(1.04-1.71)and 1.44(1.14-1.83),respectively.The relationship(“U”shape)between summer mean RH and the risk of CVD was nonlinear.Stratified analyses indicated that the risk of CVD was substantially influenced by the summer mean RH in female,older individuals,and those in southern China.Conclusion Unsuitable(too high or low)humidity environments affect the risk of CVD.Our study highlights those future policies for adapting to climate change should consider the humidity-CVD relationship.

Impacts of high temperature,relative air humidity,and vapor pressure deficit on the seed set of contrasting maize genotypes during flowering

Heat stress is a major constraint to current and future maize production at the global scale.Male and female reproductive organs both play major roles in increasing seed set under heat stress at flowering,but their relative contributions to seed set are unclear.In this study,a 2-year field experiment including three sowing dates in each year and 20 inbred lines was conducted.Seed set,kernel number per ear,and grain yield were all reduced by more than 80%in the third sowing dates compared to the first sowing dates.Pollen viability,silk emergence ratio,and anthesis-silking interval were the key determinants of seed set under heat stress;and their correlation coefficients were 0.89^(***),0.65^(***),and-0.72^(***),respectively.Vapor pressure deficit(VPD)and relative air humidity(RH)both had significant correlations with pollen viability and the silk emergence ratio.High RH can alleviate the impacts of heat on maize seed set by maintaining high pollen viability and a high silk emergence ratio.Under a warming climate from 2020 to 2050,VPD will decrease due to the increased RH.Based on their pollen viability and silk emergence ratios,the 20 genotypes fell into four different groups.The group with high pollen viability and a high silk emergence ratio performed better under heat stress,and their performance can be further improved by combining the improved flowering pattern traits.

Gypsum-based Silica Gel Humidity-controlling Composite Materials:Preparation,Characterization,and Performance

Gypsum was used as substrate,and silica gel was mixed into substrate at a certain mass ratio to prepare humidity-controlling composites;moreover,the moisture absorption and desorption properties of gypsum-based composites were compared with adding different silica gel particle size and proportion.The morphological characteristics,the isothermal equilibrium moisture content curve,moisture absorption and desorption rate,moisture absorption and desorption stability,and humidity-conditioning performance were tested and analyzed.The experimental results show that,compared with pure-gypsum,the surface structure of the gypsum-based composites is relatively loose,the quantity,density and aperture of the pores in the structure increase.The absorption and desorption capacity increase along with the increase of silica gel particle size and silica gel proportion.When 3 mm silica gel particle size is added with a mass ratio of 40%,the maximum equilibrium moisture content of humidity-controlling composites is 0.161 g/g at 98% relative humidity(RH),3.22 times that of pure-gypsum.The moisture absorption and desorption rates are increased,the equilibrium moisture absorption and desorption rates are 2.68 times and 1.61 times that of pure-gypsum at 58.5% RH,respectively.The gypsum-based composites have a good stability,which has better timely response to dynamic humidity changes and can effectively regulate indoor humidity under natural conditions.

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.

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.

Effects of temperature, particle size, and air humidity on sensibility of typical high-energetic explosives

The production and utilization of high-energetic explosives often pose a range of safety hazards,with sensitivity being a key factor in evaluating these risks.To investigate how temperature,particle size,and air humidity affect the responsiveness of commonly used high-energetic explosives,a series of BAM(Bundesanstalt für Materialforschung und-prüfung)impact and friction sensitivity tests were carried out to determine the critical impact energy and critical load pressure of four representative high-energetic explosives(RDX,HMX,PETN and CL-20)under different temperatures,particle sizes,and air humidity conditions.The experimental findings facilitated an examination of temperature and particle size affecting the sensitivity of high-energetic explosives,along with an assessment of the influence of air humidity on sensitivity testing.The results clearly indicate that high-energetic explosives display a substantial decline in critical reaction energy when subjected to micrometre-sized particles and an air humidity level of 45%at a temperature of 90℃.Furthermore,it was noted that the critical reaction energy of high-energetic explosives diminishes with an increase in temperature within 25℃−90℃.In the same vein,as the particle sizes of high-energetic explosives increase,so does the critical reaction energy for micrometre-sized particles.High air humidity significantly affects the sensitivity testing of high-energetic explosives,emphasizing the importance of refraining from conducting sensitivity tests in such conditions.

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.

Calibration of CO and CO2 Monitors Used in Periodic Inspection of Vehicles at Fixed Stations for Environmental Control

Global efforts for environmental cleanliness through the control of gaseous emissions from vehicles are gaining momentum and attracting increasing attention. Calibration plays a crucial role in these efforts by ensuring the quantitative assessment of emissions for informed decisions on environmental treatments. This paper describes a method for the calibration of CO/CO2 monitors used for periodic inspections of vehicles in cites. The calibration was performed in the selected ranges: 900 - 12,000 µmol/mol for CO and 2000 - 20,000 µmol/mol for CO2. The traceability of the measurement results to the SI units was ensured by using certified reference materials from CO/N2 and CO2/N2 primary gas mixtures. The method performance was evaluated by assessing its linearity, accuracy, precision, bias, and uncertainty of the calibration results. The calibration data exhibited a strong linear trend with R² values close to 1, indicating an excellent fit between the measured values and the calibration lines. Precision, expressed as relative standard deviation (%RSD), ranged from 0.48 to 4.56% for CO and from 0.97 to 3.53% for CO2, staying well below the 5% threshold for reporting results at a 95% confidence level. Accuracy measured as percent recovery, was consistently high (≥ 99.1%) for CO and ranged from 84.90% to 101.54% across the calibration range for CO2. In addition, the method exhibited minimal bias for both CO and CO2 calibrations and thus provided a reliable and accurate approach for calibrating CO/CO2 monitors used in vehicle inspections. Thus, it ensures the effectiveness of exhaust emission control for better environment.

Effect of Humidity on Formaldehyde Oxidation over Ce_(0.8)Zr_(0.2)O_(y)Catalyst

In the preparation of a series of Ce_(0.8)Zr_(0.2)O_(y)catalysts catalyzing the removal of formaldehyde,BET,H2-TPR,IR,SEM,XPS,and XRD were used to characterize the catalyst,and the influence of humidity on the catalyst activity was studied by adjusting the humidity during the process.The experimental results showed that the formaldehyde removal rate increased with the increase of humidity.When the humidity was higher than 50%,the formaldehyde removal rate decreased by 3%over that when the humidity was 50%.The characterization results showed that humidity facilitated the activation of oxygen and the formation of hydroxyl groups,which both promoted the formation and oxidative decomposition of intermediates and prevented the deposition of intermediates that clogged the pores,allowing more formaldehyde to be adsorbed and oxidized,which increased the activity of the catalyst.This provides new mechanistic evidence for the oxidation of formaldehyde and helps in the development of relatively low-cost materials for formaldehyde purification.

A stepwise camera calibration method incorporating compensation for eccentricity error

In visual measurement,high-precision camera calibration often employs circular targets.To address issues in mainstream methods,such as the eccentricity error of the circle from using the circle’s center for calibration,overfitting or local minimum from fullparameter optimization,and calibration errors due to neglecting the center of distortion,a stepwise camera calibration method incorporating compensation for eccentricity error was proposed to enhance monocular camera calibration precision.Initially,the multiimage distortion correction method calculated the common center of distortion and coefficients,improving precision,stability,and efficiency compared to single-image distortion correction methods.Subsequently,the projection point of the circle’s center was compared with the center of the contour’s projection to iteratively correct the eccentricity error,leading to more precise and stable calibration.Finally,nonlinear optimization refined the calibration parameters to minimize reprojection error and boosts precision.These processes achieved stepwise camera calibration,which enhanced robustness.In addition,the module comparison experiment showed that both the eccentricity error compensation and the camera parameter optimization could improve the calibration precision,but the latter had a greater impact.The combined use of the two methods further improved the precision and stability.Simulations and experiments confirmed that the proposed method achieved high precision,stability,and robustness,suitable for high-precision visual measurements.

Modelling of the variation of granular base materials resilient modulus with material characteristics and humidity conditions

This study aims to quantify the susceptibility of granular materials used in pavements to changes in moisture content and propose a correlation model to incorporate this susceptibility into seasonal analyses.The fines content and the percentage of fractured coarse aggregates were identified as direct indicators of the resilient modulus susceptibility to changes in water content.The results showed that the percentage of fractured coarse aggregates particles(FR)has a more significant impact on the resilient modulus(Er)of crushed granular materials used in pavement construction than the combined indicator of the fines content and sample volumetrics(nf).Crushed granular materials with a higher percentage of fractured coarse aggregates are relatively insensitive to changes in the degree of saturation,but become more sensitive as the fine fraction porosity decreases.An adjusted model was proposed based on the existing formulation,but considers a complex parameter to describe and adjust the sensitivity of base granular materials to variations in moisture content with respect to fabrication charac-teristics,fines content and volumetric properties.The model shows that the variation of Er values is below10%for fully crushed granular materials.However,it reaches approximately±12%for materials with 75%of crushed coarse aggregates andþ40%and-25%for materials with FR=50%.This model could help select good ag-gregates characteristics and adjust grain-size distribution for environments where significant moisture content variations can occur in the pavement system,such as in the Province of Quebec(Canada).As it is based on pa-rameters that can be easily determined or estimated,it also represents a valuable tool for detailed design and analysis that can consider material characteristics.

Parameter calibration of the tensile-shear interactive damage constitutive model for sandstone failure

The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.

Prediction of Low Heating Value of Sugar Cane Bagasse as a Fuel for Industrial Boilers in the High Relative Humidity Region: Case of Cameroon

Many attempts have been made to estimate calorific value of bagasse using mathematical equations, which were created based on data from proximate, ultimate, physical and chemical analysis. Questions have been raised on the applicability of these equations in different parts of the globe. This study was initiated to tackle these problems and also check the most suited mathematical models for the Law Heating Value of Cameroonian bagasse. Data and bagasse samples were collected at the Cameroonian sugarcane factory. The effects of cane variety, age of harvesting, source, moisture content, and sucrose on the LHV of Cameroon bagasse have been tested. It was shown that humidity does not change within a variety, but changes from the dry season to the rainy season;the sugar in the rainy season is significantly different from that collected in the dry season. Samples of the same variety have identical LHV. LHV in the dry season is significantly different from LHV in the rainy season. According to the fact that this study was done for cane with different ages of harvesting, the maturity of Cameroonian sugarcane does not affect LHV of bagasse. Tree selected models are much superior tool for the prediction of the LHV for bagasse in Cameroon compared to others. The standard deviation of these validated models is around 200 kJ/kg compared to the experimental. Thus, the models determined in foreign countries, are not necessarily applicable in predicting the LHV of bagasse in other countries with the same accuracy as that in their native country. There was linear relationship between humidity, ash and sugar content in the bagasse. It is possible to build models based on data from physical composition of bagasse using regression analysis.

Global Calibration Method for Monocular Multi-View System Using Rotational Dual-Target

To make the problems of existing high requirements of calibration tools, complex global calibration process addressed for monocular multi-view visual measurement system during measurement, in the paper, a global calibration method is proposed for the geometric properties of rotational correlation motion and the absolute orientation of the field of view without over lap. Firstly, a dual-camera system is constructed for photographing and collecting the rotating image sequence of two flat targets rigidly connected by a long rod at different positions, and based on the known parameters, such as, target feature image, world coordinates, camera internal parameters and so on, then the global PnP optimization method is used to solve the rotation axis and the reference point at different positions;Then, the absolute orientation matrix is constructed based on the parameters of rotation axis, reference point and connecting rod length obtained by this method. In the end, the singular value decomposition method is used to find the optimal rotation matrix, and then get the translation matrix. It’s shown based on simulation and actual tests that in comparison with the existing methods, the maximum attitude and pose errors is 0.0083˚ and 0.3657 mm, respectively, which improves the accuracy by 27.8% and 24.4%, respectively. The calibration device in this paper is simple, and there are no parallel, vertical and coplanar requirements between multiple rotating positions. At the same time, in view of the calibration accuracy, the accuracy requirements of most application scenarios can be met.

Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

Effects of Land Cover on Soil Temperature,Humidity and Moisture in Phoebe bournei Forest

Under the condition of high temperature, the effects of five cover patterns （clean tillage, film mulching, weed covering, branches and leaves covering and growing grass covering） on soil properties in young Phoebe boumei forest were in- vestigated. The results showed that the five cover patterns all showed significant ef- fects on soil properties in young Phoebe bournei forest under the condition of high temperature. Land cover increased land temperature in Phoebe boumei forest. Un- der the film mulching, the land temperature was increased most rapidly with the largest increment. However, weed covering, branches and leaves covering and growing grass covering decreased land temperature. Among them, growing grass covering showed the best cooling effect. The film mulching, weed covering, branch- es and leaves covering and growing grass covering all increased land humidity. The film mulching showed the best moisture-preserving effect in the early period, but in the late period, the humidity in the film mulching treatment group was lower than that in the clean tillage treatment group. Among the five mulching patterns, moisture loss in the film mulching treatment group was slowest and least, followed by those in the weed covering and branches and leaves covering treatment groups, and moisture loss in the growing grass covering treatment group was fastest and most.