Analysis of the Design and Application of a Novel CT Secondary Cable Line Calibrator

With the rapid development of electrical power systems,ensuring the accuracy and reliability of power transmis-sion has become particularly crucial.The secondary cable line calibrator for current transformers(CT)plays an essential role in calibrating electrical power systems.It is not only related to the safe operation of the system but also directly im-pacts the accuracy of energy metering.This study aims to design and analyze an efficient CT secondary cable line calibra-tor to explore its application effects in the power system.By thoroughly analyzing the characteristics of CT secondary ca-ble lines and the design requirements of the calibrator,this paper proposes an innovative design scheme for the calibrator.This device demonstrates significant effects in enhancing the accuracy and stability of power system calibration,providing robust technical support for the optimization and upgrade of the power system.This research not only offers a theoretical basis and practical guidance for the design and application of CT secondary cable line calibrators but also contributes new ideas and methods for the precise calibration and efficient management of the power system.

Low-Cost 4-20 mA Loop Calibrator

Instrument calibration is vital to a successful control system because signal inputs to the system controllers come from such instruments. This paper presents a method for actualizing a standard low-cost loop calibrator for the famous 4-20 mA electrical signaling scheme. The loop calibrator generates a linear current signal from 4 to 20 mA over a 250 ? typical process instrument load for calibration. The realization of the loop calibrator relies on a voltage-to-current converter to build a constant current source. The voltage controlled constant current source is built from discrete components and an op-amp to keep the cost low. Results from simulations and the prototype demonstrate the performance of the 4-20 mA loop calibrator which utilizes a greatly reduced number of components. The cost of these components is approximately 34% of the least expensive calibrator sampled, though other production costs are not included. This conclusion reinforces the fact that loop calibrators can be cheaper.

A Mixture of Glycosylated proBNP and BNP-32 Is a Suitable Calibrator for BNP Immunoassays

Aim: Measurement of B-type natriuretic peptide (BNP) is widely used as a diagnostic and risk assessment tool for cardiovascular disease. Recent studies have demonstrated that BNP-32 and its precursor proBNP circulate in the blood stream, and that most commercial BNP immunoassays measure both forms. However, recombinant or synthetic BNP-32 is used as the standard for those BNP immunoassays. This gap between clinical samples and the standard might be a potential source of variation in BNP measurements among assays. The purpose of this study is to validate a more suitable calibrator for BNP immunoassays. Methods: External BNP calibrators containing both BNP-32 and glycosylated proBNP were prepared at two concentration levels. Target BNP concentrations of the low and high levels were 40 and 160 pg/mL, respectively. And to reflect clinical samples, the molar ratios of BNP-32 to glycosylated proBNP in these concentration levels were adjusted to 50:50 and 25:75, respectively. BNP concentrations of plasma samples along with the external BNP calibrators were measured at two commercial labs and using an automated analyzer MI02 Shionogi®BNP (MI02). These samples and the calibrators were also measured using an immunoradiometric assay (IRMA) as a standard assay procedure. Concentrations of the plasma samples measured at the labs or using the MI02 were adjusted according to a comparison of the measured concentrations of the external BNP calibrators with the IRMA. Results: After measured concentrations of the plasma samples were adjusted using the external BNP calibrators, the correlation between each measurement and the IRMA was improved. The range of the slopes according to Passing-Bablok regression analysis narrowed from 0.628 - 0.955 to 0.911 - 1.005. Conclusions: Our data suggests that a mixture of BNP-32 and glycosylated proBNP at different ratios by concentration level is suitable for calibration to minimize variations in BNP measurements among immunoassays.

Design and Realization of a Portable Temperature Sensor Calibrator

Due to the complex erection environment of various types of automatic stations,the provincial meteorological inspection department is difficult to carry out this work in terms of equipment or staffing. For this reason,a portable temperature sensor calibrator was developed,and it uses semiconductor refrigeration technology to increase and decrease temperature quickly. It uses an intelligent PID temperature controller as a control device to provide a stable temperature environment; it is small,light and easy to operate,and it provides technical support for the calibration of temperature sensors. The structure and working principle of this equipment were analyzed,and its performance was tested. All the indicators could meet the requirements of field calibration. The calibrator will provide a strong guarantee for the reliability of temperature data obtained at automatic meteorological stations.

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.

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.

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.

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.

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.

基于5 μm厚向列相液晶的高效圆极化相控阵系统的设计、校准和实验验证

This paper presents a systematic investigation and demonstration of a K-band circularly polarized liquidcrystal-based phased array(LCPA),including the design,over-the-air(OTA)in-array calibration,and experimental validation.The LCPA contains 16 phase-shifting radiating channels,each consisting of a circularly polarized stacked patch antenna and a liquid-crystal-based phase shifter(LCPS)based on a loaded differential line structure.Thanks to its slow-wave properties,the LCPS exhibits a maximum phase-shifting range of more than 360°with a figure of merit of 78.3(°)·dB^(-1)based on a liquid crystal layer with a thickness of only 5μm.Furthermore,an automatic OTA calibration based on a state ergodic method is proposed,which enables the extraction of the phase-voltage curve of every individual LCPA channel.The proposed LCPA is manufactured and characterized with a total profile of only 1.76 mm,experimentally demonstrating a scanned circularly polarized beam from-40°to+40°with a measured peak gain of 12.5 dBic and a scanning loss of less than 2.5 dB.The bandwidth of the LCPA,which satisfies the require-ments of port reflection(|S_(11)|)<-15 dB,an axial ratio(AR)<3 dB,beam squinting<3°,and a gain variation<2.2 dB,spans from 25.5 to 26.0 GHz.The total efficiency is about 34%,which represents a new state of the art.The use of the demonstrated low-profile LCPA to support circularly polarized scanning beams,along with the systematic design and calibration methodology,holds potential promise for a variety of millimeter-wave applications.

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.

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.

Cameroon Climate Predictions Using the SARIMA-LSTM Machine Learning Model: Adjustment of a Climate Model for the Sudano-Sahelian Zone of Cameroon

It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.

Functional Confirmation Using a Medical X-Ray System of a Semiconductor Survey Meter

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.

Evaluation of Air-Kerma and Absorbed Dose to Water for External Radiotherapy Beam Using Ionization Chamber

Radiotherapy is the most widely applied oncologic treatment modality utilizing ionizing radiation. A high degree of accuracy, reliability and reproducibility is required for a successful treatment outcome. Measurement using ionization chamber is a prerequisite for absorbed dose determination for external beam radiotherapy. Calibration coefficient is expressed in terms of air kerma and absorbed dose to water traceable to Secondary Standards Dosimetry Laboratory. The objective of this work was to evaluate the level of accuracy of ionization chamber used for clinical radiotherapy beam determination. Measurement and accuracy determination were carried out according to IAEA TRS 398 protocol. Clinical farmers type ionization chamber measurement and National Reference standard from Secondary Standards Dosimetry Laboratory were both exposed to cobalt-60 beam and measurement results compared under the same environmental conditions. The accuracy level between National Reference Standard and clinical radiotherapy standard was found to be −1.92% and −2.02% for air kerma and absorbed dose to water respectively. To minimize the effect of error and maximize therapeutic dose during treatment in order to achieve required clinical outcome, calibration factor was determined for air kerma (Nk) as 49.7 mGy/nC and absorbed dose to water ND, as 52.9 mGy/nC. The study established that radiotherapy beam measurement chain is prone to errors. Hence there is a need to independently verify the accuracy of radiation dose to ensure precision of dose delivery. The errors must be accounted for during clinical planning by factoring in calibration factor to minimize the systematic errors during treatment, and thereby providing enough room to achieve ±5% dose delivery to tumor target as recommended by ICRU.