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.

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.

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.

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.

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.

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.

In-flight Energy Calibration of the GECAM Gamma-ray Detectors

The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor(GECAM)mission is designed to monitor the Gamma-Ray Bursts(GRBs)associated with gravitational waves and other high-energy transient sources.The mission consists of two microsatellites which are planned to operate at the opposite sides of the Earth.Each GECAM satellite could detect and localize GRBs in about 8 keV-5 MeV with its 25 Gamma-Ray Detectors(GRDs).In this work,we report the in-flight energy calibration of GRDs using the characteristic gamma-ray lines in the background spectra,and show their performance evolution during the commissioning phase.Besides,a preliminary cross-calibration of energy response with Fermi GBM data is also presented,validating the energy response of GRDs.

Reform of Teaching Practice of Modern Instrumental Analysis in Biotechnology Programs

Through the online and offline practical reform of the Modern Instrumental Analysis course of biotechnology majors from teaching content,teaching method,teaching demonstration to teaching effect,the traditional single offline lecture is transformed into diversified and interactive modern teaching.The practical reform enriches and optimizes the course content,perfects and improves the course assessment system,and improves the teaching quality.It achieves the student-centered and application-oriented teaching goal,and also provides reference for further cultivating high-quality applied talents.

Train wheel-rail force collaborative calibration based on GNN-LSTM

Accurate wheel-rail force data serves as the cornerstone for analyzing the wheel-rail relationship.However,achieving continuous and precise measurement of this force remains a significant challenge in the field.This article introduces a calibration algorithm for the wheel-rail force that leverages graph neural networks and long short-term memory networks.Initially,a comprehensive wheel-rail force detection system for trains was constructed,encompassing two key components:an instrumented wheelset and a ground wheel-rail force measuring system.Subsequently,utilizing this system,two distinct datasets were acquired from the track inspection vehicle:instrumented wheelset data and ground wheel-rail force data,a feedforward neural network was employed to calibrate the instrumented wheelset data,referencing the ground wheel-rail force data.Furthermore,ground wheel-rail force data for the locomotive was obtained for the corresponding road section.This data was then integrated with the calibrated instrumented wheelset data from the track inspection vehicle.Leveraging the GNN-LSTM network,the article establishes a mapping relationship model between the wheel-rail force of the track inspection vehicle and the locomotive wheel-rail force.This model facilitates continuous measurement of locomotive wheel-rail forces across three typical scenarios:straight sections,long and steep downhill sections,and small curve radius sections.

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.

一种前后台结合的Pipelined ADC校准技术

针对Pipelined模数转换器(ADC)中采样电容失配和运放增益误差带来的非线性问题,提出了一种前后台结合的Pipelined ADC校准技术。前台校准技术通过对ADC量化结果的余量分析,补偿相应流水级的量化结果,后台校准技术基于伪随机(PN)注入的方式,利用PN的统计特性校准增益误差。本校准技术在系统级建模和RTL级电路设计的基础上,实现了现场可编程门阵列(FPGA)验证并成功流片。测试结果显示,在1 GS/s采样速率下,校准精度为14 bit的Pipelined ADC的有效位数从9.30 bit提高到9.99 bit,信噪比提高约4 dB,无杂散动态范围提高9.5 dB,积分非线性(INL)降低约10 LSB。

基于特征间几何约束的指针仪表读数识别方法

在指针式仪表示值监控系统中,倾斜拍摄仪表图像时会导致识别结果出现偏差,为此提出一种基于仪表特征间几何约束的识别方法。首先利用一幅平铺于仪表上表面的平面标定板图像实现相机参数标定,生成仪表在标定平面上的虚拟图像;然后利用表壳上表面、刻度平面、指针平面与虚拟像平面间的平行关系,确定表壳圆的圆心、仪表刻度圆心、指针转轴中心特征间的透视投影约束关系,利用图像的极坐标变换和离散傅里叶变换提取仪表刻度圆心的最优位置及起始刻度线的角度,利用Hough变换的投票原理估计指针的转动角度;最后利用角度法实现仪表读数的解算。以某型号指针式温度计为测试对象进行实验验证,识别结果的平均误差为0.23℃,最大误差为0.7℃,本文方法能够准确识别仪表读数且识别精度优于人工读数。

基于双目立体视觉的多分辨率图像匹配方法研究

在双目立体视觉系统中,面对复杂场景时噪声会损害图像特征,增加提取难度,导致匹配精度和鲁棒性下降。因此,文中提出基于双目立体视觉的多分辨率图像匹配方法,旨在从不同尺度图像中有效获取信息并实现高精度匹配。该方法利用双目立体视觉模型的双目旋转相机扫描目标并进行成像,根据内、外空间标定提升双目旋转相机的位置精度,保证目标的多分辨率成像效果;将其输入金字塔立体匹配网络中,通过网络中的类金字塔多空洞卷积操作提取双目图像特征,在此基础上,基于可变卷积增强其纹理特征细节;结合细粒度特征和互注意力机制完成双目图像匹配。测试结果显示,空间标定后,左、右两个相机的成像误差最小值分别为0.6 Pixel和0.4 Pixel;匹配点坐标偏差均值和坐标偏差方差值分别低于0.012和0.011,匹配效果良好。

端粒长度与10种常见肌肉骨骼疾病的关系孟德尔随机化分析

背景:多项观察性研究表明,端粒长度与肌肉骨骼疾病之间存在潜在的关联,然而它们之间的潜在机制仍不清楚。目的:利用两样本孟德尔随机化分析来探索端粒长度与肌肉骨骼疾病之间的遗传因果关系。方法:从英国生物银行中获得端粒长度的全基因组关联研究汇总数据。从FinnGen财团中获得了关于10种常见肌肉骨骼疾病(骨坏死、骨髓炎、骨质疏松、类风湿关节炎、腰痛、椎管狭窄、痛风、肩周炎、强直性脊柱炎和下肢深静脉血栓)的全基因组关联研究汇总数据。使用逆方差加权、孟德尔随机化-Egger和加权中位数方法评估端粒长度与10种肌肉骨骼疾病的因果关系,逆方差加权作为主要的孟德尔随机化分析方法,并进行敏感性分析探讨结果稳健性。结果与结论:①逆方差加权法结果表明,遗传预测的端粒长度与类风湿关节炎(OR=0.78,95%CI:0.64-0.95,P=0.015)和骨坏死(OR=0.56,95%CI:0.36-0.90,P=0.016)风险之间存在负向因果关系,但未发现端粒长度与其他8种肌肉骨骼疾病之间存在因果关系(P均>0.05)。②敏感性分析结果表明因果关系稳健,孟德尔随机化-Egger截距分析未检测到潜在的水平多效性(P均>0.05)。③此项孟德尔随机化研究支持端粒长度对类风湿关节炎和骨坏死的保护作用的结论,然而,未来将需要更多的基础和临床研究来验证。

虚拟仪器平台下旋转机械振动应用研究

利用LabVIEW软件图形化程序和可视化前面板,基于旋转机械结构和运动特点构建虚拟仪器仿真平台基本框架,设计了与真实仪器功能相当的旋转机械齿轮振动虚拟仿真研究平台。通过仿真验证,齿轮振动虚拟仿真平台能够有效采集振动信号,并对振动信号特征进行提取和分析,测试结果与旋转机械齿轮实际振动情况一致。开发的虚拟仿真平台达到了预期功能目标,为旋转机械关键部件振动检测和故障诊断提供了可视化技术支持,降低了旋转机械故障检测成本。

Research on the Testing Methods of Instrumental System in the Marine Magnetic Survey

Testing methods of instrumental system in the marine magnetic survey have been studied in this paper, and the feasibility of each method has been testified by the observed data. The conclusion shows that the method brought out can effectively eliminate the systematic error caused by the instrumental system, and greatly improve the surveying precision and the reliability of the survey results.

GYRO BIAS ON-ORBIT CALIBRATION FOR MICRO SATELLITES

According to gyro application in micro-satellites, a new gyro bias real-time on-orbit calibration technology is presented and it is independent of any other sensors. The approach relies on gyro on-orbit measurements restricted by satellite attitude dynamics and estimates the gyro bias generated when the gyro is electrified. Observability of the calibration model is analyzed and applicable conditions of the technology are derived. Simulation results indicate that the calibration algorithm is accurate and robust at gyro sampling rate, and its convergence speed is fast. Within the given attitude dynamics model error, the convergence time is less than 100 s and the convergence accuracy is about 1.0 （°）/h. Calibration performance can meet requirements of spacecraft operations.