Lossless Compression Method for the Magnetic and Helioseismic Imager(MHI)Payload

The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.

Database of molecular masers and variable stars

We present the database of maser sources in H2 O, OH and Si O lines that can be used to identify and study variable stars at evolved stages. Detecting the maser emission in H2 O, OH and Si O molecules toward infrared-excess objects is one of the methods for identifing long-period variables(LPVs, including miras and semiregulars), because these stars exhibit maser activity in their circumstellar shells. Our sample contains 1803 known LPV objects. Forty-six percent of these stars(832 objects) manifest maser emission in the line of at least one molecule: H2 O, OH or Si O. We use the database of circumstellar masers in order to search for LPVs which are not included in the General Catalogue of Variable Stars(GCVS). Our database contains 4806 objects(3866 objects without associations in GCVS) with maser detection in at least one molecule. Therefore it is possible to use the database in order to locate and study the large sample of LPV stars. The database can be accessed at http://maserdb.net.

A New Method of Frequency Fluctuation Estimation and IPS Processing Results Based on the Downlink Signal of Tianwen-1

The radio-occultation observations taken by Tianwen-1 are aiming to study the properties of solar wind.A new method of frequency fluctuation(FF)estimation is presented for processing the down-link signals of Tianwen-1 during the occultation period to study the properties of the coronal plasma at the heliocentric distances of 4.48–19 R_(⊙).Because of low S/N as well as the phase fluctuation phenomena caused by solar activity,a Kalman based on polynomial prediction methods is proposed to avoid the phase locked loop loss lock.A new detrend method based on multi-level iteration correction is proposed to estimate Doppler shift to get more accurate power density spectra of FF in the low frequency region.The data analyze procedure is used to get the properties of the solar corona during the occultation.The method was finally verified at the point when the solar offset is 5.7 R_(⊙),frequency tracking was successfully performed on data with a carrier-to-noise ratio of about 28 dBHz.The density spectra obtained by the improved method are basically the same when the frequency is greater than 2 mHz,the uncertainty in the result of the rms of the FF obtained by removing the trend term with different order polynomials is less than 3.3%.The data without eliminating interference show a large error for different detrending orders,which justifies the need for an improved approach.Finally,the frequency fluctuation results combined with the information on intensity fluctuation obtained by the new method are compared with the results of the integrated Space Weather Analysis system and theoretical formula,which verifies that the processing results in this paper have a certain degree of credibility.

Extraction and Analysis of Coronal High-temperature Components Based on Outlier Detection

The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging Assembly.However,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the predictions.In this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del's method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many differences.In order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del's method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 MK.It is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del's method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.

基于数据-模型混合驱动的电力系统机电暂态快速仿真方法

数据驱动建模方法改变了发电机传统的建模范式,导致传统的机电暂态时域仿真方法无法直接应用于新范式下的电力系统。为此,该文提出一种基于数据-模型混合驱动的机电暂态时域仿真(data and physics driven time domain simulation,DPD-TDS)算法。算法中发电机状态变量与节点注入电流通过数据驱动模型推理计算,并通过网络方程完成节点电压计算,两者交替求解完成仿真。算法提出一种混合驱动范式下的网络代数方程组预处理方法,用以改善仿真的收敛性;算法设计一种中央处理器单元-神经网络处理器单元(central processing unit-neural network processing unit,CPU-NPU)异构计算框架以加速仿真,CPU进行机理模型的微分代数方程求解;NPU作协处理器完成数据驱动模型的前向推理。最后在IEEE-39和Polish-2383系统中将部分或全部发电机替换为数据驱动模型进行验证,仿真结果表明,所提出的仿真算法收敛性好,计算速度快,结果准确。

全流程健康体检系统的设计与实现

为提高体检效率和体检体验,提供个性化健康管理服务,提升医疗服务质量,改变传统体检系统的单一模式,并实现数据的分析与共享。本研究设计并实现了全流程健康管理系统,该系统采用面向对象程序设计(Object Oriented Programming,OOP)和分布式的系统体系架构,并与院内HIS、PACS、LIS等业务系统集成。运用后,平均体检时长由6h缩短至3h,并实现了检前智能评估、检中智慧化分检导检、检后统计分析和智能随访功能。该系统提升了服务质量和医护体检效率,建立了检前、检中和检后的全流程管理体系,促进了医院的数字化发展。

CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach

Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.

Estimating stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization

Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization （SPSO）. We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels. Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment （MSX） catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.

Neutrino energy spectrum and electron capture of Nuclides^(56))Fe,^(56)Co,^(56)Ni,^(56)Mn,^(56)Cr and^(56)V in stellar interiors

Based on the shell-model Monte Carlo method and random phase approximation theory, the neutrino energy spectrum （NES） and the electron capture （EC） of ^56Fe, ^56Co, ^56Ni, ^56Mn, ^56Cr and ^56V are investigated in presupemova surroundings. The results show that the EC rates are affected greatly at different densities and temperatures. The rates increase greatly and even exceed six orders of magnitude at lower temperature. On the other hand, the NES is very sensitive to stellar temperature and electron energy. The higher the temperature and the lower the electron energy, the larger the influence on NES is. For example, the maxima of NES in the ground state are 9.02, 160, 80, 24.01, 0.44, 1.42 me c^2 for ^56Fe, ^56Co, ^56Ni, ^56Mn, ^56Cr and ^56V respectively at ρ7 = 10.7, Ye = 0.45 and T9 = 15. Furthermore, the influence on NES due to EC for different nuclei has some otherness because of different Qo-values. For example, the spectrum of ^56Co shows a double bump structure.

Beta decay of nuclides ^(56)Fe,^(62)Ni,^(64)Ni and ^(68)Ni in the crust of magnetars

By introducing the Dirac δ-function and Pauli exclusion principle in the presence of superstrong magnetic fields （SMFs）, we investigate the influence of SMFs on beta decay and the change rates of electron fraction （CREF） of nuclides 56Fe, 62Ni, 64Ni and 6SNi in magnetars, which are powered by magnetic field energy. We find that the magnetic fields have a great influence on the beta decay rates, and the beta decay rates can decrease by more than six orders of magnitude in the presence of SMFs. The CREF also decreases by more than seven orders of magnitude in the presence of SMFs.

The neutrino energy loss by electron capture of nuclides ^(55)Co and ^(56)Ni in explosive stellar environments

Using the Shell-Model Monte Carlo method and the Random Phase Approximation theory, we carry out an estimation of neutrino energy loss （NEL） for 55Co and 56Ni by electron capture. We find that the NEL rates increase greatly at some typical stellar conditions, and can even exceed five orders of magnitude （e.g. T9 = 38.5, Ye = 0.42 for 56Ni）. On the other hand, the error factor C shows that the fit is fairly good for two results at higher density and lower temperature, and the max- imum error is - 1.2%. However, the maximum error is ,- 55.60% （e.g. T9 = 18.5, Ye = 0.45） at lower density and higher temperature.

The Co-alignment of Winged HαData Observed by the New Vacuum Solar Telescope

The New Vacuum Solar Telescope(NVST)has been releasing its novel winged Hαdata(WHD)since 2021 April,namely the Hαimaging spectroscopic data.Compared with the prior released version,the new data are further coaligned among the off-band images and packaged into a standard solar physics community format.In this study,we illustrate the alignment algorithm used by the novel WHD,which is mainly based on the optical flow method to obtain the translation offset between the winged images.To quantitatively evaluate the alignment results of two images with different similarities,we calculate the alignment accuracies between the images of different off-band and line center,respectively.The result shows that our alignment algorithm could reach up to the accuracy of about 0.″1 when the off-band of winged image is lower than 0.6?.In addition,we introduce the final product of the WHD in detail,which can provide convenience for the solar physicists to use high-resolution Hαimaging spectroscopic data of NVST.

Supernova β^- decay of nuclides ^(53)Fe,^(54)Fe,^(55)Fe and ^(56)Fe in strongly screened plasma

Electron screening has strong effects on electron energy and threshold energy of the beta decay reaction.In this paper,we study β~-decay rates of some iron isotopes.The beta decay rates increase by about two orders of magnitude due to electron screening.Strongly screened beta decay rates due to Q-value correction are more than one order of magnitude higher than those without Q-value correction.

Mathstudio在近代物理实验数据处理中的应用

为了提升大学生信息素养和实践能力,介绍了Mathstudio在5个近代物理实验数据处理中的应用,同时与其他数据处理软件的处理结果进行了对比。结果表明:①较传统手工计算和电脑端软件运算的方法,基于Mathstudio的近代物理实验数据处理方法,在符号运算、数据输入或输出和曲线拟合方面为大学生提供技术支持和便利;②本文所展示的基于Mathstudio近代物理实验数据处理方法,丰富了智能手机在物理实验数据处理中的应用,对加速教学信息化改革研究与实践、提升学生信息素养与实践能力的深度融合具有重要意义。

技术赋能下的政府数据开放风险管控体系设计

[研究目的]在政府数据开放活动面临国家秘密与个人隐私泄露、数据不当利用等一系列风险挑战的大背景下,探讨如何形成政府数据开放风险管控的有效策略,并选择特定技术工具,尝试建构技术赋能下的政府数据开放风险管控新体系。[研究方法]基于理论分析,该文将政府数据开放过程中需求方(社会主体)与供给方(行政主体)的双重匿名状态识别为关键的风险诱发条件,提出相应的风险管控策略,进而甄选区块链与数字水印作为技术工具,破解政府数据开放风险管控新体系的建构难题,并探讨该体系的优势及其给政府带来的挑战。[研究结论]基于需求侧、供给侧双向去匿名化的“全过程阳光”策略,结合区块链、数字水印两种技术工具,新建构的政府数据开放风险管控体系在追溯主体身份与行为、防止数据篡改、强化数据维护责任等方面具有显著的比较优势,同时也在治理能力、组织关系和制度环境三个方面为政府提出了新的挑战。

借助Python提高迈克尔逊干涉仪测光波波长实验数据的处理精度与图像可视化

在现代“大学物理实验”课程中,如何正确处理实验数据是非常重要的一个步骤,利用软件处理相对复杂的实验数据往往可以使得结果更加精确。Python作为一种应用广泛的计算机编程语言,在物理实验中的应用却相对少见。以迈克尔逊干涉仪实验为例,详细论述了如何利用Python语言实现数据处理,并且和传统方法处理数据得到的结果进行对比,结果表明利用Python语言处理得到的结果的相对误差为2.72%,小于逐差法计算得到的3.67%。此外,通过Python仿真实现了干涉圆环的可视化图像,有助于加深对实验本质的理解。因此,借助Python语言处理大学物理实验数据值得进一步地研究与推广。

基于Python的PN结物理特性实验数据处理方法

半导体PN结物理特性实验中的数据计算量大,如果使用普通计算器计算,不仅繁琐费时而且易引入误差.通过Python语言调用Numpy模块可实现对实验数据的多项式拟合,再通过Matplotlib模块可实现可视化绘图.结果表明,该方法能快速、准确处理实验数据,给出电流电压关系拟合曲线,对PN结实验数据的分析和处理有很好的辅助作用.

Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on PWCNet.This method transforms the transverse velocity field measurements into an optical flow field prediction problem.We evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope observations.The experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning methods.On the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 to17.0194.The optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar images.The code implementing the proposed method is available on https://github.com/lygmsy123/transverse-velocity-field-measurement.

Igor Pro在《大学物理实验》课程中的创新应用——以牛顿环实验为例

使用科学软件对实验数据的有效处理和分析是《大学物理实验》课程的教学培养目标之一。以牛顿环实验为例,介绍了使用Igor Pro能够高效地完成对实验数据的处理以及对实验图像的数据采集和分析,对于培养学生的科学素养和创新能力有很大裨益,而且可以为学生进一步学习和深造奠定良好的基础。同时,由于当前疫情等原因,线上实验课程的重要性也越发突显,使用Igor Pro这类科学软件能更好地展示大学物理实验相关课程知识要点,提升学生线上学习效果。

基于PoF与数据驱动融合算法的核电仪控卡件剩余寿命预测方法研究

作为核电厂的神经中枢,仪表控制系统的稳定性与可靠性是保证核电厂安全运行的关键。对核电仪控系统卡件进行剩余寿命预测可降低直接更换带来的维护、购置费用,可减少关键设备的损坏风险,是提高系统可靠性的重要手段。数据驱动和故障物理(PoF)是剩余寿命预测领域的两种主要方法,然而这两种方法都存在着一定的局限性,且现有融合算法难以对应力多样、结构复杂的核电产品实现准确的剩余寿命预测。针对以上问题,本文提出了一套基于PoF与数据驱动融合算法的核电产品剩余寿命预测方法,该方法采用广义阿伦尼斯模型进行PoF建模,利用维纳过程进行退化过程建模并生成退化数据,基于长短期记忆(LSTM)神经网络实现PoF数据与数据驱动方法的有效融合。依据所提方法完成了某核电站仪表控制系统DC-DC卡件的剩余使用寿命预测,通过不同算法的比较,验证了本文所提方法的有效性与准确性。研究结果可用于指导核电站仪控设备的预防性维修,也为设备可靠性管理提供了参考和方向。