Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

3D MERGE与3D SPACE STIR序列在腰椎间盘突出症检查中的应用比较

目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,所有患者均接受常规MRI扫描及3D MERGE、3D SPACE STIR序列扫描,对比3D MERGE、3D SPACE STIR序列测量神经根直径的一致性,评价两种序列的图像质量参数[信噪比(SNR)、对比噪声比(CNR)]、图像清晰度评分。结果:3D MERGE和3D SPACE STIR序列测量的L3~S1神经根直径比较差异无统计学意义(P>0.05),且两组序列测量的L3、L4、L5和S1直径均显示出较高相关性(r=0.957,0.986,0.975,0.972,P<0.05);3D MERGE序列的SNR及CNR均高于3D SPACE STIR序列,神经根显示分级、图像清晰度评分优于3D SPACE STIR序列,差异有统计学意义(P<0.05)。结论:3D MERGE、3D SPACE STIR序列在LDH神经根直径测量中具有极高一致性,3D MERGE序列较3D SPACE STIR序列能够更清晰显示神经跟的解剖形态,图像质量更好。

国内“大思政课”研究的现状、热点与展望--基于CiteSpace的核心期刊论文可视化分析

“大思政课”是近年的一个新议题。自习近平总书记作出关于“大思政课”的重要论述以来,“大思政课”建设在全国积极推进,成为新时代用党的创新理论铸魂育人的重要抓手。学界对这一领域的研究成果持续涌现。借助Cite Space文献计量软件对中国知网CNKI数据库中2021-2023年间350篇核心期刊论文的发文量、作者、机构、关键词进行可视化分析,近三年学界的研究热点聚焦“大思政课”的本体论、认识论、价值论、方法论以及一体化等方面。未来,学界还应以多学科交叉和多主体融合实现“大思政课”研究的系统化,以理论和实践结合提升“大思政课”研究的科学化,以破解技术遮蔽与AI赋能强化“大思政课”研究的数字化,以研究反哺教学,不断增强“大思政课”立德树人实效。

基于CiteSpace知识图谱的智慧养老领域研究文献综述

“十四五”规划将发展智慧养老产业上升为国家战略高度。文章借助Cite Space和Vosviewer绘图软件对2012-2022年国内外智慧养老领域研究的发文量、突现词等进行可视化分析。发现国内外研究都经历了三个发展阶段,国外研究起步早,研究成果丰富;国内研究虽然起步较晚,但进展快速。总结出“智慧养老内涵”“智慧养老模式”“智慧养老产业”“智慧养老平台”“智慧养老产品”“智慧养老用户”六大国内外都重点关注的研究议题,发现技术赋能智慧养老发展是近年来国内外共同关注的研究热点,大数据、物联网、养老金融、数字技术、区块链等成为围绕智慧养老文献的关键词。

中国人才政策研究的现状与趋势——基于Cite Space的可视化分析

科学的人才政策是实现人才强国和科教兴国战略的重要保障。借助Cite Space量化分析软件,分别从发文作者、发文机构、高被引文献、关键词等方面对人才政策研究情况进行可视化分析,在整合既有研究基础上展望未来。研究表明:我国人才政策研究主要聚焦于人才政策本身、实施存在的问题、可行性路径三个维度。当前的研究热点主题为人才战略、人才开发、人才培养,已有关键词之间关联性不强,机构合作密度不高,缺少系统研究框架和强大理论支持。据此,今后人才政策的研究应该加强跨学科融合思考,建构具有中国特色的本土化研究架构,注重人才培养的硬环境和软环境建设。

基于Cite Space的氮沉降对全球草地生态系统碳、氮和磷化学计量特征影响的可视化分析

【目的】全面分析氮沉降对全球天然草地生态系统碳、氮和磷化学计量学影响的现状、发展趋势和研究热点。【方法】基于文献计量分析方法,利用Cite Space可视化软件对Web of Science Core Collection数据库2000-2021年收录的14912篇有关氮沉降的论文进行分析。【结果】2000-2021年该领域的研究成果在国外期刊的发文量逐年上升;美国的论文数量位居前列(4199篇),中国发文量排名第2(3831篇);文献作者聚类分析参数为Q=0.4380(>0.3)、S=0.7865(>0.7),表明存在重要的聚类结构;共同引用的参考文献网络分析的模块化Q值和平均轮廓值均大于0.6,第1个大规模聚类被命名为"#0 nitrogen deposition",同时,Q=0.6792(>0.3)和S=0.8705(>0.7)。【结论】土壤温度敏感性和碳汇等主要内容是未来的研究热点。日后的研究应关注氮沉降驱动土壤微生物活动、制约土壤碳循环、多层网络稳定性机制、土壤微生物碳利用效率等方面。

基于Cite Space对螺旋藻藻蓝蛋白的研究进展与热点分析

螺旋藻(Spirulina)藻蓝蛋白具有独特的理化特性及生理功能,是药物、食品和化妆品的天然原料,具有较大的开发潜力。为探讨螺旋藻藻蓝蛋白的研究现状与发展前景,对中国知网和Web of Science数据库中1990—2023年发表的文献进行检索并筛选,使用Cite Space软件对文章发文量、研究团队及研究热点进行图谱分析。综合分析可知,国内年发文量偏少,呈平稳趋势;国外年发文量持续上升,尤其近几年发文量迅速增长,且发文量超过了100篇;国外研究热点集中于藻蓝蛋白在食品、医药行业的应用方面,而国内研究热点集中在提取纯化、稳定性、功能活性的研究与应用,下一步应结合研究现状开发适合规模化生产的提取纯化工艺,进一步加强藻蓝蛋白研究的广度与深度;国内外研究群体主要是高校的相关生物技术学院或研究机构等,总体来讲,学者间存在较为密切的合作,但研究机构间尚未形成紧密的合作关系,在地域上比较分散,各大高校和研究机构应突破地区或机构间的各种限制,促进该研究领域的深度融合和快速发展,深入挖掘藻蓝蛋白在各个领域的潜在应用。

基于CiteSpace的红色文创设计可视化分析与方法研究

目的基于CiteSpace概述国内红色文创设计的发展状况,梳理关于红色文创设计的相关方法与理论模型,探讨未来红色文创设计发展的趋势,为研究者提供相关设计思路与发展动向。方法通过对国内相关文献的研究,梳理了红色文创设计的概念与发展现状,归纳了当前红色文创设计中包括文化转译维度、文化层次理论、叙事性设计的三种主要方法与理论模型。结论目前红色文创设计方法较为单一,很难满足用户的不同需求,且缺乏具体针对性的完整理论体系与设计方法。在今后的红色文创设计中,可以综合运用叙事性及文化转译维度等设计理论和手段,对红色文创设计和方法加以探究和创新,打造具有中国特色的红色文创产品,使红色文化得到更好的普及和弘扬。

基于Cite Space可视化分析我国多发伤急救研究热点及趋势

目的梳理国内多发伤急救相关研究文献,分析研究现状、热点和趋势,为我国多发伤急救研究提供借鉴和指导。方法检索中国知网数据库中2011—2021年关于多发伤急救的相关文献,使用Cite Space 6.1.R3可视化软件对该领域的年发文量、机构、作者、关键词进行分析。结果最终纳入多发伤急救研究文献2519篇,整体发文数量较平稳,以2016年为小高峰;发文量最高的机构是华中科技大学附属同济医院。多发伤急救研究热点包括院前急救、并发症护理、风险因素分析和预后效果评估,研究前沿包括不同多发伤人群的诊断、治疗、手术和护理体会等方面。结论本文通过可视化分析国内多发伤急救研究的热点及趋势,指明了多发伤目前研究存在的问题和未来研究发展的方向,为进一步完善多发伤急救卫生服务和管理体系提供指导。

基于CiteSpace文献计量分析的智慧教育研究现状与展望

当前,以人工智能、教育大数据、学习分析等新兴技术为代表的智慧教育时代已然来临,伴随着智慧教育的快速发展,有关智慧教育研究的文献也极速增多。基于此,文章通过文献查阅及可视化分析对当前智慧教育研究现状进行梳理与分析,探求当下研究热点与未来趋势,为该领域研究者提供参考依据。可视化分析结果显示:相关研究发文量2023年达到顶峰,核心研究机构内部已经形成合力,但是机构间关系比较疏离;“智慧教育”和“人工智能”是这个阶段的热点和前沿、又是后续研究热点的研究基础,随着智慧教育进入高速发展期,智慧教育研究将更加深化、成果更趋丰富。

基于CiteSpace的我国高校公共空间研究热点及趋势分析

文章基于CiteSpace软件对CNKI数据库中相关609篇文献进行可视化分析,发现当前我国关于高校公共空间的研究具有阶段性、各地域间相互独立的特点;研究热点分布于空间扩充、功能拓展、设计优化多个角度,并朝着精细化、人性化、多学科交叉等趋势探索。总结现有研究提出加强地域间机构合作、融合多学科研究视角、引入创新性研究方法等发展方向,以期为新时代我国高校公共空间的研究提供思路和参考。

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

体育教师信念的国际研究现状与趋势——基于CiteSpace的文献计量分析

高质量教师是高质量教育发展的中坚力量。教师信念作为教师专业素养构成的关键要素,对促进教师专业发展、提升教师质量具有重要作用与影响。为借鉴国际体育教师信念研究的成果与经验,促进国内对体育教师信念的研究,研究利用CiteSpace软件,对Web of Science核心合集数据库中1960—2022年的英文文献进行可视化研究。发现:体育教师信念研究高潮出现于2021年,载文数量最多的期刊是Journal of Teaching in Physical Education;研究中心度最高的国家是美国,核心圈层的代表学者是Richards KAR、Kulinna PH和Curtner-smith MD等人;研究热点趋势集中于体力活动促进、职业社会化、批判性教学法、职前体育教师、专业发展等方面。启示:国内未来研究应重点关注体育教师信念对课程改革的影响以及促进职前、职后阶段体育教师信念的发展。

我国家政服务业研究综述——基于Cite Space知识图谱的可视化分析

家政服务业既是满足人民日益增长的美好生活需要的重要载体,也是保障民生、扩大就业、实现共同富裕的重要渠道。近年来,我国家政服务业因有“为”而有“位”,关于家政服务业的研究也逐渐受到重视。本研究以CNKI数据库中1981年1月1日至2022年12月31日期间有关家政服务业研究的1984篇文献为依据,运用Cite Space软件6.1.R3.64bit版本分析了我国家政服务研究的总体趋势、研究热点演进,并提出加快我国家政服务业研究的对策建议,推动家政服务业可持续健康发展。

乡村景观设计研究现状与发展趋势——基于Cite Space的分析

本研究利用Cite Space软件对CNKI中以乡村景观设计为主题的期刊文献进行分析,以探究国内研究现状与发展趋势。研究发现,国内乡村景观设计的研究相对独立,缺乏系统的沟通转化。随着社会发展,生态、文化逐渐成为关注的焦点。在后续研究中,应拓宽学科交流与文化表达途径,促进体系建构与应用发展,促进乡村景观和谐发展。

Simulation study of the performance of the Very Large Area gamma-ray Space Telescope

The Very Large Area gamma-ray Space Telescope(VLAST)is a mission concept proposed to detect gamma-ray photons through both Compton scattering and electron–positron pair production mechanisms,thus enabling the detection of photons with energies ranging from MeV to TeV.This project aims to conduct a comprehensive survey of the gamma-ray sky from a low-Earth orbit using an anti-coincidence detector,a tracker detector that also serves as a low-energy calorimeter,and a high-energy imaging calorimeter.We developed a Monte Carlo simulation application of the detector using the GEANT4 toolkit to evaluate the instrument performance,including the effective area,angular resolution,and energy resolution,and explored specific optimizations of the detector configuration.Our simulation-based analysis indicates that the current design of the VLAST is physically feasible,with an acceptance above 10 m^(2)sr which is four times larger than that of the Fermi-LAT,an energy resolution better than 2%at 10 GeV,and an angular resolution better than 0.2◦at 10 GeV.The VLAST project promises to make significant contributions to the field of gamma-ray astronomy and enhance our understanding of the cosmos.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

基于Cite Space对近5年发酵领域的研究进展与热点分析

近年来,我国生物产业快速发展,且发酵对身体和胃肠道健康具有很多益处,有关微生物发酵的研究逐渐增多。为探讨发酵领域近5年的研究现状与发展前景,对中国知网和Web of Science数据库中2019—2023年发表的文献进行检索并筛选,使用Cite Space软件对文章发文量、研究团队、研究热点进行图谱分析。结果表明,近5年国内发酵领域发文量偏少,年发文量平稳上升;国外发文量较多,年发文量超过100篇,2023年发文量达到200篇;国内外研究群体主要是中医药高校和研究机构;国内外研究热点集中在抗氧化和肠道菌群,国内研究重点为发酵技术,而国外研究范围较广,重点为益生菌的研究;国内外学者间合作较为密切,但研究机构间尚未形成紧密的合作关系。高校和研究机构应突破地区间的限制,促进发酵研究领域的融合。肠道菌群是近5年发酵的研究热点,也是未来发酵研究深入的对象。