Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

基于AIS轨迹和改进蚁群算法的船舶航线规划方法

在保证船舶航线安全的前提下,以最短航程为目标,提出基于AIS轨迹和改进蚁群算法的船舶航线规划方法。对船舶AIS数据进行预处理,去除船舶AIS数据中的冗余数据,完成船舶AIS数据提纯;采用基于粒子群与K均值混合聚类算法的核心转向点筛选与识别方法,筛选并识别船舶AIS数据中船舶航线核心转向点数据;通过基于改进蚁群算法的航线规划方法,以核心转向点数据为基础,构建航线网络,在此网络中,通过人工势场法对蚁群算法进行改进,对船舶航线进行寻优,实现船舶航线规划。经实验验证,本文方法能够规划出安全合理的船舶航线。

结合地理信息与AIS数据的海上航道自适应提取方法

海上航道对于海上态势感知具有重要意义。然而,现有的航道提取方法存在航道航路点易丢失、提取准确性不高等问题,本文依据船舶航路点数量与其途经岛屿、港口等地理区域数量相等的准则,提出了一种结合地理信息与自动识别系统(Automatic Identification System, AIS)数据的海上航道自适应提取方法。首先,构建港口之间往返船只AIS航迹数据集,将每条AIS航迹等间距分段并计算每段航迹的平均航速,设置航速差初始阈值,计算相邻航段平均航速差超过阈值的数量作为初始船只转弯次数。其次,统计AIS航迹途经岛屿、港口等地理区域的数量,若其与利用AIS航迹提取的船舶转弯次数不相等,则根据两者的差异自适应调整航速差阈值,重新提取船舶航路点,直至其与途经地理区域的数量相等,将平均航速差超出最终阈值的航迹段的连接点作为船舶航路点。然后,利用基于密度的聚类算法将所有船舶的航路点聚类为航路点集合,计算航路点集合的质心作为航道的航路点。最后,以航道航路点为顶点、航路点之间的连线为边构建有向图,并删除落在陆地上的连线后得到最终的航道提取结果。利用潍坊港—连云港港之间行驶的20条船舶的AIS数据开展了海上航道提取实验,结果表明,与基于交通路径异常检测的航道提取方法相比,本文方法得到的船舶航路点的平均虚检率降低了9.1%,平均漏检率降低了16.7%,显著提升了航道航路点位置提取的准确性。

基于AIS大数据耦合分析的沿海港口泊位利用率研究

针对我国沿海港口能力供给水平数据跟踪方面长期存在的时效性差、人为统计过程中易出现错漏等诸多问题,分析传统泊位通过能力统计失真的具体原因,提出将泊位利用率作为评价港口服务水平的表征指标,依托地理信息系统(GIS)平台和基于船舶自动识别系统(AIS)等数据耦合的空间拓扑分析,综合考虑空间关系、航速特征、经留时间等影响因素,研发基于AIS大数据的泊位利用率算法模型,并以上海港2019年集装箱泊位利用率为例进行算法验证。结果表明,所提出的泊位利用率算法模型是可信的;提供了一种能够反映客观实际、定量分析判断港口服务水平的技术手段,可为政府部门长期动态监测港口能力与运输需求互动平衡关系,支撑政府部门决策港口发展重点和建设时序,避免空间资源浪费、重复建设、能力过剩等问题提供技术支撑。

太阳耀斑对东海海域船舶AIS通信的影响分析

太阳耀斑是一种强烈的电磁辐射爆发,耀斑爆发时会影响地球磁场的稳定,造成电离层扰动,干扰无线电通信,并对航天器、卫星导航系统等造成影响。研究了太阳耀斑对船舶AIS通信影响,以中国东海海域为例,分析2024年5月初数次耀斑发生的前、中、后时间段的AIS数据。研究发现,船舶AIS数据本身传输的错误占比与太阳耀斑没有明显关系,但是根据AIS数据的船舶定位信息和时间计算得到的表观速度的方差在耀斑发生的前、中、后时间段有明显差异,结果表明太阳耀斑对船舶AIS的定位信息有较大影响。

基于AIS数据的船舶风险领域模型

为构建船舶风险领域及分析其特征,基于船舶自动识别系统(Automatic Identification System,AIS)数据提出一种具有风险级别的船舶领域模型。首先,根据预处理后的AIS数据,获取他船相对于本船的位置。然后,采用椭圆领域边界对船舶相对位置数据进行筛选,同时获取到代表风险级别的临界点,并使用最小二乘法对其进行拟合,从而得到船舶风险领域。最后,利用老铁山水道中149 m、190 m、229 m、300 m船舶的AIS数据对所提方法进行验证,并分析船舶风险领域的特征。结果表明,该方法可以较好地反映船舶的风险级别;在同一风险级别时,不同尺度船舶间的风险领域长、短半轴与船长之比差异较小;风险级别为1的船舶领域边界接近于圆形;他船在本船周围分布的密集程度不同。本研究所提模型对航行安全保障、航行风险研究有一定的参考意义。

“看得见”的海上通信实验设计——以AIS虚拟仿真实验为例

实验案例对应的理论课是“船舶自动识别系统”,课程讲述船舶自动识别系统(AIS)进行海上通信与船舶识别的工作原理及信息传输过程中的编解码等关键技术,旨在培养交通运输领域的创新型人才。真实AIS设备价格昂贵,仅限于界面操作,接收到的船舶数据种类有限,理论课中的难点未能在实验中解决,大大降低了学生学习的主动性。另外考虑到未来虚拟仿真实验室的建设,课程组设计了AIS信号源模拟器实验平台,可实时发送AIS各种类型的报文数据,让学生能够“看得见”船舶发送的二进制暗码数据,“看得见”船舶的位置和航速、航向等信息,提高了学生学习兴趣,解决了理论课难点;同时学生基于模拟器数据进行二次开发,提高了自身学习的主动性和创新性。

复杂环境下的窄口段水域AIS轨迹重塑方法研究

针对窄口段航道船舶自动识别系统(AIS)轨迹信息易出错和缺失的问题,对宁波舟山港条帚门水域及其船舶基本情况进行统计分析,并提出AIS船舶轨迹修复方法。对异常数据归一化处理构建预输入模型,提出一种基于DS(Dempster-Shafer)证据融合理论的双向长短期记忆的多层前馈网络修复方法,将此方法与BP(Back Propagation)、长短期记忆网络(LSTM)方法进行试验论证比较。结果表明:所提方法在轨迹修复4个维度上均优于其他方法,且连续丢失点在20个以内,机器学习平均损失率为0.048 3%,均低于其他方法。修复后的船舶轨迹信息更加完整也更符合船舶运动规律。

How Could Agricultural Land Systems Contribute to Raise Food Production Under Global Change?

To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid iferce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And ifnally, crop-speciifc agricultural intensiifcation would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.

基于2比特判决反馈的改进AIS非相干解调算法

针对星载接收机接收到的船舶自动识别系统(Automatic Identification System,AIS)信号信噪比低、高斯最小频移键控调制采用的高斯成形滤波器产生码间干扰(Inter-Symbol Interference,ISI)以及非相干解调鲁棒性差等问题,提出了一种基于2比特判决反馈的改进AIS非相干解调算法。首先,接收信号通过匹配滤波器、鉴频器和自适应检测滤波器获得基带信号;其次,构造判决反馈,补偿鉴频噪声与ISI;最后,设计一种基于长短时记忆(Long-short Term Memory,LSTM)与注意力机制(Attention)的相位路径预测模型,获取2比特反馈补偿,改进判决反馈补偿能力。仿真与实测结果表明,改进方法较1比特判决反馈法对自适应滤波器的适应能力更强,在信噪比为7.2 dB时,误码率降低至10-5;较传统方法,真实场景下AIS信号的循环冗余校验通过率提升了9.5%。

悬空波导环境下AIS信号超视距传播特性分析

大气波导可陷获无线电波信号,使其发生超视距传播现象,影响无线电系统信号传播预测和性能评估应用。本文针对由探空数据诊断的悬空波导个例,利用对流层电波传播确定性方法模拟岸-船自动识别系统(automatic identification system,AIS)传播链路,定量研究了不同悬空波导环境影响下AIS信号的超视距传播特性。结果表明:波导强度50 M单位且底高约7 m的悬空波导环境下,AIS信号发生明显超视距传播现象;波导底高约700 m且强度较大的悬空波导环境也会对AIS信号传播产生影响;相比于单一悬空波导模型,复合悬空波导(文中指两个悬空波导层结构组成)环境对AIS信号传播影响更大。本文研究成果可为大气波导监测反演、无线电系统设计、装备效应评估等应用提供重要理论参考。

船舶自动识别系统(AIS)数据在海洋渔业中研究应用现状

在全球海洋主要经济渔业资源衰退的背景下,如何保护和可持续开发利用海洋渔业资源受到全球各国、地区和组织高度重视,一直是研究热点。受传统海洋渔业数据的制约,一直难以全面了解远洋渔船的捕捞足迹,因此,无法对其实行有效监控和管理。船舶自动识别系统(automatic identification system,AIS)提供的全球远洋渔船轨迹数据可以用于量化分析从单艘到全球渔船行为,挖掘的历史捕捞强度空间信息可为海洋捕捞活动的监测管理和生态压力评估提供良好的可替代数据来源,成为近年海洋信息和海洋渔业研究的新热点。为促进AIS数据在我国海洋渔业中的研究应用,对AIS近年的研究内容和应用现状进行总结,指出AIS数据目前研究的不足和未来潜在的研究方法,以期为AIS在海洋渔业中的研究和应用提供参考。

SII、SIRI和AISI对高血压颈动脉斑块形成的预测价值

目的:探讨系统性免疫炎症指数(SII)、系统性炎症反应指数(SIRI)和系统性炎症聚集指数(AISI)对高血压颈动脉斑块形成的预测价值。方法:选择2023年5月1日至2023年6月30日于河南中医药大学第一附属医院接受颈动脉血管彩超检查的高血压患者465例,其中有颈动脉斑块形成326例(斑块组),无颈动脉斑块形成139例(对照组),进行血液生化检查,计算SII、SIRI、AISI。采用Logistic回归分析SII、SIRI和AISI与高血压颈动脉斑块形成的关联性。采用ROC曲线评价SII、SIRI和AISI对高血压颈动脉斑块形成的预测价值。结果:斑块组SII、SIRI、AISI分别为450.72(283.76,769.99)、71(50,110)、160.64(91.58,272.53),对照组分别为319.81(233.65,406.69)、45(35,59)、110.48(73.78,141.49),斑块组均大于对照组(P<0.05)。Logistic回归分析结果显示,校正了年龄、性别、收缩压、糖尿病和高脂血症等因素后,SII、SIRI和AISI与高血压颈动脉斑块形成存在关联性,OR(95%CI)分别为1.004(1.003~1.006)、1.035(1.024~1.047)、1.010(1.006~1.013)。ROC曲线分析结果显示,SII、SIRI和AISI预测高血压颈动脉斑块形成的AUC(95%CI)分别为0.685(0.637~0.733)、0.752(0.707~0.797)、0.680(0.631~0.728)。结论:SII、SIRI、AISI可能有助于高血压颈动脉斑块形成的预测。

Conventional Geothermal Systems and Unconventional Geothermal Developments: An Overview

This paper provides an overview of conventional geothermal systems and unconventional geothermal developments as a common reference is needed for discussions between energy professionals. Conventional geothermal systems have the heat, permeability and fluid, requiring only drilling down to °C, normal heat flow or decaying radiogenic granite as heat sources, and used in district heating. Medium-temperature (MT) 100°C - 190°C, and high-temperature (HT) 190°C - 374°C resources are mostly at plate boundaries, with volcanic intrusive heat source, used mostly for electricity generation. Single well capacities are °C - 500°C) and a range of depths (1 m to 20 Km), but lack permeability or fluid, thus requiring stimulations for heat extraction by conduction. HVAC is 1 - 2 m deep and shallow geothermal down to 500 m in wells, both capturing °C, with °C are either advanced by geothermal developers at <7 Km depth (Enhanced Geothermal Systems (EGS), drilling below brittle-ductile transition zones and under geothermal fields), or by the Oil & Gas industry (Advanced Geothermal Systems, heat recovery from hydrocarbon wells or reservoirs, Superhot Rock Geothermal, and millimeter-wave drilling down to 20 Km). Their primary aim is electricity generation, relying on closed-loops, but EGS uses fractures for heat exchange with earthquake risks during fracking. Unconventional approaches could be everywhere, with shallow geothermal already functional. The deeper and hotter unconventional alternatives are still experimental, overcoming costs and technological challenges to become fully commercial. Meanwhile, the conventional geothermal resources remain the most proven opportunities for investments and development.

Systems Theory-Driven Framework for AI Integration into the Holistic Material Basis Research of Traditional Chinese Medicine

This paper introduces a systems theory-driven framework to integration artificial intelligence(AI)into traditional Chinese medicine(TCM)research,enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles.Utilizing the System Function Decoding Model(SFDM),the research progresses through define,quantify,infer,and validate phases to systematically explore TCM’s material basis.It employs a dual analytical approach that combines top-down,systems theory-guided perspectives with bottom-up,elements-structure-function methodologies,provides comprehensive insights into TCM’s holistic material basis.Moreover,the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components,proposing two specific AIdriven technical applications.This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.

Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

基于联邦学习的船舶AIS轨迹预测算法

联邦学习是一种可以在弱通信环境下有效解决数据孤岛问题的分布式机器学习方法。针对海上船舶轨迹实时预测问题,提出基于Fedves联邦学习框架与卷积神经网络-门控循环单元(CNN-GRU)模型的船舶轨迹预测算法(E-FVTP)。根据Fedves联邦学习框架,通过规范客户端数据集规模以及客户端正则项,在保持原有客户端数据特征的前提下,减小数据非独立同分布特征对全局模型的影响,加快收敛速度。面向海洋通信资源短缺场景,建立基于船舶自动识别系统(AIS)数据的CNN-GRU船舶轨迹预测模型,解决了船舶终端设备计算能力不足的问题。在MarineCadastre开源和舟山海洋船舶航行AIS数据集上的实验结果表明,E-FVTP在预测误差比集中式训练降低40%的情况下,收敛速度提升67%、通信代价降低76.32%,可实现复杂海洋环境中船舶轨迹的精确预测,保障海上交通安全。

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

冷锋推进中的大气波导变化及其对AIS传播影响研究

冷锋推进中会使得海域内的气温、相对湿度、风速等气象要素分布发生显著改变,导致与气象要素紧密关联的大气波导出现的类型及分布范围随之改变。文中利用大气再分析数据分析了2018年3月中旬某次冷锋过境中不同阶段大气波导的变化过程,并利用电磁波传播模型研究了冷锋推进过程中大气波导对船舶自动识别系统(AIS)传播的影响,结果表明:冷锋开始和发展阶段,出现的蒸发波导对AIS传播影响较弱;冷锋延伸和结束阶段,出现的多类型大气波导对AIS传播影响较强。因此,为有效监控海域内的船舶状态,应增加对冷锋开始和发展阶段的补充监控措施。