Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

An Implicit Coupled 1D/2D Model for Unsteady Subcritical Flow in Channel Networks and Embayment

In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method and targeted long narrow reaches of the river networks, while the 2D model targeted broad channels and embayment and solved the 2D shallow-water equations using a semi-implicit scheme applied to an unstructured grid of triangular cells. The 1D and 2D models were solved simultaneously by building a matrix for the free surface elevation at every 1D junction and 2D cell center. Velocities were then computed explicitly based on the results at the previous time step and the updated water level. The originality of the scheme arose from a novel coupling method. The results showed that the coupled 1D/2D model produced identical results as the full 2D model in classical to benchmark problems with considerable savings in computational effort. Application of the model to the Pearl River Estuary in southern China showed that complex patterns of tidal wave propagation could be efficiently modeled.

Nonlinear Control of Magnetically Coupled Rodless Cylinder Position Servo System

Magnetically coupled rodless cylinders are widely used in the coordinate positioning of mechanical arms,electro-static paintings,and other industrial applications.However,they exhibit strong nonlinear characteristics,which lead to low servo control accuracy.In this study,a mass-flow equation through the valve port was derived to improve the control performance,considering the characteristics of the dynamics and throttle-hole flow.Subsequently,a fric-tion model combining static,viscous,and Coulomb friction with a zero-velocity interval was proposed.In addition,energy and dynamic models were set for the experimental investigation of the magnetically coupled rodless cylin-der.A nonlinear mathematical model for the position of the magnetically coupled rodless cylinder was proposed.An incremental PID controller was designed for the magnetically coupled rodless cylinder to control this system,and the PID parameters were adjusted online using RBF neural network.The response results of the PID parameters based on the RBF neural network were compared with those of the traditional incremental PID control,which proved the superiority of the optimization control algorithm of the incremental PID parameters based on the RBF neural network servo control system.The experimental results of this model were compared with the simulation results.The average error between the established model and the actual system was 0.005175054(m),which was approximately 2.588%of the total travel length,demonstrating the accuracy of the theoretical model.

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network

The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining device reliability.Existing temperature monitoring methods based on the electro-thermal coupling model have limitations,such as ignoring device interactions and high computational complexity.To address these issues,an analysis of the parameters influencing IGBT failure is conducted,and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory(MMALSTM)recursive neural network is proposed,which takes the forward voltage drop and collector current as features.Compared with the traditional electricalthermal coupling model method,it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process.The simulation model of a highspeed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature.The simulation outcomes,which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model,confirm the reliability of this approach for predicting the temperature of IGBT power modules.

Modeling of the Coupling of the Lightning Shock Wave Flow Circuit in the High Voltage Electrical Network

This article focuses on the aggression of lightning overload on the equipment of the electrical network of sites where storm activity is very dense;and the electrocution of people located in the direct environment of the high-voltage substation during the flow of lightning current to the ground through the ground socket. The modeling of the flow circuit of the shock wave consisting of guard wire, lightning arrester and ground socket couple to the transformer of the high voltage substations, thanks to the approach of a servo block, led to the synthesis of a PID regulator (corrector) whose action is to reject the effects of the overvoltage on the network equipment and to significantly reduce or even cancel the effects of the step or touch voltage due to the distribution of the potential around the ground socket;and thus improve the quality of service of the high-voltage transmission and distribution electricity network, especially in stormy times.

Reluctance Network Analysis for Complex Coupled Inductors

The use of reluctance networks has been a conventional practice to analyze transformer structures. Basic transformer structures can be well analyzed by using the magnetic-electric analogues discovered by Heaviside in the 19th century. However, as power transformer structures are getting more complex today, it has been recognized that changing transformer structures cannot be accurately analyzed using the current reluctance network methods. This paper presents a novel method in which the magnetic reluctance network or arbitrary complexity and the surrounding electrical networks can be analyzed as a single network. The method presented provides a straightforward mapping table for systematically linking the electric lumped elements to magnetic circuit elements. The methodology is validated by analyzing several practical transformer structures. The proposed method allows the analysis of coupled inductor of any complexity, linear or non-linear.

Artificial Neural Network Model for Optical Fiber Direction Coupler Design

A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time saving. So it is promising in the field of both investigation and application.

Characteristic analysis of mechanical thermal coupling model for bearing rotor system of high-speed train

Based on Newton’s second law and the thermal network method,a mechanical thermal coupling model of the bearing rotor system of high-speed trains is established to study the interaction between the bearing vibration and temperature.The influence of lubrication on the vibration and temperature characteristics of the system is considered in the model,and the real-time relationship between them is built up by using the transient temperature field model.After considering the lubrication,the bearing outer ring vibration acceleration and node temperature considering grease are lower,which shows the necessity of adding the lubrication model.The corresponding experiments for characteristics of vibration and temperature of the model are respectively conducted.In the envelope spectrum obtained from the simulation signal and the experimental signal,the frequency values corresponding to the peaks are close to the theoretical calculation results,and the error is very small.In the three stages of the temperature characteristic experiment,the node temperature change of the simulation model is consistent with the experiment.The good agreement between simulation and experiments proves the effectiveness of the model.By studying the influence of the bearing angular and fault size on the system node temperature,as well as the change law of bearing lubrication characteristics and temperature,it is found that the worse the working condition is,the higher the temperature is.When the ambient temperature is low,the viscosity of grease increases,and the oil film becomes thicker,which increases the sliding probability of the rolling element,thus affecting the normal operation of the bearing,which explains the phenomenon of frequent bearing faults of high-speed trains in the low-temperature area of Northeast China.Further analysis shows that faults often occur in the early stage of train operation in the low-temperature environment.

Right on time:measuring Kuramoto model coupling from a survey of wrist-watches

Using a survey of wrist-watch synchronization from a randomly selected group of independent volunteers, we model the system as a Kuramoto-type coupled oscillator network. Based on the phase data both the order parameter and likely size of the coupling are derived and the possibilities for similar research to deduce topology from dynamics are discussed.

Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction

Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.

热液成矿系统构造控矿理论

构造对成矿的控制是热液成矿系统的典型特征之一,系统剖析多重尺度控矿构造的几何学、运动学、动力学、流变学和热力学对认识矿床成因和预测找矿至关重要;而如何实现控矿构造格架、渗透性结构、成矿流体通道和矿化变形网络由静态到多尺度时-空四维动态的转变,查明流体通道和矿床增量生长过程与控制因素,揭示热液成矿系统的构造-流体耦合成矿机制和定位规律是亟待解决的关键科学难题。为此,我们在对已有相关成果系统梳理的基础上,提出了科学构建热液成矿系统构造控矿理论的基本要点与对应方法及应用范畴:(1)流体而非构造是构造控矿理论的中心,热液系统的流体流动与成矿作用受控于断裂带格架及其渗透性结构,其中渗透率是将流体流动与流体压力变化联系起来理解控矿构造的核心;(2)不同控矿构造组合的关键控制是构造差应力和流体压力的大小,而矿化类型的变化可能是由于构造应力场引起的容矿构造方位的不同和赋矿围岩之间的强度差异所致;(3)流体通道的生长始于超压流体储库上游围岩中孤立的微裂隙沿流体压力梯度最大的方向、随裂隙发育且相互连结而形成新的长裂隙,并最终连通形成断裂网络内的流体通道,矿床的增量生长发生在高流体通量的短爆发期,断层反复滑动驱动其内流体压力、流速和应力快速变化,当由此诱发的流体通道生长破坏了流体系统的动态平衡时,随之而来的流体快速降压就成为金属沉淀成矿的关键驱动因素;(4)以热液裂隙-脉系统野外地质观测和构造-蚀变-矿化网络三维填图为基础,通过宏观与微观各级控矿构造相结合、地质历史与构造应力分析相结合、局部与区域点-线-面相结合、浅部与深部相结合、时间与空间相结合、定性和定量相结合,对各种控矿因素开展多学科、多尺度、多层次、全方位综合研究,是应遵循的基本原则;(5)通过构造-蚀变-矿化网络填图,将蚀变-矿化体与控矿构造的类型、形态、规模、产状和间距等几何学特征联系起来,利用热液裂隙-脉系统和断裂网络拓扑学及矿体三维几何结构分析等定量方法查明控矿构造格架和渗透性结构并揭示矿化变形网络的连通性与成矿潜力;(6)合理构建地质模型,选取合适的热力学参数和动力学边界条件,利用HCh和COMSOL等方法,定量模拟成矿过程中的流体流动、热-质传递、应力变形和化学反应等的时-空变化,是揭示构造-流体耦合成矿机理和定位规律、预测矿化中心和确定找矿目标的有效途径。进而提出了构造控矿理论的研究流程:聚焦构造-流体耦合成矿机制和定位规律这一关键科学问题,选择热液裂隙-脉系统和构造-蚀变-矿化网络为重点研究对象;通过几何学描述、运动学判断、流变学分析、动力学解析和热力学综合,厘定控矿构造格架,定位矿化中心,示踪成矿流体通道和多种矿化样式的增量生长过程及其关键控制,揭示渗透性结构的时-空演变规律及构造再活化与成矿定位的成因关联,建立构造-流体耦合成矿模式,服务新一轮战略找矿突破。以胶东焦家金矿田为例,开展控矿构造理论研究和成矿预测应用实践,证实了其科学性和有效性。

山区铁路工程“路基-环境”绿色关键要素识别研究

为推动山区铁路路基工程与环境之间绿色优质协调发展,提出一种铁路“路基-环境”耦合关系分析及关键要素确定方法。首先,构建“路基-环境”交互影响网络,引入木桶理论构建绿色要素集;然后,运用系统耦合模型,测算系统耦合协调度并评估组合状态,分析铁路路基与环境的绿色协调状况;最后,借助复杂网络理论,以要素为节点,要素间影响强度为连边,形成协同、影响、扩散多层复杂关联网络,采取spearman和二维云模型相结合的方式确定多层网络要素耦合强度,并结合节点删除法确定铁路“路基-环境”的关键要素集。以某铁路路基为例进行研究,得出铁路路基与环境之间的耦合协调状态处于勉强协调和初级协调之间的结论,因此应对路基边坡植被防护、路堑开挖尺寸、路堤填筑尺寸、路基边坡工程防护4个关键要素加以重点管理,以实现铁路路基工程绿色化。

基于BP神经网络的水中双爆源爆炸冲击波峰值压力预测模型研究

为了获得水中等质量两爆源同步爆炸时冲击波耦合中心的峰值压力计算模型,利用Autodyn计算得到不同药量和爆距下的峰值压力数据.一方面根据量纲分析确定的函数形式拟合数据从而获得峰值压力的计算公式;另一方面对药量、爆距及峰值压力三类数据进行对数变换和归一化,并将其分为训练集和测试集,然后将训练集代入BP神经网络进行训练,得到结构相对简单、均方误差最小的BP神经网络预测模型.结果表明:公式计算结果和BP神经网络模型计算得到的峰值压力与实际值吻合较好,公式计算值与实际值的平均相对误差为1.08%,BP神经网络预测值与实际值的平均相对误差为0.52%,与公式计算相比,BP神经网络能够以更少的数据样本容量实现更高的精度预测.

考虑水动力条件及水系连通的平原河网圩区畅流活水方案

针对平原河网地区水动力条件不足等问题,以嘉兴市新塍镇为例,探究不同畅流活水方案对河道水质的改善效果。构建MIKE11水动力学水质耦合模型,分析“引水/水系连通—水质”驱动响应规律,以NH3-N浓度为主要指标,分析不同活水方案的水质改善效果,并提出优化调控方案。结果表明,集中引水对区域水质改善影响较小,分散设置多个引水点可明显提高水质;直接对水质较差河道进行补水,污染物消减率在8%~30%之间,水质改善效果较明显;合理的水系连通工程可进一步增加畅流活水方案水质改善效果。

中国降碳-减污-扩绿-增长协同发展空间关联网络特征及影响因素研究

协同推进降碳-减污-扩绿-增长已成为我国经济社会发展全面绿色转型的必然选择。基于我国30个省份面板数据(不包含港澳台地区以及西藏自治区数据),运用熵值法、耦合协调度模型和社会网络分析方法,分析各省份间降碳-减污-扩绿-增长协同演变趋势及空间关联网络特征。结果表明:①各省份降碳-减污-扩绿-增长协同效应的变化趋势基本一致,但在空间上呈现东部>东北>西部>中部的区域不均衡特征。②降碳-减污-扩绿-增长协同效应呈现以东部地区为核心的复杂空间网络结构,省际间空间关联性呈上升态势,但网络结构稳定性还有待提高。③北京市、天津市和上海市等地区凭借优越区位,在关联网络中处于主导地位,而宁夏回族自治区、黑龙江省和新疆维吾尔自治区等地区对其他地区的影响较小。④北京市、天津市和上海市等地区属于“主受益”板块,浙江省、广东省等地区属于“经纪人板块”,安徽省、江西省和湖北省等地区属于“净溢出”板块。⑤降碳-减污-扩绿-增长协同效应的空间关联网络受多种因素共同影响,人力资本水平、科技投入、市场化水平和数字经济发展均有利于空间关联关系的建立。研究显示,中国降碳-减污-扩绿-增长协同效应存在空间关联性,需进一步加强省份间的绿色合作与交流,共同推动降碳-减污-扩绿-增长协同发展。

基于BP神经网络的增强型地热系统产热性能:以伊通盆地花岗岩体热储层为例

探究增强型地热系统(enhanced geothermal systems,EGS)工程采热性能,对提高系统产热效率、延长储层寿命是至关重要的。以伊通盆地北部昌27井实际地质构造、岩性、地热特征及热物性测试为基础,以地下4054~4168 m花岗岩为热储层建立三水平井水热耦合数值模型,并在此水平井模型基础上建立BP神经网络热储产热能力预测模型,研究了不同影响因素对系统换热过程的影响。结果表明:BP神经网络预测生产温度、焓值差和压力差与水平井数值模型的相对误差基本不超过±1.0%、±2.0%和±3.0%,预测准确性较高。水平井地热开采中,对系统生产温度造成影响较大的因素是井间距和注入速度,对系统发电效率造成影响较大的因素是注入速度和裂隙渗透率。综合考虑各影响因素交互作用,采用三水平井开采地热能可选择开采方案井间距600 m,注入温度60℃,注入速度23 kg/s,裂隙渗透率1×10^(-10 )m^(2)。在该工况下系统的经济寿命可达32年,累积发电量为605.9 GW·h,比基础模型提高7.4%。

光储接入牵引供电系统应急供电方案下低频稳定性研究

“双碳”目标背景下,新能源较大规模接入牵引网可能成为铁路运输新趋势。为对意外失电的列车紧急牵引并打破铁路领域传统用能壁垒,实现铁路能源自给型低碳供电并与电网互为备用,各系统能量互联互通,首次提出一种应急供电方案灵活利用牵引网侧接入的光储装置牵引列车并对“光-储-车网”耦合系统的低频稳定性展开研究。首先,制定RPC双模式单相全桥逆变器的控制方法结合光储运行状态协调控制策略划分工作模态;然后,利用小信号及状态空间平均法建立光储、客车(CRH3型动车)以及投影到dq坐标系下铁路功率调节器直流侧阻抗模型;最后,利用所得“光-储-车网”3个子系统的导纳模型,引入一种适应多工况的无源判据,揭示控制器参数及多源对牵引供电系统电气稳定性的影响规律。研究结果表明:所提方案通过新能源及储能装置在昼夜多工况下均能长时持续稳定牵引列车,当新能源或电网供电出现意外二者可互补切换,故障条件下机车仍可受电稳定行走。此外,光储电压环比例增益对该系统阻抗匹配性稳定性有较强的电气敏感性,所建模型结合无源判据指导调参接入不同供电源补偿系统阻抗可治理部分失稳现象,该方案下储能系统具有良好的补偿特性。在Starsim半实物实验平台验证了“光-储-车网”耦合系统应急供电方案的可行性及稳定性研究的正确性。

局部土地利用类型变化对城市水文过程的影响研究

为探究城市下垫面局部土地利用类型变化对城市水文过程的影响,采用高效高精度城市雨洪模型(GAST)耦合管网模型(SWMM),模拟不同降雨重现期下动态改变土地利用类型(单块无下渗土地C_(1)、多块组合无下渗土地C_(2)、单块裸土地C_(3)、单块草地C_(4)和单块林地C_(5))对城市地表积水、管网排水的影响。结果表明:改变局部原始土地利用类型(B1)在小降雨重现期下降低了城市积水量,而在大降雨重现期下改变土地下渗面积和区域位置无法进一步减少积水,此时则需考虑非工程措施的极端暴雨应对模式。不同动态规划区域下渗强度导致管网排口流量增幅不同(C_(2)>C_(1)>C_(3)>C_(4)>C_(5)),隔渗能力越强对管网排口流量增幅影响越大,而管网径流控制率则呈相反趋势。结合对积水量的影响可知,增加下垫面下渗能力并结合排水系统可进一步降低城市内涝风险和管网排水负荷,且管网径流控制率同样可以作为评估动态规划可行性的一个重要指标。

基于耦合神经网络反应扩散模型的图像增强方法研究

随着人工智能技术的迅速发展,图像处理已经广泛应用于交通、医疗、农林、航空航天等领域,并且对图像质量要求也越来越高。针对图像增强问题,提出了一种利用神经网络模型,将FitzHugh-Nagumo反应扩散模型转变为微分动力系统的方法,来改进耦合神经网络,引入自适应的阈值,得到耦合神经元的非线性动力学模型,利用此模型可使图像对比度得到提升,起到增强图像的效果。该模型的适用面更广,图像对比度拉伸效果更好,视觉增强效果更为明显。

基于脉冲耦合神经网络的图像分割算法分析

为进一步提高脉冲耦合神经网络(PCNN)在图像分割中的性能,文章研究了基于PCNN模型的图像分割算法,并探讨其模型简化算法,旨在减少其中的参数量,从而提高图像分割处理效率和分割效果。借助Girl属性图像进行仿真验证,通过与OTSU算法及LevelSet算法的分割效果进行对比,文章论证了算法的有效性及可行性。实验结果表明,该算法具有显著的分割效果。