Quantum communication for satellite-to-ground networks with partially entangled states

To realize practical wide-area quantum communication,a satellite-to-ground network with partially entangled states is developed in this paper.For efficiency and security reasons,the existing method of quantum communication in distributed wireless quantum networks with partially entangled states cannot be applied directly to the proposed quantum network.Based on this point,an efficient and secure quantum communication scheme with partially entangled states is presented.In our scheme,the source node performs teleportation only after an end-to-end entangled state has been established by entanglement swapping with partially entangled states.Thus,the security of quantum communication is guaranteed.The destination node recovers the transmitted quantum bit with the help of an auxiliary quantum bit and specially defined unitary matrices.Detailed calculations and simulation analyses show that the probability of successfully transferring a quantum bit in the presented scheme is high.In addition,the auxiliary quantum bit provides a heralded mechanism for successful communication.Based on the critical components that are presented in this article an efficient,secure,and practical wide-area quantum communication can be achieved.

Modeling and Analysis of Mesh Tree Hybrid Power／Ground Networks with Multiple Voltage Supply in Time Domain

This paper proposes a novel algorithm, which can be used to model and analyzemesh tree hybrid power/ground distribution networks with multiple voltage supply in time domain.Not only this algorithm enhances common method''s ability on analysis of power/ground network withirregular topology, but also very high accuracy it keeps. The accuracy and stability of thisalgorithm is proved using strict math method in this paper. Also, the usage of both preconditiontechnique based on Incomplete Choleskey Decomposition and fast variable elimination technique hasimproved the algorithm''s efficiency a lot. Experimental results show that it can finish the analysisof power/ground network with enormous, size within very short time. Also, this algorithm can beapplied to analyze the clock network, bus network, and signal network without buffer under highworking frequency because of the independence of the topology.

Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application

In this paper,a layer-constrained triangulated irregular network（ LC-TIN） algorithm is proposed for three-dimensional（ 3 D） modelling,and applied to construct a 3 D model for geological disease information based on ground penetrating radar（ GPR） data. Compared with the traditional TIN algorithm,the LCTIN algorithm introduced a layer constraint to the discrete data points during the 3 D modelling process,and it can dynamically construct networks from layer to layer and implement 3 D modelling for arbitrary shapes with high precision. The experimental results validated this method,the proposed algorithm not only can maintain the rationality of triangulation network,but also can obtain a good generation speed. In addition,the algorithm is also introduced to our self-developed 3 D visualization platform,which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.

Three-dimensional Fusion of Spaceborne and Ground Radar Reflectivity Data Using a Neural Network–Based Approach

The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.

Study on Decision Method of Neutral Point Grounding Mode for Medium-Voltage Distribution Network

The neutral grounding mode of medium-voltage distribution network decides the reliability, overvoltage, relay protection and electrical safety. Therefore, a comprehensive consideration of the reliability, safety and economy is particularly important for the decision of neutral grounding mode. This paper proposes a new decision method of neutral point grounding mode for mediumvoltage distribution network. The objective function is constructed for the decision according the life cycle cost. The reliability of the neutral point grounding mode is taken into account through treating the outage cost as an operating cost. The safety condition of the neutral point grounding mode is preserved as the constraint condition of decision models, so the decision method can generate the most economical and reliable scheme of neutral point grounding mode within a safe limit. The example is used to verify the feasibility and effectiveness of the decision method.

Fault Line Selection Method Considering Grounding Fault Angle for Distribution Network

In the distribution network system with its neutral point grounding via arc suppression coil, when single-phase grounding fault occurred near zero-crossing point of the phase voltage, the inaccuracy of the line selection always existed in existing methods. According to the characteristics that transient current was different between the fault feeder and other faultless feeders, wavelet transformation was performed on data of the transient current within a power frequency cycle after the fault occurred. Based on different fault angles, wavelet energy in corresponding frequency band was chosen to compare. The result was that wavelet energy in fault feeder was the largest of all, and it was larger than sum of those in other faultless feeders, when the bus broke down, the disparity between each wavelet energy was not significant. Fault line could be selected out by the criterion above. The results of MATLAB/simulink simulation experiment indicated that this method had anti-interference capacity and was feasible.

Predicting Ground Effects of Omnidirectional Antennas in Wireless Sensor Networks

Omnidirectional antennas are often used for radio frequency (RF) communication in wireless sensor networks (WSNs). Outside noise, electromagnetic interference (EMI), overloaded network traffic, large obstacles (vegetation and buildings), terrain and atmospheric composition, along with climate patterns can degrade signal quality in the form of data packet loss or reduced RF communication range. This paper explores the RF range reduction properties of a particular WSN designed to operate in agricultural crop fields to collect aggregate data composed of subsurface soil moisture and soil temperature. Our study, using simulation, anechoic and field measurements shows that the effect of antenna placement close to the ground (within 10 cm) signi?cantly changes the omnidirectional transmission pattern. We then develop and propose a prediction method that is more precise than current practices of using the Friis and Fresnel equations. Our prediction method takes into account environmental properties for RF communication range based on the height of nodes and gateways.

Analysis of Chinese/English Codeswitching on Network Language by Figure/ground Theory

With the rapid development of network language,more and more phenomena of Chinese/English Codeswitching appear in our daily communications.Codeswitching research of the network language is a new aspect in the linguistic field.The paper starts from cognitive perspective to discuss the process of Chinese/English Codeswitching by Figure/ground theory.

Observations on the application of artificial neural network to predicting ground motion measures

Application of the artificial neural network （ANN） to predict pseudospectral acceleration or peak ground acceleration is explored in the study. The training of ANN model is carried out using feed-forward backpropagation method and about 600 records from 39 California earthquakes. The statistics of the residuals or modeling error for the trained ANN-based models are almost the same as those for the parametric ground motion prediction equations, derived through regression analysis; the residual or modeling error can be modeled as a normal variate. The similarity and differences between the predictions by these two approaches are shown. The trained ANN-based models, however, are not robust because the models with almost identical mean square errors do not always lead to the same predictions. This undesirable behaviour for predicting the ground motion measures has not been shown or discussed in the literature; the presented results, at least, serve to raise questions and caution on this problem. A practical approach to ameliorate this problem, perhaps, is to consider several trained ANN models, and to take the average of the predicted values from the trained ANN models as the predicted ground motion measure.

Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect

Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.

Walking Stability Control Method for Biped Robot on Uneven Ground Based on Deep Q-Network

A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.

架空配电线路故障电弧的电磁辐射特性及故障定位应用

针对架空配电线路电弧接地故障点定位难题,该文研究架空配电线路故障电弧的电磁辐射特性,探索基于电磁辐射信号的电弧故障定位方法的可行性。通过10 k V配网真型故障模拟试验平台,分析接地电弧电磁辐射的时域与频域特性及传播衰减规律,结果表明:电弧电流的电磁辐射特征频段为20~30 MHz,该特征频段不会受到中性点接地方式、电弧接地介质与线路结构参数的显著影响,且特征频段内辐射信号在传播过程中衰减较慢。在此基础上,设计一种小型化三角形单极子–环形组合平面天线,工作频率为20~500 MHz。利用自制天线开展小型电弧故障定位实验,为后续配网电弧故障定位的应用研究提供基础。

基于PMC指数模型的“十四五”基础教育数字化转型政策评价——以江浙沪地区为例

“十四五”是我国推进基础教育数字化转型的关键阶段,区域政策的制订和引领至关重要,因此有必要对其进行科学的评价和全面的审视。江浙沪是基础教育数字化转型的示范地区,相关政策的科学性和有效性值得深入研究。首先,运用社会网络分析和扎根理论研究对全国29份相关政策文件进行文本挖掘,在此基础上构建基于PMC指数模型的评价指标体系。其次,针对江浙沪地区“十四五”期间八份政策样本进行PMC指数计算,并绘制PMC曲面图,得出政策评价结果(1份优秀、6份良好、1份及格)。再次,基于评价数据发现,相关政策在制定过程中目标规划长远、制度保障完善、政策重点分布广泛合理,且地域间各具特色,但在政策时效、课程建设、教育公平等领域还存在一定问题。最后,综合研究结果和实际情况,地方政府应细化政策阶段目标、提升教育公平认同感、加强课程建设和建立跨省沟通机制。

智能天地一体化网络的卫星跟踪测控技术综述

随着卫星互联网和我国航天测控技术的不断进步,航天测控网络朝着智能化、一体化的方向发展,在自主测控、资源分配等方面进展良好。因此,建立智能天地一体化的航天测控网是我国航天未来发展的重要目标。针对智能航天测控网中的跟踪测轨、遥测和遥控三个方面,分别介绍了相关原理与技术。同时,结合CCSDS提出的空间数据链路标准协议详细介绍了TM、TC、AOS、Proximity-1以及USLP标准,分析了不同标准所使用的技术与实际应用。本文从数据链路层和物理层的角度介绍了智能航天测控系统的工作原理及技术要求,为我国智能天地一体化卫星测控通信网的研究提供参考并予以展望。

中性点柔性接地配电网故障相恢复电压暂态时域特征分析

配电网接地故障过零熄弧后的电压暂态恢复特性决定了系统过电压水平及电弧重燃特性,目前,中性点柔性接地配电网故障熄弧后暂态恢复电压变化机理尚不明确,为此建立柔性接地配电网接地故障暂态等值电路,理论解析故障过零熄弧后故障恢复电压的时域表达式。在此基础上,对比分析配电网谐振接地与柔性接地方式下,系统参数变化对故障相恢复电压暂态峰值与恢复速度的影响,揭示暂态时间尺度下故障相恢复电压特征随注入零序电流值与电流注入时机的变化规律,阐明通过中性点注入可控零序电流抑制故障暂态过电压的机理。在PSCAD/EMTDC仿真环境与10 kV真型配电网实验场中模拟各种运行和故障工况,仿真与真型实验结果均验证柔性接地配电网故障相恢复电压时域解析特征与变化规律的正确性。

地面控制网变形对盾构基线边的影响分析

在地铁施工建设过程中,测量工作对于盾构掘进、结构施工的准确性有着重要作用。在地铁施工控制测量工作中,需要将地面上的控制点通过联系测量的形式,将坐标、高程传递到地下控制点,以地下控制点作为基线点来指导施工。地面控制点的成果作为联系测量起算依据,其精度直接决定了地下控制点的精度。文章通过分析地面控制网变形对盾构基线边的影响,探讨了提高控制测量精度的相关措施,如优化地面控制网、应用高精度的仪器设备等。

社区团购农产品供应链的风险识别与风险网络结构分析——以淘菜菜为例

社区团购农产品供应链风险网络复杂,增加了风险防范的难度。准确识别风险因素,厘清风险外溢路径,对建立与完善社区团购农产品供应链风险管理体系和维持行业健康发展具有重要意义。以电商平台淘菜菜为案例,使用扎根理论及社会网络分析等方法,探究社区团购农产品供应链的风险,构建风险网络,评估风险关联程度,找出风险外溢路径。研究发现:在社区团购农产品供应链的风险网络中,物流方的履约交付、物流方的运输损耗、物流方的节点周转、平台的人员服务、平台的供应链计划、平台的采购交付是关键风险节点,有较强的风险影响能力。风险网络可根据风险节点间的关联程度划分为四个板块,四个板块间存在“溢出—影响—再溢出”的风险外溢路径。当某一风险节点出现问题时,与其隶属同一利益相关者或同一风险范畴的风险节点将会进一步放大风险外溢。鉴于此,找出社区团购农产品供应链的关键风险和外溢路径,有针对性地加强风险管理,强化路径监控,降低关键风险的影响,增强供应链的抗风险能力,可促进社区团购电商平台的可持续发展。

基于神经网络的跨越地裂缝框架结构地震损伤及预测研究

为开展特殊地质环境下结构的损伤分析,以一跨越西安f4地裂缝的五层框架结构为研究对象,基于振动台试验和ABAQUS有限元分析结果,进行了BP神经网络模型的模型训练,选取变形和能量组合形式的双参数损伤模型计算结构损伤指标,采用加权系数法,开展了构件、楼层、结构三个层面的损伤预测分析,给出了不同地震作用下结构损伤程度评估。结果表明:地裂缝场地结构表现出明显上下盘效应,结构首层为薄弱层。BP神经网络损伤预测值与有限元计算值在不同工况下均较为一致,其对于构件、层间、整体结构损伤指数预测最大误差分别为8.86%、5.66%、7.57%,该研究成果为跨越地裂缝结构的性能评估提供一种准确且高效的研究方法。

天地一体化网络关键技术研究综述

作为地面网络的补充和延伸,卫星网络有助于加速弥合区域间的数字鸿沟,扩展地面网络的覆盖和服务范围.然而卫星网络拓扑动态性高、传播时延大、星上计算能力和存储能力均受限,因此实现卫星网络与地面网络的有机融合,构建覆盖全球的天地一体化网络面临路由扩展性、传输稳定性等技术挑战.针对天地一体化网络的研究挑战,从网络架构、路由、传输和基于组播的内容分发等方面介绍了国内外的研究现状,并展望了天地一体化网络的发展趋势.

基于多域物理信息神经网络的复合地层隧道掘进地表沉降预测

复合地层中盾构掘进诱发地表沉降的准确预测是隧道工程安全建设与施工决策的关键问题。基于隧道施工诱发地层变形机制构建隧道收敛变形与掘进位置的联系,并将其耦合至深度神经网络(deep neural network,简称DNN)框架,建立了预测盾构掘进诱发地层变形的物理信息神经网络(physics-informed neural network,简称PINN)模型。针对隧道上覆多个地层的地质特征,提出了多域物理信息神经网络(multi-physics-informed neural network,简称MPINN)模型,实现了在统一的框架内对不同地层的物理信息分区域表达。结果表明:MPINN模型高度还原了有限差分法的计算结果,可以准确预测复合地层中隧道开挖诱发的地表沉降;由于融入了物理机制,MPINN模型对隧道施工诱发地表沉降的问题具有普适性,可应用于不同地质和几何条件下隧道诱发地表沉降的预测;基于工程实测数据,提出的MPINN模型准确预测了监测断面的地表沉降曲线,可为复合地层下盾构掘进过程中地表沉降的预测预警提供参考。