Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model

The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.

Analysis of Hops Length in Wireless Sensor Networks

Wireless sensor networks are provided with a limited source of power. The lifetime of such networks is an overwhelming matter in most network applications. This lifetime depends strongly on how efficiently such energy is distributed over the nodes especially during transmitting and receiving data. Each node may route messages to destination nodes either through short hops or long hops. Optimizing the length of these hops may save energy, and therefore extend the lifetime of WSNs. In this paper, we propose a theorem to optimize the hop’s length so to make WSN power consumption minimal. The theorem establishes a simple condition on hop’s length range. Computer simulation when performing such condition on Mica2 sensors and Mica2dot sensors reveals good performance regarding WSNs energy consumption.

Finding Multiple Length-Bounded Disjoint Paths in Wireless Sensor Networks

In a wireless sensor network, routing messages between two nodes s and t with multiple disjoint paths will increase the throughput, robustness and load balance of the network. The existing researches focus on finding multiple disjoint paths connecting s and t efficiently, but they do not consider length constraint of the paths. A too long path will be useless because of high latency and high packet loss rate. This paper deals with such a problem: given two nodes s and t in a sensor network, finding as many as possible disjoint paths connecting s and t whose lengths are no more than L, where L is the length bound set by the users. By now, we know that this problem is not only NP hard but also APX complete [1,2], which means that there is no PTAS for this problem. To the best of our knowledge, there is only one heuristic algorithm proposed for this problem [3], and it is not suitable for sensor network because it processes in a centralized way. This paper proposes an efficient distributed algorithm for this problem. By processing in a distributed way, the algorithm is very communication efficient. Simulation results show that our algorithm outperforms the existing algorithm in both aspects of found path number and communication efficiency.

Application of the Spectrum Peak Positioning Technology Based on BP Neural Network in Demodulation of Cavity Length of EFPI Fiber Optical Sensor

An Extrinsic Fabry-Perot Interferometric (EFPI) fiber optical sensor system is an online testing system for the gas density. The system achieves the measurement of gas density information mainly by demodulating the cavity length of EF- PI fiber optical sensor. There are many ways to achieve the demodulation of the cavity length. For shortcomings of the big intensity demodulation error and complex structure of phase demodulation, this paper proposes that BP neural net-work is used to locate the special peak points in normalized interference spectrum and combining the advantages of the unimodal and bimodal measurement achieves the demodulation of the cavity length. Through online simulation and actual measurement, the results show that the peak positioning technology based on BP neural network can not only achieve high-precision demodulation of the cavity length, but also achieve an absolute measurement of cavity length in large dynamic range.

基于空间句法的矿井应急疏散研究与应用

地下矿井作业危险,空间拓扑结构复杂,因而矿山安全事故时有发生,合理的应急疏散路线规划以及疏散指示布置,能够有效提高井下人员的逃生效率,降低人员伤亡与财产损失。结合某地下矿井实例,基于空间句法和GIS网络分析搭建地下矿井巷道网络模型,从逃生人员的空间认知角度出发,利用标准化角度选择度值选取巷道网络模型中的前景网络和背景网络作为主要避险通道,为应急疏散指示标志布置提供参考;考虑巷道宽度、巷道类型、巷道坡度等巷道基础属性信息,并结合空间句法指标——标准化角度整合度,构建巷道网络应急脆弱模型,顾及巷道空间单元的稳定性,规划井下人员的最优认知路径,进而实现了多目标的地下矿井紧急疏散路线规划。该研究不仅能够为地下矿井的巷道布局设计提供建议,还可为地下矿井的应急疏散路线规划辅助决策、应急预案制定提供理论依据,同时为紧急避险系统的建设与完善提供新的思路。

基于虚拟投影中心的抽水蓄能电站施工测量坐标系建立

抽水蓄能电站工程区面积小、高差大,常规的抽水蓄能电站施工控制网,是选取枢纽工程平均高程附近的一个高程面建立的自由网。由于工程高差较大,常规方法只能保证部分枢纽区长度归算变形小于25 mm/km,无法保证所有枢纽区长度变形值满足规范要求。为解决长度归算变形超限问题,减少长度归算变形对工程施工测量精度的影响,通过对常规施工控制网坐标系长度变形的分析,提出了基于虚拟投影中心的抽水蓄能电站施工测量坐标系统建立方法。结果表明:该方法能有效消除或减弱控制网成果对输水发电系统贯通测量误差的影响,使勘测设计阶段成果与施工阶段得到有效衔接,可满足高差约640.00 m以内的抽水蓄能电站中长度归算变形不大于25 mm/km的要求,具有一定的推广价值。

面向加密流量的社交软件用户行为识别

随着智能终端和社交网络越来越融入人们的日常生活,针对社交软件的用户行为识别在网络管理、网络环境监管和市场调研等方面发挥越来越重要的作用.社交软件普遍使用端到端加密协议进行加密数据传输,现有方法通常提取加密数据的统计特征进行行为识别.但这些方法识别的性能不稳定且需要的数据量多,这些缺点影响了方法的实用性.提出了一种面向加密流量的社交软件用户行为识别方法.首先,从加密流量中识别出稳定的控制流数据,并提取控制服务数据分组负载长度序列.然后设计了2种神经网络模型,用于自动从控制流负载长度序列中提取特征,细粒度地识别用户行为.最后,以WhatsApp为例进行了实验,2种神经网络模型对WhatsApp用户行为的识别精准率、召回率和F1-score均超过96%.与类似工作的实验比较证明了该方法识别性能的稳定性,此外,该方法能够通过很少的控制流数据分组达到较高的识别精准率,对实时行为识别的研究具有重要的现实意义.

卒中转诊网络特征与医疗费用及住院天数关系研究——基于广州医疗保险数据的回顾性分析

目的探讨医院在卒中转诊网络中的特征与医疗费用及住院天数的关系,为推进卒中分级诊疗提供政策建议。方法采用社会网络分析方法探索医疗机构在卒中转诊网络中的特征,采用多重线性回归分析网络结构指标对患者次均住院天数、次均住院总费用及个人自付费用的影响。结果在初始网络中共纳入卒中住院患者11918人,医院158家。随着网络阈值的上升,患者次均住院天数从15.76天下降至15.60天,次均住院总费用从12570.57元下降至11897.40元,次均个人自付费用从1745.66元下降至1558.91元。在阈值=16的网络中,患者共转诊89740次,其中三级医院的转出和转入人次均最多,分别为23485次和21371次。除在最紧密的网络中各级医院的度中心度无差异外,三级医院在转诊网络中各项中心度指标均最高。较高的度中心度与较低的住院总费用及个人自付费用有关联,较高的紧密中心度与较短的住院天数有关联。结论三级医院在卒中转诊网络中占据主要地位,二级医院没有充分体现在卒中分级诊疗体系中的中介作用。应提高转诊网络的紧密程度,形成医疗机构间更高质量、紧密快捷的合作关系,降低患者的医疗费用和住院天数。

基于TDR技术的电缆线长时差法测量技术

为了提升电缆线长测量精度,为输电线缆的实际铺设奠定基础,研究基于TDR技术与深度神经网络的电缆线长时差法测量技术。结合时差法与RBF深度神经网络,实现对待测电缆线起始端点的定位,通过TDR技术向所定位的起始端点位置注入脉冲电压信号,运用模拟内插值法得出入射脉冲与反射脉冲的时间间隔,运算获得脉冲的行波波速,得出待测电缆线的长度。实验结果表明,该技术可精准定位实验电缆线的起始端点位置,位置坐标的最高误差低于0.13 m,能够测量出实验电缆线的长度,所测长度与实际电缆线长度较为接近。

基于深层神经网络的信道编码类型盲识别

为了解决当前识别算法只能识别一种或者两种码字类型以及人工提取特征复杂的问题,提出了两种基于深层神经网络模型的信道编码类型识别器,即卷积神经网络(convolutional neural network,CNN)识别器和递归CNN(recursive CNN,RCNN)识别器,用于识别接收数据中不同类型的信道码字。将待识别的软解调序列作为自然语言处理中文本分类问题的句子向量进行处理,输入到预先训练好的深层神经网络识别器中进行识别,并分析了字长度对识别准确率的影响,得出了最合适的字长度。实验结果表明,两种识别器都能够有效识别接收数据中多种类型的信道编码,且在信噪比为3 dB时CNN识别器的识别准确率能够达到99%以上,而RCNN识别器在1 dB时就能够达到99%以上的识别准确率。

网络舆论主题的周期长度对主题热度的影响及预测

网络平台中舆论主题演化、起伏的周期与节律是否以及如何影响到主题的热度,是具有困惑度和缺乏研究的问题,探索该问题对于研究主题周期功能、主题演化机制等具有重要意义。本文基于今日头条平台帖子样本,探索主题的周期长度对主题热度的作用及可预测模型。采取功率谱分析计算主题周期长度,使用逻辑回归与决策树检验主题周期长度对主题热度的预测效果。结果表明:1)多数主题具有1周、1/3周、1/2周这三种周期,可称为平台三大“主频”,“主频”对主题热度起到抑制作用;2)对主题热度起到抑制作用的还有1/2周左右的“波长”集群、1周左右的“波长”集群;3)[2.6天,3天]区段(α区段)、[5.2天,6天]区段(β区段)内的周期长度,对主题热度起正向作用,它们主要是处于6天长度内的“快频率”和短波长,且在三大“主频”的抑制区波长以外;4)α区段约处于1/2周和1/3周的这2个主频中间,β区段约处于1/2周和1周这2个主频中间,且α区段与β区段之间有近似的2倍关系。上述周期长度的特征,可对主题热度起到一定的预测作用。

分段拼接坐标系在越岭公路中的局限性分析

在山区越岭公路工程中,分段投影拼接坐标系与隧道独立坐标系在隧道两端出现了控制点边长差距较大的问题。根据地面边长归化到参考椭球面的长度变形分析拼接坐标系在越岭公路的总体变形,并与以隧道工程平均高程为抵偿投影面的隧道独立坐标系比较,得到分段拼接坐标系与隧道独立坐标系的不一致性。以工程实例总结出分段拼接坐标系在山区公路遇到隧道工程时,其边长变形的局限性,以及在应用拼接坐标系的注意项及其对隧道贯通的影响。

平台与内容:网络视频的关系机制研究

网络视频在经历了PGC、UGC、PUGC的共生发展后,AIGC成为新科技带动下又一全新的内容生产方式,网络平台也在“移动化、社交化、视频化”的趋势中持续迭代,在多模态内容生产方式与多样态网络平台的快速发展下,网络视频的平台与内容也因此产生出了互为影响的关系机制。在不同的网络平台调性下,如何生产出适合平台的优质视频内容?优质的视频内容,又该如何匹配适宜的网络平台?这是网络视频平台运营者和内容创作者共同面临的重要问题。本文通过参与式观察与文献研究法梳理网络视频的历史脉络与发展前景,重点分析两者互为的影响关系,研究认为:平台的算法机制、品牌印象决定了内容风格走向;而内容的优劣,则对平台的口碑和长远发展起关键作用。

GNSS静态相对定位控制网同步环闭合差及重复基线较差限差研究

目前实际工程中,同步环坐标分量闭合差很难满足全球定位系统测量规范中的限差要求,而高速铁路工程测量规范中的同步环坐标分量闭合差限差又偏大,同时这两部规范所规定的同步环全长闭合差和重复基线长度较差限差也偏大。针对这一现象,本文基于基线解算完成后基线解算软件提供的方差协方差阵,推导了同步环坐标分量及全长闭合差限差和重复基线长度较差限差,并通过高速铁路某工程项目的CPII控制网的实测数据验证了上述限差的合理性。

时间敏感网络中的可变长整形队列调整算法

针对异步整形器(ATS)采用固定长度整形队列实现流量整形存在缓存资源利用率低、可调度流平均时延高等问题,提出了一种基于改进磷虾群算法与流量预测的可变长整形队列调整算法。综合考虑流的队列分配规则、有界时延需求及有限缓存资源,定义时间敏感网络中可调度流传输约束。引入混沌映射、反向学习与精英策略并设计自适应位置更新策略以提升传统磷虾群算法的求解能力,利用改进磷虾群算法寻找整形队列可调整上限。基于卷积神经网络与长短期记忆模型(CNN-LSTM)预测流量,根据预测值计算队列长度调整步幅。仿真结果表明,与采用固定长度整形队列的方法相比,所提算法能有效提高可调度流数量,降低调度流(ST)平均时延,并提升网络缓存资源利用率。

An MDL approach to efficiently discover communities in bipartite network

An minimum description length(MDL) criterion is proposed to choose a good partition for a bipartite network. A heuristic algorithm based on combination theory is presented to approach the optimal partition. As the heuristic algorithm automatically searches for the number of partitions, no user intervention is required. Finally, experiments are conducted on various datasets, and the results show that our method generates higher quality results than the state-of-art methods, cross-association and bipartite, recursively induced modules. Experiment results also show the good scalability of the proposed algorithm. The method is applied to traditional Chinese medicine(TCM) formula and Chinese herbal network whose community structure is not well known, and found that it detects significant and it is informative community division.

Distributed Packet-Aware Routing Scheme Based on Dynamic Network Coding

In this paper, we study transmission of packets with time constraints in cooperative 5G wireless networks. As we know, the packets which are transmitted with large delay become useless and have to be dropped. In order to minimize packet dropping probability, we consider multiple transmission methods and integrate packet scheduling with adaptive network coding method selection. Firstly we introduce queue length to obtain the gain of network. Based on this, we present the dynamic coding-aware routing metric, which can increase potential coding opportunities. Moreover, we propose a distributed packet-aware transmission routing scheme based on the above routing metric, which can discover the available paths timely and efficiently. Simulation results show that the proposed method can reduce average packet dropping probability with lower computational complexity.

Complex Networks: Traffic Dynamics, Network Performance, and Network Structure

This paper explores traffic dynamics and performance of complex networks. Complex networks of various structures are studied. We use node betweenness centrality, network polarization, and average path length to capture the structural characteristics of a network. Network throughput, delay, and packet loss are used as network performance measures. We investigate how internal traffic, through put, delay, and packet loss change as a function of packet generation rate, network structure, queue type, and queuing discipline through simulation. Three network states are classified. Further, our work reveals that the parameters chosen to reflect network structure, including node betweenness centrality, network polarization, and average path length, play important roles in different states of the underlying networks.

The Impact of Vehicular Networks on Urban Networks

The objective of this paper is to study the impact of a vehicular network on a physical (road) network consisting of several intersections controlled by traffic lights. The vehicular network is considered to be a random graph superimposed on a regular Hamiltonian graph. The two graphs are connected by hyperlinks. The evolution of traffic at intersections given the existence of vehicular networks is measured by the method of reflective triangles.

Terrain Details Effect on Connectivity in Ad hoc Wireless Networks

In this work, we conduct a research on the effects of the details of the terrain on the path establishment in wireless networks. We discuss how the terrain induced variations, that are unavoidably caused by the obstructions and irregularities in the surroundings of the transmitting and the receiving antennas, have two distinct effects on the network. Firstly, they reduce the amount of links in the network connectivity graph causing it to behave more randomly, while decreasing the coverage and capacity of the network. Secondly, they increase the length of the established paths between the nodes. The presented results show how the terrain oblique influences the layout of the network connectivity graph, in terms of different network metrics, and gives insight to the appropriate level of details needed to describe the terrain in order to obtain results that will be satisfyingly accurate.