A Flat Mobile Core Network for Evolved Packet Core Based SAE Mobile Networks

In the current mobile IPv6 (MIPv6) systems for the System architecture evaluation (SAE) networks, such as 4th generation (4G) mobile network, the data delivery is performed basing on a centralized mobility network anchor between Evolved Node B (eNB) and Serving Gateways (S-GW), and also between S-GW and Packet Data Network Gateway (P-GW). However, the existing network has many obstacles, including suboptimal data routing, injection of unwanted data traffic into mobile core network and the requirement of capital expenditure. To handle these challenges, here we describe a flat mobile core network scheme donated by F-EPC, based SAE mobile network. In the proposed scheme, the P-GW and S-GW gateways are features as one node named Cellular Gateway (C-GW). Further, we proposed to distribute and increase the number of C-GW in mobile core network, the Mobility Management Entity (MME) functioned as centralizing mobility anchor and allocating the IP address for the User Entity (UE). In this paper, the explained results of a simulation analysis showed that the proposed scheme provides a superior performance compared with the current 4G architecture in terms of total transmission delay, handover delay and initial attach procedure.

Flatness predictive model based on T-S cloud reasoning network implemented by DSP

The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

Particulate Matter Exposure of Rural Interior Communities as Observed by the First Tribal Air Quality Network in the Yukon Flat

A tribal-owned network of aerosol monitors and meteorological stations was installed at Ts’aahudaaneekk’onh Denh (Beaver), Gwichyaa Zheh (Fort Yukon), Jalgiitsik (Chalkyitsik), and Danzhit Khànlaii (Circle) in the Yukon Flats, Alaska. Surface inversions occurred under calm wind conditions due to radiative cooling. In May, local emissions governed air quality with worst conditions related to road and river dust. As the warm season progressed, worst air quality was due to transport of pollutants from upwind wildfires. During situations without smoke or when smoke existed at layers above the surface inversion, concentrations of particulate matter of less than 2.5 micrometer in diameter or less (PM2.5) were explainable by the local emissions;24-h means remained below 25 μg·m-3. Absorption of solar radiation in the smoke layer and upward scattering enhanced stability and fostered the persistence of the surface inversions. During smoke episodes without the presence of a surface inversion, daily mean concentrations exceeded 35 μg·m-3 often for several consecutive days, at all sites. Then concentrations temporally reached levels considered unhealthy.

FLAT网络在发酵过程建模中的应用

本文使用FLAT网络对一种工业规模的连续发酵过程进行了辨识 .发酵过程作为一种复杂的生化反应 ,它比一般的非线性系统更加复杂 ,FLAT网络用于发酵过程的辨识 ,可以得到较高的辨识精度 ,而且辨识速度快。

结合神经文本生成的FLAT模型的中文电子病历命名实体识别

随着医疗信息化的发展,电子病历命名实体识别受到了广泛关注。电子病历中包含大量的专业词汇,而专业词汇的切分错误会使命名实体识别效果不佳。FLAT模型在引入词边界信息时能有效避免分词错误信息的传播,提高命名实体识别效果,但FALT模型依赖于高质量的词典信息。针对这一问题,提出了结合神经文本生成的FLAT模型,使用神经文本生成方法生成大量新病历文本,通过提出的评分函数筛选通顺的文本训练词向量作为FLAT模型的词典信息。实验表明:结合神经文本生成的FLAT模型在CCKS2017数据集上取得了95.32%的F1分数,比BiLSTM CRF模型提高了1.16%,比BERT CRF模型提高了0.89%;在CCKS2019数据集上取得了85.87%的F1分数,比BiLSTM CRF模型提高了5.19%,比BERT CRF模型提高了1.34%。

Tier_Flat:P2P网络并行模拟器(HiFiP2P)的一种路由算法

为了给P2P网络并行模拟器HiFiP2P提供正确高效的路由,使其能够高效地执行大规模P2P网络并行模拟,基于互联网中的层次路由模型和Flat本地静态路由计算和查找算法,采取边界路由最小化的并行网络拓扑划分机制,设计了Tier_Flat路由算法,用以实现HiFiP2P的远程和本地静态路由,它以最低O((N^4)^(1/3))的空间开销,取得了O(1)的查找效率。结果表明,Tier_Flat路由算法路由计算时间短,路由表内存占用小,路由查询速度快,为HiFiP2P平台的大规模P2P网络并行模拟提供了高效的路由服务。

Flatness Control Based on Dynamic Effective Matrix for Cold Strip Mills

Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced： the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method.

Research on confirmation of basic technological parameters of tension levellers based on neural network and genetic algorithm

Confirmation of basic technological parameters of tension levellers is the most important factor of leveling strip. Up to now, most factories have used experts’ experience to decide these parameters, without any established rule to follow. For better quality of strip, a valid method is needed to decide technological parameters precisely and reasonably. In this paper, a method is used based on neural network and genetic algorithm. Neural network has a good ability to extract rules from work process of tension levellers. Then using neural network, which has learned from a lot of working samples, to be the evaluation of fitness, genetic algorithm could easily find the best or better technological parameters. At the end of this paper, examinations are given to show the effect of this method.

Energy Consumption in Wireless Sensor Networks

Wireless sensor networks(WSNs) can be used to collect surrounding data by multi-hop.As sensor networks have the constrained and not rechargeable energy resource,energy efficiency is an important design issue for its topology.In this paper,the energy consumption issue under the different topology is studied.We derive the exact mathematical expression of energy consumption for the flat and clustering scheme,respectively.Then the energy consumptions of different schemes are compared.By the comparison,multi-level clustering scheme is more energy efficient in large scale networks.Simulation results demonstrate that our analysis is correct from the view of prolonging the large-scale network lifetime and achieving more power reductions.

基于改进海鸥算法结合Elman网络的板形模式识别方法

为了提高板形模式识别精度,提出了一种基于改进海鸥算法结合Elman网络的板形模式识别方法。将改进的海鸥算法对Elman网络权值阈值进行优化,用于板形模式识别,选取20组数据进行测试,并将结果与基于BP神经网络的板形模式识别和基于传统Elman网络的板形模式识别方法进行比较,结果表明本文算法精度更高、效果更好,均方误差MSE相比其他算法低2个数量级。

A Summary of the Large-Scale Access Convergence Network Structure

Under the requirement of everything over IP, network service shows the following characteristics:(1) network service increases its richness;(2) broadband streaming media becomes the mainstream. To achieve unified multi-service bearing in the IP network, the largescale access convergence network architecture is proposed. This flat access convergence structure with ultra-small hops, which shortens the service transmission path, reduces the complexity of the edge of the network, and achieves IP strong waist model with the integration of computation, storage and transmission. The key technologies are also introduced in this paper, including endto-end performance guarantee for real time interactive services, fog storing mechanism, and built-in safety transmission with integration of aggregation and control.

结合Graph-FPN与稳健优化的开放世界目标检测

开放世界目标检测(OWOD)要求检测图像中所有已知和未知的目标类别,同时模型必须逐步学习新的类别以自适应更新知识。针对ORE方法存在的未知目标召回率低以及增量学习的灾难性遗忘等问题,提出一种基于图特征金字塔的稳健优化开放世界目标检测方法(GARO-ORE)。首先,利用Graph-FPN中的超像素图像结构以及上下文层和层次层的分层设计,获取丰富的语义信息并帮助模型准确定位未知目标;之后,利用稳健优化方法对不确定性综合考量,提出了基于平坦极小值的基类学习策略,极大限度地保证模型在学习新类别的同时避免遗忘先前学习到的类别知识;最后,采用基于知识迁移的新增类别权值初始化方法提高模型对新类别的适应性。在OWOD数据集上的实验结果表明,GARO-ORE在未知类别召回率上取得较优的检测结果,在10+10、15+5、19+1三种增量目标检测(iOD)任务中,其mAP指标分别提升了1.38、1.42和1.44个百分点。可以看出,GARO-ORE能够较好地提高未知目标检测的召回率,并且在有效缓解旧任务灾难性遗忘问题的同时促进后续任务的学习。

大学生“低欲望躺平主义”的表征、溯因和破解策略

大学生“低欲望躺平主义”呈现出唯心主义、娱乐主义、功利主义、虚无主义等样态。它是现实生活内部矛盾、网络社会环境、亚文化和消费主义四个方面综合作用的产物。为了解决该问题,国家和政府要提升社会治理水平,党组织和共青团要重视网络空间的宣传工作,高校要坚守大学生思想政治教育主阵地,大学生要加强主体自觉性。

基于改进径向基网络的电动舵机滑模控制

针对电动舵机小角度平顶现象引发的弹道抖振问题,以小角度平顶问题为中心,对平顶问题展开研究分析,并提出了一种改进的径向基滑模控制(RBF-SMC)方法,通过设计滑模控制器,削弱摩擦和间隙的不利影响,以提高系统的跟踪精度;针对模型中的不确定项,引入径向基网络进行逼近,提高控制精度,同时降低滑模控制算法的抖振问题;为提高径向基网络的迭代效率,使用2个单参数代替权值矩阵,大幅减少需要迭代估计的参数数量。最后,搭建电动舵机系统实验平台,采用PID控制器和改进的径向基滑模控制器进行对比分析。实验结果表明,相比于传统PID算法,在跟踪0.1°4 Hz正弦指令时,采用改进的径向基滑模控制算法将位置平顶时间从64 ms降低至9 ms,位置跟踪误差从0.123°降低至0.04°;在半物理试验时,弹道抖动频率从14.7 Hz降低至6.8 Hz,抖振幅值从0.079°降低至0.034°。采用改进的径向基滑模控制能有效提高系统的跟踪性能,削弱平顶问题,抑制弹道极限环震荡。

网络“躺平”亚文化的审思及其消解策略

网络是观点、意见表达的重要舆论场,也是亚文化的重要生成地。2021年以来,网络“躺平”亚文化问题广受学界关注。它依托网络新媒体,以其独特的话语表达方式影响着“躺平”群体的“三观”,在网络文化圈层内对主流意识形态的话语引导力产生了一定的解构威胁。网络“躺平”亚文化的生成背后,有着“亚文化网络圈层的收编”“逃避资本收割”和“劳动兴趣的缺失”等深层原因,折射出“躺平”群体面对社会压力时的消极应对心态,体现出“躺平”群体在释压中放逐自己的外在表征。消解网络“躺平”亚文化带来的负面影响,应建立弹性多元的引领策略和治理方法。在信仰层面要充分发挥主流意识形态对网民的价值引领作用,在文化层面要充分发挥中华优秀传统文化和红色文化对网民的滋养作用,在政策层面要充分发挥政策体系对网民的激励与保障作用。

基于深度置信网络的缝纫平整度客观评价模型

针对织物缝纫平整度进行客观自动评估时评价精度不高的问题,提出一种基于特征参数与深度置信网络(deep belief network,DBN)的织物缝纫平整度客观评价模型。首先,对织物缝纫样本图像进行图像灰度化处理、中值滤波去噪、直方图均衡强化等图像预处理,提取织物的纹理特征,获取更高质量、利于后续处理的织物特征图像;其次,构建灰度共生矩阵,并在0°、45°、90°与135°下提取织物图像能量、熵、对比度、相关性4个关键特征参数,在此基础上构建基于DBN的织物缝纫平整度自动评价模型,并使用缝纫图像对该模型进行训练;最后,通过提取的织物缝纫图像进行验证。实验结果显示该模型的评价精度达98.74%,与多元线性回归模型和基于BP网络模型2种方法相比,提出的评价方法可以有效对缝纫平整度进行客观评价,为织物服装外观质量控制提供理论依据。

网络亚文化冲击下高职院校大学生“低欲望躺平”精神样态的形成与破解

在网络亚文化的冲击下,“低欲望躺平”精神样态呈现出无欲无求、洒脱淡泊等极简主义形式和特征,其实质是一种脱离主流价值领域的自我放逐和消极遁世情绪,这对高职院校大学生理想信念的塑造、心理健康的形成造成了严重危害。从网络亚文化冲击视角,分析亚文化冲击所导致的青年大学生“低欲望躺平”的内涵与行为表征,发现理想信念和价值标准的模糊虚化、心理落差加大和机会供给失衡、多元文化冲击和群体性焦虑等是青年大学生“低欲望躺平”的诱发因素,建议通过思政和价值观教育、社会公平正义的治理、积极健康文化环境的营造,以及心理教育的强化和引导等策略加以破解。

建设扁平化新型校园网络

为解决校园网络传统结构中存在的问题,北京大学医学部依据扁平化的网络设计思路,以高性能路由设备为网络核心,利用QinQ技术,建设新型校园网络.该结构充分发挥高性能核心设备的优势,实现用户、业务的集中控制和精细化管理,提高了网络吞吐性能、安全性及稳定性.

网络实验室的设计与实现

介绍了一个高性能网络实验室的架构及其实现方法 .该实验室包含局域网、城域网、广域网、无线网及其互连 ,同时包含UNIX ,LINUX ,WINDOWSNT等不同的主机平台 .该实验室能模拟仿真各种网络环境 ,支持各种路由及组播协议 ,如RIPI&II,OSPF ,IGRP ,EIGRP ,BGP4 ,DVMRP ,IGMP ,CGMP等 .该实验室除了能够提供常规的Internet服务 ,如WWW ,DNS ,FTP ,PROXY ,EMAIL ,DIAL外 ,还可以模拟IP电话。

扁平化组织与网络化沟通

21世纪是一个迅速变化的时代。为适应经济全球化、信息网络技术和知识经济的挑战与冲击 ,企业的组织设计不能再恪守依据职权划分和层级管理来机械设置管理层次和职能部门的传统形式 ,而是应充分体现组织对环境的适应性和应变力 ,使之能够在第一时间内对环境变化做出快速反应。由此 ,企业组织结构的扁平化和内部沟通的网络化 ,正在成为现代组织结构变革的最新趋向和目标模式。