面向电信承载网的监控系统设计与实现

面对电信承载网连接的日益增长的海量终端设备，运营商需要结合网络拓扑对终端设备产生的数据进行高效的汇聚统计、异常分析、故障定位处理等操作。针对已有系统存在的操作困难、分析效率低等问题，设计与实现了一个面向电信承载网的高效监控系统，提供实时与离线数据分析和多维可视化分析的能力。对网管、认证、终端等系统及设备采集的数据进行结构化存储，对采集的数据进行拓扑相关性和时间序列方法分析，根据分析结果实现基于动态阈值控制的异常实时告警、定位等操作，并提供多维度可视化分析对网络状态进行实时监控。实际应用结果表明，该系统性能优异，具有良好交互性，能较好地满足承载网运维人员业务分析需求。     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

综采放顶煤工作面高位巷抽采关键技术研究

为了保证九里山矿1508工作面采空区瓦斯治理效果，通过现场数据测定、实验考察和理论分析，研究了推进度、抽采负压、抽采管网等高位巷抽采关键参数。研究结果表明，工作面煤层越厚、推进度越快，瓦斯涌出量越大；孔抽期间理想抽采负压为25～30 kPa、巷抽期间理想抽采负压为5～10 kPa，高位巷并联支路及管路阻力会影响高位巷的抽采效果。     

基于多元神经网络融合的分布式资源空间文本分类研究

针对实体产业对科技资源的服务需求,以服务效应作为资源文本分类标准,提出一种基于多元神经网络融合的分布式资源空间文本分类模型。设计了包含词嵌入层、卷积层、双向门控循环单元层、注意力机制层和softmax层的多元神经网络通路;在此基础上采用基于需求—效应—资源分类策略,完成了从定性科技资源需求到定量资源服务效应求解,再到定性科技资源输出的映射变换,重点解决了分布式科技资源局部和全局语义特征形式多样、文本长距离依赖特征显著、重要资源信息难以准确识别的问题,进而从分布式科技资源空间中快速准确地获取效应知识,提升实体产业产品研发效率和创新能力;通过万方专利科技资源数据集验证了所提方法的可行性和有效性,为更加全面地挖掘资源文本特征和按需服务实体产业提供了一种新的思路和手段。     

NSGA-Ⅱ和NSGA-Ⅲ应用于换热网络多目标优化的对比

针对换热网络多目标优化问题，采用目前应用较广泛的两种多目标遗传算法，即NSGA-Ⅱ和NSGA-Ⅲ，对两种算法的性能进行对比研究。案例研究结果表明，NSGA-Ⅱ算法比NSGA-Ⅲ算法运行效率更高，NSGA-Ⅲ的运行时间是NSGA-Ⅱ的2倍以上；NSGA-Ⅱ算法的应用并不严格地受限于3个目标的最大目标数量，NSGA-Ⅱ在求解大于3个目标的多目标优化问题时也可能具有良好的性能，目标数量并非选择NSGA-Ⅱ和NSGA-Ⅲ算法的严格标准。对10×5换热网络案例进行4个相关目标改造优化时，从换热网络的单一性能指标来看，NSGA-Ⅱ算法更容易获得各目标的极值。从最小年度总费用的评价指标来看，两种算法的最优方案效果相近。对7×3换热网络案例进行6个目标的优化时，NSGA-Ⅲ算法得到各目标的极值较优。从最小年度总费用的评价指标来看，NSGA-Ⅲ算法获得的投资费用和年度总费用更小。对于目标函数数量不超过3个，或者3个以上具有一定相关性的多目标优化问题，推荐优先使用NSGA-Ⅱ算法实现快速寻优；而NSGA-Ⅲ算法由于其基于参考点的选择机制，运行效率较慢，更适合用于收敛困难的高维多目标优化问题。     

Strength of pure alumina ceramics above 1 GPa

Up to now, commercially available alumina ceramics were claimed to have strength between 400 and 550 MPa. However, our study shows strength ~ 2 times higher for commercially available alumina than commonly believed. The average and characteristic strength, measured on 31 pure alumina ceramic discs by ball on three balls (B3B) test, were 1205 ± 93 MPa and 1257 MPa, respectively, with a Weibull modulus of m = 11.8. Tested specimens were in form of discs with a diameter of 5 mm and thickness 0.5 mm. The grain size distribution of the alumina is bimodal with an average grain size of ~ 850 nm measured at the surface. The fracture reveals a mixed transgranular / intergranular failure mode. To avoid incorporation of additional flaws, the discs were tested as sintered. The characteristic flexural strength measured in B3B was recalculated according to Weibull theory for standard 4-point bending bars of size 3 × 4 × 45 mm as bend 856 MPa. The measured strength of nearly 900 MPa shows the potential of strength for high purity alumina ceramics.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

User activities and the heterogeneity of urban space:The case of Dahiyat Al Hussein park

Urban researchers have maintained a constant interest in the complexity and continuity of urban space usage. Some have applied actor–network theory (ANT) to investigate the heterogeneity of spaces and present them through the networks of their users’ activities. However, these accounts are predominantly limited in examining the extent to which these spaces may be heterogeneous when exploring such networks. This paper draws on recent ANT scholarship, which employs an ethnographic research conducted in a main park in a housing project at Dahiyat Al Hussein in Amman, Jordan. The findings describe the complex and unpredictable negotiations that occur within spaces by documenting the varieties and interrelations among user activity networks within this common and shared urban space. This research reveals the extent to which spaces, parks in this case, may be heterogeneous by unpacking their usage. The conclusions and insights assert the necessity of paying attention to design detail and creating designs that are responsive to evolving user activities.     

黄浦江下游吴淞口站低潮位重现期研究

黄浦江为感潮河流,低潮位重现期的确定对黄浦江航道的设计具有重要意义。为了准确估计黄浦江低潮位的重现期,以黄浦江下游吴淞口站年最低潮位序列为例,在对样本进行三性审查的基础上,分别采用传统的数理统计方法和时变矩方法进行水文频率分析。结果表明:吴淞口站年最低潮位序列在1996年发生了变异;还现修正后的序列服从位置参数线性时变的GEV模型,低潮位存在缓慢的上升趋势;一致性条件下百年一遇低潮位约为0.261 m,在非一致性条件下其重现期增大为150 a。非一致性条件下的重现期增大说明黄浦江现有的航道设计标准偏安全,航道通航保证率提高。     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。