基于地图云的党建服务平台设计实践

党建平台建设在党员管理和联系群众中发挥重要作用,网络党建是紧跟时代的新服务模式。以重庆市大渡口区为例,提出基于地图云的党建服务平台建设方案,介绍总体框架和软硬件环境、数据库组织分类和内容、功能设计、系统实现过程和创新点,并指出打造移动互联立体信息网及实现线上线下互动闭环是下一步工作重点。     

Greedily computing associative aggregations on sliding windows

We present an algorithm for combining the elements of subsequences of a sequence with an associative operator. The subsequences are given by a sliding window of varying size. Our algorithm is greedy and computes the result with the minimal number of operator applications.     

无线移动感知网络上的数据聚集传输规划

随着城市交通日益增多,智能交通系统研究受到工业界和学术界的极大关注.智能交通系统上的实时导航、交通监控等应用都需要大量实时车辆速度、位置等信息.由智能车组成的无线移动感知网络的链路变化频繁并且无线干扰严重,现有的车辆网络上的数据聚集算法没有考虑无线链路变化的具体情况,就进行数据聚集和传输的规划.文中分析并求解了移动感知网络上的无冲突数据聚集传输规划问题,将该问题形式化为移动通信拓扑图上的单传聚集传输规划问题,并证明了该问题是NP完全的;提出了移动聚集路由树,将无线冲突分为树内冲突和树间冲突,首先构建树间冲突图描述树间冲突,过滤节点的传输候选时刻集以消除树间冲突,然后利用动态规划思想进行聚集路由树上的数据聚集传输时刻规划来消除树内冲突.并且在真实出租车移动轨迹数据集上进行了实验,实验结果表明文中的算法比现有的算法在数据收集率上提高了1/4左右,并且收集上来的数据的平均延迟也更小.     

制造物联网中高吞吐率复杂事件检测技术研究

复杂事件处理能非常有效地检测到事件流中有价值的模式,高吞吐率意味着单位时间内获取更多有价值的信息.为复杂事件处理提出一种提高吞吐率的查询技术,通过聚集活动实例栈中的连接,并批量执行序列构造,减少了相同连接的遍历,降低了序列构造处理的运行频率,从而提高了复杂事件处理的吞吐率.仿真实验表明:提出的方案相比于SASE提出的传统方案,吞吐率有数倍的提升.     

一种基于群组判断离差的偏好集结方法

针对群决策偏好集结中违反Pareto 最优性的情况, 设计一种基于群组判断几何离差的同质性集结方法. 该方法在集结前进行几何离差测试, 以确定个体决策信息的离差水平. 离差较小时, 可基于几何平均集结; 对于离差较大且修正复杂度较高的决策信息, 采用主成分分析(PCA) 从高维决策信息中提取大多数相关信息, 在不依赖主观分析的情况下进行加权集结. 仿真实验表明, 所提出的方法能够在不违背Pareto 最优性的基础上集结离差较大的群决策信息.

     

一种基于线性时间概率计数算法的数据聚集技术

无线传感器网络中,通过数据聚集操作在中间节点预先对数据进行处理,可去除大量冗余,减少数据传输,实现节能。针对多路径路由下数据聚集操作的重复计数问题,研究对副本不敏感的概要结构并优化某些特性,在线性时间概率计数算法的数学模型基础上提出一种新的数据聚集技术FA( Fan Aggregation)技术,实现高能效的数据聚集。理论分析和仿真实验均表明, FA技术相较于FM( Flajolet Martin)技术和LC( Linear Counting)技术在存储空间和准确率上均有更好的性能体现。     

Aggregate‐induced emission in light‐emitting liquid crystal display technology

Nowadays, liquid crystal displays (LCDs) with light‐emission are considered as energy‐efficient devices and are promising alternatives to conventional LCDs. To realize such possibility, strong fluorescent materials with a dichroic properties are required. Aggregate‐induced emission (AIE) is an unusual photophysical phenomenon shown by some luminogenic materials that will be highly emissive in their aggregate state. In this work, we studied the AIE effect of a luminescent liquid crystalline molecule TPE‐PPE in our LC system as a luminophore dopant. The result showed the excellent AIE effect that higher concentration of luminogen in the nematic LC host induced stronger luminescent intensity. Through exposure of a photoisomeriable alignment material sulfonic‐dye‐1, the photopatterning of a light‐emitting LC device was achieved with the use of the TPE‐PPE/nematic LC mixture.     

SRA-MSDU: Enhanced A-MSDU frame aggregation with selective retransmission in 802.11n wireless networks

The main goal of the IEEE 802.11n standard is to achieve more than 100 Mbps of throughput at the MAC service access point. This high throughput has been achieved via many enhancements in both the physical and MAC layers. One of the MAC enhancements is the frame aggregation in which multiple frames are concatenated into a single large frame before being transmitted. The 802.11n MAC layer defines two types of aggregation, aggregate MAC service data unit (A-MSDU) and aggregate MAC protocol data unit (A-MPDU). The A-MPDU outperforms A-MSDU due to its large aggregation size and the subframes retransmission in erroneous channels. However, in error free channels and under the same aggregation size the A-MSDU performs better than the A-MPDU due to its smaller headers. Thus, adding a selective retransmission capability to the A-MSDU would improve the system performance. In this paper, we have proposed an MSDU frame aggregation scheme that enables selective retransmission at the MSDU level without altering the original MAC header. In this proposed scheme an implicit sequence control mechanism has been introduced in order to keep the frames in sequence and preserve their correct order at the receiver side. The results show that the proposed scheme improves the system performance in terms of throughput and delay even under highly erroneous channels.     

一种基于数值积分的过程神经元网络训练算法

针对过程神经元网络的训练问题,提出了一种基于数值积分的学习算法。直接采用数值积分进行网络中动态样本与连接权函数的时域加权聚合运算,采用梯度下降法实现连接权函数特征参数及网络性质参数的调整。设计了基于梯形积分、辛普森积分、柯特斯积分等3种过程神经元网络数值积分训练方法,以太阳黑子数据预测为例进行仿真实验,结果表明,基于数值积分的过程神经元网络训练算法是有效的,其中辛普森积分算法的性能最优。     

基于蚁群算法的无线传感网络中心点融合算法研究

建立级别梯度场是无线传感器网络中心点融合算法的重要组成部分。采用泛洪方法向外扩散梯度,会造成严重的能量消耗问题。为了节省传感器网络中的能量,将蚁群算法用于建立级别梯度场阶段,提出了基于蚁群算法的无线传感网络中心点融合算法。仿真实验验证了该方法的可行性。研究结果表明,基于蚁群算法的中心点融合算法可以显著地减少无线传感器网络的能量消耗,延长无线传感器网络的生命周期。