多个水下机器人动态任务分配和路径规划的信度自组织算法

针对多个水下机器人(autonomous underwater vehicles,AUVs)动态任务分配和路径规划速度跳变问题,引入栅格信度函数概念,给出一种改进的栅格信度自组织(belief function self-organizing map,BFSOM)算法.目的是控制一组AUV有效地到达所有指定的目标位置,同时保证AUV能够自动的避开障碍物.首先,自组织神经网络(self-organizing map,SOM)算法对多AUV系统进行任务分配,使得每个目标位置都有一个AUV去访问.整个分配过程包括定义SOM神经网络的初始权值、获胜者选择、邻域函数的计算3个步骤;其次,根据栅格信度函数和环境信息更新SOM获胜神经元的权值,使得每个AUV在访问对应目标的过程中能够自动避障并且克服速度跳变,实现AUV自动有效路径规划.最后,通过仿真实验证明了本文提及算法的有效性.     

EV-DO监测系统中R-P接口业务信令追踪的研究与实现

在EV-DO网络中,信令监测系统对于用户数据业务运营的分析尤为重要。针对EV-DO网络中的重要数据接口R-P接口间A11及PPP信令CDR(Call Data Record)的合成,结合主叫和寻呼过程的业务特征,提出一系列优化及改进方案。经过EV-DO现网数据的测试以及产生的相应实验结果表明,优化信令与业务数据消息间的关联机制,缩短了数据网络寻呼与用户主叫业务信令的追踪统计时间最高可达30 s,指标的细化提升了故障定位准确性。这项改进方案对于优化网络运维的分析平台有着重要意义。     

Using Memory in the Right Way to Accelerate Big Data Processing

Big data processing is becoming a standout part of data center computation. However, latest research has indicated that big data workloads cannot make full use of modern memory systems. We find that th...     

一种休眠机制下无线传感器网络的自治愈方法

为减少无线传感器网络(WSNs)大规模应用中出现的节点同步失效、数据丢失的问题,通过对现有WSNs应用系统的研究,提出了一种全网节点同步休眠机制下WSNs的自治愈方法.详细介绍了该方法的设计思路、实现方法及测试过程.测试结果表明:此方法可针对数据丢失和时间同步失效的节点进行节点自救,实现丢失节点的再入网、再同步,提高WSNs系统的稳定性,为WSNs的应用提供更好的条件.     

基于MultiBoost 的集成支持向量机分类方法及其应用

针对网络故障诊断中的模式识别问题, 提出一种基于多重提升(MultiBoost) 的优化支持向量机集成学习方法. 首先, 利用自适应的荷尔蒙调节遗传算法(HMGA), 对支持向量机基分类器进行建模参数优化; 然后, 通过构建MultiBoost 集成学习方法将多个基分类器集成, 建立以多分类器优化集成为核心的故障诊断系统. 实验结果表明, 所提出的方法在网络故障诊断中, 迭代次数少、建模时间短, 并且能够明显提高故障分类的准确率.     

基于云模型的灾难恢复能力评价方法研究

鉴于现有灾难恢复能力评价体系对动态指标的考虑不足,不能全面地反映现实系统,评价方法未对系统的整体稳定性做分析且权重计算偏于主观,易受人为因素影响等情况,构建了一套新的灾难恢复能力评估体系和评价方法。提炼归纳了体现灾难恢复能力的综合性动态指标,丰富了指标体系。引入云理论,提出了基于正态分布区间数的指标评价值规范化方法和改进的G1-熵权法的综合赋权模型,最终得出了系统灾难恢复能力的云模型表示方法,完成了定性到定量的转换。通过实例仿真验证了该方法的可行性。     

微型航姿系统抗加速度干扰姿态估计研究

基于加速度计、陀螺仪和磁强计的航姿参考系统(AHRS)可提供航向角、俯仰角和横滚角等信息,但加速度计读数中包含的载体运动加速度会影响AHRS航姿精度.针对常用的基于扩展卡尔曼滤波(EKF)的姿态估计算法,讨论了对运动加速度进行估计和补偿的两种不同方法,即内部估计法和外部估计法.仿真和实验结果表明,加速度外部估计法比内部估计法计算量小且补偿效果好,在动态环境下能获得较高的航姿精度.     

Think,feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.     

Quantum commitments from complexity assumptions

Bit commitment schemes are at the basis of modern cryptography. Since information-theoretic security is impossible both in the classical and in the quantum regime, we examine computationally secure commitment schemes. In this paper we study worst-case complexity assumptions that imply quantum bit commitment schemes. First, we show that QSZK \({\not\subseteq}\) QMA implies a computationally hiding and statistically binding auxiliary-input quantum commitment scheme. We then extend our result to show that the much weaker assumption QIP \({\not\subseteq}\) QMA (which is weaker than PSPACE \({\not\subseteq}\) PP) implies the existence of auxiliary-input commitment schemes with quantum advice. Finally, to strengthen the plausibility of the separation QSZK \({\not\subseteq}\) QMA, we find a quantum oracle relative to which honest-verifier QSZK is not contained in QCMA, the class of languages that can be verified using a classical proof in quantum polynomial time.     

Factors of low individual degree polynomials

Kaltofen (Randomness in computation, vol 5, pp 375–412, 1989) proved the remarkable fact that multivariate polynomial factorization can be done efficiently, in randomized polynomial time. Still, more than twenty years after Kaltofen’s work, many questions remain unanswered regarding the complexity aspects of polynomial factorization, such as the question of whether factors of polynomials efficiently computed by arithmetic formulas also have small arithmetic formulas, asked in Kopparty et al. (2014), and the question of bounding the depth of the circuits computing the factors of a polynomial. We are able to answer these questions in the affirmative for the interesting class of polynomials of bounded individual degrees, which contains polynomials such as the determinant and the permanent. We show that if \({P(x_{1},\ldots,x_{n})}\) is a polynomial with individual degrees bounded by r that can be computed by a formula of size s and depth d, then any factor \({f(x_{1},\ldots, x_{n})}\) of \({P(x_{1},\ldots,x_{n})}\) can be computed by a formula of size \({\textsf{poly}((rn)^{r},s)}\) and depth d + 5. This partially answers the question above posed in Kopparty et al. (2014), who asked if this result holds without the dependence on r. Our work generalizes the main factorization theorem from Dvir et al. (SIAM J Comput 39(4):1279–1293, 2009), who proved it for the special case when the factors are of the form \({f(x_{1}, \ldots, x_{n}) \equiv x_{n} - g(x_{1}, \ldots, x_{n-1})}\). Along the way, we introduce several new technical ideas that could be of independent interest when studying arithmetic circuits (or formulas).