南水北调东干渠工程安全监测数据分析与处理技术研究

东干渠工程是北京市南水北调配套工程之一,是中心城环线供水系统重要组成部分,承担第十水厂、通州水厂等东部区域供水任务.东干渠工程建设了安全监测系统,监测内容包括环境量、变形、渗流、应力、应变及专项监测等.但监测系统存在监测数据中断、仪器失效及缺少初值等问题.对某典型断面的应力、应变及温度等内观监测数据以及东五环段的沉降监测数据进行了梳理、对比验证分析,明确了监测数据的合理性,初步确定了沉降风险点,为东干渠工程安全监控提供技术支撑.     

基于深度神经网络的高位滑坡范围预测

以贵州尖山营滑坡为工程背景,通过对深度学习的总结与分析,建立多层感知器模型以对该滑坡危险区范围进行非线性预测研究。通过对深度神经网络算法的优化,构建64-128-32-1四层多层感知器模型,并以滑坡最大高差、滑坡体积、滑源区坡度、坡脚坡度、地层倾角作为输入量,以滑坡最大水平运动距离作为输出量对该模型进行训练,实现影响因素与运动距离的非线性映射。根据对贵州省尖山营滑坡调查和研究,尖山营滑坡区域面积约648 700 m2,体积约1 200万 m3,属于特大型滑坡。依据最优模型对该滑坡进行滑距预测,滑坡平面直线距离1 769 m区域内为危险区域。     

The HaLoop approach to large-scale iterative data analysis

The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce has enjoyed particular success. However, MapReduce lacks built-in support for iterative programs, which arise naturally in many applications including data mining, web ranking, graph analysis, and model fitting. This paper (This is an extended version of the VLDB 2010 paper “HaLoop: Efficient Iterative Data Processing on Large Clusters” PVLDB 3(1):285–296, 2010.) presents HaLoop, a modified version of the Hadoop MapReduce framework, that is designed to serve these applications. HaLoop allows iterative applications to be assembled from existing Hadoop programs without modification, and significantly improves their efficiency by providing inter-iteration caching mechanisms and a loop-aware scheduler to exploit these caches. HaLoop retains the fault-tolerance properties of MapReduce through automatic cache recovery and task re-execution. We evaluated HaLoop on a variety of real applications and real datasets. Compared with Hadoop, on average, HaLoop improved runtimes by a factor of 1.85 and shuffled only 4 % as much data between mappers and reducers in the applications that we tested.     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

The stable topology for residuated lattices

The spectrum of a residuated lattice L is the set Spec(L) of all prime i-filters. It is well known that Spec(L) can be endowed with the spectral topology. The main scope of this paper is to introduce and study another topology on Spec(L),?the so called stable topology, which turns out to be coarser than the spectral one. With this and in view, we introduce the notions of pure i-filter for a residuated lattice and the notion of normal residuated lattice. So, we generalize to case of residuated lattice some results relative to MV-algebras (Belluce and Sessa in Quaest Math 23:269–277, 2000; Cavaccini et?al. in Math Japonica 45(2):303–310, 1997) or BL-algebras (Eslami and Haghani in Kybernetika 45:491–506, 2009; Leustean in Central Eur J Math 1(3): 382–397, 2003; Turunen and Sessa in Mult-Valued Log 6(1–2):229–249, 2001).     

A fuzzy cerebellar model articulation controller based visual servo system for robot

This paper presents a novel online learning visual servo controller integrating the FCMAC with proportion controller for the control of position of manipulator end-effector. Since the FCMAC has good learning capability and fast learning speed, and can save much computer memory space by fuzzy processing of input space division and memory unit activation, it is used to develop an adaptive control law by learning the relationship between the image feature errors and manipulator input, and the aim of online learning of the FCMAC is to minimize the output of proportion controller. Furthermore, the FCMAC has no need for models of robot manipulator and image feature extraction, so that the capability of proposed controller for tasks under uncertain environment can be improved. Finally, the proposed controller is proved to be effective by the experiment, and compared with BP neural network.     

基于9种无约束优化算法的岩爆预测BP模型优选

为研究不同训练样本数量和无约束优化算法对岩爆BP(Back Propagation)神经网络模型预测准确率的影响,选取围岩最大切向应力与岩石单轴抗压强度比σθ/σc、岩石单轴抗压强度与单轴抗拉强度比σc/σt和弹性能量指数Wet作为预测指标,广泛搜集整理100组典型岩爆实例建立了训练样本数据库.在样本数量分别为40、70和100时,基于标准算法和9种无约束优化算法建立了10个岩爆BP神经网络预测模型,并提出了考虑不同样本规模影响的岩爆烈度等级预测指数——综合准确值N.比较研究结果表明:BP模型的预测准确率随样本数量增加而提高,3种样本数量下的模型平均预测准确率分别为62.5％、76.4％和87.5％;基于9种优化算法建立的BP网络模型的N值均高于标准BP模型;基于Ploak-Ribiere共轭梯度法优化的BP模型的N值(195)和预测准确率(99.0％)均最高,且在5个工程实例中的预测结果完全符合现场实际,优于标准BP模型、支持向量机模型和其他优化模型,为岩爆烈度等级预测的最佳模型.     

N,N−二甲基−二硫代甲酰胺丙磺酸钠对THPED体系化学镀铜的影响

研究了N,N?二甲基?二硫代甲酰胺丙磺酸钠(DPS)作为添加剂对以四羟丙基乙二胺(THPED)为单一配位剂的化学镀铜体系的沉积速率、镀层形貌和晶体结构的影响。结果发现,当DPS的质量浓度从0 mg/L增大到1.0 mg/L时,沉积速率从2.91μm/h提高到6.73μm/h,所得镀层结晶均匀、细致。线性扫描伏安测量结果表明,DPS是通过促进甲醛的阳极氧化来加速化学镀过程。本体系的Cu镀层主要呈面心立方多晶取向,DPS的添加会令晶面取向从(220)转变为(111)。     

酚醛树脂为原料制备双电层电容器用电极材料的工艺研究

以酚醛树脂为原料，NaOH为活化剂制取双电层电容器用高比表面积活性炭电极材料，考察了炭化温度、活化温度、活化剂用量、活化时间等工艺参数对活性炭比电容的影响。实验结果表明，在炭化温度为600℃，活化温度为900℃，碱炭比为4，活化时间为1h的工艺条件下，制得的高比表面积活性炭比电容可达58．8F／g，用它组装成的电容器具有良好的充放电性能和循环性能，既能在大电流下快速充放电也能在小电流下缓慢充放电，但存在微孔所占比例较高引起的分散电容效应，这是大电流下放电容量有所下降的主要原因。     

陶瓷隧道窑的节能监测与节能途径

窑炉节能监测是能源管理的一个重要组成部分.本文讨论了陶瓷隧道窑节能监测的意义、项目、方法和节能途径,以供参考.