湘钢220KV备用电源自投方式商榷

针对湘钢三中央变电站２２０ｋＶ备用电源自投装置存在的问题，提出了新的方式和设想．分析采用新的方式后，停电时间由３．５ｓ缩短至０．０４～０．０９ｓ的可行性．同时还提出在ＳＦ６断路器的变电站，自投装置应采用的方式．     

基于多维彩色向量空间的火焰图像模糊聚类分割算法

针对火灾探测过程中早期火焰的分割技术研究,提出了一种基于多维彩色向量空间的火焰图像模糊聚类分割算法,该算法以运动目标序列图像之间变化的区域作为聚类模板,提取该聚类模板的RGB多维彩色特征向量,然后将图像的像素与聚类模板通过模糊聚类的方式进行分割。这种分割算法计算简单,时间开销较小,可以较好地获取火焰图像的边缘形态特征,并且能够明显消除不同光线下分割误差,实现快速无监督自动分割。     

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.     

条件函数依赖及其在领域无关数据清洗中的应用

条件函数依赖(Conditional Functional Dependeny,CFD)是对函数依赖(Functional Depencency,FD)加入语义约束扩展而来,它在数据库一致性检测、数据清洗方面更优于后者.讨论了条件函数依赖的相关概念及其基本性质,讨论如何将它应用于数据清洗,并对已提出的基于CFD的数据清洗方案提出改进措施,并通过实验说明改进措施的可行性.     

安全级低压配电柜耐温极限研究

为应对安全级低压配电柜存在较高环境温度运行的情况,采用保护元件降容和高温环境试验的方法对低压配电柜的耐温极限进行分析研究.通过优化计算方法,得出不同工况低压配电柜的散热量,分析配电柜内部的温升,对保护元件进行了降容计算和定值修正,设计逐步逼近的试验方案,完成了高温环境试验.研究结果表明,安全级低压配电柜能在50℃环境温度正常运行72 h.研究提出的降容计算和环境试验方法可为高温环境中安全级电气设备的设计提供参考.     

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。