Stability-based preference selection in affinity propagation

Recently, as one of the most popular exemplar-based clustering algorithms, affinity propagation has attracted a great amount of attention in various fields. The advantages of affinity propagation include the efficiency, insensitivity to cluster initialization and capability of finding clusters with less error. However, one shortcoming of the affinity propagation algorithm is that, the clustering results generated by affinity propagation strongly depend on the selection of exemplar preferences, which is a challenging model selection task. To tackle this problem, this paper investigates the clustering stability of affinity propagation for automatically selecting appropriate exemplar preferences. The basic idea is to define a novel stability measure for affinity propagation, based on which we can select exemplar preferences that generate the most stable clustering results. Consequently, the proposed approach is termed stability-based affinity propagation (SAP). Experimental results conducted on extensive real-world datasets have validated the effectiveness of the proposed SAP algorithm.     

基于FPGA的多路矿用气体传感器智能调校系统

矿用气体传感器因煤矿安全被大量使用,国家规定必须定期进行调校,目前的传感器调校系统可同时对5-8台传感器进行调校,针对大中型煤矿生产过程中需要调校的矿用气体传感器数量大、调校耗时长的现状,提出了一种基于单片机与现场可编程门阵列（FPGA）的64路气体传感器调校系统,介绍了系统的整体设计并详细阐述了该系统的气路控制模块和传感器输出信号采集模块,使用Verilog语言在QuartusⅡ工具中实现FPGA的逻辑设计、仿真和综合。结果证明：该系统能有效地完成对64路气体传感器同时调校的目的,很大程度节省了人力消耗,提高了调校效率,节约了调校时间。     

基于专家系统的汽车衡故障传感器判别

传感器是自动化设备的核心部件,传感器故障检测显得尤为重要。针对目前汽车衡维护、检修的困难,为有效准确判断故障传感器,提出了以径向基函数神经网络（RBFNN）预估值的初始化数据库专家系统判别方法。经现场测试准确率达到96%以上,从而有效简单地判定传感器好坏和识别故障传感器的位置。     

活性污泥法污水处理厂的运行模拟

结合ASM1和二沉池模型组成活性污泥模拟系统,将某污水处理厂的常规检测数据转化为活性污泥系统模型所需的组分数据,然后调整模型参数,使污水处理厂出水指标最接近于污水处理厂实际数据,从而确定适合该污水处理厂的模型参数,最后对该污水处理厂的工艺参数进行了模拟改造。     

高效液相色谱法分析混合甲基萘

建立了甲基萘的高效液相色谱分析方法，确定了紫外检测器波长、流动相及其组成等实验条件。结果表明：用高效液相色谱法分析混合甲基萘，α-甲基萘和β-甲基萘可以得到有效分离。     

Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

Synthesis of carbon nanocapsules containing Fe, Ni or Co by arc discharge in aqueous solution

A simple, inexpensive and one-step synthesis method of metal-containing carbon nanocapsules using an arc discharge in aqueous solution is reported. It was found that Ni, Co and Fe nanoparticles could be in situ encapsulated in carbon shells when the arc was performed respectively in aqueous solutions of NiSO₄, CoSO₄ and FeSO₄. Transmission electron microscopy, energy dispersive X-ray spectroscopy and electron diffraction patterns of selected areas were used to determine the crystalline phase of the metal cores. To explain the formation mechanism of metal-containing carbon nanocapsules, a model of discharge in solution is proposed. This result presents a simply controllable way to synthesize metal-containing carbon nanocapsules.     

Supported Vanadia Catalysts for Dehydrogenation of Ethylbenzene with CO2

Alumina supported vanadia catalysts (V/Al) for selective oxidehydrogenation of ethylbenzene with CO₂ were prepared by impregnation method. During preparation the effect of promoters and calcined temperature was investigated, it was found these two items had a strong influence on the activity of V/Al catalysts. Dehydrogenation reaction with CO₂ was happened in the fixed-bed reactor at 450 °C. Results showed that 15.2% ethylbenzene conversion and 99.2% styrene selectivity were acquired when V₂K/Al catalyst was used.     

改进的局域均值分解在机械故障诊断中的应用

为了准确地提取出故障特征,把局域均值分解算法应用于机械故障振动信号的特征提取中.然而在对信号进行局域均值分解时,由于端点的趋势无法预知,在分解时会污染到整个信号序列,而且滑动平均造成了信号的过平滑处理,导致故障特征不能准确地提取.采用波形匹配解决端点处信号的走势,之后再利用线性插值求得信号的局域均值函数与包络函数,通过在Matlab环境下仿真调幅信号验证了该算法在端点处能很好的保持原有信号的特征;并采用实际的机械振动信号进行实验验证,引入端点效应评价指标,根据分解后的生产函数与原信号的相关系数验证了改进的局域均值分解更能准确地提取故障特征.     

结合形态学的结构化车道线快速识别算法

为解决结构化车道线识别算法中存在的约束条件多,计算过于复杂等问题,提出一种基于形态学的车道线快速识别算法. 该算法首先对道路图像进行中值滤波,Sobel算子增强边缘,利用直方图特征分割图像,然后划分感兴趣区域,接着构造具有车道特征的形态学结构元素去提取车道线,最后概率霍夫变换拟合车道线. 实验对比结果表明,针对结构化道路,该算法简单有效,计算量小,具有良好的实时性.