灰色关联分析法用于优选乳化切削液配方方案

乳化切削液的最佳配方方案的选择往往从评价油品的综合质量着手，然而影响油品质量的评价指标很多，包括油品防锈性、抗氧化性、抗磨性及减摩性能等，如何准确地评价不同的配方方案是一个十分重要且很复杂的工作．引用层次分析法(AHP)来确定各性能指标的权重，通过灰色系统理论的灰关联分析法，对乳化切削液配方方案进行评价和优选．结果表明，灰关联分析法是配方方案择优的一个有效的工具．     

基于ICC的色彩管理在数码彩扩机上的应用研究

在数字图像的输入、复制和输出的过程中,色彩的一致性一直是困扰人们多年的问题。以ICC为标准的色彩管理技术很好地解决了这个问题。数码彩扩作为一种计算机图像输出方式,同样面临色彩不一致的问题,因此迫切需要运用色彩管理采对整个冲印过程进行色彩一致性管理。该文综述了将色彩管理运用到数码彩扩机的方法,研究结果对ICC标准的普及应用以及色彩管理应用水平的提高都有着指导意义。     

BS大字符集编辑输入系统的设计与实现

本文首先介绍了多文种编码字符集的国际新标准ＩＳＯ／ＩＥＣ１０６４６的总体结构、使用形式与发展前景．然后对该字符集标准下开发的编辑格入系统（包括系统内码体系，软件实现，功能特色等）做了一些简要的分析与介绍．     

IP黑名单关联聚类算法对恶意簇检测的优化研究

互联网中复杂的恶意活动都是由IP地址集群共同执行的,通过处理在网络中收集的数据来寻找恶意IP簇成为重要的研究方向。提出一种IP黑名单关联聚类算法(IPBACA),首先,构建IP-IP无向图;然后,利用测量统计相关性来测量IP黑名单与IP的相关性,并使用给定的IP黑名单来找到最佳阈值得出IP簇,判断其标准化残差是否达标;最后,识别出具有高精度的恶意簇。仿真结果表明,对比ICAMO算法,CAIIB算法和DABR算法,本文提出的IPBACA算法在精确率、召回率、F1指标和归一化互信息等4个主要性能指标方面均有明显改善,显著提高了对检测恶意簇的检测能力。     

基于时域模型分析谐波复合音的音高感知机制

为了探讨中等次数的谐波复合音是否依赖于时间精细结构信息进行音高感知, 测量了谐波复合音的基频辨别阈。目标复合音可以单独存在, 或者和掩蔽复合音同时存在。二者经过相同的带通滤波区域, 具有不同的相位组合和基频差异。结果表明: 相位组合对音高感知没有显著影响; 滤波区域和基频差异对音高感知具有显著影响。采用时域模型分析发现, 中等次数的谐波复合音可能依赖于时间精细结构信息获得较好的音高感知结果。研究结果能帮助建立复杂声音场景下的音高感知机制。     

天然有机溶剂回收聚苯乙烯泡沫塑料的研究

利用天然有机溶剂使废弃的聚苯乙烯泡沫塑料减容后溶解,选择液体沉淀剂使聚苯乙烯沉淀,另选择一种助剂,该助剂为一种常见液体,可与沉淀剂互溶,从而使沉淀剂与溶剂分离.溶剂、沉淀剂、助剂均无毒、无害,不会造成二次污染.3种试剂均可循环使用,其回收率都达96%以上.     

滨海盐沼植物种子扩散过程对潮流阻断的响应机制

滨海盐沼湿地中,种子扩散模式主要由潮汐作用决定,在潮汐沿高程梯度的运动中形成了种子流。滨海围垦活动可以直接影响到盐沼湿地的潮汐作用,进而造成盐沼湿地中种子扩散模式的变化。本研究对不同高程盐沼湿地的种子流通量进行调查,阐明了潮流干扰下盐沼湿地中植物种子扩散模式的响应机制。本研究发现在潮流驱动下,低位盐沼和中位盐沼的种子将会向海陆两端迁移,低位盐沼上部是重要的种子源区,中位盐沼下部成为了种子汇区。而高位盐沼的围垦活动将会改变盐地碱蓬种子扩散模式。中位盐沼下部的种子扩散模式受到的影响最为严重。围垦活动占据或破坏高位盐沼之后,中位盐沼下部将由种子的汇区变为源区,大量的种子净流出将给该区域盐地碱蓬定植造成潜在威胁。      

East Asian monsoon change for the 21st century: Results of CMIP3 and CMIP5 models

Forty-two climate models participating in the Coupled Model Intercomparison Project Phases 3 and 5 were first evaluated in terms of their ability to simulate the present climatology of the East Asian winter (December-February) and summer (June-August) monsoons. The East Asian winter and summer monsoon changes over the 21st century were then projected using the results of 31 and 29 reliable climate models under the Special Report on Emissions Scenarios (SRES) mid-range A1B scenario or the Representative Concentration Pathways (RCP) mid-low-range RCP4.5 scenario, respectively. Results showed that the East Asian winter monsoon changes little over time as a whole relative to the reference period 1980-1999. Regionally, it weakens (strengthens) north (south) of about 25°N in East Asia, which results from atmospheric circulation changes over the western North Pacific and Northeast Asia owing to the weakening and northward shift of the Aleutian Low, and from decreased north- west-southeast thermal and sea level pressure differences across Northeast Asia. In summer, monsoon strengthens slightly in East China over the 21st century as a consequence of an increased land-sea thermal contrast between the East Asian continent and the adjacent western North Pacific and South China Sea.     

图像目标语义层级结构及其非监督学习算法

根据场景图像中某些目标的特征间具有空间关联性和几何连贯性的空间语义属性,对特定场景的图像序列进行学习,首先提取各图像的尺度不变特征变换(SIFT)特征点,分析每对图像中各匹配特征点对间的几何连贯性,然后根据图像序列特征点矢量间的空间紧密程度进行聚类获得目标对象,从而得到基于空间关联性的一种新的图像语义层级结构(SBOSH),此层级结构图是通过非监督自动学习的过程获得的.由SBOSH结构图不仅可得到各节点上的目标对象,而且能够获得各节点间的空间交互性.所得的SBOSH结构图符合人对场景的逻辑分析,可以应用于场景分析、目标及目标局部精确定位和类目标的识别中.     

Application of artificial neural networks in global climate change and ecological research: An overview

Fields that employ artificial neural networks (ANNs) have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence. ANN has been adopted widely and put into practice by researchers in light of increasing concerns over ecological issues such as global warming, frequent El Niño-Southern Oscillation (ENSO) events, and atmospheric circulation anomalies. Limitations exist and there is a potential risk for misuse in that ANN model parameters require typically higher overall sensitivity, and the chosen network structure is generally more dependent upon individual experience. ANNs, however, are relatively accurate when used for short-term predictions; despite global climate change research favoring the effects of interactions as the basis of study and the preference for long-term experimental research. ANNs remain a better choice than many traditional methods when dealing with nonlinear problems, and possesses great potential for the study of global climate change and ecological issues. ANNs can resolve problems that other methods cannot. This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available. It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.