基于接收方信用量调整的TCP新算法

传输控制协议(transport control protocol, TCP)发送窗口的大小由拥塞窗口和接收方确认窗口中较小的值来确定。在高速网络中,通过合理地增加拥塞窗口,可以提高TCP的性能。然而,一个相对较小的接收方确认窗口将限制发送窗口的增加,如果根据TCP接收方的数据变化率恰当地增加接收方确认窗口赋予发送方的信用量,就能减少这种限制。提出一种增强的TCP算法,命名为接收方信用量调整传输控制协议（receiver credit adjustment transport control protocol, RCA-TCP）,该算法根据TCP接收方缓冲区的动态数据变化率和TCP连接的最小往返时间之间的乘积的估计值来获得信用量增量。在ns-2(network simulator, version 2)下仿真表明,该算法能获得更好的性能。     

Clustering patents using non-exhaustive overlaps

Patent documents are unique external sources of information that reveal the core technology underlying new inventions.Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments.This research incorporates an ontology schema to extract and represent patent concepts.A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery.The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims,a feature that enables the grouping of patents that define related key innovations.Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation.The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.     

从资产相关性计算信用质量相关性

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营销决策综合效果的DEA评价方法

首先构建了一套评价营销决策综合效果的指标体系,然后运用数据包络分析(DEA)方法对之进行了有效性评价。最后,以新兴公司为依托进行了实证研究,具有一定的实际意义。     

公路建设中土石方调运方法研究

本文研究了公路建设中土石方调运的实际背景,提出了处理该问题的数学模型和计算方法.     

时间序列曲线盒维数的一种快速算法

盒维数是应用最为广泛的维数之一,它在信息处理、预测等领域都有成功的应用。目前,对盒维数的计算主要采用网格法,但该算法需处理分形集合在网格中的计数问题,所以计算机处理起来很不方便。针对一维时间序列,提出了在不同间隔时间内寻求最大离差的方法,避免了集合的计数问题。应用该算法在上证指数的时间序列曲线的盒维数的计算表明上证指数存在着长程正相关。Ｗｉｅｎｅｒｆ过程曲线的盒维数的计算同样表明该算法是可行和有效     

基于神经网络的舰船全磁场部位识别

利用人工神经网络将舰船磁场的三个分量进行序贯融合处理 ,从而进行对舰船关键部位的精确识别 ,给出了处理方法及检验结果 ,并分析了提高网络推广性能的措施。     

基于神经网络的一类非线性系统的变结构控制

在已知名义系统的基础上 ,将小脑关节模型控制器 (CMAC)神经网络用于一类状态反馈可线性化的多输入多输出连续时间非线性系统的变结构控制中。利用自适应技术估计了估计误差的大小 ,减小了系统的不确定性 ,并利用模糊控制技术调整了变结构增益 ,改善了系统的性能。在很弱的假设条件下 ,应用Lyapunov稳定性定理证明了闭环系统内的所有信号为均匀最终有界。算法在导弹控制系统中的应用进一步证明了本文方法的有效性。     

Combined effects of solar UV radiation and CO2-induced seawater acidification on photosynthetic carbon fixation of phytoplankton assemblages in the South China Sea

We carried out short term pCO₂/pH perturbation experiments in the coastal waters of the South China Sea to evaluate the combined effects of seawater acidification (low pH/high pCO₂) and solar UV radiation (UVR, 280–400 nm) on photosynthetic carbon fixation of phytoplankton assemblages. Under photosynthetically active radiation (PAR) alone treatments, reduced pCO² (190 ppmv) with increased pH resulted in a significant decrease in the photosynthetic carbon fixation rate (about 23%), while enriched pCO₂ (700 ppmv) with lowered pH had no significant effect on the photosynthetic performance compared to the ambient level. The apparent photosynthetic efficiency decreased under the reduced pCO₂ level, probably due to C-limitation as well as energy being diverged for up-regulation of carbon concentrating mechanisms (CCMs). In the presence of UVR, both UV-A and UV-B caused photosynthetic inhibition, though UV-A appeared to enhance the photosynthetic efficiency under lower PAR levels. UV-B caused less inhibition of photosynthesis under the reduced pCO₂ level, probably because of its contribution to the inorganic carbon (Ci)-acquisition processes. Under the seawater acidification conditions (enriched pCO₂), both UV-A and UV-B reduced the photosynthetic carbon fixation to higher extents compared to the ambient pCO₂ conditions. We conclude that solar UV and seawater acidification could synergistically inhibit photosynthesis.     

农机与社会、经济协调发展分析

由于农业系统的复杂性和灰色性,用灰色系统理论来解决农业系统中的问题是科学的。本文利用灰色系统理论建立了农机、社会、经济协调发展模型,并用此模型对吉林省某村作了实证研究,得到了许多有益的结论。