Semi-physical mean-value NOx model for diesel engine control

A semi-physical model has been developed to predict nitrogen oxide (NO_x) emissions produced by diesel engines. This model is suitable for online NO_x estimation and for model-based engine control. It is derived from a zero-dimensional thermodynamic model which was simplified by only retaining main phenomena contributing to NO_x formation. The crank angle evolution of the burned gas temperature, which has a strong impact on NO_x formation rate, is described by a semi-empirical model whose key variable is the maximum burned gas temperature. This variable presents a good correlation with the molar fraction of NO_x at the end of combustion and can be expressed as a function of the intake burned gas ratio and the start of combustion. The maximum burned gas temperature sub-model is then coupled to an averaged NO_x formation kinetic model (based on the Zeldovich mechanism) to form a mean-value model for NO_x computation. This latter model was validated using data sets recorded in two diesel engines for steady-state operating conditions as well as for several driving cycles including parametric variations of the engine calibration.     

基于蚁群聚类的RBF NN海洋环境下武器效能评估

海洋环境复杂多变,武器装备作战效能影响要素众多并且机理复杂,效能评估问题难度加大。针对海洋环境影响下武器效能的评估问题,将蚁群聚类算法与RBF神经网络相结合,提出了基于蚁群聚类的RBF神经网络武器作战效能评估方法并建立评估模型。通过蚁群聚类算法确定RBF神经网络隐含层神经元的中心值,并且为了得到最优聚类结果,在蚁群聚类中加入了局部搜索。最后,利用样本数据对该模型进行训练,并对特定条件下武器作战效能进行评估,实验证明此评估模型具有一定的可行性和有效性。     

催化柴油管式液相加氢脱硫数值模拟

采用计算流体力学(CFD)软件FLUENT,以用户自定义函数(UDF)添加化学反应和反应热,对实验室带有陶瓷膜管分散器的催化柴油管式液相加氢脱硫反应器进行模拟计算,得出反应床层不同部位的硫化物含量分布和温度分布状况。从反应床层的入口到出口,催化柴油的硫化物含量逐渐下降,且下降速度趋缓。在压力6.5 MPa、混氢量0.84%(m)、空速2 h~(-1)、进口温度633 K的条件下,位于床层高度0.15 m处出现最高温度点643.8 K,径向温差最大2.1 K,表明催化柴油管式液相加氢脱硫反应器催化剂装填合适的高径比为4~6。工艺条件的模拟结果表明:随着进口温度上升、混氢量增加、空速减小脱硫率提高,与实验数据吻合程度较好,说明模拟研究过程中采用的模型和控制方程准确性较高。     

PR状态方程二元相互作用参数的拟合及对柴油中氢气溶解度的预测

通过Aspen Hysys模拟计算了温度(323~623)K和压力(4~10)MPa条件下,氢气在0~#柴油、直馏柴油、催化柴油和焦化柴油中的溶解度。计算结果对比实验数据,得到氢气在4种柴油中溶解度的平均误差分别为17.49%、11.48%、20.09%和14.00%。因二元相互作用参数与柴油性质以及温度之间存在着一定的关联,为了得到更加准确的溶解度数据,提出了计算PR状态方程中二元相互作用参数的关联式,并用Matlab进行关联式参数的拟合和氢气溶解度的计算,得到溶解度数据的平均误差分别为8.12%、7.51%、5.73%和6.10%,有效地提高了数据的准确性。因此,提出的二元相互作用参数的计算关联式可以为更加准确地预测氢气在柴油中的溶解度提供有效的方法。     

Zipf定律与网络信息计量学

作为文献计量学重要定律的Zipf定律已在许多领域得到较广泛的应用,网络信息计量学伴随着网络信息的激增而受到人们越来越大的关注。该文结合搜索结果数量的分布情况,提出了在网络信息计量学中仍然存在Zipf定律的猜想,并采用公开的词语集在几个代表性的搜索引擎中进行实验验证,证实了搜索结果数目近似服从Zipf定律的结论,其中Baidu与So搜索结果的Zipf指数为0.003。     

节目传输机房接地系统浅析

根据无线局发射台节目传输机房具有的功能,分析了机房设备安全、人员操作安全和设备稳定运行所需的接地要求,并根据这些要求重点分析了电磁适应接地。     

基于 Activiti 流程管理关键技术设计与实现

Activiti 是用于管理业务流程的工作流引擎,它的使用降低了复杂业务流程开发与维护的难度。但在实际应用中,流程节点无法自由跳转,给管理和使用带来诸多不便。本文研究了 Activiti 中的系统框架和接口,基于现有接口设计实现了流程节点自由跳转功能,并进行了框架整合。测试结果表明,该功能满足各项既定功能需求,具有一般性,可满足实际工作中相关要求。     

A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP)

Wireless sensor networks have become increasingly popular because of their ability to cater to multifaceted applications without much human intervention. However, because of their distributed deployment, these networks face certain challenges, namely, network coverage, continuous connectivity and bandwidth utilization. All of these correlated issues impact the network performance because they define the energy consumption model of the network and have therefore become a crucial subject of study. Well-managed energy usage of nodes can lead to an extended network lifetime. One way to achieve this is through clustering. Clustering of nodes minimizes the amount of data transmission, routing delay and redundant data in the network, thereby conserving network energy. In addition to these advantages, clustering also makes the network scalable for real world applications. However, clustering algorithms require careful planning and design so that balanced and uniformly distributed clusters are created in a way that the network lifetime is enhanced. In this work, we extend our previous algorithm, titled the zone-based energy efficient routing protocol for mobile sensor networks (ZEEP). The algorithm we propose optimizes the clustering and cluster head selection of ZEEP by using a genetic fuzzy system. The two-step clustering process of our algorithm uses a fuzzy inference system in the first step to select optimal nodes that can be a cluster head based on parameters such as energy, distance, density and mobility. In the second step, we use a genetic algorithm to make a final choice of cluster heads from the nominated candidates proposed by the fuzzy system so that the optimal solution generated is a uniformly distributed balanced set of clusters that aim at an enhanced network lifetime. We also study the impact and dominance of mobility with regard to the variables. However, before we arrived at a GFS-based solution, we also studied fuzzy-based clustering using different membership functions, and we present our understanding on the same. Simulations were carried out in MATLAB and ns2. The results obtained are compared with ZEEP.     

Google and Privacy

Abstract

Google has emerged as the preeminent Internet search engine, but more important, it has achieved an iconic status. It is solidly entrenched in our language and popular culture. But there is a darker side to Google. Google collects personal information about its users, and it aggregates third party information more effectively than many third world governments. While there has been no abuse (that we know of) by Google of personal information, that potential exists, and there are numerous instances of third parties using Google to aggregate personal information for dubious purposes.