还原增感浓度对AgCl光电子衰减的影响

还原增感是三大化学增感类型之一.近10年来,还原增感的机理以及过程的控制,越来越受到感光科学工作者的关切和注意.主要利用微波介电检测技术,测得自由光电子与浅束缚光电子衰减行为随还原增感浓度的变化.实验发现:还原增感浓度低时,增感中心起空穴陷阱作用;还原增感浓度高时,增感中心起深电子陷阱作用.根据光电子衰减行为随增感浓度的变化得到了最佳增感浓度.     

益阳市域发展战略研究

从区域发展的角度,全面分析了益阳市域发展的主要条件及其对益阳发展的重要影响,并在此基础上,提出了益阳的发展规划和战略定位．     

用微分方程方法研究红绿灯控制下的交通流模型

红绿灯模型是交通流动力学模型研究中一个重要而困难的问题,首先给出交通流模型,然后给出红绿灯交通流模型与特征线的求解,根据红绿灯模型与特征线意义,对交通流的红绿灯转换周期的设置具有重要意义．     

基于Windows的DSM系统中线程迁移的实现

随着多线程DSM系统的发展,线程迁移已经成为DSM系统中一个非常重要的研究课题。在分析Windows操作系统的存储和控制结构的基础上,讨论了基于Windows操作系统的多线程DSM系统的基本框架,提出了一种对线程进行动态迁移的方法。并且在一个基于Windows操作系统工作站机群的Smonn系统中成功实现,表明了该方法的有效性。     

黄河口凹陷明化镇组下段网状浅水三角洲沉积

为了认识湖盆萎缩期网状浅水三角洲的沉积特征和沉积模式,利用测井、录井、取芯、粒度、地震等资料,对黄河口凹陷明化镇组下段低位域地层进行了分析.研究认为,黄河口凹陷明化镇组下段低位域网状浅水三角洲河道砂体主要是垂向加积作用的产物,河道平面上呈网状、交织状,具有宽深比小、稳定性强等特点,砂体发育程度中等;其形成于低坡降背景,...     

WO3丙酮传感器及其多传感器融合技术的研究

高浓度丙酮会刺激黏膜和引起中毒,在医疗健康领域丙酮也可作为呼出气体标记物。为实现对丙酮气体的有效检测,利用WO₃纳米材料制备旁热式气敏传感器,系统测试该传感器的基本特性,并在此基础上与SnO₂传感器组成双气敏传感器阵列,结合反向传播神经网络方法对丙酮混合气体进行识别。在体积浓度10^-5丙酮气体测试中,WO₃丙酮传感器的响应值达到9.3,响应与恢复时间均为4 s,重复性误差小于10^-8;双气敏传感器阵列对混合气体中丙酮和乙醇的识别误差小于10^-8。结果表明,制备的WO₃传感器响应灵敏度、稳定性、选择性、瞬态响应等特性良好,采用多传感器融合技术可以实现混合气体的有效识别。     

Novel cued search strategy based on information gain for phased array radar

A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.     

考虑悬架几何特性及作动器延时的整车模糊控制

利用ADAMS软件建立了整车悬架及转向机械模型,考虑了悬架的几,阿特性,结合变论城模糊控制方法,设计了主动悬架变论域模糊控制器,同时考虑了作动器的延时特性,通过matlab/simulink联合仿真,并与被动悬架模型进行了对比,得出所设计的模糊控制器对汽车的平顺性有较大改善.     

Interval Time Series Analysis with an Application to the Sterling-Dollar Exchange Rate

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.     

Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.