四冲程汽油机缸内斜轴涡流研究

利用双特征角气道稳流试验台,对一款二气门四冲程单缸试验机的3种不同进气道方案进行了不同测试平面的气流运动试验和分析.结果表明,提高原气道涡流强度来改善燃烧的同时会降低气道的流通能力,但适度提高滚流强度的同时还可以提高流通能力.利用测得的无因次涡流比和滚流比,提出了一个简便的斜轴涡流特征参数计算公式,并通过试验数据给予了有效性证明.另外对特征角α=60°时的无因次涡流比和利用涡流与滚流合成的无因次斜轴涡流比进行了线性回归分析,3种方案数据的相关系数分别是0.96、0.97、0.97,因此,通过稳流气道试验台测取某一特征角度下的无因次涡流比可以评价进气道斜轴涡流的性能.     

新疆阿勒泰地区区域旱涝变化特征分析

利用1961-2008年阿勒泰地区7个气象站的降水量资料计算其区域旱涝指数（W,简称W指数）,主要采用线性趋势法、Mann-Kendall法（简称M-K法）分析其变化特征;选取5个典型干旱、湿润年的NCEP/NCAR资料,对比分析其500hPa环流形势的异同;选取前一年500hPa部分环流特征量与当年W指数进行相关分析。结果表明：年和各季的W指数均具有明显的年际和年代际波动,均有旱涝交替、连旱或连涝的特征。M-K突变检测分析结果表明,年、季W指数突变主要发生在20世纪60年代中后期和80年代中期。分析典型干旱与湿润年500hPa距平场表明,干旱年东欧至西西伯利亚为较强的正距平,新疆受暖脊控制,湿润年则相反。若前一年极涡面积偏小,强度偏弱,而西太副高强度偏强,面积偏大,并盛行纬向环流,次年W指数则容易偏大,即易出现涝年。科学准确认识该地区旱涝规律,把握未来旱涝趋势变化,及时进行农业结构调整,合理安排农业生产,是防御旱涝夺丰收的有效措施。     

国营方旺林场森林收获预定探讨

在森林收获调整作出之后,以什么顺序进行收获是一需待研究的问题。为了将各分期的收获量落实到小班,本文以云南省国营方旺林场作为研究对象,应用线性规划的方法对现有华山松人工林分各小班的收获预定进行了初步的尝试。     

非充分灌溉中作物——水模型的建模与识别

本文认为:作物水分生产函数的名称不能确切反映作物产量与水分关系的数学特点。作物—水模型(MCRW)的名称可能较为适合,这种模型实质上是一种模拟模型。本文从理论特征,建模主要假设及确认方法等方面,对线性模型和阶段非线性模型的建模问题进行了较系统的研究,对国外主要的阶段模型进行了比较分析,提出了模型适用条件;对线性模型进行了带有置信区间的改进。可为非充分灌溉中模型选择和应用提供指导以及模型的建立提供理论依据。     

巨峰葡萄叶面积测定方法的研究

本文用数理统计的方法,对不同立地条件、不同树龄巨峰葡萄树的叶片面积与其叶片长度、宽度和长×宽分别进行线性回归分析,得出各自高度相关的回归方程,为巨峰葡萄叶面积的测定,提供一个简单实用、可靠性较高的方法。     

南方贮木场原木库存线性随机最优反馈控制系统

本文根据贮木场原木库存生产的随机性和动态性,用CAR模型自动辨识机辨识上贮木场原木库存动态系统的阶,时滞和参数,建立系统的状态方程,在此基础对库存动态进行kalman滤波和预报,并进行了最优控制。     

Prediction of CIELab data and wash fastness of nylon 6,6 using artificial neural network and linear regression model

We tried to predict the CIELab data and wash fastness values of scoured nylon 6.6 knitted fabric dyed with 1:2 metal-complex acid dyes and aftertreated using three different methods named as syntan, syntan/cation and full backtan by artificial neural network (ANN) with Levenberg-Marquardt algorithm and regression models. Afterward, the predicting performance of these models was tested and compared with each other using unseen data sets. We were able to achieve to predict the all colorimetric data satisfactorily such as L*, a*, b*, C, h ^o and wash fastness performance using both models. The statistical findings indicated that the regression models provide more accurate prediction for all colour data with an average error of 1% contrast to previous study. In terms of prediction of fastness, artificial neural network is a bit more useful than regression models for prediction of staining value on the nylon part of adjacent multifiber.     

基于农用地分等成果的不同质量耕地保有量预测方法与实证研究——以江苏淮安市为例

以农用地分等成果为基础,探讨了不同等级耕地保有量预测方法,并以江苏省淮安市为例进行了实证研究,结果表明,该方法可以合理确定种植粮食作物的最低保护方案和各等级土地保护面积,可用于农业结构调整耕地面积的最高方案。     

Comparison of illuminations to identify wheat classes using monochrome images

Wheat class identification using machine vision is an objective method which can be used for online testing to automate handling, binning and shipping operations in grain industry. The efficiencies of a monochrome camera-based vision system with three different illuminations (incandescent light (IL), fluorescent ring light (FRL), fluorescent tube light (FTL)) were determined to identify eight western Canadian wheat classes at four moisture levels (11%, 14%, 17% and 20%). The monochrome images of the bulk wheat samples were acquired at each moisture level (3 illuminations × 8 classes × 4 moisture contents × 100 replications = 9600 images). A linear discriminant function was used for the classification of wheat samples using 32 gray level textural features extracted from the monochrome images. The mean gray values of the wheat classes were in the ranges of 75–103, 73–115, and 107–143 for IL, FRL and FTL, respectively. The mean gray values of wheat classes were significantly different within each illumination and between different illuminations (α = 0.05). Mean gray value was the highest for FTL and the lowest for IL illumination. The moisture content of the wheat samples had significant effect on the mean gray values. The overall classification accuracies were 90%, 81% and 96% for IL, FRL and FTL, respectively, when all the wheat classes were at the same moisture levels. It was 66%, 53% and 85% for IL, FRL and FTL, respectively, when the wheat classes were at different moisture levels. The classification accuracies of a 2-stage classification system for the classes with different moisture levels were 68%, 56% and 90% for IL, FRL and FTL, respectively.