Pd—DBS—偶氮胂新显色体系的分光光度法测定钛合金中微量钯

在较高酸度的H_3PO_4介质中钯与DBS—偶氮肿的新显色反应有较高的选择性。钯含量在0～10μg/10ml符合比尔定律,ε_(610)=2.37×10~4。提出不经分离,准确、快速测定钛合金中微量钯的方法。     

Rh(Ⅲ)—钍试剂—高碘酸钾新体系催化光度法测定痕量铑

在pH3.9醋酸盐缓冲溶液中及加热条件下,Rh(Ⅲ)对高碘酸钾氧化钍试剂这一褪色反应有强烈的催化作用,可藉自来水冷却中止反应,研究了催化光度测定痕量铑的影响因素,建立了一个检出限和测定范围分别为0.06ng/ml和0.12～4.5ng/ml铑的催化光度新方法。     

基于PCA-Logistic模型的围岩稳定性分类研究

为了建立易于理解和应用的围岩稳定性分类模型,采用主成分分析法对围岩样本数据进行降维处理,以Logistic模型来描述围岩稳定性影响因素与围岩稳定性之间的经验关系。选取影响围岩分类的5个主要因素,根据分类标准,采用在每级标准中随机内插的方法,得到100个标准样本,用于建立PCA-Logistic模型。分析结果显示,PCA-Logistic模型拟合效果良好,对工程实例的预测结果同投影寻踪模型和分形-插值模型完全一致,表明该模型在围岩稳定性评价中具有可行性和有效性。     

高压切割喷嘴冲击磨损的数值分析

以复用矿井含煤颗粒的水对高压切割喷嘴的磨损物理问题为研究对象,采用计算流体力学理论进行数值计算,分析讨论了质量浓度为10、15、20、25、30、40 mg/L条件下速度、剪应力、磨损的分布以及变化关系。计算、分析结果表明:磨损主要发生在出口部位和锥体的后半部份,入口部份和锥体前半部份磨损较小;入口部位、锥体部位磨损量、剪应力随质量浓度增加变化较小;出口部位磨损量在质量浓度为10～20 mg/L减小较快,超过20 mg/L后变化不大;出口部位流体速度、磨损量、剪应力与质量浓度间存在对应关系。煤粒入口速度与混合物速度相同、锥体部位煤粒流体速度低于锥体部位混合物速度(平均值)约79.4%;煤粒出口部位速度随质量浓度的增大而呈现先增加后减小的趋势,速度最大值发生在浓度20 mg/L。     

软件开发框架模型——现实世界问题的结构化分析

对软件开发框架模型提出了一种新的构化的分析方法,同时介绍了常用的结构化分析的工具及其更实用情况。     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Limit load and shakedown analysis of plastic structures under stochastic uncertainty

Problems from plastic analysis are based on the convex, linear or linearised yield/strength condition and the linear equilibrium equation for the stress (state) vector. In practice one has to take into account stochastic variations of several model parameters. Hence, in order to get robust maximum load factors, the structural analysis problem with random parameters must be replaced by an appropriate deterministic substitute problem. A direct approach is proposed based on the primary costs for missing carrying capacity and the recourse costs (e.g. costs for repair, compensation for weakness within the structure, damage, failure, etc.). Based on the mechanical survival conditions of plasticity theory, a quadratic error/loss criterion is developed. The minimum recourse costs can be determined then by solving an optimisation problem having a quadratic objective function and linear constraints. For each vector a(·) of model parameters and each design vector x, one obtains then an explicit representation of the “best” internal load distribution F^∗. Moreover, also the expected recourse costs can be determined explicitly. Consequently, an explicit stochastic nonlinear program results for finding a robust maximal load factor μ^∗. The analytical properties and possible solution procedures are discussed.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

RoadCompass: following rural roads with vision + ladar using vanishing point tracking

We present a vision- and ladar-based approach to autonomous driving on rural and desert roads that has been tested extensively in a closed-loop system. The vision component uses Gabor wavelet filters for texture analysis to find ruts and tracks from which the road vanishing point can be inferred via Hough-style voting, yielding a direction estimate for steering control. The ladar component projects detected obstacles along the road direction onto the plane of the front of the vehicle and tracks the 1-D obstacle “gap” presumed due to the road to yield a lateral offset estimate. Several image- and state-based tests to detect failure conditions such as off-road poses (i.e., there is no road to follow) and poor lighting due to sun glare or distracting shadows are also explained. The system’s efficacy is demonstrated with analysis of diverse logged data including from the 2005 DARPA Grand Challenge, as well as tests with full control of a vehicle over 15 km of difficult roads at up to 37 km/h with no waypoints.

基于Hyper Mesh的商用车驱动桥壳有限元分析

驱动桥壳是汽车的重要部件之一,应具有足够的强度和刚度。先利用UG建立桥壳的几何模型,然后在Hyper Mesh中进行网格划分、加载和约束,对驱动桥壳进行有限元分析计算,最后提出改进方案。