基于表面反应和扩散迁移控制的灰岩单裂隙渗流—溶解模型及其数值模拟

针对碳酸盐类可溶岩地区水电站坝址流场、化学场以及固相介质属性等随时间发生改变,从而对工程安全运行造成不利影响等问题,研究了灰岩地区地下水运移过程中各物理场间的相互作用,分析了影响灰岩溶解速率的两个因素,即表面反应控制和扩散迁移控制。在此基础上,建立了单裂隙中的渗流—溶解耦合模型,并进行数值求解。模拟结果表明,在垂直裂隙延伸方向,其溶蚀锋面为非齐整平面,而是呈似“虫洞”状非均一变化,而沿裂隙延伸方向即自上游侧向下游方向溶蚀程度逐渐减轻;而通过裂隙的流量呈现随时间逐渐增大的趋势,但变幅不大;根据流量求得的等效水力隙宽,其增幅和增长速率均小于实际平均隙宽;同时,化学场中Ca2+浓度的分布与裂隙开度变化具有相似性,不同时刻下上游侧反应速率R均大于下游侧。就反应机制而言,在初期均受表面反应控制,而随反应进行,位于上游补给区部位转为受扩散迁移控制,但在下游位置仍受表面反应控制。     

金属材料屈服强度的应变率效应和热激活理论

对４种金属材料进行了应变率为１０＾－４－１０＾３ｓ＾－１的拉伸试验，结果表明，屈服中度随应变率的升高分两阶段线性升高，提出了位错线热振动对加载频率的响应的热激活作用机理，依据该机理计算所得Ⅰ，Ⅱ区临界应变εｃ与实测值相符，并在此基础上将应变率对掘强度影响的机制图重新划分为：Ｉ区，富热激活区；Ⅱ区，贫热激活区。     

粘土砂旧砂再生效果与其含水量的关系

在影响粘土砂旧砂再生效果的诸多因素中,旧砂中水分含量是最重要的因素。本文就水分含量对再生效果的影响规律、再生效果最佳的水分含量等进行了研究和分析,得出了如下结论:并非含水量越低再生效果就越好,而是存在一个去泥率低谷区;再生效果最佳的适宜水分含量为1～2%。     

Dual–primal proximal point algorithms for extended convex programming

This paper presents a class of dual–primal proximal point algorithms (PPAs) for extended convex programming with linear constraints. By choosing appropriate proximal regularization matrices, the application of the general PPA to the equivalent variational inequality of the extended convex programming with linear constraints can result in easy proximal subproblems. In theory, the sequence generated by the general PPA may fail to converge since the proximal regularization matrix is asymmetric sometimes. So we construct descent directions derived from the solution obtained by the general PPA. Different step lengths and descent directions are chosen with the negligible additional computational load. The global convergence of the new algorithms is proved easily based on the fact that the sequences generated are Fejér monotone. Furthermore, we provide a simple proof for the O(1/t) convergence rate of these algorithms.     

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。     

Efficient wavelet-based ECG processing for single-lead FHR extraction

This paper describes a novel model for fetal heart rate (FHR) monitoring from single-lead mother?s abdomen ECG (AECG) measurements. This novel method is divided in two stages: the first step consists on a one-step wavelet-based preprocessing for simultaneous baseline and high-frequency noise suppression, while the second stage efficiently detects fetal QRS complexes allowing FHR monitoring. The presented structure has been simplified as much as possible, in order to reduce computational cost and thus enable possible custom hardware implementations. Moreover, the proposed scheme and its fixed-point modeling have been tested using real abdominal ECG signals, which allow the validation of the presented approach and provide high accuracy.     

故障自动防护微机械角速度传感器

微机械角速度传感器是目前市场上能够进行批量生产的最复杂的传感器之一。它由在真空中做复杂运动的惯性质量块和驱动该设备、分析其响应的多种复杂电路组成,并集中于一个在极狭小的空间内。本文介绍了由奥地利SensorDynamics公司成功研制的一种MEMS角速率传感器,可以满足现代应用的全部要求,尤其是汽车工业所要求的小尺寸、坚固的机械结构、长期稳定性、不受限制的故障自动防护性和AEC-Q100认证。     

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.