TYPHOON PROCESS AND ITS IMPACT ON THE SURFACE CIRCULATION IN THE NORTHERN SOUTH CHINA SEA

A severe typhoon Utor,occurring between July 3 and 8,2001,brought heavy rainfall,strong wind and storm surge.Utor was responsible for tremendous destruction and economic losses in Philippines,Taiwan and Guangdong.An air-sea model system (MM5 and Princeton Ocean Model(POM))was built to simulate meteorological dynamics and ocean circulation in the South China Sea(SCS).In the POM the output of MM5 was used as the input data.With an increased number of vertical levels,a high-resolution planetary boundary layer scheme and updated landuse/vegetation data,the accuracy of computing wind,temperature and other meteorological fields are improved in near surface and upper levels in MM5 simulations.The simulated trajectory and wind speed of Utor are close to the observed results.The simulated distribution of rainfall is accorded well with measured data in the Pearl River Delta(PRD)area.At different meteorological stations in Hong Kong,the wind,temperature and sea surface pressure are well simulated.The simulated ocean surface current and surface temperature fields have an obvious rightward-biased response to the typhoon Utor,and the maximum velocity and the lowest temperature region appear in the 30 km of the right side of the typhoon track.The typhoon Utor could make the water 50m under the surface ocean unwell to surface and the ocean surface temperature decrease by about 2°C.     

辛集挡潮蓄水闸交通桥维修加固与管理浅谈

随着社会经济高速发展,公路运输量日益增大,部分建于20世纪70年代的桥梁因材料老化、设计承栽能力低而出现病害.根据旧桥维修加固工作实践,对桥梁维修加固设计方案与施工方法进行探计.     

基于ASP技术的校园网新闻发布系统的设计与实现

在互联网飞速发展的今天,校园网站建设已成为各个学校信息化建设的重要组成部分.本文针对学校的实际应用需求,提出了一个适用于校园的校园网新闻的发布系统设计方案.实践证明该方案高效可行.     

基于Moodle的网络教学模式构建与使用

本文介绍一种功能强大、开源免费的学习管理系统(LMS)- -Moodle,阐述了利用该系统的功能模块进行课程设计的方法和构建网络教学模式的流程,并提出了这种教学模式在实际使用过程中的建议.     

New insights on survivability in multi-domain optical networks

With the development of intelligent optical networks and the general multi-protocol label switching (GMPLS) technique, the seamless convergence between IP network and optical network is no longer be a dream but a practical reality. Similar to the Internet, current optical networks have been divided into multiple domains each of which has its own network provider and management policy. Therefore, the development of multi-domain optical networks will be the trend of new-generation intelligent optical networks, and GMPLS-based survivability for multi-domain optical networks will become a hot topic of research in the future. This paper provides a comprehensive review of the existing survivable schemes in multi-domain optical networks and analyzes the shortcomings of current research. Based on previous studies, we present possible challenges and propose new ideas to design efficient survivable schemes to guide the future work of researchers in multi-domain optical networks.     

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.     

多相DC-DC电路中PWM控制技术的研究

本文对多相DC-DC电路中的PWM控制技术进行了研究,对电压型PWM控制和电流型PWM控制进行了简单比较,提出了基于快速主动均流技术的电流型控制方法,并将这种方法应用于工程设计。实验证明,采用快速主动均流技术消除了器件性能不一致带来的影响。     

改进自组织映射神经网络在指纹识别中的应用

改进自组织映射神经网络方法是将常规自组织映射神经网络方法结合确定性水平,对网络的输入矢量进行预处理。通过实验比较了这种改进的自组织映射神经网络识别方法与常规的自组织映射神经网络识别方法的识别效果,在识别性能上有了很大的提高。     

平移不变小波去噪法在胎儿心率提取中的应用

用传统的小波阈值法对超声多普勒胎儿心脏回波信号进行去噪处理时会产生Psuedo Gibbs现象,影响了去噪效果和胎儿心率值的提取。为了解决这个问题,在此基础上提出了平移不变小波法去噪。实验结果表明,该方法有效的抑制了Psuedo Gibbs现象,剔除了信号中的噪声,识别出被噪声淹没的有用信号,有利于心率值的提取。     

一种改善无线TCP性能的链路层重传方案

提出一种提高无线环境中TCP性能的链路层选择性重传的改进方案mARQ.根据每个数据包在链路层被拆分成多个无线帧这一特点,对包中所有帧的重传次数之和进行限定,使其可以根据各帧的需求进行自适应分配.系统仿真表明:与传统方案相比,新设计方案mARQ在高误帧率的环境下以较低的误包率获得了较高的TCP吞吐量,提高了无线环境下TCP的性能.