高静态动作压力下粉尘爆炸泄放标准的可靠性

粉尘爆炸泄放设计标准的可靠性对能否实现爆炸防护尤为关键.以中径70μm石松子粉尘为介质,采用20 L球形粉尘爆炸装置进行了4种泄放口径、静态动作压力在0.12~0.6 MPa的爆炸泄放实验,对比了实验值与NFPA 68和EN 14491计算值.结果表明,NFPA 68在预测高静态动作压力下的泄放面积时,随着泄放口径的增大,预测结果由保守变为危险,但预测结果精度较好且数值稳定;EN 14491在预测高静态动作压力下的泄放面积时非常保守,预测精度较差,数据不可靠.依据两种标准推荐的经验函数关系,分析了NFPA 68和EN 14491在预测高静态动作压力时产生这种差异的原因.     

利用反预报方法从截面预言近滴线丰质子核的结合能

在Origin等数据处理软件中,当实验数据较少时,自由参数的不同初始化设置会导致较大的结果差异,这为物理结果的确定带来较大不确定性.通过最小二乘法分析了345MeV/u ~(78) Kr+~9Be反应中产生的丰质子同位素的截面和结合能,并得到线性回归方程.通过回归方程,利用结合能预报部分丰质子核素的截面,以及通过实验截面对近质子滴线核素的结合能进行反预报测量.这对于近质子滴线的丰质子核素实验测量具有较好的借鉴意义.     

基于网络最大流的城市雨水管网系统脆弱性评价

为了对城市雨水管网系统进行针对性的改建,在暴雨天提高事故抢修速度,需要对城市雨水管网系统进行脆弱性评价并找出脆弱管段.首先介绍了城市雨水管网系统排出雨水的干扰因素,提出了城市雨水管网系统脆弱性的概念,并逐一分析了影响城市雨水管网系统的因素,综合考虑各方面因素,建立了城市雨水管网系统的脆弱性评价模型.在此基础上,结合图论中网络最大流理论,寻找城市雨水管网系统中的脆弱管段.最后以西安某高校的雨水管网系统为例进行研究,测得了该高校雨水管网系统的脆弱度为0.877,证明该区域雨水管网脆弱性较低,不易形成内涝,并找出该高校雨水管网的脆弱管段.     

线性回归分析与能源需求预测

结合实例介绍了回归模型方法在能源预测中的应用，具体地讨论了最简单、最基本的直线回归模型结构及参数估计方法，对于其他一些曲线回归模型则可通过变量代换转化为直线回归模型。     

Robustness of alternative non‐linearity tests for SETAR models

In recent years there has been a growing interest in exploiting potential forecast gains from the non‐linear structure of self‐exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR‐type non‐linearities in an observed time series. It is important to study the power and robustness properties of these tests since erroneous test results might lead to misspecified prediction problems. In this paper we investigate the robustness properties of several commonly used non‐linearity tests. Both the robustness with respect to outlying observations and the robustness with respect to model specification are considered. The power comparison of these testing procedures is carried out using Monte Carlo simulation. The results indicate that all of the existing tests are not robust to outliers and model misspecification. Finally, an empirical application applies the statistical tests to stock market returns of the four little dragons (Hong Kong, South Korea, Singapore and Taiwan) in East Asia. The non‐linearity tests fail to provide consistent conclusions most of the time. The results in this article stress the need for a more robust test for SETAR‐type non‐linearity in time series analysis and forecasting. Copyright © 2004 John Wiley & Sons, Ltd.     

离散边界条件系统的最优控制

研究离散边界条件系统的最优控制问题，给出了最优控制存在的必要条件，证明了此类系统最优控制仍有最大值原理成立。     

动态校正的PI型广义预测控制器及其在反应釜系统中的仿真研究

提出了一种新型的广义预测控制器。由于在控制器的设计中,进行了预测误差的动态校正,并且控制器采用了比例积分型(PI)目标函数,因此该控制器对未建模动态具有很强的鲁棒性。应用低阶的模型对一个强放热的化学反应釜模型进行的仿真研究检验了该方法的有效性。     

也谈分段函数的初等性

主要讨论一类能表示成最大值函数、最小值函数的分段函数的初等性。证明了只要能表示成最大值函数或最小值函数的分段函数一定是初等函数。     

云南数字台网谱震级的测定和地震辐射能的计算

运用Duda等提出的谱震级测定公式,结合云南区域数字台网宽频带记录资料测定了发生在网内的中强震(4.0相似文献   

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.