烷烃在分子筛孔中吸附和扩散的分子模拟

用巨正则系综蒙特卡罗(grand canonical monte carlo,GCMC)和构型偏倚蒙特卡罗(configurational-bias monte carlo,CB- MC)相结合的模拟方法,研究300 K下,烷烃(甲烷、乙烷、丙烷、丁烷)在ISV、MEL型分子筛上的吸附,饱和吸附量的大小顺序为:甲烷>乙烷>丙烷>丁烷。模拟甲烷-乙烷、乙烷-丙烷混合物在分子筛(ISV、MOR、MEL型)中的吸附分离,结果甲烷-丙烷比乙烷-丙烷混合物更易分离。采用分子动力学(molecular dynamics,MD)模拟方法,研究甲烷在MEL、MOR分子筛上的扩散系数,分别为2.76-3.86 m~2·s~(-1)、3.14-13.30 m~2·s~(-1),烷烃在分子筛中的扩散与吸附相关,且扩散系数的大小与分子筛的结构有关。模拟所得的有关吸附扩散性质的数据对于化工生产、研发和设计具有指导意义。     

A method based on interval-valued fuzzy soft set for multi-attribute group decision-making problems under uncertain environment

In this paper, we develop a new method for multiple attributes group decision-making problems under uncertain environment, in which the information about attribute weights is incompletely known or completely unknown, and each maker’s decision information is expressed by an interval-valued fuzzy soft set. Moreover, this paper takes account of the decision makers’ attitude toward risk. In order to get the weight vector of the attributes, we construct the score matrix of the final fuzzy soft set. From the score matrix and the given attribute weights information, we establish an optimization model to determine the weights of attributes. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. According to these models, a method based on interval-valued fuzzy soft set, which considers the decision makers’ risk attitude under uncertain environment, is given to rank the alternatives. Finally, a numerical example is used to illustrate the applicability of the proposed approach.     

Performance analysis on Lv distribution and its applications

A new signal analysis method, known as Lv distribution (LVD), has been reported recently to provide improved estimation accuracy of centroid frequency and chirp rate. In this paper, performances of the LVD on signal concentration, detection, representation errors and computational complexity are discussed and compared with polynomial Fourier transform (PFT) and fractional Fourier transform (FrFT). Based on the results of our theoretical analysis and Monte Carlo simulations, it is shown that the LVD achieves desirable performance improvement compared with those achieved by other methods. By using the accurate estimation of chirp rate provided by the LVD, the performance of local polynomial periodogram (LPP) is investigated. Comparisons with other time–frequency representations, such as the inverse LVD (ILVD) and the PFT-based LPP, are made on signal concentration in the time–frequency domain.     

基于MSP430的二线制高精度表头设计

针对传统的二线制表头在高温下测量精度差、温漂大的特点,提出了一种在高温下的高精度数显表头的设计方案。分别介绍了其软硬件的实现方法,即采用超低功耗微处理器MSP430F4250采集回路中的电流值,采用24位ADC转换器ADS1244进行模数转换,最后将经过运算的变送器输出信号值在LED数码管中加以显示。实测结果证明,该表头在高温下测量精度高、工作可靠、抗干扰性强、实用性强,势必会成为无源二线制仪表的发展方向。     

校园迎新报到系统的网络供应解决方案——以扬州工业职业技术学院为例

校园迎新报到系统为数字化迎新提供了可能,而数字化迎新又以稳定的网络保障为后盾,如何为报到系统提供稳定而安全的网络保障,成为部分高职院校面临的一个问题。本文以扬州工业职业技术学院为例,简单探析了几种网络保障的解决方案。     

Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.     

二次规划问题的神经网络方法及其数值仿真

针对具有边界约束的凸二次规划问题,利用离散神经网络模型的建模原理,构造了一个新的离散投影神经网络模型.为了优化求解,运用李亚普诺夫稳定性理论,通过构造一个新的广义李亚普诺夫函数,得到了神经网络模型全局指数稳定的线性矩阵不等式条件.同时将结果的保守性与传统方法所得结果进行了比较.数值仿真表明,改进方法采用新条件,有求解方便,保守性弱的特点,具有较强的实用性.     

Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images

Based on seed region growing method, lesion segmentation for ultrasound breast tumor images often requires manual selection of the seed point, which is both time-consuming and laborious. To overcome this limit, this paper attempts to explore an automatic method for finding the seed point inside the tumor. Two criteria combining iterative quadtree decomposition (QTD) and the gray characteristics of the lesion are thus designed to locate the seed point. One is to seek the biggest homogenous region and the other is to select the seed region where the seed point is found. Furthermore, this study validates the proposed algorithm through 110 ultrasonic breast tumor images (including 58 malignant tumor images and 52 benign tumor images). According to the needs of the seed region growing algorithm, if the seed point is found inside the tumor, it means the proposed method is correct. Otherwise, it means that the method is a failure. As the quantitative experiment results show, the proposed method in this paper can automatically find the seed point inside the tumor with an accuracy rate of 97.27 %.     

MGC,consumers’ engagement with MGC,WOM and consumers’ purchase intention: the case of Weibo platform

Information Systems and e-Business Management - Nowadays, consumers spend more time on and pay more attention to information on social media. Thus, firms are constantly concerned if...