基于交叠社团相似性的生物网络关键节点识别

生物网络是研究生物特性的一个重要工具,目前已经有许多方法用于研究生物网络,关键节点分析是其中最常用的方法之一。关键节点分析通常是根据一定的规则为网络中的各节点分配一个函数值,并由此来确定网络中各节点的重要程度,目前已经发表了一些方法。然而,这些方法在单独使用的情况下,获得的关键节点的生物学意义一般较低,存在一定的缺陷。本文从节点对社团贡献的角度建立关键节点识别方法,首先提取网络中富含生物学功能意义的社团,然后依据交叠社团的相似性为各节点分配贡献值,最后通过两个生物网络实例论证了方法的有效性。     

一种检测单晶炉热场温度的新方法

为去除单晶炉热场温度测量信号中的低频干扰、提高温度信号的估计精度,本文提出了一种基于跨维模拟退火(trans dimensional simulated annealing,TDSA)的单晶炉热场温度检测方法.该方法首先将AIC(Akaike information criterion)信号个数判断准则纳入采样机制中以便对干扰个数采样估计,进而利用所设计的基于信号频谱和轮盘赌思想的Metropolis Hastings机制对相应个数的干扰频率进行采样,最后设计了混合均匀和高斯采样机制对单晶炉热场温度进行采样估计.仿真和单晶炉工程实验结果表明,该方法在干扰个数未知的情况下,能够有效地抑制低频干扰、准确检测单晶炉热场的温度.     

利用煤层气发展山西合成油工业探讨

介绍了国外利用天然气生产油口的生产工艺，探讨了山西省利用丰富的煤层气资源发展合成油的可能性和优势，并提出了相应的发展建议。     

A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine

Adjusting parameters iteratively is a traditional way of training neural networks, and the Rough RBF Neural Networks (R-RBF-NN) follows the same idea. However, this idea has many disadvantages, for instance, the training accuracy and generalization accuracy etc. So how to change this condition is a hot topic in Academics. On the basis of Extreme Learning Machine (ELM), this paper proposes a Weighted Regularized Extreme Learning Machine (WRELM), taking into account both minimizing structured risk and weighted least-squares principle, to train R-RBF-NN. The traditional iterative training method is replaced by the minimal norm least-squares solution of general linear system. The method proposed in this paper, increasing controllability of the entire learning process and considering the structured risk and empirical risk, can improve the performance of learning and generalization. Experiments show that it can reach a very superior performance in both time and accuracy when WRELM trains the Rough RBF Neural Networks in pattern classification and function regression, especially in pattern classification, which can improve the generalization accuracy more than 3.36 % compared with ELM. Obviously, the performance of the method proposed in this paper is better than the traditional methods.     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Prediction-based realistic 3D model compression

The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced.     

空客飞机编码译码组件DEU B故障分析与系统优化

针对机龄较长的A320飞机多次重复出现DEU B或DEU B 300 RH等故障信息,难以排除故障,本文将研究空客排故手册TSM和图解目录手册IPC中的排故信息,以及编码译码计算机软件和硬件的情况,判断故障的原因,彻底解决这一故障,提高航空器的可靠性,控制维修成本。最终,在大量研究之后,发现是编码译码计算机在互换性和混装性方面出了问题,并经过系统设备优化升级、手册修改等措施,取得了预期的效果。     

浅析计算机网络维护工作和管理

随着现代社会的进步,科学技术取得了飞速发展,计算机网络得到了大量普及,极大地促进了社会生产和人们生活水平的提高,对社会的整体发展起着至关重要的作用.本次研究将从网络维护、管理的意义和应注意的问题出发,对网络网络维护与管理相关策略进行深入分析和探究.     

Studies of structure and cycleability of LiMn2O4 and LiNd0.01Mn1.99O4 as cathode for Li-ion batteries

Lithium manganese oxides LiMn₂O₄ and rare earth elements doped LiNd_0.01Mn_1.99O₄ were synthesized by microwave method. The structure and the electrochemical performances of the samples were characterized. XRD data shows both samples exhibit the same pure spinel phase. But due to the introduction of Nd³⁺ ion into the unit cell, the lattice parameter of the Nd-doped spinel was larger than that of the undoped one. The two samples had a similar morphology including small particle size and homogeneous particle distribution as tested by SEM. The cyclic voltammmetry and constant-current charge-discharge tested that Nd-doped spinel displayed a better reversibility and cycleability.     

The kinetics of B‐a and P‐a type copolybenzoxazine via the ring opening process

The structure of benzoxazines is similar to that of phenolic resin through thermal self‐curing of the heterocyclic ring opening reaction that neither requires catalyst nor releases any condensation byproduct. These polybenzoxazine resins have several outstanding properties such as high thermal stability and high glass transition temperature. To better understand the curing kinetics of this copolybenzoxazine thermosetting resin, dynamic and isothermal differential scanning calorimetry measurements were performed. Three models, the Kissinger method, the Flynn–Wall–Osawa method, and the Kamal method, were used to describe the curing process. Dynamic kinetic activation energies based on Kissinger and Flynn–Wall–Osawa methods are 72.11 and 84.06 KJ/mol, respectively. The Kamal method based on an autocatalytic model results in a total order of reaction between 2.66 and 3.03, depending on curing temperature. Its activation energy and Arrhenius preexponential are 50.3 KJ/mol and 7959, respectively. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 95: 730–737, 2005