Study on structure-property relationship of polyimide blends

Summary Three pairs of polyimide/polyimide blends (50/50 wt%) with different molecular structures were prepared by two ways, i.e. mixing of the polyamic acid precursors with subsequent imidization, and direct solution mixing of the polyimides. The blends were studied with DMA technique. The results obtained show that all the blends prepared with these two different ways are miscible, as there existed only one glass transition temperature (Tg) for all the blends. It is suggested that the miscibility of these polyimide/polyimide blends is a result of the strong inter-molecular charge-transfer interaction between the chains of their components.     

Analysis over factors of innovation in China’s fast economic growth since its beginning of reform and opening up

The technological progress makes great contribution to the rapid economic growth of China during its past three decades of reform and opening up. An empirical analysis conducted over China’s total factor productivity certifies this conclusion but it also reveals that China’s TFP growth rate is not very high. We further explore the various stages of change of China’s total factor productivity and the causes of these changes and finally take an analytical calculation over the present flaws of China’s innovation system and offer some advices.     

Data-based adaptive online prediction model for plant-wide production indices

A data-based adaptive online prediction model is proposed for plant-wide production indices based on support vector regression, a general method which we customized specifically to model very large data sets that are generated dynamically and periodically. The proposed model can update its parameters online according to the statistical properties of the training samples. Further, in order to improve the prediction precision, each sample is weighted with a dynamic penalty factor that considers the effect of each sample on the prediction model accuracy. Moreover, a customized procedure is introduced to handle large training sets. After having been convincingly evaluated on benchmark data, effectiveness and performance of our approach for plant-wide production indices is demonstrated using industrial data from an operating ore dressing plant over a range of scale in training data set size. The higher accuracy and shorter computation times than existing methods suggest that it may prove advantageous in actual application to dynamic production processes.     

Safe Landing Analysis of a Quadrotor Aircraft With Two Legs

We proposed a multi-propeller multifunction aerial robot (MMAR) that is composed of a quadrotor with two legs. The legs can increase the function of the robot. We found that the legs can increase the safety region of the robot during the landing. To study the safety problem, the dynamics of MMAR interacting with environment should be solved. On the basis of the interaction dynamics, this paper investigates the velocity constraint for safe landing of MMAR. The investigation begins from the kinematics and dynamics of MMAR in flight mode. Then the impact dynamics of MMAR during landing is developed. Furthermore, the landing safety problem is formulated by a series of constraint equations which can give a safety region of the velocity during landing. The safety region can give the safe velocity of the aerial robot before landing. Finally, simulation examples are presented using the proposed methodology. In the examples, a detailed safety region of a simulated MMAR is constructed and shown.     

A novel self-adaptive extreme learning machine based on affinity propagation for radial basis function neural network

In this paper, a novel self-adaptive extreme learning machine (ELM) based on affinity propagation (AP) is proposed to optimize the radial basis function neural network (RBFNN). As is well known, the parameters of original ELM which developed by G.-B. Huang are randomly determined. However, that cannot objectively obtain a set of optimal parameters of RBFNN trained by ELM algorithm for different realistic datasets. The AP algorithm can automatically produce a set of clustering centers for the different datasets. According to the results of AP, we can, respectively, get the cluster number and the radius value of each cluster. In that case, the above cluster number and radius value can be used to initialize the number and widths of hidden layer neurons in RBFNN and that is also the parameters of coefficient matrix H of ELM. This may successfully avoid the subjectivity prior knowledge and randomness of training RBFNN. Experimental results show that the method proposed in this thesis has a more powerful generalization capability than conventional ELM for an RBFNN.     

Wavelet twin support vector machine

Twin support vector machine (TWSVM) is a research hot spot in the field of machine learning in recent years. Although its performance is better than traditional support vector machine (SVM), the kernel selection problem still affects the performance of TWSVM directly. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and it is suitable for the analysis of local signals and the detection of transient signals. The wavelet kernel function based on wavelet analysis can approximate any nonlinear functions. Based on the wavelet kernel features and the kernel function selection problem, wavelet twin support vector machine (WTWSVM) is proposed by this paper. It introduces the wavelet kernel function into TWSVM to make the combination of wavelet analysis techniques and TWSVM come true. The experimental results indicate that WTWSVM is feasible, and it improves the classification accuracy and generalization ability of TWSVM significantly.     

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.