顶底板围岩不稳的缓倾斜中厚以上矿体的采矿新方案

密集平行束孔落矿的底盘漏斗采矿方案的特点是:用密集严行束孔落矿;凿岩与出矿水平合二为一;实行“掘井、劈漏、落矿三位一体”的回采工艺。该方案结构简单、采切比低、工人劳动强度低,适于开采缓倾斜顶底围岩不稳固的中厚以上矿体。     

《自动控制原理》课程中闭环脉冲传递函数算法研究

针对一般方法推导离散系统闭环脉冲传递函数过程繁琐费时的问题,给出一种先连续化求出闭环传递函数再离散化求解的简便方法,并给出使用本方法的单一化条件。对于满足该条件的系统,可直接求用简便方法求取系统的闭环脉冲传递函数,不满足该使用条件的系统,对其中间变量的求取可使用本方法,因而大大简化求解闭环脉冲传递函数的过程,为采样控制系统的分析和设计提供了很大的方便。     

Effect and Evolution of Oxide Film in the HDH-Ti Powder Surface on Densification Behavior During Sintering

Metallurgical and Materials Transactions A - In this work, we used hydride-dehydride Ti (HDH-Ti) powders (with the oxygen levels of 0.17 and 0.51 wt pct, respectively) combined with microstructural...     

冲压件CAPP系统中几何信息提取实现

基于对AutoCAD的DXF文件格式的详细分析，充分利用多义线的表达简洁性优点，并结合冲压件自身特点，提出了冲压件几何信息获取的方法及程序框架，详细说明了几何信息库的创建方法，并进一步采用Delphi6．0语言编程，实现了冲压几何信息库的建立和几何信息自动提取。     

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.