基于模型缩聚的θ指标损伤识别方法

为实现大型三维空间结构的损伤检测,提出了一种基于模型缩聚技术的检测方法。该方法利用有限数量的加速度传感器,得到大型三维桁架结构的自振频率和模态振型,以完成损伤的识别。数值仿真结果表明：该方法在损伤的定位识别和定量识别上能达到预期的适用性和可靠性。     

Production of niobium powder by magnesiothermic reduction of niobium oxides in a cyclone reactor

In the last years, a variety of processes respectively process steps have been investigated for the production of niobium powder. This is due to the fact that niobium capacitors could be a viable alternative to tantalum capacitors from a performance, availability, and price point of view. The reduction of niobium pentoxide by magnesium results in fine powders with high specific surface area but has the disadvantages of a very exothermic nature and the formation of magnesium niobate. It is shown in this work that the application of a continuously operating cyclone reactor and the use of niobium(IV) oxide as raw material solve these problems. A good control of the highly exothermic reaction within the cyclone reactor was achieved in the cyclone reactor by the ratio between gas flow rate and powder flow rate as well as by a proper preheating of the gas.     

Performance comparison of reduced models for leak detection in water distribution networks

This paper presents a methodology for comparing the performance of model-reduction strategies to be used with a diagnostic methodology for leak detection in water distribution networks. The goal is to find reduction strategies that are suitable for error-domain model falsification, a model based data interpretation methodology. Twelve reduction strategies are derived from five strategy categories. Categories differ according to the manner in which nodes are selected for deletion. A node is selected for deletion according to: (1) the diameter of the pipes; (2) the number of pipes linked to a node; (3) the angle of the pipes in the case of two-pipe nodes; (4) the distribution of the water demand; and, (5) a pair-wise combination of some categories.The methodology is illustrated using part of a real network. Performance is evaluated first by judging the equivalency of the reduced network with the initial network (before the application of any reduction procedure) and secondly, by assessing the compatibility with the diagnostic methodology. The results show that for each reduction strategy the equivalency of networks is verified. Computational time can be reduced to less than 20% of the non-reduced network in the best case. Results of diagnostic performance show that the performance decreases when using reduced networks. The reduction strategy with the best diagnostic performance is that based on the angle of two-pipe nodes, with an angle threshold of 165°. In addition, the sensitivity of the performance of the reduced networks to variation in leak intensity is evaluated. Results show that the reduction strategies where the number of nodes is significantly reduced are the most sensitive.Finally this paper describes a Pareto analysis that is used to select the reduction strategy that is a good compromise between reduction of computational time and performance of the diagnosis. In this context, the extension strategy is the most attractive.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

Test frame updating in CPM testing of Prolog programs

Category Partition Method (CPM) is a general approach to specification-based program testing, where test frame reduction and refinement are two important issues. Test frame reduction is necessary since too many test frames may be produced, and test frame refinement is important since during CPM testing new information about test frame generation may be achieved and considered incrementally. Besides the information provided by testers or users, implementation related knowledge offers alternative information for reducing and refining CPM test frames. This paper explores the idea by proposing a call patterns semantics based test frame updating method for Prolog programs, in which a call patterns analysis is used to collect information about the way in which procedures are used in a program. The updated test frames will be represented as constraints. The effect of our test frame updating is two-fold. On one hand, it removes “uncared” data from the original set of test frames; on the other hand, it refines the test frames to which we should pay more attention. The first effect makes the input domain on which a procedure must be tested a subset of the procedure’s input domain, and the latter makes testers stand more chance to find out the faults that are more likely to show their presence in the use of the program under consideration. Our test frame updating method preserves the effectiveness of CPM testing with respect to the detection of faults we care. The test case generation from the updated set of test frames is also discussed. In order to show the applicability of our method an approximation call patterns semantics is proposed, and the test frame updating on the semantics is illustrated by an example.

遗传算法与文本特征选择方法相结合的研究

对文本特征选择的传统方法进行了深入分析并对各自的优缺点进行了详细的对比;其次,通过利用遗传算法自身的寻优特点,把它与传统特征选择方法相结合,并得出了算法。最后,在对各自算法进行了分析,表明了在性能上有了明显的提高。     

一种基于浓缩树结构的属性约简算法

属性约简是粗糙集理论的重要研究内容之一,其中基于区分矩阵的约简算法是一种高效的约简算法,但算法具有很高的空间复杂度.为了减少区分矩阵的空间开销,利用浓缩树结构,结合区分矩阵单个属性一定为核属性的特征,提出改进的生成浓缩树算法,压缩存储区分矩阵中的非空数据项,且不丢失原区分矩阵的所有信息;利用生成的浓缩树结构结合启发式策略,给出属性约简算法.实验结果表明,算法正确有效并且空间复杂度有明显降低.     

基于队列的动态电源管理策略

动态电源管理技术是一项系统级电源优化策略,它通过有选择性的关闭(或者降低性能)空闲的可控设备来降低功耗.在回顾以往系统级动态电源管理策略的基础上,提出了一种基于队列的新型系统级动态电源管理策略,利用队列不但可以提高对于可控模块何时被唤醒时间的预测命中率,并且能够根据不断变化的任务负载情况进行自身的快速调节,以适应不同任务的要求,通过搭建的SkyEye环境进行仿真,结果验证了在性能和节能方面,该方法优于传统方法.     

一种基于粗糙集理论的快速并行属性约简算法

将并行计算的思想融入基于粗糙集理论的快速属性约简中,提出了一种基于粗糙集理论的快速并行属性约简算法.该算法在保证约简结果是Pawlak约简的情况下,将属性约简任务划分到多个处理器中同时处理,从而大大提高了属性约简的效率.仿真实验结果说明了该算法的高效性.     

一种基于相对粒度的决策表约简算法

在知识粒度的基础上,针对决策表提出了相对粒度和属性相对重要性的概念,证明了知识的相对粒度随着知识粒度的增大而单调增加的变化规律,在此基础上提出了一种基于相对粒度的启发式约简算法,以弥补基于正区域的约简方法处理不一致决策表时存在的不足.通过理论分析和实例验证表明,该算法是有效的,且其时间复杂度相对较低.