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。     

30CrMnSiNi2A钢件镀硬铬的氢脆分析

本文叙述了用延迟破坏法试验超高强度钢(υb=1700MPa)不超过三次镀硬铬的氢脆情况。     

Ti和V对Fe中氢与位错交互作用的影响

研究了Fe-0.5wt-%Ti和Fe-0.5wt-%V合金中由氢与位错交互作用所形成的氢致冷加工内耗峰(H-CWP)。实验结果表明:合金元素对H-CWP、对氢与位错之间的交互作用有显著的影响,与氢作用愈强的元素,对H-CWP的影响愈大,内耗实验结果与力学性能之间存在着一定的联系.     

表面氧化层对TiMn1.25Cr0.25储氢合金活化性能的影响

通过TiMn1.25Cr0.25合金粉活化前在空气中暴露不同的时间,探讨了表面氧化层的形成对其活化性能的影响。活化测试结果表明：合金的活化难度随着合金在空气中暴露时间的延长而加大。俄歇电子能谱(AES)结果表明,在空气中暴露后的合金表面形成了一层大约6nm-10nm厚的表面氧化层,正是这层氧化层的形成加大了合金的活化难度。扫描电镜(SEM)结果表明合金的活化过程与合金的碎化有着密切的联系。     

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。     

考虑天然气混氢的园区综合能源系统电制氢优化配置

电制氢和天然气混氢技术在促进可再生能源消纳、降低系统碳排放量方面具有良好的理论研究和工程应用前景。面向含高比例可再生能源的园区综合能源系统,提出一种计及天然气混氢及跨季节存储的电制氢优化配置方法。首先梳理了含氢园区综合能源系统的运行框架和能量流动关系,建立园区内部能源生产、转换与存储设备的数学模型,其次以设备的年化投资成本、园区综合能源系统的年度运行成本和碳交易成本最优为目标,提出电制氢优化配置模型。最后通过算例分析表明电制氢及天然气混氢技术的引入可提升可再生能源的消纳能力,降低系统的整体经济成本和碳排放量,并分析了电解槽投资成本、混氢体积分数上限以及经济性和低碳性成本权重系数变化对规划运行结果的影响。关键词：园区综合能源系统;电制氢;天然气混氢;碳交易;跨季节储氢;优化配置 中图分类号：TM732