资水梯级电站施工期闸孔通航方式工程实践

有通航要求的水利水电工程施工期通航问题,是施工导流设计和工程实施期间需要妥善解决的问题之一。以资水柘溪电站以下河段修建的修山水电站、东坪水电站和株溪口水电站工程为例,介绍了施工期利用工程既有的闸孔通航并取得较好技术经济效果的实际做法。工程实践表明,利用闸孔通航,技术上是可行的,并具有导流程序简单、施工期通航工程措施费用低、电站发电时间可提前等优点,经济效益显著。其工程经验可在类似工程中推广应用。     

The weak aggregating algorithm and weak mixability

This paper resolves the problem of predicting as well as the best expert up to an additive term of the order o(n), where n is the length of a sequence of letters from a finite alphabet. We call the games that permit this weakly mixable and give a geometrical characterisation of the class of weakly mixable games. Weak mixability turns out to be equivalent to convexity of the finite part of the set of superpredictions. For bounded games we introduce the Weak Aggregating Algorithm that allows us to obtain additive terms of the form .     

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。     

模拟时滞化学反应系统中的DK-Leap算法

最近发展的可精确模拟时滞化学反应系统的动力学状态的时滞随机模拟算法(delay stochastic simulation algorithm,DSSA)的模拟效率很低。本文提出了一个模拟时滞化学反应系统的加速DK-Leap(Delay K-Leap)算法。DK-Leap算法首先确定满足Leap条件的总反应次数K,然后利用得到τ的概率密度函数随机确定时间区间[t,t τ)。由于每个反应通道的反应次数由K确定,该算法可得到较好的模拟精度。数值试验表明DK-Leap算法在模拟时滞化学反应系统时能取得很好的性能。     

坦克设计新发展

本文提出“针对式坦克发展战略”主要内容,它要求“突破性新概念高新技术”,获得“减法设计方法”,因而形成“坦克发展新道路”.运用“系统总体取胜”的系统工程理论,处理好坦克全系统“综合”、全系统与分系统“配置”、分系统与分系统“匹配”、全系统与人及作战环境“适应”四大关系,以“相互同水平组合”替代“单项高水平爬坡”.通过“从系统总体着眼、由边际零部件突破”的途径,善于从各个部分或部件之间的结合部加以突破,实现坦克设计新发展.     

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     

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.