铁路信号电缆断线故障在线监测与定位方法研究

铁路信号电缆是保证列车安全运营的重要组成部分,其故障严重影响列车的安全运营;由于其经多个分线盒接线柱连接的敷设特点,存在中间接头过多,致使传统的电缆故障检测方法应用受到了极大的限制;为此,通过在电缆中间分线盒的备用芯线对接线柱中接入固定阻值的电阻,将备用芯线对形成闭合回路网络,然后在线监测其电阻阻值,利用回路阻值的变化来反映电缆状态,并以在线监测备用芯线对代替在用芯线对,进而在不影响正常列控信号传输的情况下实现了信号电缆的故障在线监测;经实际测试,有效地解决了铁路信号电缆断线故障的在线监测及定位。     

从“超级网银”看我国PKI交叉认证体系建设

"超级网银"是我国第一个大规模跨CA的交叉认证平台,采用的是桥接交叉认证模式。不同的领域,应根据其自身特点,采用不同形式的交叉认证模型。最终的目的,是要在全国范围内建立一个交叉认证、相互融合又相互独立、相互协调的PKI整体体系,在这个过程中,国家和政府应发挥更加积极的作用。     

Commenting in the Online Arab Public Sphere: Debating the Swiss Minaret Ban and the “Ground Zero Mosque” Online

This article focuses on the emerging online Arab public sphere that the web has enabled. It explores how the “local” interacts with the “global,” critically examining their implications on global politics. Specifically looking at online readers' comments about the Swiss minaret ban the so‐called “Ground Zero Mosque,” how does the Arab online public sphere respond to and frame these issues? What implications do these reader frames portend locally and globally? The study analyzes online comments and responses that readers of Al Arabiya.net and Al Jazeera.net posted on related news articles. The article concludes that the new online public sphere does make it possible for Arab citizens to circumvent and challenge traditional authoritarian controls.     

Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

避暑山庄的选址

造园首在选址。选址得当,可以事半功倍。计成在《园冶》中指出,只有“相地合宜”,才能“构园得体”。地址要好,清洁卫生,有丘有壑,草地和树林相间”。避暑山庄之所以能够在造园史上独树一帜,选址得当是其中一个重要原因。避暑山庄当时所以选择这块地方建造宫苑,除政治上的原因之外,主要是因为它具有优越的自然条件。避暑山庄的园林艺术成就,已得到世人的公认,造园家相地择址的匠心,也会给我们许多有益的启示。     

Semantic analysis in XTRA,an English — Chinese machine translation system

This paper concerns the resolution of lexical ambiguity in a machine translation environment. We describe the integration of principles of selection restrictions. Preference Semantics, and intelligent relaxation of constraints in handling lexical ambiguity. The approach differs from many previous MT systems in that it is more powerful than brute force systems, while more realistic than systems that assume a large degree of coded encyclopedia information for full understanding.     

航空发动机传感器故障鲁棒检测方法

研究发动机传感器故障准确检测问题,现代航空发动机数字电子控制系统对传感器的可靠性要求日益提高。针对航空发动机结构复杂,又工作在高温和高压下,常规采用的传感器故障检测方法的准确性易受到建模误差与外界扰动的影响,造成漏报或误报。为了提高检测精度,提出建立航空发动机数控系统传感器未知输入故障模型,采用特征结构配置的方法,通过配置闭环系统左特征向量实现故障检测残差对不确定性因素的干扰解耦,降低扰动对故障诊断结果的影响。用某型涡扇发动机数控系统传感器故障数字仿真试验表明,所设计的方法对范数有界的不确定量可以实现干扰解耦,抑制干扰对故障检测的影响,改善检测算法的鲁棒性,提高检测结果的准确性,同时满足在线运算的实时性要求。提高了航空发动机的可靠性,保证了安全飞行。     

一种面向SOA业务恢复的服务选择算法

针对SOA业务恢复的服务选择问题,本文提出了一种QoS近似全局最优的服务选择算法GALSA:首先利用GA算法将全局QoS约束分解为最优的局部QoS约束,然后利用LSA算法选取出满足局部QoS约束的最优服务。算法不仅能作用于服务重构的服务选择过程中,而且在单个服务节点失效时亦能选取出满足全局QoS约束的服务进行替换。理论分析与实验结果说明了算法的可行性和有效性。     

An improved algorithm for finding the median distributively

Given two processes, each having a total-ordered set ofn elements, we present a distributed algorithm for finding median of these 2n elements using no more than logn +O(logn) messages, but if the elements are distinct, only logn +O(1) messages will be required. The communication complexity of our algorithm is better than the previously known result which takes 2 logn messages.