基于多神经网络分类器的军事目标识别方法研究

对军事目标类型的识别是军事信息处理中的一个重要环节.本文首先对军事目标进行特征提取,以矩和圆度两种不变矩特征向量作为神经网络的输入,分别采用了BP神经网络、自组织竞争网络、Hopfield网络对军事目标进行识别,最后采用了分类器多数投票法对识别结果进行融合,仿真实验结果表明采用多神经网络分类器融合的方法比单一神经网络识别率高,这对提高军事信息处理的准确性具有重要意义.     

关于判定及防范多数票循环的研究

多数票循环在社会选择领域是引人关注的问题.对于偏好选举在过半数原则下是否发生"无Condorcet胜出者的多数票循环",提出并证明了判定定理,分析了影响多数票循环产生的条件及其根源,在不对偏好进行限制的条件下,提出并证明了偏好选举中避免多数票循环产生的优胜条件和办法.     

网络投票系统的防注入数据技术探讨

网络投票系统作为一种大众投票系统，正在被广泛的使用，如果设计不合格的话，投票系统变的形同虚设，投票数据的真实性会大打折扣。为了解决投票系统数据注入问题，使投票系统更可靠，数据更真实，本文将要讨论网络投票系统存在的一些技术漏洞和改进的方法，     

Reliability measure assignment to sonar for robust target differentiation

This article addresses the use of evidential reasoning and majority voting in multi-sensor decision making for target differentiation using sonar sensors. Classification of target primitives which constitute the basic building blocks of typical surfaces in uncluttered robot environments has been considered. Multiple sonar sensors placed at geographically different sensing sites make decisions about the target type based on their measurement patterns. Their decisions are combined to reach a group decision through Dempster-Shafer evidential reasoning and majority voting. The sensing nodes view the targets at different ranges and angles so that they have different degrees of reliability. Proper accounting for these different reliabilities has the potential to improve decision making compared to simple uniform treatment of the sensors. Consistency problems arising in majority voting are addressed with a view to achieving high classification performance. This is done by introducing preference ordering among the possible target types and assigning reliability measures (which essentially serve as weights) to each decision-making node based on the target range and azimuth estimates it makes and the belief values it assigns to possible target types. The results bring substantial improvement over evidential reasoning and simple majority voting by reducing the target misclassification rate.     

The Quasistability Criterion for a Vector Path Problem with a Majority Principle of Optimality

A multicriteria path problem of majority optimization is considered. The partial criteria are MINSUM, MINMAX, and MINMIN. Necessary and sufficient conditions are found under which "small" independent perturbations of the vector criterion parameters preserve the majority efficiency of all the solutions and do not exclude emergence of new solutions.     

基于最大熵模型与投票法的汉语动词与动词搭配识别

提出一种基于最大熵模型和投票法的汉语动词与动词搭配识别方法.该方法通过组合目标动词与候选搭配词的上下文词性信息以及关联程度的统计信息构成5种复合特征模板,然后利用最大熵方法获得它们对应搭配识别器,最后采用最好搭配识别器占优的投票法构造组合识别器.实验结果表明,同时包含上下文词性信息和统计信息的识别器优于单纯包含上下文词性信息或统计信息的识别器,但最好搭配识别器占优的组合识别器效果更佳.     

基于量子高维Bell态纠缠交换的分发式投票方案

利用了高维Bell态,设计一种新的量子投票方案.方案分为4个阶段:首先,高维Bell纠缠态粒子被作为投票信息载体被分发给投票者;然后投票者利用最小复杂度搜索寻找竞选者,并用相移操作投出选票;接着代理人收到选票后,运用纠缠交换过程来检测窃听,通过检查粒子的测量结果来发现是否非法攻击;最后代理人利用计票算子统计票数并公布.所有人都只知道总的投票结果,单个投票者的投票信息是保密的.协议分析了各种攻击下的抵抗,证明了这是一个合理,安全,高效的量子投票方素.     

基于ARM的远程电子表决网络基站的设计与实现

设计并实现了一种应用于远程电子表决系统的网络基站.该基站硬件上以嵌入式ARM为基础,扩展了具有以太网和CAN总线功能的外设;软件上移植了嵌入式操作系统μC/OS-Ⅱ和TCP/IP协议栈LwIP,实现了以太网和CAN总线驱动,以及应用程序,成功完成其通信枢纽的作用.     

A novel SVM-kNN-PSO ensemble method for intrusion detection system

In machine learning, a combination of classifiers, known as an ensemble classifier, often outperforms individual ones. While many ensemble approaches exist, it remains, however, a difficult task to find a suitable ensemble configuration for a particular dataset. This paper proposes a novel ensemble construction method that uses PSO generated weights to create ensemble of classifiers with better accuracy for intrusion detection. Local unimodal sampling (LUS) method is used as a meta-optimizer to find better behavioral parameters for PSO. For our empirical study, we took five random subsets from the well-known KDD99 dataset. Ensemble classifiers are created using the new approaches as well as the weighted majority algorithm (WMA) approach. Our experimental results suggest that the new approach can generate ensembles that outperform WMA in terms of classification accuracy.     

Group conversion versus group expansion as modes of change in majority and minority positions: All losses hurt but only some gains gratify.

This study examined reactions to minority and majority positions that were either stable or reversed through group conversion that transformed opponents (supporters) of the minority (majority) into supporters, (opponents) or through group expansion that brought new supporters (opponents) for the minority (majority) into the group. Minorities who became majorities through group expansion, compared with those who changed through group conversion, perceived their supporters and the overall group as significantly more similar to the self, and had significantly higher expectations for future positive interactions within the group. Perception of similarity with the supporters mediated the effect of the experimental conditions on perception of the overall group-self similarity. Implications of changes through conversion and expansion for the functioning of social groups are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)