An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

In cognitive radio networks, spectrum sensing is one of the most important functions to identify available spectrum for improving the spectrum utilization. Due to the open characteristic of the wireless electromagnetic environment, the wireless network is vulnerable to be attacked by malicious users(MUs), and spectrum sensing data falsification(SSDF) attack is one of the most harmful attacks on spectrum sensing performance. In this article,an algorithm based on the evidence theory and fuzzy entropy is proposed to resist SSDF attacks. In this algorithm, secondary users(SUs) obtain the corresponding degree of membership function and basic probability assignment function based on the local energy detection result. The new conflicting coefficient is calculated based on the evidence distance and classical conflicting coefficient, and the conflicting weight of the evidence is obtained.The fuzzy weight is calculated by the fuzzy entropy. The credibility weight is obtained by updating the credibility. On this basis, the probability assignment function of the evidence is corrected, and the final result is obtained by using the fusion formula. Simulation results show that the proposed algorithm has a higher detection probability and lower false alarm probability than other algorithms.It can effectively defend against SSDF attacks and improve the performance of spectrum sensing.

Risk assessment of water security in Haihe River Basin during drought periods based on D-S evidence theory

The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.

The Scientific Evidence That Today’s Psychiatry Cripples Itself—By Excluding‘Intent’

PSYCHIATRISTS DISAGREE profoundly with lawyers,about what we human beings are capable of.The one says we have‘intent’—the other that we do not.They cannot both be right.All non-psychiatrist doctors must perforce agree with the lawyers.This paper argues that these harmful discrepancies will continue,until we undo the separate watertight human knowledge silos,which have grown up between legal procedures,general medicine,and psychiatric practice.All three would benefit.Psychiatry in particular,suffers from a grievously narrow view of scientific evidence,one which is open to fundamental criticism.There are radical differences in how the fuzzy concept of‘intent’is regarded in law,in general clinical medicine and in psychiatry.Once‘intent’is accorded its due weight,our understanding of justice,health and sanity is vastly improved,allowing us hugely more optimism.This paper is based on two earlier papers—The Scientific Evidence That‘Intent’Is Vital for Healthcare and Why Quakerism Is More Scientific Than Einstein.These are deployed here,to unpick the unhealthy tangle in which today’s psychiatry now finds itself.Its six sections are—(1)why‘intent’matters in law,in medicine&in psychiatry;(2)scientific quagmires;(3)a working definition for‘madness’;(4)“children are impressionable”;(5)“trust me,I’m a doctor”;and(6)skin heals,why can’t minds?The breakthrough is that verbal fuzziness means that words can mean different things at different times––not that they are 100%meaningless.Only a better understanding of trust,autonomy and consent can open the way to something that is painfully absent from today’s psychiatry––a cure for any and all mental disease.

ε-OPTIMAL SOLUTION FOR CONTINUOUS MINIMAX PROBLEMS WITH MAXIMUM-ENTROPY FUNCTION

This paper describes and explores a maximum-entropy approach to continuous minimax problem, which is applicable in many fields, such as transportation planning and game theory. It illustrates that the maximum entropy approcach has easy framework and proves that every accumulation of {x_k} generated by maximum-entropy programming is -optimal solution of initial continuous minimax problem. The paper also explains BFGS or TR method for it. Two numerical exam.ples for continuous minimax problem are

A novel adaptive temporal-spatial information fusion model based on Dempster-Shafer evidence theory

In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an adaptive temporal-spatial information fusion model is proposed.Firstly,an adaptive evaluation correction mechanism is constructed by the evidence distance and Deng entropy,which realizes the credibility discrimination and adaptive correction of the spatial evidence.Secondly,the credibility decay operator is introduced to obtain the dynamic credibility of temporal evidence.Finally,the sequential combination of temporal-spatial evidences is achieved by Shafer’s discount criterion and Dempster’s combination rule.The simulation results show that the proposed method not only considers the dynamic and sequential characteristics of the temporal-spatial evidences com-bination,but also has a strong conflict information processing capability,which provides a new refer-ence for the field of temporal-spatial information fusion.

基于D-S证据理论改进AHP-熵权的流域洪涝灾害评估研究

考虑致灾因子危险性、孕灾环境敏感性以及承灾体易损性,选取指标构建小清河流域洪涝灾害风险评估指标体系,提出一种基于D-S证据理论的改进AHP-熵权法计算指标权重,求取洪涝灾害风险指数,运用自然断点分级法确定洪涝灾害风险等级,分析小清河流域洪涝灾害风险空间分布情况。结果表明:小清河流域洪涝灾害风险总体上表现出南低北高的趋势,其中高风险区和较高风险区分别占流域面积的8.7%和14.3%,主要分布在小清河干流以及主要支流两岸。所得评估结果同“利奇马”台风发生期间实际洪灾风险分布情况一致,对比证明基于D-S证据理论的改进AHP-熵权法优于AHP和熵权法,可为小清河流域防洪减灾决策提供依据。

基于循证的飞行签派员岗位胜任力评估画像模型构建

为提升航空公司的安全运行水平,采用熵权法对60份研究样本从核心胜任力、心理胜任力和作风胜任力三个维度构建飞行签派员岗位“画像”的评估模型,根据柯氏模型,从反应层、学习层、行为层、结果层四方面完成对飞行签派员岗位胜任力评估画像的建议。

基于生成对抗网络的UWSNs恶意节点检测方法

水下无线传感器网络(Underwater Wireless Sensor Networks,UWSNs)节点通常部署于无人值守的恶劣环境中,恶意节点的存在对网络的安全构成了极大威胁。为了在缺乏节点行为证据的情况下有效检测恶意节点,提出了一种基于生成对抗网络(Generative Adversarial Network,GAN)的恶意节点检测方法。首先,构建了基于GAN的异常预测模型;其次,收集数据、链路、能量等多维信任证据,构建出训练数据集;最后,训练异常预测模型,并通过信任决策检测恶意节点。仿真结果表明,该方法可以在不需要大量信任证据的情况下有效地进行恶意节点检测,并且与同类型方法相比,所提方法具有较高的恶意节点检测率、通信成功率及较低的假阳率。

证据理论框架下主动学习代理模型驱动的CFD模拟不确定性量化方法

计算流体力学(CFD)模拟中存在模型参数、数值离散和边界条件等诸多形式各异的不确定因素。鉴于证据理论具有灵活的建模框架,且能同时量化CFD模拟中的随机和认知不确定性,基于其提出了一种证据理论框架下主动学习代理模型驱动的CFD模拟不确定性量化方法,以较少的CFD仿真模型调用次数实现对CFD模拟结果的不确定性量化。该方法采用最优最大最小距离策略生成空间分布良好的候选样本点,通过动态熵权-TOPSIS主动学习策略平衡了代理模型的全局探索、局部开发和鲁棒性。此外,提出基于Hartley测度和Jousselme距离的复合收敛准则以判断终止代理模型训练的时间并量化输出响应的不确定性。最后,以采用NASA SC(2)0410翼型剖面的超临界机翼流场CFD模拟为例,分析来流参数和湍流模型参数的不确定性对机翼输出响应升阻比的不确定性量化结果。

符号熵与证据理论集成的转子故障识别研究

针对转子振动信号的非线性、非平稳性造成的故障状态难以准确识别问题,提出了一种符号熵与证据理论集成的转子故障识别方法。首先对转子系统的监测数据进行去均值处理和差分运算得到波动序列,继而通过二进制处理得到二进制序列,再通过编码得到符号序列而计算符号熵。通过一个符号熵均值相对增长率的指标来确定符号熵计算中的编码长度。通过实验分析,以转子四个通道的符号熵作为识别向量,再与历史数据得到的识别标准向量做对比,得到各状态的识别概率,最后通过D-S证据理论将其中两次测试的结果进行融合得到最终的判别结果。通过与传统的基于符号熵的故障识别方法的对比,能准确识别转子系统的常见状态,为振动信号处于非线性、非平稳条件下的转子故障状态识别问题提供了一个解决方案。

基于信任度的供应链战略合作企业推选模型

将供应链合作、信任关系作为理论支撑,以历史合作数据为研究基础,对供应链节点企业信任影响因素进行系统性分析,建立信任度评估体系,融合贝叶斯理论,计算企业节点的直接信任度;根据皮尔逊相关系数的理论知识,计算出节点企业的间接信任度;再利用信息熵理论知识,判断出直接信任度和间接信任度对综合信任度的影响大小。最后就模型中各指标对综合信任度的影响大小进行仿真计算,对数据的测算结果表明:该信任评估模型中直接信任度对于权重的选取以及综合信任度大小的影响更大,同时权重的设定可以降低主观带来的结果偏差,为推选出企业战略合作伙伴提供一个参考。

基于联系云和改进冲突证据融合算法的泥石流易发性评价

泥石流作为一种常见的地质灾害,其形成机制复杂,影响指标众多且呈现多重不确定性,为综合考虑多种影响因素协同作用,基于信息融合和不确定性分析理论,提出一种基于证据理论和云模型的泥石流易发评价方法。首先,应用联系云模型(CCM)计算泥石流易发性关键评价指标的基本概率分配函数(BPA);然后,引入兰氏距离和邓氏熵,分别修正指标BPA的可信度和不确定度,得到修正后的BPA;最后,基于D-S证据理论对修正BPA进行证据融合,实现泥石流易发性评价,并进行实例验证。结果表明:所采用的CCM能够克服正态云模型计算BPA时要求指标为正态分布的缺陷,并考虑的指标分布的随机性和不确定性;提出的方法与其他4种常用的证据融合方法评价结果基本吻合,用于泥石流易发性评价有效可行;基于兰氏距离和邓氏熵改进的冲突证据融合算法可提高证据融合的收敛速度和精度,并且结果更准确可靠。

结合近邻密度和信息修正的基本概率赋值生成方法

为了解决D-S证据理论应用中基本概率赋值(BPA)获取困难、生成模型适用度低的问题,提出一种结合近邻密度和信息修正的基本概率赋值生成方法:通过基于KNN算法得出的密度峰值点与样本间的距离为依据生成单焦元BPA函数,通过信念χ~2散度对全子集事件赋值并基于可信度对BPA进行信息修正,用改进的信念熵公式计算各证据的不确定性权重,进行证据的再分配.利用生成的BPA解决少样本和不均衡类样本的实际应用问题,经多个数据集验证诊断精度均达85%以上,优于其他方法.

基于模糊变换的多模型速度结果融合

在不确定分析基础上,为融合多源模型事故再现速度结果,本文提出一种基于模糊变换的多模型结果融合方法。首先对多模型事故再现速度结果离散化,采用K-means聚类获得聚类结果,以各聚类区间数据极值作子区间;根据熵值法得到各模型的信度,用最小信息熵原理将D-S证据理论融合得到的各专家对模型的打分融入到熵值法模型信度中去,对其进行修正;最后以模型信度为权重集与评判矩阵作模糊变换进行综合评判,得最终融合结果。并通过一真实事故案例进行实际应用。

EBT视域下飞行签派员DRM能力评估模型构建研究

为构建一个具有客观性、定量性的签派资源管理能力评估体系,改善国内民航飞行签派员胜任力不足的现状,笔者以本单位正常履职的飞行签派员为研究样本,基于循证训练“实证数据”理论,开展构建签派资源管理能力定量评估体系的研究。结合国内外民航管理机构、培训机构对飞行签派员签派资源管理技能的要求,提炼飞行签派员应具有的签派资源管理技能评估指标,咨询专家级样本后初步建立评估指标库。为保证评估数据的客观全面,选取基于组织学习的动态自评、基于合作关系度的同级互评和基于人力资源管理模型的领导评估作为数据源,运用层次分析法与熵权法主客观相结合的方式确定了各评估指标权重。最后,依据循证训练“岗位胜任力框架”从航空公司管理层角度,建立了基于柯氏模型的签派资源管理能力的评估体系,提出保障签派资源管理训练课程教学效果的方法。

基于最大信息系数法和邓熵的D-S证据理论改进

物联网中大量传感器采集的数据存在不确定性,针对D-S证据理论在处理冲突证据时融合决策结果与事实相悖的问题,文章提出一种新的基于改进D-S证据理论的多传感器数据融合算法,首先使用最大信息系数法计算证据间的可信度;然后结合信息熵对证据的不确定度进行分析,以确定新的权重;最后使用Dempster组合规则得到融合结果。算例分析表明,文章所提方法能有效融合冲突证据,较经典算法有较高的基本概率分配。将所提方法用于传感器数据处理,不仅能降低数据中存在的不确定性,还能有效处理D-S理论中的冲突问题,从而得到较为准确的融合结果。

基于演化博弈的大数据信息泄漏风险访问控制模型设计

大数据信息泄漏会导致网络主机所承担的运行风险增加,严重时下级网络终端会出现无法访问大数据信息服务器的情况;针对上述问题,设计了基于演化博弈的大数据信息泄漏风险访问控制模型;以演化博弈模型为基础,完成对大数据信息均衡点的演化处理,求解信息博弈概率,推导演化博弈模型表达式,从而完善基于演化博弈的大数据信息模糊化处理流程;计算大数据信息泄漏行为的信任度标准,参考信息监控器设置条件,实现对大数据信息泄漏风险的评估;针对风险评估结果,完成大数据信息的访问证据取样,计算访问控制阈值,实现对信息泄漏风险访问的控制,完成基于演化博弈的大数据信息泄漏风险访问控制模型的设计;实验结果表明,所提方法的大数据信息瞬时泄漏量始终小于2.3×10^(11)MB,网络主机所承担的运行风险得到有效控制,下级网络终端不会出现无法访问大数据信息服务器的情况。

基于改进D-S证据理论的相控阵雷达作战效能评估

针对相控阵雷达作战效能评估中战场环境等不确定因素的影响,采用改进Dempster-Shafer(D-S)证据理论对其进行作战效能评估方法的优化。首先,通过构建相控阵雷达作战效能评估指标体系,明确评估指标并赋值权。其次,引入Kulsinski差异和模糊熵方法,对传统的D-S证据理论进行改进,减少指标在融合过程中的融合权重不确定性的影响。最后,通过改进D-S证据理论对评估指标进行数据融合,建立评估准确性较高的相控阵雷达作战效能评估模型。通过算例验证分析,所提方法能够有效减少融合过程中的不确定性影响,可实现相控阵雷达作战效能评估的目的,提高作战效能评估精度。

基于云D-S证据理论的极端天气下电力系统应急能力评判

以提高应对极端天气下电力系统突发事件能力为目标,将电力系统应急能力作为研究对象,提出基于云模型和证据理论融合的评估算法。采用层次分析法和熵值法相结合的方法确定应急指标权重,运用云模型做为基础对电力系统应急能力进行评估,以解决评价指标模糊性和随机性的问题,并引入D-S证据理论对指标进行综合评估,构建极端天气下电力系统的应急能力评估模型。通过案例证明,该方法可以有效准确地定量和定性评估电力网系统的应急能力,为提高电力网系统的应急管理能力提供科学依据。

基于模糊证据理论的大中型水利水电项目社会稳定风险评估方法

针对大中型水利水电项目面临的社会稳定风险管理问题,提出了基于熵权法和模糊证据理论的风险评估模型。首先构建社会稳定风险指标体系,邀请相关专家为各项指标赋权;然后使用熵权法计算各项指标权重,并运用模糊综合法构建风险评估矩阵;最后,利用D-S证据推理法进行证据融合,根据计算结果明确项目所处风险等级,从而为大中型水利水电项目优化社会稳定风险管理提供理论技术支持。