基于知识融合的汽车覆盖件模具设计方法研究

针对产品设计过程中,分散异构知识融合不足导致知识检索准确性不高、知识重用效率低等问题,提出一种知识融合模型.利用知识需求模型传递知识需求信息,采用知识元对各类异构知识进行统一表示,通过概念本体对语义概念进行有序组织,与知识元之间形成映射关系.通过语义距离计算获取候选知识元集,并采用基于贝叶斯和遗传算法的知识融合方法将分散的关联知识元融合成知识单元,实现知识重用.以覆盖件模具结构设计为案例验证了所提出方法的可行性和有效性.     

基于贝叶斯与因果岭回归的物联网流量预测模型

针对物联网流量预测困难的问题,提出了一种基于贝叶斯与因果岭回归的物联网流量预测模型.该模型首先根据物联网流量传输波动影响链路变化等因果关系,深入刻画物联网流量局部特征,并利用薛定谔方程优化识别模型,同时结合贝叶斯拟合因果关系联合岭回归方法建立预测模型.最后,通过仿真实验研究了该模型与其他方法之间的性能状况,结果表明该模型在平均队列、阻塞率和延迟率等方面具有较大优势.     

基于贝叶斯理论的多模型结构识别的试验研究

基于贝叶斯理论的抽样方法,对结构的多模型结构识别问题进行试验研究.采用基于贝叶斯理论的多模型结构识别的概念与基本框架,以及马尔科夫链-蒙特卡洛模拟(MCMC),建立了有限元模型库.针对MCMC在参数维度较高时不易收敛和计算效率低下等问题,提出了一种改进的MCMC抽样方法来进行多模型结构识别.利用Matlab-Strand7的交互访问技术(API)能够进行大型结构有限元模型的参数自动修正,在获得校验后的有限元模型库后,能基于有限元模型的后验概率分布进行预测.为了验证该理论的可行性和有效性,针对一根简支梁的数值算例和一座实际大跨钢管混凝土桁架系杆拱桥进行了基于贝叶斯理论的结构识别研究与响应评估,并使用传统的单模型结构识别方法——遗传算法(GA)进行对比分析,结果表明本文提出的基于贝叶斯理论的多模型结构识别方法能够更好地进行结构响应预测.     

基于多尺度卷积神经网络的交通标志识别

针对自然场景中交通标志识别问题涉及的识别准确率和实时性改善需求,提出了一种改进的基于多尺度卷积神经网络(CNN)的交通标志识别算法.首先,通过图像增强方法比选实验,采用限制对比度自适应直方图均衡化方法作为图像预处理方法,以改善图像质量.然后,提出一种多尺度CNN模型,用于提取交通标志图像的全局特征和局部特征.进而,将组合后的多尺度特征送入全连接SoftMax分类器,实现交通标志识别.采用德国交通标志基准数据库(GTSRB)测试了所提算法的有效性,测试结果表明,算法在GTSRB基准数据集上获得98.82%的识别准确率以及每幅图像0.1ms的识别速度,本文算法具有一定的先进性.     

贝叶斯预测蜂群算法在无线传感器网络优化中的应用

针对无线传感器网络(WSN,wireless sensor network)节点分布不合理,存在较多的监测盲区等不足,提出了利用贝叶斯预测人工蜂群算法(BPABC,Bayesian predictive artificial bee colony algorithm)制定节点分布方案。BPABC算法借鉴贝叶斯预测算法的思想对蜂群算法中各蜜源存在最优解的概率进行预测,并以此为依据指导跟随蜂寻优工作。采用BPABC算法对WSN中的节点分布进行优化,与人工蜂群算法、全局人工蜂群算法制定的优化方案进行比较。结果表明,BPABC在平均覆盖率、最差覆盖率等方面均优于其他两种算法,并且BPABC算法在迭代收敛速度方面也有明显的优势。为了进一步验证改进算法的实用性,采用BPABC制定不同监测区域的WSN节点分布方案。WSN的覆盖率均在97%左右,并且标准差不超过0.005%。由此可见,基于BPABC的WSN节点分布优化方案具有较高的覆盖率、良好的适应性和稳定性。     

MASS TRANSFER EXPERIMENTS ON SINGLE IRREGULAR-SHAPED PARTICLES

Mass transfer from irregular-shaped naphthalene particles (100-200 inn in size) was studitd in an electrodynamic balance. Charged particles were suspended in an electrostatic field directly in line with a calibrated air jet. Mass and size change histories were obtained under ambient conditions, and under steady- and pulsed-flow conditions. For natural convection, the time-averaged Sherwood number was similar to that for spheres. Forced-convection Sherwood number under steady-flow conditions was strongly dependent on particle shape and panicle Reynolds number, and was consistently higher than values predicted for spheres at comparable Reynolds numbers. This study validates the technique and indicates the shape effect on mass transfer form single particles.     

一种基于贝叶斯的渐进式图像匹配框架研究

提出了一种新的渐进式图像匹配框架,将图像匹配与图像概率更新结合在一起解决这一问题.该框架在当前匹配结果上,利用贝叶斯方法对最可信目标图像进行高效的重新估算,并且可保证提高后续的图像匹配效果.实验结果表明,与一次性图像匹配方法相比,所提方法的匹配性能更优.此外,即使是对外观发生变化及包含远离主体对象的图像场合,所提方法性能仍然健壮.     

An optimal co-reinsurance strategy

This article considers a co-reinsurance strategy that (1) protects insurance companies against catastrophic risks; (2) enables insurers to gather sufficient information about the different risk attitudes of reinsurers and diversify their reinsured risks; (3) enables insurers to create better risk-sharing profiles by balancing the risk tolerances of reinsurers; (4) has the benefit of allowing reinsurers to accumulate experience with risks with which they are unfamiliar; (5) reduces the overall direct cost of a reinsurance contract; (6) allows a government to back some insurance products, such as the terrorism insurance programs that were established in many countries after the September 11th terrorist attacks; and (7) reflects the practical reinsurance industry of some countries, such as Iran. Such a co-reinsurance strategy can be fully determined by estimating its parameters whenever three optimal criteria are satisfied and prior information about the unknown parameters is available. Two simulation-based studies have been conducted to demonstrate (1) the practical applications of our findings and (2) the possible impact of any type of dependency between the co-reinsurance’s parameters and the evaluated optimal co-reinsurance strategy.     

Bayesian knowledge base tuning

For a knowledge-based system that fails to provide the correct answer, it is important to be able to tune the system while minimizing overall change in the knowledge-base. There are a variety of reasons why the answer is incorrect ranging from incorrect knowledge to information vagueness to incompleteness. Still, in all these situations, it is typically the case that most of the knowledge in the system is likely to be correct as specified by the expert (s) and/or knowledge engineer (s). In this paper, we propose a method to identify the possible changes by understanding the contribution of parameters on the outputs of concern. Our approach is based on Bayesian Knowledge Bases for modeling uncertainties. We start with single parameter changes and then extend to multiple parameters. In order to identify the optimal solution that can minimize the change to the model as specified by the domain experts, we define and evaluate the sensitivity values of the results with respect to the parameters. We discuss the computational complexities of determining the solution and show that the problem of multiple parameters changes can be transformed into Linear Programming problems, and thus, efficiently solvable. Our work can also be applied towards validating the knowledge base such that the updated model can satisfy all test-cases collected from the domain experts.     

Bayesian robustness under a skew-normal class of prior distribution

We develop a global sensitivity analysis to measure the robustness of the Bayesian estimators with respect to a class of prior distributions. This class arises when we consider multiplicative contamination of a base prior distribution. A similar structure was presented by van der Linde [12]. Some particular specifications for this multiplicative contamination class coincide with well known families of skewed distributions. In this paper, we explore the skew-normal multiplicative contamination class for the prior distribution of the location parameter of a normal model. Results of a Bayesian conjugation and expressions for some measures of distance between posterior means and posterior variance are obtained. We also elaborate on the behavior of the posterior means and of the posterior variances through a simulation study.