Random vibration analysis of switching apparatus based on Monte Carlo method

The performance in vibration environment of switching apparatus containing mechanical contact is an important element when judging the apparatus’s reliability. A piecewise linear two-degrees-of-freedom mathematical model considering contact loss was built in this work, and the vibration performance of the model under random external Gaussian white noise excitation was investigated by using Monte Carlo simulation in Matlab/Simulink. Simulation showed that the spectral content and statistical characters of the contact force coincided strongly with reality. The random vibration character of the contact system was solved using time (numerical) domain simulation in this paper. Conclusions reached here are of great importance for reliability design of switching apparatus.     

集值映象与随机集值映象的几个不动点定理

在本文中我们得到了关于集值映象与随机集值映象的几个新的不动点定理,扩充了[3]中结果,主要结果是定理1—3与定理6—7     

爱因斯坦对布朗运动的解释与现代统计动力学

回顾爱因斯坦对布朗运动的统计解释.介绍用连续时间随机行走模型对布朗运动粒子行为进行描述,与爱因斯坦的结论进行了比较而得出结论:由连续时间随机行走模型得出的结论比爱因斯坦的结论更具有一般性.     

试论巴金的抒真情文学历程

巴金从异国他乡走上文坛,一生都在以不朽作品践行＂真情论＂。＂爱那需要爱的,憎那摧残爱的＂,这是巴金一贯的文学主张。虽然巴金的真诚与热情在＂文革＂中受到沉重的打击,但在《随想录》中,他却怀着博爱而忏悔之心,对自己曾经被迫于淫威、违心批判过的朋友表示诚挚的歉疚,以＂真情＂还复历史,以大无畏英雄气概与殉道精神无情地控诉了＂文革＂的种种令人发指的罪行。巴金用心地追寻着＂真情＂丰富的内涵,终生都在探索表现＂真情＂的各种文学手段。     

基于FCM-PFS与PSO-RF的隐性知识外显案例适配研究

对隐性知识外显案例实施有效适配,于知识应用乃至创新、实现乃至增值知识资源的价值,具有重要意义。本文深入研究了隐性知识外显案例适配机理。首先,采用毕达哥拉斯模糊集对知识属性值进行处理,建立知识表达系统;接着,运用K-Means算法对FCM聚类算法进行改进,压缩匹配空间、提升案例匹配效率;而后,基于PFS相关系数求解知识供需间的视图相似度,从而获得适配案例集。在此基础上,构建随机森林适配模型,并采用粒子群算法对其优化,以确保适配效果。与传统算法的对比实验验证了本文算法的比较优势。     

反例教学法在概率论与数理统计教学中的应用与研究--以随机事件和概率为例

在概率论与数理统计教学中,以随机事件与概率一章为教学案例,举出3个典型性反例案例,在教学中加以应用,培养学生的逆向思维和敏捷性思维。     

基于混合图的在线社交网络朋友推荐算法

针对在线社交网络朋友推荐问题,尝试融合多个社会网络为一个混合图模型,采用基于混合图模型的重启动随机游走算法,为用户提供个性化的朋友推荐,并通过参数调节多个网络的权重。实验表明,该算法提高了在线社交网络朋友推荐的准确性。     

随机环境下ARCH模型的几何遍历性

本文对随机环境下的非线性自回归条件异方差(简记为ARCH(p))模型进行了分析,运用一般状态空间马氏链的有关知识和方法,研究由其决定的时间序列{X1}的(伴随)几何遍历性.     

利用随机变量的相关性质证明恒等式

组合恒等式在组合数学中占有重要地位,本文运用概率方法对几个重要的组合恒等式给出了直观简洁的证明。     

Meta-Circuit machine: Inferencing human collaborative relationships in heterogeneous information networks

Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks.