路灯人影和离家出走改进的黑猩猩优化算法

为提高黑猩猩优化算法(ChOA)的求解精度和局部极值逃逸能力,提出一种路灯人影和离家出走改进的黑猩猩优化算法(SSR-ChOA)。首先,采用SPM混沌序列初始化种群,增加初始种群分布均匀性。其次,由夜间路灯下人影变化的物理现象设计一种新的光学类改进方式——路灯人影,用于优化原有ChOA算法开发精度不高问题。同时设计一种名为离家出走的全局优化策略,使普通黑猩猩个体拥有更强的主动探索能力,避免因领导者判断错误陷入局部极值而导致整个种群搜索停滞、过早收敛。测试了25个基准测试函数和CEC2014测试函数,对比了ChOA算法、4种不同类型改进ChOA算法以及粒子群等算法,分析了改进策略有效性。最后,对航拍无人机飞行路径中存在高耸电塔、信号塔的应用情景进行了研究,验证了SSR-ChOA有效性。实验结果表明,SSR-ChOA与ChOA和4种改进ChOA对比不仅具有显著性差异,而且在寻优精度和稳定性上表现更佳。无人机3D路径规划上,SSR-ChOA平均总开销相比ChOA减少3.06%。

Twitter Data Analysis Using Hadoop and‘R’and Emotional Analysis Using Optimized SVNN

Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over different desti-nation systems.In this paper,the Hadoop cluster with four nodes integrated with RHadoop,Flume,and Hive is created to analyze the tweets gathered from the Twitter stream.Twitter stream data is collected relevant to an event/topic like IPL-2015,cricket,Royal Challengers Bangalore,Kohli,Modi,from May 24 to 30,2016 using Flume.Hive is used as a data warehouse to store the streamed tweets.Twitter analytics like maximum number of tweets by users,the average number of followers,and maximum number of friends are obtained using Hive.The network graph is constructed with the user’s unique screen name and men-tions using‘R’.A timeline graph of individual users is generated using‘R’.Also,the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized sup-port vector neural network(OSVNN)classification model.To attain better classi-fication accuracy,the performance of SVNN is enhanced using a chimp optimization algorithm(ChOA).Extracting the users’emotions toward an event is beneficial for prediction,but when coupled with visualizations,it becomes more powerful.Bar-chart and wordcloud are generated to visualize the emotional analysis results.

ON COLLISION LOCAL TIME OF TWO INDEPENDENT FRACTIONAL ORNSTEIN-UHLENBECK PROCESSES

In this article, we study the existence of collision local time of two indepen- dent d-dimensional fractional Ornstein-Uhlenbeck processes X＋^H1 and Xt^H2 with different parameters Hi ∈ （0, 1）,i = 1, 2. Under the canonical framework of white noise analysis, we characterize the collision local time as a Hida distribution and obtain its＇ chaos expansion. Key words Collision local time; fractional Ornstein-Uhlenbeck processes; generalized white noise functionals; choas expansion

基于自适应VMD和DD-cCycleGAN的滚动轴承剩余寿命预测

为准确预测强噪声干扰小样本情况下的滚动轴承剩余寿命(remaining useful life, RUL),提出一种基于自适应变分模态分解(variational mode decomposition, VMD)和双判别器条件循环一致对抗网络(double-discriminator conditional CycleGAN, DD-cCycleGAN)的滚动轴承RUL预测方法。将黑猩猩优化算法(chimp optimization algorithm, ChOA)与VMD相结合,给出一种基于ChOA的自适应VMD算法,选取有效模态分量进行重构,降低强背景噪声的干扰;开发一种DD-cCycleGAN生成新样本,这些生成的新样本不但保留了源域的样本信息,还与目标域的样本相似;将训练样本的重构样本和生成的新样本作为输入,训练长短时记忆(long short-term memory, LSTM)网络,用训练后的LSTM网络预测测试样本中滚动轴承的RUL。通过采用XJTU-SY滚动轴承加速寿命试验数据集验证该方法的有效性,试验结果表明该方法具有较强的抗噪能力和较高的轴承RUL预测精度。