Robust optimization of foam-filled thin-walled structure based on sequential Kriging metamodel

Deterministic optimization has been successfully applied to a range of design problems involving foam-filled thin-walled structures, and to some extent gained significant confidence for the applications of such structures in automotive, aerospace, transportation and defense industries. However, the conventional deterministic design could become less meaningful or even unacceptable when considering the perturbations of design variables and noises of system parameters. To overcome this drawback, a robust design methodology is presented in this paper to address the effects of parametric uncertainties of foam-filled thin-walled structure on design optimization, in which different sigma criteria are adopted to measure the variations. The Kriging modeling technique is used to construct the corresponding surrogate models of mean and standard deviation for different crashworthiness criteria. A sequential sampling approach is introduced to improve the fitness accuracy of these surrogate models. Finally, a gradient-based sequential quadratic program (SQP) method is employed from 20 different initial points to obtain a quasi-global robust optimum solution. The optimal solutions were verified by using the Monte Carlo simulation. The results show that the presented robust optimization method is fairly effective and efficient, the crashworthiness and robustness of the foam-filled thin-walled structure can be improved significantly.     

Square-root unscented Kalman filtering-based localization and tracking in the Internet of Things

The Internet of Things (IoT), which is usually established over architectures of wireless sensor networks, provides an actual platform for various applications of personal and ubiquitous computing. Recently, moving target localization and tracking in an IoT environment have been paid more and more attention. This paper proposes a square-root unscented Kalman filtering (SR-UKF)-based algorithm to discover real-time location of a moving target in an IoT environment where there exist quantities of sensors. The data generated from wireless sensor nodes of the IoT make contributions to localization and tracking of the moving target. First, a least-square (LS) criterion-based mathematical model is proposed for localization initialization in an IoT scenario. Next, we employ an SR-UKF idea for the further localization and tracking. By using the data coming from sensor nodes near the target, real-time location of the moving target can be estimated by implementation of SR-UKF in an iterative fashion so as to achieve target status tracking. Simulation results show that the proposed algorithm achieves good performance in estimation of both position and velocity of the target with either uniform linear motion or variable-speed curve motion. Compared with some existing conventional extended Kalman filtering (EKF) or UKF-based methods, the proposed algorithm shows lower location/velocity estimation error under the same computational complexity, which demonstrates its potential significance in ubiquitous computing applications for an IoT environment.     

Toward inference attacks for k-anonymity

Current research still cannot effectively prevent an inference attacker from inferring privacy information for k-anonymous data sets. To solve the issue, we must first study all kinds of aggressive reasoning behaviors and process for the attacker thoroughly. Our work focuses on describing comprehensively the inference attack and analyzing their privacy disclosures for k-anonymous data sets. In this paper, we build up a privacy inference graph based on attack graph theory, which is an extension of attack graph. The privacy inference graph describes comprehensively the inference attack in k-anonymous databases by considering attacker background knowledge and external factors. In the privacy inference graph, we introduce a concept of valid inference path to analyze the privacy disclosures in face of inference attack. According to both above, we design an algorithm to compute the n-valid inference paths. These paths can deduce some privacy information resulting in privacy disclosure. Moreover, we study the optimal privacy strategies to resist inference attack by key attribute sets and valid inference paths in the attack graph. An approximate algorithm is designed to obtain the approximate optimal privacy strategy set. At last, we prove the correctness in theory and analyze the performance of the approximate algorithm and their time complexity.     

回归正交设计在PEK-C增韧酚醛树脂研究中的应用

采用计算机辅助回归正交设计的方法对酚酞型聚醚酮(PEK-C)增韧酚醛树脂进行研究，通过回归正交设计，对共混体系的组分配比和成型工艺参数进行优化，预测出最佳配比和工艺参数，并通过试验得到验证。     

基于BPEL的MDO流程建模与调度系统设计和实现

在分析多学科设计优化特点的基础上,充分集成Web services技术和BPEL技术,设计一个基于BPEL的多学科设计优化的流程建模与调度系统。重点介绍该系统的两个模块即可视化流程建模工具模块和流程调度监控平台模块的设计和实现方法。最后给出一个优化实例说明该系统的可行性。     

超临界烯烃聚合

论述了超临界流体对烯烃聚合的影响，总结了超临界聚合技术的研究与应用。认为：在单体临界点以上进行的超临界聚合技术的研究开发已比较成熟；而新近开发的超临界流体介质中的烯烃取合技术更为重要，可以充分发挥超临界技术的优点，提高聚合产率，并能制备性能特殊的取合物。特别是采用超临界CO2为介质的聚合过程，为实现烯烃聚合的绿色生产奠定了基础。     

氨氮废水降解技术进展

介绍了氨氮废水的主要来源，论述了氨氮废水降解的各种技术及其工艺过程，重点论述了生物处理技术及工艺、物理化学处理技术及工艺以及它们在工业中的应用和特点，并探讨了氨氮废水降解机制，综述了目前国内外研究进展状况。     

HPVC/BR共混体系的性能研究

采用动态硫化方法制备高聚合度聚氯乙烯／顺丁橡胶(HPVC／BR)共混型热塑性弹性体，考察了单一组分增容剂丁腈橡胶(NBR)、氯化聚乙烯(PE—C)和(氢化苯乙烯／丁二烯／苯乙烯)嵌段共聚物(SEBS)，复合增容剂SEBS／NBR和PE—C／NBR及交联程度对HPVC／BR共温体系相容性的影响。结果表明，使用复合增容剂可明显改善HPVC／BR共混物的性能；动态硫化在改善共混物力学性能方面起主要作用。     

聚乳酸-聚氨酯微球的合成及其形态的研究

以低相对分子质量生物可降解D，L-聚乳酸(D，L-PLA)二醇和六亚甲基二异氰酸酯(HDI)为原料、1，4-丁二醇(BD)为扩链剂及聚乙烯基吡咯烷酮(PVP)为悬浮剂，通过悬浮聚合法初步合成了一种新型聚氨酯微球(PUMS)。用扫描电子显微镜(SEM)考察了BD的含量对微球表面形态的影响，通过激光粒度分析仪讨论了搅拌速率和PVP浓度对微球粒径及其分布的影响，用傅立叶变换红外光谱(FTIR)粒度分析仪对微球的化学结构进行了表征。结果表明，合成的微球的平均粒径随搅拌速率和PVP浓度的增加而减少，粒径分布变宽；当搅拌速率为400r／min，PVP质量分数为1．5％时。微球的平均粒径约47μm，粒径分布最窄。约在10～90μm；微球表面有孔，但随着BD含量的增加，微球表面变得相对粗糙，孔数减少，孔径减小，直至孔消失。     

反应精馏过程模拟研究进展

对均相反应精馏和非均相催化精馏过程的各种模拟计算方法进行了评述，分析了各种算法的优缺点和适用范围，指明了目前研究中存在的不足和今后的发展方向。