Topology optimization for crashworthiness of thin-walled structures under axial crash considering nonlinear plastic buckling and locations of plastic hinges

Although topology optimization is established for linear static problems, more effort is required for solving nonlinear plastic problems. A new topology optimization approach with equivalent static loads (ESLs) is suggested to find the optimum topologies and locations of plastic hinges of thin-walled crash boxes by considering crash-induced deformation, the main crash energy-absorbing mechanism. Together with finite element method crashworthiness analyses, considering all nonlinearities with rate-dependent plasticity, the method was developed using an appropriate time-incremental scheme of ESLs without removing any high values of loads. Analyses show that the crash boxes with optimum topologies have energy-absorbing capabilities equivalent to the original structure. The proposed method is evaluated for two crashes: a crash box at low speed and a double cell subjected to high-speed collision. The results indicate that this method captures nonlinear crushing behaviours and accurate locations of plastic hinges where, if proper reinforcements are made, energy absorption can be enhanced.     

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large‐scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi‐Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.     

最优化算法与纯元素谱剥离相结合的能量色散X射线荧光谱解谱方法

在进行能量色散X射线荧光光谱(EDXRF)解谱时,如果给定样品的元素构成,采用纯元素谱图剥离的方式会更加合理,但由于每个元素的谱线都不止一条,如果仅仅针对主线对齐做剥离,会导致结果的严重失真,同时剥离次序对解谱结果有较大影响。介绍了一种最优化算法与纯元素谱剥离相结合的EDXRF解谱方法,该方法以多个纯元素谱的强度大小和峰位漂移道数作为变量构建残差方程,并用最优化计算的方法去调整,直至残差达到极小。实验采用强度顺序剥离、能量顺序剥离和最优化算法拟合3种方法分别对谱线重叠较为严重的La-Ce-Pr-Nd混合液样品的L系谱线进行了解析,结果表明最优化计算方法拟合的谱图与原始谱的残差 (1415.0)比另两种方法(166094.0和3192.7)大幅度缩小,拟合谱与原始谱更为吻合,并且方法在实现时对初值不敏感,解谱精度也不依赖于剥离次序。     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

Revisiting the Parallel Strategy for DOACROSS Loops

Journal of Computer Science and Technology - DOACROSS loops are significant parts in many important scientific and engineering applications, which are generally exploited pipeline/wave-front...     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Maximization of quadruple phase boundary for alkaline membrane fuel cell using non-stoichiometric α-MnO2 as cathode catalyst

Oxygen can only be reduced at the quadruple phase boundary (catalyst, carbon support, ionomer and oxygen) of the cathode catalyst layer with non-conducting electrocatalyst. To maximize the quadruple phase boundary sites is crucial to increase the peak power density of each membrane electrode assembly. The quadruple phase boundary is depending on the ratio of catalyst, carbon support and ionomer. The loading of catalyst layer is also crucial to the fuel cell performance. In this study, non-stoichiometric α-MnO₂ manganese dioxide nanorod material has been synthesized and the ratios of carbon, ionomer and catalyst loadings were optimized in alkaline membrane fuel cell. In total, ten membrane electrode assemblies have been manufactured and tested. Taguchi design method has been applied in order to understand the effect of each parameter. The conclusion finds out the ionomer has more influence on the alkaline membrane fuel cell peak power performance than carbon and loading.     

求解约束优化问题的自适应人工蜂群算法

针对约束优化问题，提出一种自适应人工蜂群算法。算法采用反学习初始化方法使初始种群均匀分布于搜索空间。为了平衡搜索过程中可行个体和不可行个体的数量，算法使用自适应选择策略。在跟随蜂阶段，采用最优引导搜索方程来增强算法的开采能力。通过对13个标准测试问题进行实验并与其他算法比较，发现自适应人工蜂群算法具有较强的寻优能力和较好的稳定性。     

车门铰链系统与车门下沉刚度的相关性

针对某型车门下沉问题,通过台架试验获得车门、铰链和车身等各单因素下沉量和车门绞链系统整体下沉量,对单因素下沉量与系统整体下沉刚度进行线性拟合分析,得到车门铰链系统各单因素与系统下沉刚度的相关度排序.对前、后车门分别选取相关度较高的单因素进行优化,最终改进方案的仿真和试验结果证明该方案可有效地提升车门下沉刚度.采用定量分析法可快速找出影响下沉刚度的敏感因素,并能够快速生成优化方案,为新车型设计提供参考.