An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

压水堆核电厂采用富集硼的优势与可行性分析

压水堆核电厂通常采用天然硼进行反应性的化学补偿控制,研究表明采用富集硼替代天然硼,可以优化一回路水化学,降低一回路结构材料腐蚀风险和堆芯沉积风险,降低职业照射剂量。本文分析压水堆核电厂采用富集硼的优势与可行性,介绍国内外核电厂富集硼的应用情况。最后对我国华龙一号堆型和在役压水堆核电厂富集硼的应用提出建议。     

Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

This article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimization problems of a price-taking generation company (GenCo) in the electricity market. To simulate the binary-nature PBUC problem, the continuous, real-value whale position/location is mapped into binary search space through various transfer functions. This article introduces three variants of BWOA using tangential hyperbolic, inverse tangent (arctan) and sigmoidal transfer functions. The effectiveness of the BWOA approaches is examined in test systems with different market mechanisms, i.e. an energy-only market, and energy and reserve market participation with different reserve payment methods. The simulation results are presented, discussed and compared with other existing approaches. The convergence characteristics, solution quality and consistency of the results across different BWOA variants are discussed. The superiority and statistical significance of the proposed approaches with respect to existing approaches is also presented.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

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...