基于人工智能的钛合金热变形工艺参数优化

在深入分析热变形工艺参数对Ti-15-3合金显微组织及成形载荷的影响的基础上,以变形温度、变形程度和变形速率等热变形工艺参数作为设计变量,以显微组织和成形力的最佳综合为目标,建立了该合金热塑性成形工艺参数的多目标优化数学模型。以显微组织参数和成形力的人工神经网络预测模型作为优化算法的知识源,将人工神经网络与修正的遗传算法相结合,对Ti-15-3合金的热塑性成形工艺参数进行优化。结果表明,提出的修正的遗传算法是有效的,采用将其与人工神经网络相结合的方法对钛合金的热塑性成形工艺参数进行优化是可行的。     

基于遗传算法的产品制造的优化调度

研究了企业产品制造的调度问题 ,并建立了调度的最小模型。引入效率函数 ,运用遗传退火进化算法实现了调度决策的优化 ;提出了将遗传退火算法和分派规则相结合的调度策略和动态编码的方法 ,放宽了约束条件 ,提高了搜索效率和速度 ,保证其全局收敛性。     

基于优进策略的遗传算法对重油热解模型参数的估计

针对常规遗传算法全局寻优效率偏低的弱点，提出了一种优进策略，用以改进常规遗传算法。该策略将从繁衍过程中获取进化信息，自适应地改进子代分布，适时引入确定性操作，以提高全局寻优性能。提出的相关技术包括维持种群的多样性、改进交叉算子、增加Powell寻优算子等。实例测试表明这种优进策略效果良好，并已成功地应用于重油热解三集总动力学复杂数学模型的非线性参数估计。     

生物工程及其在化学工业中的应用

生物工程是近年来崛起的一门综合性的高新科学技术，也是当代新技术革命中最为活跃的领域。文章简要地介绍和评述了生物工程的主要内容及其在化学工业中应用的概况及发展趋势。     

A genetic tango attack against the David–Prasad RFID ultra‐lightweight authentication protocol

Radio frequency identification (RFID) is a powerful technology that enables wireless information storage and control in an economical way. These properties have generated a wide range of applications in different areas. Due to economic and technological constrains, RFID devices are seriously limited, having small or even tiny computational capabilities. This issue is particularly challenging from the security point of view. Security protocols in RFID environments have to deal with strong computational limitations, and classical protocols cannot be used in this context. There have been several attempts to overcome these limitations in the form of new lightweight security protocols designed to be used in very constrained (sometimes called ultra‐lightweight) RFID environments. One of these proposals is the David–Prasad ultra‐lightweight authentication protocol. This protocol was successfully attacked using a cryptanalysis technique named Tango attack. The capacity of the attack depends on a set of boolean approximations. In this paper, we present an enhanced version of the Tango attack, named Genetic Tango attack, that uses Genetic Programming to design those approximations, easing the generation of automatic cryptanalysis and improving its power compared to a manually designed attack. Experimental results are given to illustrate the effectiveness of this new attack.     

Using the genetic algorithm to enhance nonnegative matrix factorization initialization

Nonnegative matrix factorization (NMF) algorithms have been utilized in a wide range of real applications; however, the performance of NMF is highly dependent on three factors including: (1) choosing a problem dependent cost function; (2) using an effective initialization method to start the updating procedure from a near‐optimal point; and (3) determining the rank of factorized matrices prior to decomposition. Due to the nonconvex nature of the NMF cost function, finding an analytical‐based optimal solution is impossible. This paper is aimed at proposing an efficient initialization method to modify the NMF performance. To widely explore the search space for initializing the factorized matrices in NMF, the island genetic algorithm (IGA) is employed as a diverse multiagent search scheme. To adapt IGA for NMF initialization, we present a specific mutation operator. To assess how the proposed IGA initialization method efficiently enhances NMF performance, we have implemented state‐of‐the‐art initialization methods and applied to the Japanese Female Facial Expression dataset to recognize the facial expression states. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of relative error and fast convergence.     

江苏省耐盐生物质能源植物选育和生物柴油制备研究

江苏省有近67万公顷沿海盐土滩涂资源,发展适应这些土地资源的油料植物对促进江苏省生物质能的产业化具有极大的促进作用.本研究针对江苏省土地资源的特点,自2001年,从江苏盐城种植了近10年的美国耐盐油料植物海滨锦葵6万株自然群体中筛选了40个优良单株,并进行了子代测定.目前已经初步选育出了海滨锦葵优良无性系,含油量从原群体平均的18 %提高到23 %.2005年首次从耐盐油料植物海滨锦葵中成功获得生物柴油实验室样品,并已经得到中石化权威机构检测数据.     

基于概念设计阶段的解耦模式车门截面形状优化方法

为了提高车门静态刚度, 采用基于HDMR理论的多参数解耦优化方法与Morph技术结合, 对一款处于概念设计阶段车门的主截面形状进行优化。这种建模方法的特点在于通过少量的样本点识别形状变量之间的耦合关系, 进而将高维问题进行分解, 在保证精度的前提下提高计算效率。采用Kriging插值方法对Cut-HDMR的各项进行构造, 得到车门各刚度下的考察点位移和质量的近似模型, 进而利用非支配排序多目标遗传算法（NSGA-Ⅱ）对近似模型进行优化, 在车门静态刚度均达到设计要求的前提下合理进行减重, 使垂直刚度和扭转刚度得到了很大的提高。     

Compounding and injection molding of polybutene‐1/polypropylene blends

The effect of shear‐controlled orientation injection molding (SCORIM) was investigated for polybutene‐1/polypropylene blends. This article reports on the methods and processing conditions used for blending and injection molding. The properties of SCORIM moldings are compared with those of conventional moldings. SCORIM is based on the application of specific macroscopic shears to a solidifying melt. The multiple shear action enhances molecular alignment. The moldings were investigated with mechanical tests, differential scanning calorimetry studies, and polarized light microscopy. The application of SCORIM improved Young's modulus and the ultimate tensile strength. The gain in stiffness was greater for higher polybutene‐1 content blends. A drastic decrease in the strain at break and toughness was observed in SCORIM moldings. The enhanced molecular orientation of SCORIM moldings resulted in a featureless appearance of the morphology. Interfacial features due to segregation were visible in the micrographs of SCORIM moldings. Both conventional and SCORIM moldings exhibited form I′ in polybutene‐1. This article explains the relationship between the mechanical properties and micromorphologies. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 806–813, 2003     

RGA-D：一种无线传感器网络覆盖优化方法

研究了无线传感器网络覆盖优化问题,针对传感器节点随机部署、分布不均,传统覆盖优化算法一般只考虑网络部署后单次优化的问题,提出基于节点冗余和覆盖集冗余的计算方法,用网络的局部特征表征全局特征,改进了网络覆盖模型。并在此基础上提出RGA-D算法,利用遗传算法计算覆盖集,同时考虑节点和覆盖集冗余度,对网络整个生存期进行全局优化。仿真实验表明,RGA-D算法能在活跃节点数和网络覆盖率之间达到平衡,解决了网络生存期后期容易出现覆盖盲区的问题。