燃烧合成TiC-Al_2O_3/Fe梯度材料及其抗热震行为

采用自蔓延高温合成结合准热等静压（ＳＨＳ／ＰＨＩＰ）制备出了具有对称结构的 ＴｉＣ－Ａｌ２Ｏ３／Ｆｅ梯度材料,并对其抗热冲击及抗热疲劳行为进行了测试和分析·结果表明, ＳＨＳ／ＰＨＩＰ制备的 ＴｉＣ－Ａｌ２Ｏ３／Ｆｅ梯度材料具有预期的梯度式组成,在热冲击和热疲劳实验过程中均无梯度层间横向贯穿裂缝,克服了传统陶瓷／金属直接接合界面的热应力剥落．     

肌肉介导心钠素基因对L-NNA高血压大鼠的作用

用重组人心钠素基因真核表达质粒ｐｃＤＮＡ３／ＡＮＦ，通过基因缝线转移至Ｌ－ＮＮＡ高血压大鼠股四头肌，发现可明显降低Ｌ－ＮＮＡ引起的高血压，并产生明显的利钠利尿作用。提示肌肉介导心钠素基因，作为一种ＤＮＡ药物治疗高血压，可能具有潜在应用价值。     

计算机辅助构造PRS工具的实现

通过对企业活动之间基本联系方式的分析以及过程规则集基本类型研究，借助功能－功能矩阵ＦＦＭ（Ｆｕｎｃｔｉｏｎ－ＦｕｎｃｔｉｏｎＭａｒｔｒｉｘ）及其操作算法，建立了计算机辅助构造过程规则集（ＰｒｏｃｅｄｕｒａｌＲｕｌｅＳｅｔ，ＰＲＳ）工具－ＣＡＢＰＲＳ，并在其基础上将ＰＲＳ自动转化成０－０表达和行为关系图（ＢｅｈａｖｉｏｒＲｅｌａｔｉｏｎｓｈｉｐＧｒａｐｈ，ＢＲＧ）以验证ＰＲＳ的一致性和完整性，以及实     

氮化铁梯度薄膜的制备和磁性研究

用对向靶溅射方法制行出氮化铁梯度薄膜。利用Ｘ射线光电子能谱（ＸＰＳ）对样品进行了深度剖面分析，铁原子和氮原子的百分含量沿膜厚方向呈梯度变化。Ｘ射线衍射表明膜中含有α＝Ｆｅ，－Ｆｅ１６Ｎ２，ε－ＦｅｘＮ（２〈ｘ≤３）和δ－Ｆｅ２Ｎ各相。随着氮分压增加，膜中含氮量高的相比例亦增加，饱和磁通密度逐渐降低。     

氮化铁梯度薄膜的制备

用对向靶溅射仪制备了具有梯度结构的氮化铁薄膜材料．卢瑟福背散射分析结果表明，铁、氮两种原子的密度沿膜厚度方向呈梯度变化．在薄膜中有ζ－Ｆｅ２Ｎ，ε－ＦｅｘＮ（２＜ｘ＜３），γ′－Ｆｅ４Ｎ和α″－Ｆｅ１６Ｎ２等物相．振动样品磁强计测试结果表明部分梯度膜的饱和磁化强度值接近纯铁膜，膜面富铁的梯度样品的矫顽力与基底富铁的梯度样品的矫顽力相差很大。     

新型陶瓷刀具材料的热应力分析

运用有限元方法计算了梯度功能陶瓷刀具ＦＧ－２内的热应力,计算结果表明,在高速切削条件下,梯度功能陶瓷刀具内的热应力明显小于普通陶瓷刀具;同时进行的切削实验也表明在高速切削的条件下,ＦＧ－２的破损寿命明显好于ＳＧ－４．说明梯度功能陶瓷刀具适应于高速切削．     

考虑SSI影响的结构神经网络控制

本文将人工神经网络控制应用于地基上建筑结构地震反应的主动控制中。通过训练神经控制器ＮＮＣ（Ｎｅｕｒａｌ－ｎｅｔｗｏｒｋ Ｃｏｎｔｒｏｌｅｒ）和神经模仿器ＮＮＥ（Ｎｅｕｒａｌ－ｎｅｔｗｏｒｋ Ｅｍｕｌａｔｏｒ）和神经模仿器（Ｎｅｕｒａｌ－ｎｅｔｗｏｒｋ Ｅｍｕｌａｔｏｒ），实现结构的主动控制。算例表明，神经网络控制系统能够有效地控制结构的地震反应，且在训练神经网络时应考虑土－结构支力相互作用（ＳＳＩ     

人GM—CSF在链霉菌中的表达

报道了ＰＩＪ４５９／ＧＭ－ＣＳＦ基因表达质粒的构建，以及该重质粒在变铅青链霉菌Ｓ．ｌｉｖｉｄａｎｓＴＫ５４中的表达，结果表明：ＰＩＪ４５９／ＧＭＣＳＦ质粒具有表达ｈＧＭ－ＣＳＦ的功能。重组菌株细胞裂解液烃ＴＦ－１细胞培养及集落形成实验均证实了其生物学活性。     

Fe—Mn—Si形状记忆合金中元素对材料耐蚀性的影响

研究了Ｆｅ－Ｍｎ－Ｓｉ形状记忆合金中元素对材料耐腐蚀性能及形状记忆性能的影响。结果表明：在Ｆｅ－Ｍｎ－Ｓｉ合金中，随着Ｍｎ，Ｓｉ含量的增加，其耐腐蚀性能略有提高，但Ｍｎ含量过高会导致合金形状记忆性能下降。而在Ｆｅ－Ｍｎ－Ｓｉ合金中加入适量的Ｃｒ，可明显提高该合金的耐腐蚀性能，并使之具有良好的形状记忆性能。     

Fe－Mn－Si形状记忆合金中元素对材料耐蚀性的影响

研究了Ｆｅ－Ｍｎ－Ｓｉ形状记忆合金中元素对材料耐腐蚀性能及形状记忆性能的影响。结果表明：在Ｆｅ－Ｍｎ－Ｓｉ合金中，随着Ｍｎ、Ｓｉ含量的增加，其耐腐蚀性能略有提高，但Ｍｎ含量过高会导致合金形状记忆性能下降。而在Ｆｅ－Ｍｎ－Ｓｉ合金中加入适量的Ｃｒ，可明显提高该合金的耐腐蚀性能，并使之具有良好的形状记忆性能。     

一种改进的模糊神经网络控制器学习算法

提出了一种改进的模糊神经网络混合学习算法,运用遗传算法优化构成隶属函数的网络结构,运用最小二乘法进行解模糊,具有更高的学习精度和更快的收敛速度,解决了在多变量系统中采用模糊神经网络时学习收敛慢且易陷入局部极小点的问题。     

水声通信中变步长神经网络盲均衡算法研究

在水声通信领域多途引起的码间干拢可以用均衡消减。盲均衡不需要训练序列,这将有效的节省通信带宽,提高通信效率及通信性能。实际中的通信信道不可能是完全线性的,神经网络作为一种非线性动态系统,具有大规模并行处理及高度的鲁棒性特征,将其应用于水声信道盲均衡切实可行。文中对变步长BP算法的前馈神经网络进行了理论和算法分析,并通过计算机对其实现水声信道盲均衡进行了仿真。仿真结果表明采用变步长BP算法比采用传统BP算法的神经网络盲均衡器收敛速度快,均衡性能明显提高。     

Scheduling of a no-wait two-machine flow shop with sequence-dependent setup times and probable rework using robust meta-heuristics

This paper investigates the scheduling of a no-wait two-machine flow shop considering anticipatory sequence-dependent setup time and a probable rework for both machines to minimise mean completion time (MCT). To tackle the problem, a robust meta-heuristic algorithm, namely the adapted imperialist competitive algorithm (AICA), has been proposed and is compared with two common and popular meta-heuristic algorithms (i.e. genetic algorithm (GA) and population-based simulated annealing (PBSA)). In this study, we have adapted a traditional imperialist competitive algorithm (ICA) with some considerable changes. First of all, a revolution procedure is added to the algorithm for imperialists similar to colonies. Furthermore, the revolution is only performed when the new solution is better than the previous solution, and chief among them for preservation of premature convergence, the concept of global war is applied. However, the performance of AICA is sensitive to the choice of the best parameter values. Thus, to obtain optimal performance, a comprehensive calibration methodology called response surface methodology is employed to obtain the best combination of parameter values. In order to evaluate the effectiveness and efficiency of proposed algorithms, several test problems are generated and the results obtained from algorithms are then compared in terms of relative percentage deviation. Computational experiments indicate that AICA outperforms GA and PBSA in the MCT performance measure, and GA outperforms the others in terms of computational time.     

基于改进遗传算法的胶印质量控制方法研究

针对前馈学习算法容易陷入局部最优以及收敛速度慢等缺点,采用遗传算法来优化模糊神经网络中的参数,通过仿真实验证明,该控制器算法具有较快的收敛速度和较强的局部搜索能力.     

An integrated model for assembly line re-balancing problem

In this paper, an integrated approach for assembly line rebalancing problem (IALRP) is proposed to quickly react and find an optimal rebalancing of the line when disruptive event occurs because of product demand changes. This model is motivated by real-life application of an automotive cable manufacturer which provides more realistic constraints. To solve the problem, we propose a genetic algorithm (GA) hybridised with a heuristic priority rule-based procedure. This hybridisation is used to add more rich seeds to the initial population and consequently to improve the convergence capability and performance of the GA. After the disturbance, we aim to find a rebalance with the proposed approach to maximise the line efficiency and distributing the idle time across the workstations as equally as possible. To evaluate the efficiency of the proposed algorithm, set of samples collected from the literature are used. The real case study and the experiment results show the proposed approach is very effective and competitive.     

A surrogate assisted evolutionary optimization method with application to the transonic airfoil design

A multi-layer perceptron neural network (NN) method is used for efficient estimation of the expensive objective functions in the evolutionary optimization with the genetic algorithm (GA). The estimation capability of the NN is improved by dynamic retraining using the data from successive generations. In addition, the normal distribution of the training data variables is used to determine well-trained parts of the design space for the NN approximation. The efficiency of the method is demonstrated by two transonic airfoil design problems considering inviscid and viscous flow solvers. Results are compared with those of the simple GA and an alternative surrogate method. The total number of flow solver calls is reduced by about 40% using this fitness approximation technique, which in turn reduces the total computational time without influencing the convergence rate of the optimization algorithm. The accuracy of the NN estimation is considerably improved using the normal distribution approach compared with the alternative method.     

带时间窗的车辆路由问题的改进遗传算法

提出了一种改进的遗传算法,使用了一种新的染色体编码方式,和与之对应的启发式交叉算子,同时采取了竞争选择的淘汰机制,通过对Solomon提出的100个点的标准算例的计算验证,证明了该算法能够很好地解决各类带时间窗的车辆路由问题,通过和混和遗传算法的比较,证明了该算法在计算时间、收敛速度上都有大的优势.该算法计算得到的解在总行驶距离相差不大的情况下使用车辆数较少.     

前馈神经网络混合训练算法及其应用

介绍了单隐层前馈神经网络的混合训练算法(HFM)和正则化混合训练算法(RHFM),然后将该算法应用于UCI数据库上的实际回归例子中,并将其与BP、NNRW以及FM算法进行了比较.仿真实验表明,HFM算法的收敛速度优于其它几种算法,RHFM算法有较好的泛化性能,而NNRW算法在训练时间上占优,尽管如此,HFM算法在时间上还是大大优于BP算法.说明,混合训练算法是一种有效的算法.     

基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。     

A Markov chain analysis of genetic algorithms with individuals having different birth and survival rates

This article studies the convergence characteristics of a genetic algorithm (GA) in which individuals of different age groups in the population possess different survival and birth rates. The inclusion of this feature into the algorithm makes the algorithm mimic the natural evolutionary process more closely than the conventional GA. Although numerical experiments have demonstrated that the proposed algorithm tends to perform better than the conventional GA when used as a function optimizer, the population size of the algorithm is affected by the survival and birth rates of the individuals, which may lead to an unstable search process. Hence, this research develops the condition which governs the birth and survival rates for maintaining a stationary population size during the search process. The Markov chain approach is also used to analyze the convergence characteristics of the algorithm. The proposed algorithm is shown to converge to the global optimal solution if the best candidate solution is maintained over time. The mathematical analysis thus provides a theoretical foundation for the application of the proposed approach as a function optimizer. The performance of the proposed algorithm is tested by solving two benchmark test problems and the results are compared to those obtained by using the conventional GA. Indeed, comparison of the results clearly shows that the proposed approach is superior to the canonical genetic algorithm in terms of the quality of the final solution. The algorithm is described in some detail in the hope of thus stimulating the use of the proposed genetic approach to the solution of important problems in industrial engineering practice.