弯曲回弹逆解公式及其在焊管预弯机组设计中的应用

根据小曲率弯曲成形的特点，考虑了板料弯曲的纯弹性变形部分对回弹的影响，推导出根据板料强度、厚度和最终成形半径直接计算弯曲半径的回弹逆解公式，并应用于大口径UOE焊管预弯成形的设计上。根据计算公式给出了B-X100钢级板料UOE焊管预弯成形的预弯辊半径优化设计诺谟图。     

半固态钢铁材料轧制产品的力学特性

将弹簧钢（60Si2Mn)和不锈钢（1Cr18Ni9Ti)在半固态下1道次轧制成形，对轧制产品进行拉伸实验，研究其在室温条件下的力学性能，并对拉伸变形过程中的塑性变形机理进行了分析，研究结果表明：不同的固相率对轧制产品的力学性能有明显的影响，在研究范围内随着固相率的提高，其力学性能亦提高，半固态轧制过程中所产生的液固相分离，导致半固态轧制产品组织分布差异，使轧制产品不同区域的力学性能不同，同时由于半固态轧制产品特有的球形固相颗,其伸拉时的塑性变形机理也有自身的特点。     

板料成形数值模拟的关键技术及难点

本文结合国内外板料成形数值模拟的最新研究动态，从关键算法、屈服函数、本构方程、有限元列式、接触问题、破裂准则、回弹计算、起皱分析八个方面叙述了板料成形有限元计算的关键技术及主要难点。     

无线网络中TCP/NC协议性能分析与仿真验证

为改进传统传输控制协议( TCP)在无线网络中的性能,运用Matlab对TCP Reno,TCP Vegas和基于网络编码的TCP/NC吞吐率分析模型进行数值求解,分析3个模型在不同网络环境下应达到的理论值,验证TCP/NC模型在理论上能否提高其吞吐率。基于NS-2对3种吞吐率模型进行仿真,结果表明,TCP/NC及其分析模型可有效改进TCP协议。     

大规模定制环境下基于产品结构复杂度的工时定额制定研究

针对大规模定制环境下产品种类多、工时制定困难的特点,为了能够快速且准确获取产品工时,研究了产品结构复杂度与工时之间的关系,提出了一种基于产品结构复杂度的工时估算方法.综合考虑影响产品结构复杂度的各种因素后,针对决策结果的排序向量决定决策者权重,引入灰色关联理论,建立了产品结构复杂度计算模型.基于数据库中的工时信息,运用MATLAB软件进行编程确定产品结构复杂度和工时之间的函数关系.最后,以某企业产品加工工时估算实例,证明了所提出的估算方法的可行性.     

润滑脂生产自动组态生产线控制研究与设计

为了解决现有润滑脂单元操作设备"一对多"或"多对一"生产线灵活多变,易发生因人工连接生产线操作失误,导致物料转移错误造成浪费等问题。提出润滑脂自动组态生产线控制方案,该方案采用批号控制策略,实现批号自动传递,保证物料只能在生产相同批号产品的单元设备之间进行传递,防止组合生产线失误,提高生产线连接的可靠性和生产效率。     

Two general integral inequalities and their applications to stability analysis for systems with time‐varying delay

Integral inequalities have been widely used in stability analysis for systems with time‐varying delay because they directly produce bounds for integral terms with respect to quadratic functions. This paper presents two general integral inequalities from which almost all of the existing integral inequalities can be obtained, such as Jensen inequality, the Wirtinger‐based inequality, the Bessel–Legendre inequality, the Wirtinger‐based double integral inequality, and the auxiliary function‐based integral inequalities. Based on orthogonal polynomials defined in different inner spaces, various concrete single/multiple integral inequalities are obtained. They can produce more accurate bounds with more orthogonal polynomials considered. To show the effectiveness of the new inequalities, their applications to stability analysis for systems with time‐varying delay are demonstrated with two numerical examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation

Extreme learning machine (ELM) is a single-hidden layer feed-forward neural network with an efficient learning algorithm. Conventionally an ELM is trained using all the data based on the least square solution, and thus it may suffer from overfitting. In this paper, we present a new method of data and feature mixed ensemble based extreme learning machine (DFEN-ELM). DFEN-ELM combines data ensemble and feature subspace ensemble to tackle the overfitting problem and it takes advantage of the fast speed of ELM when building ensembles of classifiers. Both one-class and two-class ensemble based ELM have been studied. Experiments were conducted on computed tomography (CT) data for liver tumor detection and segmentation as well as magnetic resonance imaging (MRI) data for rodent brain segmentation. To improve the ensembles with new training data, sequential kernel learning is adopted further in the experiments on CT data for speedy retraining and iteratively enhancing the image segmentation performance. Experiment results on different testing cases and various testing datasets demonstrate that DFEN-ELM is a robust and efficient algorithm for medical object detection and segmentation.     

Novel prototyping method for microfluidic devices based on thermoplastic polyurethane microcapillary film

Thermoplastic polyurethane microcapillary film (TPU-MCF), as a novel extruded product, inherently contains an array of circular micron-sized capillaries embedded inside the polymer matrix. With the aid of simple laser cutting and conventional sealing technologies, a rapid prototyping method for microfluidic devices is proposed based on the ready-made microstructure of MCFs. Two functionalized microfluidic devices: serpentine micromixer and multi-droplet generator, are rapidly fabricated to demonstrate the advantages and potential of employing this new method. The whole proof-of-concept fabrication process can be completed in 8–10 min in a simple way; each procedure is repeatable with stable performance control of microfluidic devices; and the material cost can be as low as $0.01 for each device. The TPU-MCF and this novel method are expected to provide a new perspective and alternative in microfluidic community with particular requirements.