Automatic generation of variants depending on changes of product properties in a flexible manufacturing environment

Competition forces manufacturing systems to be flexible and to increase product variety and process complexity. These tasks depend on the flexible design of a bill of materials (BOM), one of the most important inputs in manufacturing planning and control systems. Product variety forces systems to generate BOMs with regard to product properties through a BOM pattern. A variant bill of materials provides a structure to manage product variability. In this study, an algorithm is designed to build a BOM pattern using computer-aided design and computer-aided manufacturing (CAD/CAM) data, and another algorithm is designed to generate variants with regard to product specifications. Genetic algorithm is used to generate new products to provide high product variability for testing algorithms. After the test, both algorithms are applied to a real industry problem. The BOM pattern is built automatically using CAD/CAM data, and variants are generated with regard to the pattern, and the results are discussed.     

Numerical study of entropy generation in Darcy-Forchheimer (D-F) Bödewadt flow of CNTs

Three-dimensional Bödewadt flow (fluid rotates at a large enough distance from the stationary plate) of carbon nanomaterial is examined. Single walled and multi walled CNTs are dissolved in water and gasoline oil baseliquids. Darcy-Forchheimer porous medium is considered. Stationary disk is further stretched linearly in radial direction. Heat transfer effect is examined in presence of radiation and convection. Effect of viscous dissipation is accounted. Entropy generation rate is studied. By using adequate transformation (von Kármán relations), the flow field equations (PDEs) are transmitted into ODEs. Solutions to these ODEs are constructed via implementation of shooting method (bvp4c). In addition to Entropy generation rate, Bejan number, heat transfer rate (Nusselt number), skin friction and temperature of fluid are examined through involved physical parameters. Axial component of velocity intensifies with increment in nanoparticles volume fraction and ratio of stretching rate to angular velocity parameter while it decays with higher porosity parameter. Higher nanoparticles volume fraction and porosity parameter lead to decay in radial as well as tangential component of velocity. However it enhances with higher ratio of stretching rate to angular velocity parameter. Temperature of fluid directly varies with higher ratio of stretching rate to angular velocity parameter, radiation parameter, Eckert number, Biot number and nanoparticles volume fraction. Rate of Entropy generation is reduced with higher estimations of porosity parameter, nanoparticles volume fraction and radiation parameter. Skin friction coefficient decays with higher porosity parameter and ratio of stretching rate to angular velocity parameter. Intensification in porosity parameter, nanoparticles volume fraction and Biot number leads to higher Nusselt number. Prominent impact is shown by multiple-walled CNTs with gasoline oil basefluid than single-walled CNTs with water basefluid.     

基于梯度提升决策树的车轮轮缘厚度磨耗预测

轮对在列车走行过程中起着导向、承受以及传递载荷的作用，其踏面及轮缘磨耗对地铁列车运行安全性和钢轨的寿命都将产生重要影响。根据地铁列车车轮磨耗机理，分析车轮尺寸数据特点，针对轮缘厚度这一型面参数，基于梯度提升决策树算法构建轮缘厚度磨耗预测模型。在该模型的基础上，任意选取某轮对数据进行验证分析，结果表明：基于梯度提升决策树的轮对磨耗预测模型具有较好的预测精度，可预测出1~6个月的轮缘厚度变化趋势范围，预测时间范围较长，可为地铁维保部门对轮对的维修方式由状态修转为预防修提供指导性建议。     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

Experimental study on the heat transfer and flow characteristics of nanorefrigerants inside a corrugated tube

The heat transfer and flow characteristics of MWCNT-R141b nanorefrigerant with different mass fractions have been studied through experiments. Experimental results were compared with existing correlations. A two-step method was used to prepare the nanorefrigerants. Span-80 was used as surfactant with an average particle diameter of 20 nm. Transmittance method was used to evaluate the stability of nanorefrigerants. Results showed that the stability of MWCNT-R141b nanorefrigerant, which is the added dispersant, was good during the experiments. The 0.3 wt% MWCNT-R141b nanorefrigerants had optimal heat transfer enhancement effects compared with pure refrigerants. The maximum Nusselt number increased by 40%. The specific pressure drop of nanorefrigerant increased as the Reynolds number (Re) increased, and the specific pressure drop of the pure refrigerant was minimum, which is similar to R141b.     

水泥窑余热发电系统用耐磨材料的研制与应用

本研究针对水泥窑余热发电系统的工作条件,根据耐磨材料的损毁机理,采用高铝矾土、煅烧氧化铝粉、黏土、硅微粉等为原料,以磷酸二氢铝为结合剂,以铝酸钙水泥为促硬剂,通过添加掩蔽剂和优化配料工艺,制备了水泥窑余热发电系统用耐磨材料,并对不同处理温度对耐磨材料的性能影响进行了分析。结果表明增强骨料与基质的结合能力有利于耐磨性的提高,磷酸及磷酸盐与氧化铝之间会随着温度的升高生成不同的磷酸铝相,都能够起到结合作用,比采用水泥结合更有利于提高材料在不同温度下的强度和耐磨性。使用结果表明,制备的耐磨材料施工性能优良,凝结硬化时间适当,强度高,抗冲刷性能好,在水泥窑余热发电系统中取得了良好的使用效果。     

基于GFU和分层LSTM的组群行为识别研究方法

提出一种以"关键人物"为核心，使用门控融合单元（GFU，Gated Fusion Unit）进行特征融合的组群行为识别框架，旨在解决两个问题：①组群行为信息冗余，重点关注关键人物行为特征，忽略无关人员对组群行为的影响；②组群内部交互行为复杂，使用GFU有效融合以关键人物为核心的交互特征，再通过LSTM时序建模成为表征能力更强的组群特征.最终，通过softmax分类器进行组群行为类别分类.该算法在排球数据集上取得了86.7%的平均识别率.     

换能器结构参数对聚合物超声塑化黏弹生热的影响

为研究超声换能器结构参数对聚合物超声塑化过程黏弹性生热的影响，首先确定超声黏弹性生热系统的组成，进行纵振超声换能器结构设计；然后分析超声黏弹性生热过程及超声黏弹性生热原理；最后采用单一变量法分析超声换能器的主要结构参数对其纵振频率及工具头前端质点最大振幅的影响，将其实际输出的纵振激励加载于熔融聚合物，研究其结构参数对聚合物超声黏弹性生热过程及达到聚合物玻璃化转变温度所用时间的影响。结果表明，随纵振激励作用时间的增加，聚合物温度非线性升高；放大比对聚合物温度变化影响最大，前盖板厚度和工具头长度次之，影响最小的是变幅杆长度。     

Costs Impact of a Transition to Hydrogen-fueled Vehicles on the Spanish Power Sector

It is expected that demand response might provide soon ancillary services to the power system. This could be done, for example, by managing the use of Electric Vehicles (EV) batteries, or the production of flexible energy commodities such as hydrogen (H₂), that can be used for fuel cell vehicles (H2EV) or in industrial processes. This paper analyses the impact of a transition to H2EV as an alternative to EV for passengers’ cars on a Spanish-like power sector. A simple H₂ demand estimation is developed and provided to CEVESA, an operation and expansion model for the Iberian Power System Electricity Market (MIBEL). For this study, CEVESA was extended to include the investments and operation decisions of H₂ production. Simulations were performed to determine the optimal evolution of the H₂ production capacity and of the electricity generation mix, considering scenarios with different shares of EV and H2EV. The impact of H2EV vs EV mobility is assessed based on the recent Spanish National Plan for Energy and Climate (NECP) as the base case scenario. Results show that, even if H2EV mobility alternative is still more costly than EV, H₂ production could provide a significant flexibility to the system that should also be appraised. Indeed, H2EV mobility could become a feasible and complementary alternative to decarbonize mobility by powering H₂ production with the renewable generation surplus. This, together with the on-going learning process of this technology that will decrease its production costs and increase its efficiency in the coming years, could boost, even more, the development of the H₂ economy.     

基于词法特征的恶意域名快速检测算法

针对互联网中恶意域名攻击事件频发，现有域名检测方法实时性不强的问题，提出一种基于词法特征的恶意域名快速检测算法。该算法根据恶意域名的特点，首先将所有待测域名按照长度进行正则化处理后赋予权值；然后利用聚类算法将待测域名划分成多个小组，并利用改进的堆排序算法按照组内权值总和计算各域名小组优先级，根据优先级降序依次计算各域名小组中每一域名与黑名单上域名之间的编辑距离；最后依据编辑距离值快速判定恶意域名。算法运行结果表明，基于词法特征的恶意域名快速检测算法与单一使用域名语义和单一使用域名词法的恶意域名检测算法相比，准确率分别提高1.7%与2.5%，检测速率分别提高13.9%与6.8%，具有更高的准确率和实时性。