考虑过程能力和成本的刀具策略研究

为了提高定期维修的刀具所加工的产品质量,减少停机时间和生产成本.考虑生产过程中工件尺寸的残差是时间的函数,利用Glejser检验方法,拟合工件尺寸残差符合的递增型异方差模型,提出了刀具磨损过程的过程能力指数计算方法;使用Bernstein概率密度函数描述刀具寿命和刀具磨损过程的一般特征,在每一个维修周期内均满足顾客要求的最小过程能力指数基础上,考虑质量损失、换刀成本、由刀具的突然失效引发的惩罚成本及维修刀具所需的成本,将过程能力与成本相结合,提出了用于确定最佳刀具初始状态和维修周期的模型.该模型为实际生产提供了一种离线的刀具管理方法.     

制造任务分解与制造单元级资源配置协同优化

针对网络化制造环境下制造任务分解与资源配置脱节的问题,提出了制造任务逐层分解与制造单元级资源配置协同优化的方法,建立了协同优化流程.在对制造任务特点进行分析的基础上,建立了制造任务模型和制造单元资源模型,建立了多目标优化模型和目标的综合评价体系,利用在遗传算法求解搜索过程中所获得的动态信息进行综合评价,并随着求解搜索过程的深入对变异幅度进行调整,以达到全局优化和加快收敛速度的目的.最后以实例验证了该方法的可行性与有效性.     

含给电子Z-N催化剂催化乙烯/1-丁烯共聚的研究

研究了两种含有给电子体分别为对硝基苯胺、乙二醇二甲醚的Ziegler-Natta催化剂催化乙烯与1-丁烯高压的共聚反应性能,主要考察了反应条件对聚合活性、聚合物物理性质的影响,同时采用红外光谱法测定了聚合物的支化度.研究表明,给电子体为乙二醇二甲醚的催化剂催化活性高于对硝基苯胺.反应条件如反应压力、温度、铝钛比对聚合活性、丁烯的插入率以及聚合物性质都有明显影响.高的反应压力有利于1-丁烯插入和聚合物堆密度的提高;反应温度在50℃时的聚合活性最大,聚合物的支化度较高,聚合物熔点较低;铝钛比为500时聚合活性高于低铝钛比的聚合活性.     

Highly ion-conductive solid polymer electrolytes based on polyethylene non-woven matrix

Highly ion-conductive solid polymer electrolyte (SPE) based on polyethylene (PE) non-woven matrix is prepared by filling poly(ethylene glycol) (PEG)-based crosslinked electrolyte inside the pores of the non-woven matrix. The PE non-woven matrix not only shows good mechanical strength for SPE to be a free-standing film, but also has very porous structure for high ion conductivity. The ion conductivity of SPE based on PE non-woven matrix can be enhanced by adding sufficient non-volatile plasticizer such as poly(ethylene glycol) dimethyl ether (PEGDME) into ion conduction phase without sacrificing mechanical strength. SPE with 20 wt.% crosslinking agent and 80 wt.% non-volatile plasticizer shows 3.1 × 10⁻⁴ S cm⁻¹ at room temperature (20 °C), to our knowledge, which is the highest level for SPEs. It is also electrochemically stable up to 5.2 V and has high transference number about 0.52 due to the introduction of anion receptor as an additive. The interfacial resistance between Li electrode and SPE is low enough to perform charge/discharge test of unit cell consisting of LiCoO₂/SPE/Li at room temperature. The discharge capacity of the unit cell shows 87% of theoretical value with 86% Coulombic efficiency.     

乳胶制品生产的工序能力分析及质量控制

本文分析了乳胶制品的生产过程，并对工序能力指数进行了分析，从而推断出产品的质量。     

The optical properties of hot-pressed magnesium fluoride and single-crystal magnesium fluoride in the 0.1 to 9.0 μm range

A polycrystalline high-density magnesium fluoride, fabricated into plates or shapes by hot-pressing, exhibits high in-line transmittance from 2.5 to 6.0 m, and single-crystal magnesium fluoride extends from 0.1 to 6.0 m. The ultimate and practical transmittance of hot-pressed magnesium fluoride using intrinsic and extrinsic reflectance, absorptance and scattering mechanisms, are investigated. The intrinsic scattering mechanism due to the polycrystalline structure is basically responsible for the tremendous difference in transmittance in the short wavelength region of the spectrum. The in-line transmittance of polycrystalline and singlecrystal MgF₂ is discussed in terms of sample thickness.     

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

Protein interaction networks: centrality,modularity, dynamics,and applications

In the post-genomic era, proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies. Especially the rapid accumulation of protein-protein interactions (PPIs) provides a foundation for constructing protein interaction networks (PINs), which can furnish a new perspective for understanding cellular organizations, processes, and functions at network level. In this paper, we present a comprehensive survey on three main characteristics of PINs: centrality, modularity, and dynamics. 1) Different centrality measures, which are used to calculate the importance of proteins, are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information; 2) Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced; 3) The dynamics of proteins, PPIs and sub-networks are discussed, respectively. Finally, the main applications of PINs in the complex diseases are reviewed, and the challenges and future research directions are also discussed.