ZAlSi12Cu2Mg1微弧氧化陶瓷膜层形成过程研究

通过控制微弧氧化时间,研究了ZAlSi12Cu2Mg1在不同氧化时间下膜层的生长过程,分析了氧化过程中正向电流密度、膜层厚度的变化.结果表明微弧氧化阶段又分为氧化过渡阶段和氧化烧结阶段.氧化过渡阶段陶瓷层以向外生长为主;生成Al2O3非晶氧化物;氧化烧结阶段膜层以向内生长为主,此阶段膜层生长速率最快,生成Al2O3晶体和莫来石.氧化烧结阶段对陶瓷膜层的形成起重要作用,对氧化烧结阶段的控制可直接影响膜层的厚度以及膜层中Al2O3晶体含量.     

组合有理二次Bezier曲线及其在数控弯管加工仿真中的应用

根据推导所得出的有理二次Bezier曲线表示圆弧曲线的表达式，给出了描述一类由直线和圆弧曲线构成的组合曲线分段参数方程，采用该方法能够自动保证圆弧曲线与直线达到G^1连续，并将其应用于数控弯管加工仿真中导管的动态成形，使用结果表明，该方法能够显著提高数控弯管加工仿真系统的运行效率。     

酸浸+等离子体刻蚀YG6微米-纳米金刚石复合涂层工具研究

金刚石涂层工具一直是金刚石膜工具应用研究的主流.制约其产业化的主要因素是涂层的附着力低和微晶金刚石涂层工具的加工精度差.通过对衬底的有效预处理和CVD沉积过程控制的研究,开发在硬质合金基体上沉积高结合强度、低粗糙度的金刚石涂层新技术,对于实现CVD金刚石涂层刀具高效、高精度切削加工具有重要意义.对旨在提高金刚石涂层附着力的预处理技术,本文探索了将酸蚀脱钴+等离子体刻蚀处理衬底法.利用优化的沉积工艺,在酸浸+等离子刻蚀处理的YG6刀片上沉积的两层金刚石复合膜表面粗糙度为0.13μm,附着力压痕测试临界载荷大于1500N.金刚石涂层工具的切削加工性能明显高于无涂层硬质合金工具.在加工ZAlSi12合金时,单层和两层金刚石涂层车刀片的切削寿命分别是无涂层车刀片切削寿命的21倍和28倍.     

反应熔渗法制备Mo(Si,Al)2-SiC复合材料的研究

通过尝试对MoSi2+C坯体中熔融渗Al制备了Mo(Si,Al)2-SiC复合材料,并对其组织及性能进行了研究.反应熔渗Al法制备Mo(Si,Al)2-SiC复合材料中,反应生成物主要为Mo(Si1-x,Alx)2和SiC相,还有少量Mo5Si3C和Al相.随着x值的增加,Mo5Si3C相和Al相逐渐减少,并消失.其组织为片状Mo(Si,Al)2组织间隙中分布着针尖状SiC颗粒;从断口形貌看,SiC颗粒非常细小,团聚在大的Mo(Si,Al)2颗粒周围.根据断口形貌,部分形成的SiC为晶须状,当x=0.4时,形成的SiC多为晶须状,并且形成的晶须状的SiC和Mo(Si,Al)2连成片.反应熔渗Al法制备复合材料抗弯强度是随着x值先增加后降低,在设计值x=0.237时取得最高值.     

连续型Δ∑调制器设计综述

Δ∑调制器作为数字转换器中重要的类型,以其较高精度及对CMOS工艺演进的更强适应性得到广泛应用.阐述了Δ∑调制器中连续型的Δ∑调制器的主要设计要点,介绍了国内外在连续型Δ∑调制器上的研究现状,并对相应设计方法进行分类总结.分析了连续Δ∑调制器的工作原理、系统结构、电路误差及模型,对其发展趋势以及面临的挑战进行了总结.结果说明合理地利用新工艺及新型积分器可以有效提高连续型Δ∑调制器的带宽及能效.     

RGB-D Hand-Held Ob ject Recognition Based on Heterogeneous Feature Fusion

Ob ject recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying o...     

一种基于FPGA的实时NPR系统

在进行非真实感渲染(NPR)处理时需要大量的计算,这对高帧率、高分辨率的视频做实时的NPR渲染是一个难题。根据FPGA以并行运算为主的特点,对传统的NPR算法做了改进和简化,最终设计了一种基于FPGA的实时NPR系统。实验结果表明,该系统对高帧率、高分辨率的视频做实时NPR处理具有运算速度快、系统稳定等特点,同时还具有体积小、便于携带等优点。     

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Smart transportation has a significantly impact on city management and city planning, which has received extensive attentions from academic and industrial communities. Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, which is usually used for smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. This paper advances Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm (VSAFCEA) as a coverage-enhancing means in multimedia directional sensor networks. Firstly, a concept of virtual streams, based on traditional artificial fish-swarm algorithm, is proposed. Then, the traditional behaviors of fishes in artificial fish-swarm algorithm are modified and expanded with several new behaviors. Finally, the presented VSAFCEA is adopted for coverage-enhancing issue in the situation of directional sensor networks with rotational direction-adjustable model. With a sequence of steps of artificial fishes in virtual stream, the presented VSAFCEA can figure out the approximation to the highest area coverage rate. Based on comparison of these simulation results (results of presented VSAFCEA and that of other typical coverage-enhancing ways in directional sensor networks), the conclusion can be drawn that VSAFCEA could attain higher area coverage rate of directional sensor networks with fewer iterative computing times.

     

Corpus based part-of-speech tagging

In natural language processing, a crucial subsystem in a wide range of applications is a part-of-speech (POS) tagger, which labels (or classifies) unannotated words of natural language with POS labels corresponding to categories such as noun, verb or adjective. Mainstream approaches are generally corpus-based: a POS tagger learns from a corpus of pre-annotated data how to correctly tag unlabeled data. Presented here is a brief state-of-the-art account on POS tagging. POS tagging approaches make use of labeled corpus to train computational trained models. Several typical models of three kings of tagging are introduced in this article: rule-based tagging, statistical approaches and evolution algorithms. The advantages and the pitfalls of each typical tagging are discussed and analyzed. Some rule-based and stochastic methods have been successfully achieved accuracies of 93–96 %, while that of some evolution algorithms are about 96–97 %.