纳米金刚石的应用

纳米金刚石不仅具有金刚石所固有优异特性，而且还具有纳米材料所拥有的奇异性能。正是这些综合技术特性，使得它在传统的和新的技术领域中得到应用，并初见成效，本文从超精抛光、润滑、复合镀、磁性录音系统和医疗等方面的应用做了扼要的叙述，指出，纳米金刚石颗粒具有球形和准球形的特点，使得摩擦表面形成滚珠轴承效应，而表现出良好的润滑性，从而避免于摩现象的发生，而对一些主要因接触疲劳或高温磨损而失效的工件，纳米金刚石金属的复合涂层，则具有明显的优越性。     

硬质合金磨削温度的实验研究

采用半人工和人工热电偶法，对硬质合金YG6和YT30平面磨削的磨削温度进行了实验研究。并对磨削参数与磨削温度的关系，进行了理论分析及实验验证。结果表明：磨削深度和工作台速度增加，磨削温度升高，但工作台速度的改变，对磨削温度的影响较小．     

耐候钢连铸工艺的改进

分析耐候钢连铸出现的问题,认为凝固时应力过大和磷铜元素偏析造成晶界脆化是出现铸坯纵裂的主要原因;结晶器中喂稀土丝恶化保护渣性能,影响结晶器与坯壳间的润滑与传热,并使热流不均匀是连铸漏钢的主因。采用钙处理代喂稀土丝工艺,以及减缓连铸一、二次冷却强度,改进保护渣后,连铸缺陷获得极大改善。     

厚实工作台的铸造工艺

对厚大工作台铸件表面结疤和石墨孔洞缺陷的原因进行了分析，并采取相应措施：采用过滤网挡渣，确保造型、刷涂料、浇注之间有充分的时间间隔，控制涂料的配制和涂刷，控制铁液成分，采用冷铁等，使铸件达到质量要求。     

高钒高速钢碳化钒形态的数值化分析

针对碳化钒的不同形态,设计出定量分析软件,并利用形状因子K,实现了高钒高速钢不同碳化物形态数值化处理,当K介于0.785～1.000之间碳化钒形态为团球状;K≤0.223,碳化钒呈开花状.高钒高速钢当碳含量达到4.2%时,K为0.86和当量直径D为0.78μm,碳化物呈团球状均匀分布.     

基于Word文档的数据隐藏方法

在分析当前数据隐藏与恢复相关技术研究基础上,参照微软官方最新公布的复合文档存储规范,从利用载体Word文档现有空间中未真正使用部分和适度扩展新空间两条思路出发,设计一种利用Word文档作载体隐藏秘密信息的解决方案,包括在文字、各种嵌入式对象及其相应控制结构数据中隐藏信息。实践表明,隐藏数据后的载体文档可以被正常拷贝和使用电子邮件传送,根据需要隐藏的信息随机选择使用该方案,被隐藏信息更不容易被侦测出来,被二次编辑后被隐藏信息一般不会被破坏,鲁棒性不错,具有一定的实用价值。     

利用曲线拟合对触觉数据的压缩

为减轻离线触觉数据压缩带来的失真变形,提出利用曲线拟合的压缩方法.在假定触觉数据所对应原触觉信号能用分段二次参数曲线表示的前提下,通过反向采样过程来构造触觉数据拟合曲线,该曲线由分段二次曲线组成,在连接处达到切线连续,且每段二次曲线对原触觉信号具有二次多项式逼近精度.为避免保存拟合曲线的所有系数以进一步提高数据减少率,对拟合曲线系数进行了迭代求解,从而只需编码保存部分系数即可解码.实验结果表明,该方法在保持高数据减少率的前提下减轻了压缩失真变形.     

Strong Convergence Analysis of Batch Gradient-Based Learning Algorithm for Training Pi-Sigma Network Based on TSK Fuzzy Models

By combining of the benefits of high-order network and TSK (Tagaki-Sugeno-Kang) inference system, Pi-Sigma network is capable to dispose with the nonlinear problems much more effectively, which means it has a compacter construction, and quicker computational speed. The aim of this paper is to present a gradient-based learning method for Pi-Sigma network to train TSK fuzzy inference system. Moreover, some strong convergence results are established based on the weak convergence outcomes, which indicates that the sequence of weighted fuzzy parameters gets to a fixed point. Simulation results show the modified learning algorithm is effective to support the theoretical results.     

Recover Fault Services via Complex Service-to-Node Mappings in Wireless Sensor Networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node’s perspective i.e., migrating the service of faulty node to it’s nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don’t fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level.