摆动滚子从动件盘形凸轮的CAD动画设计与加工

在AutoCAD基础上利用速度瞬心进行摆动滚子从动件凸轮机构轮廓曲线的CAD动画设计,同时可以生成刀具中心的加工轨迹、计算出压力角,能够实现CAD/CAM一体化,是一种简捷实用的新方法,可以直接应用于生产实际和教学.     

弧面分度凸轮理论轮廓面的计算

提高凸轮的加工质量是目前弧面分度凸轮研究的一个重点。但无论是其静态几何量测量,还是加工误差分析,首先要解决的是凸轮理论轮廓面的计算。本文在对弧面分度凸轮轮廓面详尽分析的基础上,论述了弧面分度凸轮理论轮廓面的计算以及ＶｉｓｕａｌＣ＋＋５．０开发的面向凸轮加工精度分析的理论轮廓面计算软件包。该软件包可应用于凸轮ＣＡＤ、ＣＡＭ、ＣＡＱ等子系统。     

基于有限元的大型铝型材拉伸矫直设备设计方法研究

介绍了大型铝型材拉伸矫直设备的研究现状,分析了现有拉伸矫直设备的缺点,提出并设计了新型的大型铝型材拉伸矫直设备。采用有限元数值模拟方法,对大型铝型材拉伸矫直机的关键零部件受力变形状况进行了数值模拟,得到了设备关键零部件如拉伸头、移动横梁等的应力和应变分布情况,并对结构进行了优化。此种方法降低了设备制造成本,提高了设备的精度和刚度。生产实践表明:采用此方法设计的拉伸矫直机运行平稳可靠。     

Quantification of secondary structure prediction improvement using multiple alignments

The use of multiple sequence alignments for secondary structurepredictions is analysed. Seven different protein families, containingonly sequences of known structure, were considered to providea range of alignment and prediction conditions. Using alignmentsobtained by spatial superposition of main chain atoms in knowntertiary protein structures allowed a mean of 8% in secondarystructure prediction accuracy, when compared to those obtainedfrom the individual sequences. Substitution of these alignmentsby those determined directly from an automated sequence alignmentalgorithm showed variations in the prediction accuracy whichcorrelated with the quality of the multiple alignments and distanceof the primary sequence. Secondary structure predictions canbe reliably improved using alignments from an automatic alignmentprocedure with a mean increase of 6.87percnt;, giving an overallprediction accuracy of 68.5%, if there is a minimum of 25% sequenceidentity between all sequences in a family.     

异型材定型模优化设计

描述了PVC型材挤出真空定型模冷却排布的一种优化设计方法。这种方法使用下述的最小化标准可以同时确定冷却水道的直径和位置:型材平均温度、型材温度随时间积分的变化、不同型材表面的温度,基于Fletcher Reeves优化运算法则。     

双通道喷嘴渣油气化过程(Ⅰ)冷模实验

基于对3套8.53MPa双通道喷嘴渣油气化装置普遍存在的工程现象的思考,组织了模型气化炉冷态试验。测试了3种模型在6种射流速度下的停留时间分布,7种模型在2种射流速度下的速度分布。给出了停留时间分布密度函数、无因次半速度半径轴向增长、径向射流最大速度轴向衰减和径向速度分布计算公式。     

Trickle Flow Multiplicity: The Influence of the Prewetting Procedure on Flow Hysteresis

The existence of multiple hydrodynamic studies (MHS) in trickle flow is a well-known phenomenon. It is also known that different prewetting procedures result in major differences in MHS when the hydrodynamic variables pressure drop, liquid holdup and gas–liquid mass transfer are considered. Given a certain prewetting procedure one still has the option to perform flow hysteresis cycles to achieve an even wider variety of MHS. Although numerous studies have been performed on trickle flow hysteresis, none have attempted to decouple the hysteresis behaviour from the prewetting procedure followed. Accordingly there are numerous hysteresis possibilities that have not been investigated. In this work a single liquid and gas cycle were performed for four distinct prewetting procedures described here as a dry bed, a Levec type prewetted bed, Kan prewetted bed (achieved by increasing either the liquid or the gas flow rate until the pulsing flow regime is reached) and a Super prewetted bed. Pressure drop, liquid holdup and gas–liquid mass transfer are the hydrodynamic parameters studied to quantify the various MHS. It is shown that the shape and extent of the hysteresis cycle are strongly dependant on the prewetting procedure. In terms of flow structure, similar hysteresis trends on the Kan Liquid and Super prewetting modes indicate that these modes are hydrodynamically similar. The additional measurement of the hysteresis behaviour of gas–liquid mass transfer proofs that neither holdup nor pressure drop can be used as an indicator of the distribution uniformity.     

粉煤灰在油田开发中的应用

在对某热电厂粉煤灰的粒度、化学成分、水化性等性能分析的基础上，介绍了一种以粉煤灰为主剂的水井用调剖剂。经室内实验分析和现场应用的效果分析，证明FP-2型粉煤灰类调剀剂的堵水调剖效果好，为粉煤灰的综合利用找出了一条新的应用途径。     

域自适应学习研究进展

传统的机器学习假设测试样本和训练样本来自同一概率分布. 但当前很多学习场景下训练样本和测试样本可能来自不同的概率分布. 域自 适应学习能够有效地解决训练样本和测试样本概率分布不一致的学习问题,作为 机器学习新出现的研究领域在近几年受到了广泛的关注. 鉴于域自适应学习技术 的重要性,综述了域自适应学习的研究进展. 首先概述了域自适应学习的基本问 题,并总结了近几年出现的重要的域自适应学习方法. 接着介绍了近几年提出的 较为经典的域自适应学习理论和当下域自适应学习的热门研究方向,包括样例加 权域自适应学习、特征表示域自适应学习、参数和特征分解域自适应学习和多 源域自适应学习. 然后对域自适应学习进行了相关的理论分析,讨论了高效的度 量判据,并给出了相应的误差界. 接着对当前域自适应学习在算法、模型结构和 实际应用这三个方面的研究新进展进行了综述. 最后分别探讨了域自适应学习在 特征变换和假设、训练优化、模型和数据表示、NLP 研究中存在的问题这四个方面 的有待进一步解决的问题.