基于Ansys Workbench的齿轮副有限元分析

摆线针轮减速器作为重要的机械传动部件具有体积小、重量轻、传动效率高、传动比大、承载能力强和使用寿命长等优点,广泛应用于现代工业诸多领域中.本文针对变桨减速器技术要求,通过典型摆线针轮减速器的传动原理及结构特点分析,建立齿轮、摆线轮等关键零部件的计算模型,并进行静力学有限元分析,其中对代表性的齿轮副进行了有限元模型的建立...     

一种基于改进高斯过程隐变量模型的多角度人脸识别算法

针对传统谱算法在人脸识别中的局限,该文提出一种基于改进高斯过程隐变量模型(GP-LVM)的多角度人脸识别算法。首先,通过高斯过程(GP)对人脸流形建立概率模型,得到高斯过程隐变量模型(GP-LVM);其次,分析GP-LVM得到共有信息(shared information)和独有信息(private information),利用概率最大化与拉格朗日乘子法得到参照矩阵和参照值;最后,实现多角度人脸识别。选取Yale, JAFFE, FERET, CMU-PIE 4类数据集进行对比实验,实验结果表明：该文提出的算法可以有效地识别多角度人脸,针对无角度人脸识别也具有良好的效果。     

Face Feature Selection and Recognition Using Separability Criterion and Binary Particle Swarm Optimization Algorithm

Discrete cosine transform （DCT） is an ef- fective method to extract proper features for face recog- nition. Discrete cosine transform can only map the re- source data to another data field instead of compress data. How to select the DCT coefficients that are most effec- tive for classification is an important problem. This paper proposes a novel method to search the best discriminant combination of DCT coefficients. A feature selection al- gorithm according to the separability criterion is used to preselect the DCT coefficients, and then follows a search algorithm based on binary particle swarm optimization and support vector machine to find an optimal combination of the DCT coefficient. The performance of the algorithm is assessed by computing the recognition rate and the num- ber of selected features on ORL database and Cropped Yale database.     

不同来源150BS光亮油的抗氧化性能研究

采用旋转氧弹试验、烘箱氧化试验和极压润滑油氧化性能测定法研究不同来源150BS光亮油的抗氧化性能。结果表明：采用加氢工艺制取的进口150BS-1和国产150BS-3基础油对抗氧剂的感受性比溶剂精制基础油150BS-2好;饱和烃中多环环烷烃的存在不利于提高加氢150BS基础油的氧化安定性; 150BS基础油的氧化安定性越好,用其调合的工业齿轮油抗氧化性能越优异。     

Joint face and head tracking inside multi-camera smart rooms

The paper introduces a novel detection and tracking system that provides both frame-view and world-coordinate human location information, based on video from multiple synchronized and calibrated cameras with overlapping fields of view. The system is developed and evaluated for the specific scenario of a seminar lecturer presenting in front of an audience inside a “smart room”, its aim being to track the lecturer’s head centroid in the three-dimensional (3D) space and also yield two-dimensional (2D) face information in the available camera views. The proposed approach is primarily based on a statistical appearance model of human faces by means of well-known AdaBoost-like face detectors, extended to address the head pose variation observed in the smart room scenario of interest. The appearance module is complemented by two novel components and assisted by a simple tracking drift detection mechanism. The first component of interest is the initialization module, which employs a spatio-temporal dynamic programming approach with appropriate penalty functions to obtain optimal 3D location hypotheses. The second is an adaptive subspace learning based 2D tracking scheme with a novel forgetting mechanism, introduced to reduce tracking drift and increase robustness. System performance is benchmarked on an extensive database of realistic human interaction in the lecture smart room scenario, collected as part of the European integrated project “CHIL”. The system consistently achieves excellent tracking precision, with a 3D mean tracking error of less than 16 cm, and is demonstrated to outperform four alternative tracking schemes. Furthermore, the proposed system performs relatively well in detecting frontal and near-frontal faces in the available frame views. This work was performed while Zhenqiu Zhang was on a summer internship with the Human Language Technology Department at the IBM T.J. Watson Research Center.     

DWT based HMM for face recognition

A novel Discrete Wavelet Transform (DWT) based Hidden Markov Module (HMM) for face recognition is presented in this letter. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence ex- traction. Extensive experiments are conducted on two public databases and the results show that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available.     

海洋工程平台齿轮箱齿轮本体温度场仿真

为齿轮强度提高、轮齿修形及优化设计提供理论支持,研究不同转速和扭矩工况下齿轮本体温度场的变化。以海洋工程升降平台齿轮箱单元高速传动轴齿轮副为研究对象,基于热学理论计算齿轮啮合面的热流密度和不同表面的对流换热系数,利用大型有限元软件Ansys将计算出来的热流密度和对流换热系数作为边界条件加载到Ansys中,对齿轮进行稳态热分析,并分析不同转速和不同扭矩齿轮在正常工况下的稳态温度场。仿真结果表明：在正常稳态工况下,主动轮与从动轮的稳态温度场分布情况相似,最高温度分布在齿轮啮合面中间区域,往周围温度逐渐降低,但主动轮的最高温度高于从动轮;主动轮和从动轮的最高温度随着转速或扭矩的逐渐增大而升高,且对应的最低温度也升高;但主动轮和从动轮单齿本体温度场分布趋势不会随着齿轮转速或扭矩的改变而改变;从温度变化来看,在不同的扭矩和转速下,齿面温度变化的斜率均较为平稳,没有出现突变情况,温度集中于齿面,并且向齿轮轴心扩散,但在距离齿根圆2.5倍模数的区域,齿轮温度梯度变化的影响较小。     

Robust face detection and analysis

This paper presents a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. The two maps and local similarity map are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. The experimental results demonstrate its validity and robustness to identify faces under certain variations.     

基于多特征Mean Shift的人脸跟踪算法

该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift跟踪算法,提出了基于多特征的Mean Shift人脸跟踪算法以解决Mean shift跟踪算法的鲁棒性问题。通过对LTP纹理特征的分析、研究,提出了一个LTP关键纹理模型,既增强了目标的关键纹理信息,又简化了LTP纹理模型。在此基础上,提出一种基于LTP关键纹理特征和肤色特征的Mean Shift人脸跟踪算法,有效地解决了Mean Shift算法的鲁棒性问题。为进一步提高对快速运动目标的跟踪速度和跟踪性能,该文引入了卡尔曼滤波器对目标进行预测。实验结果表明,该文的算法在目标定位的准确性和跟踪性能上比Mean Shift算法均有明显的提高。     

用于大库人脸识别的脸型分类研究

提出了一种基于”分集”的人脸识别方法,该方法在人脸识别前加入脸型预分类环节,将一个大规模的人脸数据库分解为依脸型分类的若干个子库。这样做一方面可通过分集降低后续识别处理的数据量,提高人脸识别的速度,另一方面可利用脸型特征对候选人脸集合进行粗筛选,降低系统的错误接受率。为了实现脸型分类,进一步提出了一种基于人体测量学的分类方法,即首先借助AAM技术提取脸部特征点,然后在此基础上计算面型指数,并由此实现对脸型的分类。对较大规模的人脸数据库所进行的实验结果表明,所提出的方法可有效提高人脸识别系统的识别率和识别速度。