线性八元树表示的三维客体群的连通分量标号和计数

本文提出二种算法分别标号和计数线性八元树表示的三维客体群的连通分量.这些操作典型地需要检查线性八元树中诸八分体在三个主方向上的面邻接对.根据这些邻接对,可以建造在计算机中用关联矩阵表示的邻接图.连通分量标号和计数是在关联矩阵上分别执行相应的操作而完成的.连通分量标号的执行时间是O(n+m·logm),而连通分量计数需要O(n)时间,其中m和n分别是线性八元树中八分体和邻接对的数目.     

从动齿轮内孔超差补救方法

通过两种工艺试验证明,采用保护气氛加热入缓冷筒冷却进行收缩齿轮内孔的方法,较好地解决了从动齿轮因孔大超差报废问题,使90%的齿轮达到了产品技术要求.     

邻域自适应的三维点云滤波算法

针对现有的点云滤波算法存在的精度丢失和收缩的不足,提出邻域自适应选择的算法,有效地改善了点云滤波中丢失精度的问题.算法首先针对原始点和均值点滤波出现的收缩问题,提出混合增采样策略.其次采用邻域自适应选择保持特征部分的滤波精度.最后定义每个采样点以对应的似然函数,并按照其梯度方向进行迭代,通过最大似然估计得到最优滤波结果.实验部分表明,本文三维点云滤波算法对点云滤波精度的保持具有更好的效果.适用于工业生产与检测领域的三维扫描.     

快固化、无收缩特种MMA-R17树脂在设备基础灌浆,锚固及修补中应用

本文介绍国外聚合物砂浆的性质和应用、施工配比及物理机械性能，供设计人员参考。     

基于加速残差网络的图像超分辨率重建方法

针对深层网络架构的图像超分辨率重建任务中存在网络参数多、计算复杂度高等问题,提出了一种基于加速残差网络的图像超分辨率重建方法。首先,构建一个残差网络对低分辨率图像与高分辨率图像之间的高频残差信息进行重建,以减少冗余信息的深层网络传输过程,提高重建效率;然后,通过特征收缩层对提取的低分辨率特征图进行降维,从而以较少的网络参数实现快速映射;之后,对高分辨率特征图通过特征扩展层进行升维,从而以较丰富的信息重建高频残差信息;最后,将残差与低分辨率图像求和得到重建的高分辨率图像。实验结果表明,该方法取得的峰值信噪比（PSNR）及结构相似性（SSIM）均值结果较基于卷积神经网络的图像超分辨率（SRCNN）取得的结果分别提升了0.57 dB和0.013 3,较基于中间层监督卷积神经网络的图像超分辨率重建（ISCNN）取得的结果分别提升了0.45 dB和0.006 7;在重建速度方面,以数据集Urban100为例,较现有方法提高了1.5~42倍。此外,将该方法应用于运动模糊图像的超分辨率重建时,获得了优于超深卷积神经网络的图像超分辨率（VDSR）的性能。所提方法以较少的网络参数快速获得较好的重建质量,并且为图像超分辨率重建提供了新的思路。     

摩托车右曲轴箱盖渗漏分析及对策

根据铸件的结构和压铸工艺参数特点,结合其浇注系统,解剖和分析了125型摩托车右曲轴箱盖易产生渗漏的部位,发现渗漏的主要原因是凝固补缩不够,造成了最后凝固部位出现大量的缩松缩孔。为此,在原浇注系统上增加了分支浇道,优化了压铸工艺、精炼工艺参数,保证凝固终了时易渗漏部位有足够的补缩铝合金液,生产出了合乎要求的右曲轴箱盖,产品合格率达92%以上。     

Increasing silver leaching rate from leaching-resistant zinc residues by thiourea leaching method with pressurized preoxidation process

1　ＩＮＴＲＯＤＵＣＴＩＯＮＡｔｐｒｅｓｅｎｔ ,ｚｉｎｃｉｓｐｒｏｄｕｃｅｄｂｙｔｈｅｈｙｄｒｏｍｅｔａｌｌｕｒｇｙ ｅｌｅｃｔｒｏａｎａｌｙｓｉｓｍｅｔｈｏｄｉｎｍｏｓｔｚｉｎｃｓｍｅｌｔｉｎｇｐｌａｎｔｓ .Ｅａｃｈｙｅａｒ,ｍｉｌｌｉｏｎｓｔｏｎｓｏｆｈｙｄｒｏｍｅｔａｌｌｕｒｇｉｃａｌｚｉｎｃｒｅｓｉｄｕｅｓａｒｅｄｉｓｃｈａｒｇｅｄｆｒｏｍＣｈｉｎｅｓｅｓｍｅｌｔｉｎｇｐｌａｎｔｓ ,ｗｈｉｃｈａｒｅｈｉｇｈｌｙａｃｉｄｉｃａｎｄｃｏｎｓｉｓｔｏｆｇｒｅａｔｄｅａｌｏｆｓｉｌｖｅｒａｎｄｏｔ…     

低码率图像的快速SV拟合及其最优码流结构的描述方法

给出了一个在低码率图像描述中应用最优支持向量(SV)描述的编码算法,提出一个训练样本集缩减策略,在支持向量机(SVM)模型中用于快速拟合重要的交流(AC)系数,它能够在基本保持原有精度的前提下,有效地提高SVM的回归速度,减少输出参数;提出了直流(DC)系数的按位差分预测编码方法和压缩性能更好的码流产生方法。实验结果表明,该方法不仅缩减了编码时间,而且在相同码率下能获得更好的图像质量。以往基于SV的编码未提及码流结构的描述方法,而快速SV拟合和数据组织方法克服了以往算法可行性差的缺点。     

Histogram-Based Estimation of Distribution Algorithm: A Competent Method for Continuous Optimization

Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. This paper utilizes histogram probabilistic model to describe the distribution of population and to generate promising solutions. The advantage of histogram model, its intrinsic multimodality, makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram model more efficiently explore and exploit the search space, several strategies are brought into the algorithms： the surrounding effect reduces the population size in estimating the model with a certain number of the bins and the shrinking strategy guarantees the accuracy of optimal solutions. Furthermore, this paper shows that histogram-based EDA （Estimation of distribution algorithm） can give comparable or even much better performance than those predominant EDAs based on Gaussian models.     

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian’s is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian’s behavior detection. Kalman filter has been used to detect pedestrian’s using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.