基于DSP的高精度激光干涉仪的研制

当前激光干涉仪的信号处理系统主要采用单片机技术和4细分辨向电路,存在硬件电路复杂、易受外界干扰、难以实现复杂算法等问题.因此,提出了将DSP技术引入到激光干涉仪的信号处理系统中,设计了一种用于处理干涉条纹信号的专用电路.具体研究了改进的8细分算法,并给出了系统的主要程序.实验结果表明,基于DSP的激光干涉仪具有电路简单、功能强大等优点,进一步提高了激光干涉仪的测量精度和分辨力.     

有监督的无参数核局部保持投影及人脸识别

针对发掘人脸图像中的高维非线性结构,将加核及构造无参数近邻图两种思想同时引入到局部保持投影算法中,在有监督的模式下,提出了一种新的有监督的无参数核局部保持投影(Parameter-less Supervised Kernel Locality Preserving Projection,PSKLPP)算法并给出了其推导过程。该算法通过将欧氏距离改为对离群数据更为鲁棒的余弦距离,构造无参数近邻图,利用核方法提取人脸图像中的非线性信息,并将其投影在一个高维非线性空间,运用局部保持投影算法得到一线性映射,有效避免了在计算相似矩阵过程中面临的复杂参数选择问题。在ORL和Yale人脸库上的仿真实验验证了所提算法的有效性。     

基于改进的自适应局部保持投影算法的人脸识别

局部保持投影(LPP)通过构造近邻图来保持样本的局部结构,在构造近邻图的过程中,LPP会遇到两个参数K和σ的选择问题。近邻图的构建对算法的识别效果起着重要的作用,因而这两个参数的选择会在很大程度上影响LPP的识别率。为了避免参数的选择对识别率造成影响,提出了一种基于改进的自适应局部保持投影的人脸识别算法。首先,构造无参数的近邻图,其能够自适应地选取样本的近邻点并确定其相应的边权。其次,由于在计算过程中出现了矩阵维数过高的问题,因此采用QR分解进行降维处理。最后,利用共轭正交化使得投影轴具有统计不相关性,以降低特征矢量间的统计相关性,提高识别率。在ORL人脸库和YALE人脸库上进行了实验,结果表明改进的算法在识别率方面整体上好于LPP算法、DLPP算法、LMMC算法。     

Visualizing Waypoints‐Constrained Origin‐Destination Patterns for Massive Transportation Data

Origin‐destination (OD) pattern is a highly useful means for transportation research since it summarizes urban dynamics and human mobility. However, existing visual analytics are insufficient for certain OD analytical tasks needed in transport research. For example, transport researchers are interested in path‐related movements across congested roads, besides global patterns over the entire domain. Driven by this need, we propose waypoints‐constrained OD visual analytics, a new approach for exploring path‐related OD patterns in an urban transportation network. First, we use hashing‐based query to support interactive filtering of trajectories through user‐specified waypoints. Second, we elaborate a set of design principles and rules, and derive a novel unified visual representation called the waypoints‐constrained OD view by carefully considering the OD flow presentation, the temporal variation, spatial layout and user interaction. Finally, we demonstrate the effectiveness of our interface with two case studies and expert interviews with five transportation experts.     

Efficient and robust deep learning with Correntropy-induced loss function

Deep learning systems aim at using hierarchical models to learning high-level features from low-level features. The progress in deep learning is great in recent years. The robustness of the learning systems with deep architectures is however rarely studied and needs further investigation. In particular, the mean square error (MSE), a commonly used optimization cost function in deep learning, is rather sensitive to outliers (or impulsive noises). Robust methods are needed to improve the learning performance and immunize the harmful influences caused by outliers which are pervasive in real-world data. In this paper, we propose an efficient and robust deep learning model based on stacked auto-encoders and Correntropy-induced loss function (CLF), called CLF-based stacked auto-encoders (CSAE). CLF as a nonlinear measure of similarity is robust to outliers and can approximate different norms (from \(l_0\) to \(l_2\)) of data. Essentially, CLF is an MSE in reproducing kernel Hilbert space. Different from conventional stacked auto-encoders, which use, in general, the MSE as the reconstruction loss and KL divergence as the sparsity penalty term, the reconstruction loss and sparsity penalty term in CSAE are both built with CLF. The fine-tuning procedure in CSAE is also based on CLF, which can further enhance the learning performance. The excellent and robust performance of the proposed model is confirmed by simulation experiments on MNIST benchmark dataset.     

虚拟LCD显示模块的设计

通过在BTF020单片机的RAM中建立缓冲区数组并对虚拟LCD的显示数据进行存储,然后通过单片机的串口将该数据传送给PC机,再用VB6.0语言对这些数据进行处理并构建虚拟LCD的界面,使其能够正确显示字符或者图形。实验结果表明,所建的虚拟LCD显示模块具有开发简便、快速、通用性强的特点。     

B/S模式应用系统的一种新型结构及其实现

由于目前Ｂ／Ｓ模式应用系统的缺陷,提出了一种新型的系统结构并从原理上阐述了它的合理性,最后从模块间的通信。处理线程和数据库操作程间的同步和互斥以及系统等方面讲述系统的实现。     

APACS353在压缩机控制系统中的应用

介绍ＡＰＡＣＳ３５３智能控制器及其在离心压缩机控制系统中的应用,具体介绍ＡＰＡＣＳ３５３智能控制器特点,压缩机控制系统及系统应用实例等。