汽轮发电机组负荷波动的原因分析及解决措施

详细分析了汽轮发电机组在运行过程中出现电负荷波动和瞬间突降的原因，重点介绍了如何调节、维持汽轮发电机组的经济运行以及解决电负荷波动的措施，从而保证汽轮发电机组的稳定运行。     

超临界流体技术应用研究

超临界流体技术具有许多传统技术所没有的快速，高效，低能耗，污染少等优点，在材料制备，医药工业，生物工程，食物，环境，化工等许多方面展现了广阔的应用前景。综述了其在萃取，材料制备和作为反应介质等方面的应用。     

固相萃取技术在农药残留分析中的应用

固相萃取是一种新型的样品预处理技术，操作简便，节省有机溶剂，易于实现自动化操作，已广泛应用于环境保护、药物检测、食品检验检疫等领域。在农药残留分析中，固相萃取技术的应用还使得提取不同类别农药的选择性增强，样品回收率提高，尤其是结合HPLC—MS、GC-MS、CE-MS等仪器的联合鉴定，可实现自动化分析，能简单、快速、灵敏地提取定量许多物质，从而大大提高了分析效率和精密度，具有广阔的发展前景。     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

基于GPGPU的实体表面实时提取

为实时提取三维实体表面,提出一种基于GPGPU并行计算的实体表面实时提取方法。在分析深度剥离算法原理和GPU图形绘制管线的基础上,给出在GPU上利用深度剥离算法实现实时提取三维实体表面的算法;通过OpenGL的高级着色语言GLSL控制GPU的图形绘制管线实现了该算法,给出其伪代码。以龙、叶轮和刀具扫描体的模型为应用实例验证了该算法效果良好,特别是对于刀具扫描体表面的提取,可满足实时性要求。     

基于直方图的水墨画艺术风格研究

基于内容的照片或自然图像分析和检索已经得到广泛的研究,但多数都基于颜色特征,不能直接用于水墨画艺术作品的分析。对图像进行预处理,提出基于灰度直方图描述水墨画笔道力度分布信息及构图风格,并利用神经网络学习水墨画整体技法风格特征,依据此特征进行不同艺术家间的分类。实验结果表明,通过对整体风格特征的提取,直方图模型能够有效表征画家不同的艺术风格,实现了水墨画的自动分类。     

基于退化四元小波变换的纸币识别

针对如何提取纸币图像特征和提高识别率的问题,综合利用退化四元小波变换具有的相位特性,提出一种基于退化四元小波变换的纸币识别方法.该方法首先对采集的纸币图像进行倾斜校正和边缘检测,然后运用退化四元小波对纸币图像进行分解操作,并对分解系数进行统计分析,将每个分解子带系数的能量和标准差作为该纸币图像的特征向量,最后将支持向量机作为分类器对纸币图像进行识别.本文方法在资源约束的嵌入式清分系统上实现,实验结果表明采用本文提出的算法突破了传统纸币识别系统识别率很难再提高的瓶颈,同时能够满足清分系统的实时性要求.     

基于数据分布的聚类联邦学习

联邦学习(federated learning)可以解决分布式机器学习中基于隐私保护的数据碎片化和数据隔离问题。在联邦学习系统中,各参与者节点合作训练模型,利用本地数据训练局部模型,并将训练好的局部模型上传到服务器节点进行聚合。在真实的应用环境中,各节点之间的数据分布往往具有很大差异,导致联邦学习模型精确度较低。为了解决非独立同分布数据对模型精确度的影响,利用不同节点之间数据分布的相似性,提出了一个聚类联邦学习框架。在Synthetic、CIFAR-10和FEMNIST标准数据集上进行了广泛实验。与其他联邦学习方法相比,基于数据分布的聚类联邦学习对模型的准确率有较大提升,且所需的计算量也更少。     

An efficient approach for face recognition based on common eigenvalues

In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination.