黎曼几何在计算机视觉中的应用研究进展

在计算机视觉领域，许多任务相关数据具有非欧结构，近年来基于黎曼几何的数据表征与应用研究受到了越来越多的关注。如何充分利用数据的几何结构，来提高目标识别、目标跟踪及目标检测算法的性能，是其中的一些研究热点。本文主要从三个方面介绍黎曼流形在计算机视觉中的应用研究进展。首先，从数学基本概念出发，阐述黎曼流形与图像的关系以及视觉应用的可行性，并介绍计算机视觉中具有重要应用的几种黎曼流形。其次，对黎曼流形在计算机视觉中若干常见应用进行了概述，重点介绍了与深度学习相结合的相关进展。最后，对引入黎曼流形的机器学习方法的未来发展进行了分析和讨论。     

Weighted marginal fisher analysis with spatially smooth for aircraft recognition

Due to limitations to extract invariant features for recognition when the aircraft presents various poses and lacks enough samples for training, a novel algorithm called Weighted Marginal Fisher Analysis with Spatially Smooth （WMFA-SS） for extracting invariant features in aircraft rec- ognition is proposed. According to the Graph Embedding （GE） framework, Heat Kernel function is firstly introduced to characterize the interclass separability when choosing the weights of penalty graph. Furthermore, Laplacian penalty is applied to constraining the coefficients to be spatially smooth in this algorithm. Laplacian penalty is able to incorporate the prior information that neigh- boring pixels are correlated. Besides, using a Laplacian penalty can also avoid the singularity of Laplacian matrix of intrinsic graph. Once compact representations of the images are obtained, it can be considered as invariant features and then be performed in classification to recognize different patterns of aircraft. Real aircraft recognition experiments show the superiority of our proposed WMFA-SS in comparison to other GE algorithms and the current aircraft recognition algorithm; the accuracy rate of our proposed method is 90.00% for dataset BH-AIR1.0 and 99.25% for dataset BH-AIR2.0.     

磁悬浮柔性转子系统变学习速率单神经元PID控制

磁悬浮轴承柔性转子系统的转速范围较大,固定的控制参数很难同时保证系统所需的低频和高频特性。本文在一般单神经元PID控制的基础上,将转速信号引入磁悬浮轴承数字控制器,在不同转速段选择不同的学习速率以调节控制参数得到合适的磁悬浮轴承的刚度和阻尼。试验结果表明,变学习速率单神经元自适应PID控制能够明显减小转子在各阶临界转速时的振幅,有助于磁悬浮轴承柔性转子系统的安全稳定运行。     

高职计算机专业学生自主学习能力培养策略

计算机专业课程具有实践性与操作性非常强的特点,采用传统的先教后学的教学方式学习效率低,不能适应时代的发展。自主学习方式可以更好的引导计算机专业学生从做中学,从学中做,提高学习效率,培养学生终身学习能力。在探讨自主学习理论的基础上,分析了自主学习的意义及高职计算机专业学生自主学习现状,并提出了适合高职计算机专业学生的自主...     

Trajectory Tracking Control for a GMM Actuator Based on a Heuristic ILC Method

A heuristic iterative learning control （ILC） method is presented and applied to the trajectory tracking control of a giant magnetostrictive material （GMM） actuator. A GMM actuator is used as experimental equipment for micro-displacement trajectory tracking control. The advantage of the presented approach lies in quitting the model of the GMM actuator. The experimental results attest to the high efficiency of the presented method for the micro-displacement trajectory tracking control.     

一种改进的适应性超媒体学习单元模型

在netUniversité这一平台上,结合IMS LD标准建立学习单元模型,并以此标准根据新的教学需求对已有模型进行改进.将HBDI 全脑模型结合入学习内容管理工具,利用设计时工具Reload Editor和运行时工具CopperCore Player构造学习单元模型.     

大学英语课堂教学有效任务的设计

本文探讨了目前在英语教学界影响较大的任务型教学法中任务的设计与运用,介绍了在课堂教学中的一些有效任务及其优越性。     

基于DHNN人工神经网络的指纹识别技术

给出一种基于DHNN人工神经网络的人的指纹识别技术 ,讨论了DHNN实现指纹识别的基本原理 ,在一定领域内不失为一种有效的识别手段。     

强夯法加固湿陷性黄土地基实例分析

通过陕西渭南开发区某建筑地基的加固实例，对使用强夯法消除黄土湿陷性，提高黄土地基强度的施工技术与质检方法进行了分析研究，得出了在湿陷性黄土地区含水量适中，地下水位较深时采用强夯法效果显著，对实际施工具有一定的指导意义。     

A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise

In data-driven fault diagnosis for turbo-generator sets, the fault samples are usually expensive to obtain, and inevitably with noise, which will both lead to an unsatisfying identification performance of diagnosis models. To address these issues, this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network (W-ENN). W-ENN is a novel neural network which has three types of connection weights and an improved correlation function. The performance of the proposed model is validated against Extension Neural Network (ENN), Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Extreme Learning Machine (ELM) based models. The results indicate that, on noisy small sample sets, the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability. The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.