Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

基于LabVIEW的惯性测量单元信号采集及处理系统设计

利用LabVIEW设计了惯性测量单元信号采集及处理软件,主要包括串口通信、数据分离及信号处理等模块;串口通信模块采用VISA方式编程,建立了惯性测量单元与上位机之间的通信;根据惯性测量单元输出数据的格式,利用LabVIEW中的匹配模式函数分离出沿三轴向的加速度信号和角速率信号;设计了一种用于去除加速度信号中低频成份的数字滤波器;在建立的陀螺漂移模型基础上,分别进行小波基、小波分解层数、阈值函数及阈值估计方法等小波参数的选取.     

基于双重故障模式的微型电网电压自愈控制方法研究

针对故障干扰容易造成电网负荷损失的问题与负荷裕度较小且分布不均等问题,提出双重故障模式的微型电网电压自愈控制系统等效处理微型电网运行模式。通过有向图路径矩阵及其关联矩阵确定供电路径计算传输功率,构建网络故障关联与拓扑矩阵然后对双重故障形成关联,实现负荷裕度损失较小的条件下进行微型电网电压自愈控制的目的。实验表明,故障检测过程中负荷裕度提升值较高且分布均匀,紧急负荷补偿能力较高,故障定位下的微型电网负荷性能得到提升且具有良好的稳定性。     

指路标志版面评价与优化

为客观度量指路标志版面设计的优劣,从人类工效学角度应用多维量化数据评价道路交叉口指路标志版面工效性.综合考虑指路标志视认中的信息需求和信息处理过程,引入信息需求度、理解度和信息量,建立了指路标志版面工效性评价模型.发现指路标志指引交叉口直接衔接的道路信息及周边的重要地点信息更加合理,文字排列方式与道路线形保持一致、图形符号与文字结合都将提高标志内容理解水平.结果表明:版面工效性评价能很好地反映指路标志满足驾驶员信息需求、符合驾驶员认知规律、利于驾驶员感知道路线形的程度,是一种研究驾驶员、指路标志及道路之间协调性的有效方法.     

轨道空间线形的运动检测算法

基于惯性测量平台,对惯性测量单元(IMU)信息进行等空间采样.采用经验模态分解算法(EMD)消除信号中的干扰成分,设计数字积分器对惯性信息进行积分计算,获取相对空间运动轨迹.实验结果表明:上述方法所测量的轨道三轴绝对位移精确度大于97.17%.     

Directional pedestrian counting with a hybrid map-based model

Detection-based pedestrian counting methods produce results of considerable accuracy in non-crowded scenes. However, the detection-based approach is dependent on the camera viewpoint. On the other hand, map-based pedestrian counting methods are performed by measuring features that do not require separate detection of each pedestrian in the scene. Thus, these methods are more effective especially in high crowd density. In this paper, we propose a hybrid map-based model that is a new directional pedestrian counting model. Our proposed model is composed of direction estimation module with classified foreground motion vectors, and pedestrian counting module with principal component analysis. Our contributions in this paper have two aspects. First, we present a directional moving pedestrian counting system that does not depend on object detection or tracking. Second, the number and major directions of pedestrian movements can be detected, by classifying foreground motion vectors. This representation is more powerful than simple features in terms of handling noise, and can count the moving pedestrians in images more accurately.

     

仿真软件在计算机网络通信教学中的应用

基于PacketTracer仿真软件组建了跨路由器的虚拟VLAN网络实验,分析了路由器的端口设置、IP地址分配以及交换机的详细配置方法,丰富了实验教学手段。通过具体的实验对虚拟网络进行了仿真测试分析,实现了跨路由器间的VLAN通信以及跨网络的DHCP中继服务,并在实际的网络设备上进行了试验验证。     

以本体为核心的图像情感语义检索模型

针对目前图像检索系统较难实现情感语义检索的问题,构建了以本体为核心的图像情感语义检索模型。在对核心本体库的构建中,采用将Mpeg-7和概念格理论与本体构建相结合的方法,其难点在于将Mpeg-7标准描述符与图像情感本体的属性构造的结合,以及自动产生新的概念结果的方法;实现了图像情感领域本体框架的半自动构建。通过系统的初步实现与相关实验的验证,证明了此检索模型在实现图像情感语义检索中的可行性和优越性。     

无线充电系统电磁屏蔽与效率优化技术研究

在电动汽车无线电能传输系统中,磁屏蔽效果往往以牺牲传输效率为代价,如何减少磁泄漏的同时提高传输效率是一 个难题。 为此本文提出了一种新型强耦合磁屏蔽结构来减少系统磁泄漏。 首先,提出了一种强耦合磁屏蔽线圈结构,基于提出 的屏蔽线圈结构,分析了屏蔽线圈阻抗特性对系统漏磁与传输效率的影响;其次,给出了一种漏磁优化流程,运用提出的优化流 程,得到了满足设计要求的各线圈参数;最后,根据得到的线圈参数研制了一套基于电动汽车带磁屏蔽结构的无线充电系统,通 过仿真和实验验证了所提结构与方法的有效性。 结果显示,提出的新型强耦合磁屏蔽线圈不仅使系统的磁泄漏有效降低了 28%,且传输效率提升了近 4%。     

A method for the compound fault diagnosis of gearboxes based on morphological component analysis

An improved morphological component analysis (MCA) method is proposed for the compound fault diagnosis of gearboxes. When gear fault and bearing fault occur simultaneously, the compound fault signal of the gearbox contains meshing components (related to the gear fault) and periodic impulse components (related to the bearing fault). The corresponding fault characteristics can be separated by MCA according to the morphological differences of the components. In the proposed method, the optimal dictionary, which can represent the characteristics of bearing faults, is first selected based on the principle of minimum information entropy. Then, the compound fault signal is decomposed into the meshing component and the periodic impulse component using MCA. Finally, the separated components are subjected to the Hilbert envelope spectrum analysis. The faults of the gear and the bearing can be diagnosed according to the envelope spectra of the separated fault signal components. Simulation and experimental studies validate the effectiveness of the proposed method for the compound fault diagnosis of gearboxes.