Re-ranking for microblog retrieval via multiple graph model

As a new information sharing platform, microblog has got explosive growth in recent years and has become an important source for public opinion mining. A variety of information like the reviews of brands/products or the trends of events can be socially sensed from such kind of data. However, it is still a challenging task to search relevant microblogs as the user generated content tends to be mixed with noise. Besides short text, image is getting popular in microblogs due to its power in visual information conveying. In this paper, we leverage textual and visual cues integratedly and propose a general re-ranking approach for microblog retrieval via multi-graph semi-supervised learning. We argue that the different types of information in microblogs correspond to different relationships among microblogs and each type of the relationship can be represented as a similarity graph. We then integrate different graphs into a unified framework and solve them simultaneously for microblog re-ranking. Extensive experiments on a recently published Brand-Social-Net dataset showed the effectiveness of the proposed method and marginal improvements have been achieved in accuracy as compared to the single graph model based method.     

A deep bidirectional LSTM approach for video-realistic talking head

This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations.     

一种内嵌导磁槽的圆感应同步器

圆感应同步器是一种高精度角度传感器,传统的圆感应同步器的转子需外引激励线以驱动其工作,鉴于圆感应同步器在使用过程中需要连续旋转,这将使得外引激励线存在绞线的问题,本文提出一种新的设计方法,在圆感应同步器的定、转子基体上嵌有环状的金属导磁槽,导磁槽内放入环状金属导线.通过给定子导磁槽内的环状金属导线施加激励信号,此激励信号可耦合到转子导磁槽内的环状金属导线中以驱动转子工作,这使得转子无需外引激励线,进而提高了系统应用的便利性以及可靠性.     

CAM自带仿真模块与VERICUT仿真对比与综合运用

对CAM自带仿真模块与VERICUT仿真情况进行对比,以加工腔体零件前罩为例,从图形显示、干涉检查、程序的显示验证、尺寸检验以及程序优化等方面进行了对比,并分析了两类机械加工仿真模式的优劣,为不同需求的工程技术人员提供了仿真方法的借鉴和参考.为了更好的利用两类仿真软件,提出把两类仿真软件结合使用,并结合PDM工作环境,对CAM软件和第三方仿真软件的优势进行了整合,建立了全新生产准备模式下的信息模型,提出了新的新的生产准备方式.     

基于小波变换的卡尔曼滤波动力电池SOC估算

混合动力电动汽车的动力电池和电池管理系统的工程实际应用环境是非常恶劣的,导致信号采样过程中包含大量的系统噪声和测量噪声.因此,提出一种基于小波变换的卡尔曼滤波动力电池SOC估算方法,通过在一系列充放电实验所建立的动力电池系统模型上,利用小波变换自相似过程的去相关作用和多尺度多分辨的特性对噪声进行抑制,实时对测量信号多尺度滤波分解,再采用卡尔曼滤波的迭代递推的线性无偏最小方差估计特性对电池SOC进行估算.实验结果表明,提出的基于小波变换的卡尔曼滤波SOC估算方法效果优于标准的卡尔曼滤波算法,在实际工况中,具有较强的适应性.     

基于i.MX35的视觉扩展系统设计和实现

视觉扩展技术是当前机器视觉领域中的热点问题之一。本文研究和设计了一种视觉扩展方法下的自动规避交通危险系统,采用专业汽车嵌入式处理器,对整车后端进行全方位观测,对驾驶员视觉进行补偿。介绍了系统的硬件结构,阐述了车载终端软件系统的驱动层和应用层程序的实现过程,并通过自主搭建的实验平台加以验证。试验结果表明视觉扩展明显,且系统智能化程度高,具有较好的实用价值。     

水电站对外公路设计特点及主要技术标准分析——以乌东德水电站工程为例

水电站对外公路设计既要满足水电站施工期间对外交通运输要求,又要便于水电站完工后移交地方交通部门统一管理。结合金沙江上游乌东德等梯级水电站对外公路设计实践,通过对水电站对外公路的任务、功能及特点的分析,从公路设计等级、设计速度、建筑限界、荷载标准、平曲线设计等方面,对主要技术标准和指标的运用进行了论述;总结了设计经验和体会。对类似工程设计具有参考作用。     

改进的蚂蚁算法在矩形优化排料中的应用

矩形件优化排料问题是一类具有NP完全难度的组合优化问题.将改进的蚂蚁算法应用到矩形件优化排料问题求解中,优化试验结果表明可获得比基本蚂蚁算法更好的效果,为矩形件优化排料这类NP完全问题提供了新的思路和方法.     

FPGA动态局部重配置在软件无线电中的应用

论文简单概述了Xilinx现场可编程门阵列(FPGA)动态局部重配置的技术特点以及在现代软件无线电(SDR)中的应用.并以一个工程示例着重介绍了在某波形处理模块上采用飞思卡尔处理与FPGA内部微处理器配合的方案对FPGA进行局部功能动态配置的工程实现.     

A mathematical morphology-based multi-level filter of LiDAR data for generating DTMs

Light detecting and ranging(LiDAR)technology has become an efective way to generate highresolution digital terrain models(DTMs).To generate DTMs,point measurements from non-ground features,such as buildings,vegetation and vehicles,have to be identifed and removed while preserving the terrain points.This paper proposes an efcient mathematical morphology-based multi-level flter to generate DTMs from airborne LiDAR data.Preliminary non-ground points are frst identifed with the characteristics of the multiecho airborne LiDAR data.The localized mathematical morphology opening operations are then immediately applied to the remaining points.By gradually increasing the window size of the flter and using a dynamic critical gradient threshold,the non-ground points are removed,while the ground points are preserved.Eight samples were chosen from eight sites provided with the ISPRS Commission III,Working Group 3,to evaluate the accuracy of our algorithm.Both the qualitative and quantitative experiment analyses show that our morphologybased multi-level flter method achieves promising results,not only in flat urban areas but also in rural areas,especially in preserving complex terrain details,while non-ground spatial objects are removed.