RISC-V架构芯片实现的无线触控系统

本文介绍了一款基于RISC-V架构芯片实现的无线触控系统方案,本方案实现了将触控芯片的触控I2C信号实时通过蓝牙BLE信号上传,具备集成度高、性价比高、传输稳定、传输距离远、兼容性好等优点.经测试,该方案完美实现了触控信号的无线传输,实现了在手机、电脑周边系列触摸产品的友好交互.     

Actuator and Sensor Fault Detection and Isolation for Uncertain Switched Nonlinear System Based on Sliding Mode Observers

International Journal of Control, Automation and Systems - In this paper, we discuss actuator fault and sensor fault detection and isolation (FDI) problems for a class of switched nonlinear systems...     

断层模型约束反演在MTH地区煤层横向预测中应用研究

利用相干体确定断层平面的走向特征,在剖面上采用井点处断点引导小断层解释,准确落实其空间位置,并建立精细的断层模型,在此基础上,利用断层约束反演技术,进行煤层的横向预测。MTH地区实例表明,基于断层模型约束反演技术的煤层横向预测方法,可精细描述煤层变薄带、分叉合并带、厚度变化等赋煤状态。     

In situ study of the mechanical properties of airborne haze particles

Particulate pollution has raised serious concerns regarding its potential impacts on human health in developing countries. However, much less attention has been paid to the threat of haze particles to machinery and industry. By employing a state-of-the-art in situ scanning electron microscope compression testing technique, we demonstrate that iron-rich and fly ash haze particles, which account for nearly 70% of the total micron-sized spherical haze particles, are strong enough to generate abrasive damage to most engineering alloys, and therefore can generate significant scratch damage to moving contacting surfaces in high precision machineries. Our finding calls for preventive measures to protect against haze related threat.     

液相流动对柱晶一次间距的影响

利用带有对流驱动系统的定向凝固装置,研究了液相稳定流动时透明合金SCN-2wt-％Ace定向凝固的动态过程.结果表明:流动显著改变定向凝固柱状晶的一次间距枝晶的间距增大,主要是流动与枝端浓度场耦合作用的结果;胞晶的间距变小,主要是侧向分枝转向生长的结果     

急弯段闸孔出流的弯道水流特性研究

在河弯处修建水工建筑物时,研究弯道水流特性对水工建筑物布置的影响十分重要。采用RNCκ-ε紊流模型对急弯段闸孔出流的弯道水流进行了三维数值模拟。通过模拟计算,比较分析了急弯段不同闸位、不同流量条件下闸孔过流时的流速分布、紊流动能、压强分布和环流等相关水流特性与变化规律。分析研究结果表明,闸坝位于弯道上游时,弯道对闸坝过流的影响最小,但需对闸坝下流弯道凸岸岸坡附近区域采取一定的防护措施;闸坝布置在弯道中游时,过闸水流受弯道的影响最小,闸室水流的稳定性、闸门的工作安全以及闸墩的空蚀破坏都是实际工程中需要注意的重点。数值模拟验证与物理模型试验结果吻合较好,计算精度较高,能够满足相关计算研究要求。     

基于RGB-D数据的目标分割与实时重建方法

应用RGB-D传感器进行三维重建具有速度和成本的优势,并可获取纹理信息,但扫描数据易受干扰。针对有干扰的外部环境,提出一种基于RGB-D数据的目标分割与重建方法。首先为改善输入图像质量,采用多帧叠加的中值滤波对信息缺失的位置进行预处理。接着,利用静态减除和动态3D Mean-shift相结合的方法分割目标,实现对目标物体的实时定位。最后使用一种优化的点到面ICP方法进行配准,并在此基础上利用随机抽样算法对点云的配准进行加速。采用该方法实现的原型系统能够支持含有背景和一定干扰下的小物体配准,且能有效提高三维重建的速度和自动化程度。     

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.     

Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots

Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other’s positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %.     

基于Tri-Training的事件关系分类方法研究

事件关系分类是一项研究事件之间存在何种逻辑关系的自然语言处理技术。针对事件关系分类任务中训练语料不足的问题,提出了基于Tri-Training的事件关系分类方法。该方法首先根据已标注的语料训练三个不同的分类器,以多数投票的方式从未标注集中抽取置信度较高的样本对训练集进行扩充,然后利用新的训练集重新训练分类器,反复迭代,不断完善分类模型,最终达到提升事件关系分类性能的目的。实验结果表明,以F1值为评价标准,基于Tri-Training的事件关系分类方法在四大类事件关系上的分类性能为64.36%。