带式输送机回程空段双向清扫器研究与设计

针对北方某燃煤发电厂输煤系统带式输送机工作表面粘煤和结冰问题,设计了一种带式输送机回程双向空段清扫器.该双向清扫器由刮扫部分和双向转换部分组成.刮扫部分的多个刮刀单元在刮刀安装板上错落排布,形成一个垂直于输送胶带运行方向的完整刮扫面;为降低相邻刮刀之间的物料漏扫率,其横向装配尺寸小于刮刀片宽度.清扫器双向转换部分由四连杆机构实现,通过设定驱动电机转动合适的角度,可以保证清扫器刮扫单元移动到目标位置,从而实现正反向清扫状态的转换.本装置结构精简、加工方便、易于安装,很好地保证胶带输送机的正常运行.     

VB环境下基于内存共享技术的串行通讯程序设计

VB由于具有强大的界面生成能力及灵活的数据库操作功能,是上位软件的主要编程工具之一,在很多场合领域得到了应用。然而,直接采用VB编程的主要问题是可靠性较差,特别是在与其它低层设备,如PLC通讯时。本文提出一种采用内存共享技术的程序之间数据交换方法,将VB的通讯子程序和主程序分离,以解决通讯子程序死锁等故障对主程序的影响。本方法用在汽车安全气囊装配生产线的软件设计中,取得了良好的效果。     

Speech driven photo realistic facial animation based on an articulatory DBN model and AAM features

This paper presents a photo realistic facial animation synthesis approach based on an audio visual articulatory dynamic Bayesian network model (AF_AVDBN), in which the maximum asynchronies between the articulatory features, such as lips, tongue and glottis/velum, can be controlled. Perceptual Linear Prediction (PLP) features from audio speech, as well as active appearance model (AAM) features from face images of an audio visual continuous speech database, are adopted to train the AF_AVDBN model parameters. Based on the trained model, given an input audio speech, the optimal AAM visual features are estimated via a maximum likelihood estimation (MLE) criterion, which are then used to construct face images for the animation. In our experiments, facial animations are synthesized for 20 continuous audio speech sentences, using the proposed AF_AVDBN model, as well as the state-of-art methods, being the audio visual state synchronous DBN model (SS_DBN) implementing a multi-stream Hidden Markov Model, and the state asynchronous DBN model (SA_DBN). Objective evaluations on the learned AAM features show that much more accurate visual features can be learned from the AF_AVDBN model. Subjective evaluations show that the synthesized facial animations using AF_AVDBN are better than those using the state based SA_DBN and SS_DBN models, in the overall naturalness and matching accuracy of the mouth movements to the speech content.     

Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the ‘tracking error accumulation problem’. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel ‘twin-pipeline’ training framework to find the latent semantic topics of ‘moving people’, the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.     

高温脉冲工况下钛酸锂电池热特性

钛酸锂电池具有可高倍率充放电特点,常用于一些需要快速充电的场景,比如大型电化学储能装置、需快速充电的电动汽车等.为避免钛酸锂电池在高温脉冲工况下容易因表面温度升高而引起爆炸等危险情况发生,研究钛酸锂电池热行为是很有必要的.按照不同充放电倍率(0.3 C、0.6 C、1.0 C)对钛酸锂电池进行实验,对高温脉冲工况下的钛酸锂电池热行为进行实时监测,以钛酸锂电池电化学过程为基础,对其生热机理、产热功率进行定性定量研究.实验结果表明,低倍率下充电生成热功率比较高倍率下充电要低些,且强制空气冷却比自然冷却降温效果好,冷板散热冷却效果最佳.     

基于MCU并行接口的尿湿监测系统

尿湿监测对残疾人、瘫痪者等患有行动不便症的病人以及医院护理工作具有重要的意义。介绍了一种基于单片机并行接口方式的病床尿湿监测系统,该系统以STC89C58RD+单片机为核心,从信号采集、存储到时钟记录、液晶显示、打印输出全部采用并行接口器件;采用循环扫描,优先报警,不处理报警的设计思路,配以蜂鸣器、指示灯等声、光器件,能够实时有效监测多达96个病床的尿湿情况,并通知护理人员进行处理。该系统对报警信号响应快速、灵敏,满足实际应用要求。     

高性能罗兰-C接收天线设计

为了提高罗兰-C磁天线的接收能力,设计了一种顺接串联多层线圈的磁天线。理论推导了顺接串联多层线圈磁天线的信噪比和灵敏度,最终设计的磁天线大小为150 mm×150 mm。远场测试接收到了较高信噪比的罗兰-C信号,在相同信号调理电路条件下,与单层单组线圈的磁天线相比,顺接串联多层线圈的磁天线接收到3.47 V的电压信号,且信噪比高达30.87 dB,灵敏度和信噪比分别提高了82.42%和63.20%。顺接串联多层线圈磁天线提高了罗兰-C磁天线的灵敏度和信噪比,对罗兰-C系统在定位方面的应用具有重要意义。     

基于分形理论的SAR图像分割

SAR(合成孔径雷达)图像包含有相干斑噪声,传统方法不能很好对其进行分割,文章将SAR图像的特点和分形理论相结合,提出了一种新的SAR图像分割方法。该方法首先对原始SAR图像每个像元为中心取两种不同窗口,计算在该两种窗口下基于区域自选的分形维数并求均值,将其作为分形纹理特征。然后根据SAR图像噪声在小波域中的分布特点对SAR图像进行滤波,最后以SAR图像分形纹理特征和滤波后的灰度组成特征向量对SAR图像进行分割。实验结果分析表明,该方法是一种有效的SAR图像分割方法。     

基于小波变换的SAR图像分割

SAR(合成孔径雷达)图像包含有相干斑噪声，传统方法不能很好地对SAR图像进行分割，该文结合SAR图像和小波变换多分辨分析的特点，提出了一种新的SAR图像分割方法。首先利用小波变换提取SAR图像的纹理特征信息，然后根据SAR图像噪声在小波域中的分布特点对SAR图像进行滤波，最后以SAR图像小波能量纹理特征和滤波后的灰度组成特征向量对SAR图像进行分割。实验结果表明，该方法是一种有效的SAR图像分割方法。     

光照变化条件下的人脸识别研究

本文提出了两种研究光照变化条件下人脸识别的方法.第1种方法光照子空间方法,它适用于训练集中存在与测试人脸图像相同或者相似光照的人脸训练图像.当这个条件不满足时,可得用径向基函数产生虚拟光照条件下的人脸图像样本图像特征加入训练集,该方法适用于更一般的情况。实验结果证明文中提出的方法可以有效提高识别率。