Study of new flow field geometries to enhance water redistribution and pressure head losses reduction within PEM fuel cell

Efficiency of fuel cell is dependent on reactant distribution, products evacuation, pressure losses and many of these factors is dependent on the design of flow field plate. With an effective design, reactant distribution, pressure drop, and water and heat management can be further improved. In this work, two new designs, as multi-serpentine set-up with additional slots and hybrid geometry, on stainless steel bipolar plates, are presented. Electrical performance, and pressure head losses are analyzed by electrochemical methods such as polarization curve and use of electrochemical noise as a diagnostic tool to further understand the impact of water management on performance. On the one hand, multi-serpentine design shows the best electrical performance with an increase of 0.2 V (66%) at 0.9 A/cm² in comparison of traditional serpentine design. On the other hand, hybrid design reveals the lowest pressure head losses, with a decrease of 2 mbar (about 50%) in comparison of traditional serpentine design, and a higher stability with time that can be useful to downsize compressor and provide lower impact on fuel cell stack durability.     

四川省植被覆盖时空演变及未来变化趋势分析

四川省地处长江上游,是长江流域重要的生态屏障;解析该区域植被覆盖的时空演变特征,对长江上游的生态环境修护与治理具有重要意义。基于长时间序列的MODIS NDVI数据,解析2000~2016年间四川省植被的空间分布格局与时间变化特征;采用趋势分析法和Hurst指数,预测未来植被的变化趋势及持续性。结果表明:(1)四川省NDVI多年平均值为0.50,植被覆盖整体上呈现川东高于川西的空间分布特征;(2)2000~2016年间,四川省的NDVI值在0.48至0.53之间波动,整体呈上升趋势,增长率为0.002 5/a;(3)植被覆盖度在未来变化将呈现改善大于退化的趋势;植被持续性改善的面积大于持续性退化的面积。     

基于时空马尔可夫随机场模型的车辆跟踪算法研究

鲁棒的车辆跟踪是实现交通事件自动检测的重要前提,车辆跟踪中的车辆相互遮挡则是影响车辆跟踪结果的关键因素。针对这一难题,设计自适应的车辆跟踪算法,并依据交通图像序列的时空相关性,根据马尔可夫的基本理论和贝叶斯方法,应用MRF-MAP理论分析框架,并结合了彩色图像序列的纹理信息建立了图像序列的时空马尔可夫随机场模型。采用随机松弛算法中的Metropolis算法来求解时空马尔可夫随机场模型,对车辆跟踪得到的目标标号图进行优化,从而解决车辆跟踪中的遮挡问题。初步实验结果,跟踪不遮挡的车辆时达到的跟踪成功率为95%。遮挡情况时成功率也可达到83%。实验结果表明,该跟踪算法在不遮挡时效果非常理想,在遮挡情况下跟踪鲁棒性也较好。     

考虑参数时空演化规律的黄土边坡极限平衡分析法研究

考虑黄土特殊力学性质的边坡稳定性分析方法研究对于保障国家"西部大开发"战略和"一带一路"倡议的实施具有重要的意义。利用渗流—软化—稳定性分析顺序耦合的研究思路,对地下水位上升条件下的黄土边坡稳定性进行分析。首先借助非饱和非稳定渗流分析理论进行地下水位上升条件下黄土边坡的渗流分析;其次基于渗流分析的计算成果借助水致结构性劣化方程动态更新土体强度参数的空间分布;基于总重度、黏聚力、内摩擦角和孔隙水压力的时空分布规律,采用面力法提出了考虑参数时空演化规律的黄土边坡极限平衡分析法。最后以地下水位上升条件下白鹿塬边某黄土边坡为例,通过考虑参数时空演化规律的黄土边坡稳定性Spencer法和M-P法计算成果之间的相互对比分析,同时将这些成果与现场勘察迹象进行对比,从而说明考虑参数时空演化规律的黄土边坡极限平衡分析法的合理性和可靠性。     

基于多源大数据的传统商业中心区动静特征与规律解析——以广州上下九商业区为例

空间形态大数据在空间上具有静态、精细等特征,是研究城市静态规律的基本;手机信令大数据具有时间上的动态性、个体定位的精准性,是研究城市动态规律的重要工具。广州上下九商业区,是中国传统商业中心区的代表。以广州上下九商业区为例,一方面,利用空间形态大数据,进行高度,强度,密度的静态空间刻画;另一方面,利用手机信令大数据,从出行距离、时长、时段等多个视角,刻画上下九地区人群活动动态结构,以及动态时空出行行为关系,总结上下九人群时空分布规律以及与外界的时空行为联系特征。综上所述,从动静两个维度,总结出上下九商业中心区空间的活动特征和变化规律,为传统城市商业中心区的空间活力规律研究提供了实证支持。     

共享交通的时空轨迹检索与群体发现

为解决共享交通下的共乘用户群体发现效率低、准确率不高问题，依据R-树原理建立GeoOD-Tree索引，并在此基础上提出以最大化共乘率为目标的群体发现策略。首先，对原始时空轨迹数据进行特征提取与标定处理，挖掘有效出行起讫点（OD）轨迹；其次，针对用户起讫点轨迹的特征，建立GeoOD-Tree索引进行有效的存储管理；最后，给出以最大化共乘行程为目标的群体发现模型，并运用K最近邻（KNN）查询对搜索空间剪枝压缩，提高群体发现效率。采用西安市近12000辆出租车营运轨迹数据，选取动态时间规整（DTW）等典型算法与所提算法在查询效率与准确率上进行性能对比分析。与DTW算法相比，所提算法的准确率提高了10.12%，查询效率提高了约15倍。实验结果表明提出的群体发现策略能有效提高共乘用户群体发现的准确率和效率，可有效提升共乘出行方式的出行率。     

Adaptive visual target detection and tracking using weakly supervised incremental appearance learning and RGM-PHD tracker

Multiple visual target tracking is a challenging problem due to various uncertainties including occlusion, miss-detection and noisy measurement. Most tracking approaches utilize an object-specific detector, pre-trained on many labeled images, to provide suitable measurements for their tracking system. In this paper, we use a simple background subtraction detector which only needs the background image to localize targets independent of their shape or type. In order to cope with the uncertainties resulted by the detector, we propose an adaptive appearance model and develop an incremental appearance learning algorithm to learn the target appearances in time. The proposed method employs the background information and our defined keypoints’ miss-matched history to adapt the target appearances within different frames. Furthermore, we combine Refined Gaussian Mixture Probability Hypothesis Density (RGM-PHD) tracker with the detectors to keep target trajectories and handle uncertainties. The experiments conducted on several video datasets show the effectiveness of our proposed method.