Pavement crack detection using hessian structure propagation

Pavement images are widely used in transportation agencies to detect cracks accurately so that the best proper plans of maintenance and rehabilitation could be made. Although crack in a pavement image is perceived because the intensity of crack pixels contrasts with that of the pavement background, there are still challenges in distinguishing cracks from complex textures, heavy noise, and interference. Unlike the intensity or the first-order edge feature of crack, this paper proposes the second-order directional derivative to characterize the directional valley-like structure of crack. The multi-scale Hessian structure is first proposed to analytically adapt to the direction and valley of cracking in the Gaussian scale space. The crack structure field is then proposed to mimic the curvilinear propagation of crack in the local area, which is iteratively applied at every point of the crack curve to infer the crack structure at the gaps and intersections. Finally, the most salient centerline of the crack within its curvilinear buffer is exactly located with non-maximum suppression along the perpendicular direction of crack. The experiments on large numbers of images of various crack types and with diverse conditions of noise, illumination and interference demonstrate the proposed method can detect pavement cracks well with an average Precision, Recall and F-measure of 92.4%, 88.4%, and 90.4% respectively. Also, the proposed method achieves the best performance of crack detection on the benchmark datasets among methods that also require no training and publicly offer the detection results for every image.     

A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network

This paper focuses on consensus reaching process (CRP) under social network in which the trust relationship expressed by linguistic information. A new feedback mechanism in social network group decision making (SN-GDM) is proposed, which mainly consists of the following two aspects: (1) The propagation of distributed linguistic trust is investigated to study trust relation among experts; (2) A maximum self-esteem degree based feedback mechanism is developed to produce personalized advice for reaching higher group consensus. To do so, a novel linguistic trust propagation method is proposed to obtain the complete trust relationship among group. The self-esteem degree is used to define the extent that an individual makes concessions. Then, a maximum self-esteem degree based optimal feedback mechanism is built to produce personalized advice to help inconsistent experts make change of their opinion. Its novelty lies in the establishment of an optimization model with the nonlinear group self-esteem degree function as the objective function while group consensus threshold as the restrictions. Therefore, the inconsistent experts will reach a group consensus with the minimum loss of self-esteem degree, and then, it achieves the optimal balance between individual self-esteem and group consensus. Finally, a ranking process is applied to derive the appropriate consensus solution.     

基于国产处理器增量式实时交通流预测算法及实现

针对城市交通难以处理大量数据且实时性差等问题,提出了根据增量式城市交通流数据预测拥堵情况的一种基于国产处理器的L-BFGS(limited-memory BFGS)算法。该算法通过存储向量序列计算Hessian矩阵,改进Two-Loop算法求下降方向,在Spark集群中并行处理时收敛速度快,适用于实时性要求强的城市交通场景。实验结果证明,L-BFGS预测算法完全可以在国产平台上对大规模的实时交通数据流进行快速建模、预测,在改善城市交通管理水平提供有效支撑的同时也丰富了国产芯片的应用领域。     

融入类贡献抑制因子的灰度级模糊C均值图像分割

基于灰度级模糊C均值图像分割算法具有分割速度快的优势。由于无损检测图像中背景类和目标类差异较大,该算法不能有效地将目标分割出来,故提出改进的基于灰度级的模糊C均值算法。构建了一种与类大小反向相关的类贡献抑制因子表达式,将之融入目标函数后能够降低较大类对目标函数的贡献,这可避免较小类的聚类中心受较大类的影响而靠近较大类的聚类中心。最小化新的目标函数可得新形式的隶属度和聚类中心表征形式。采用类大小差异较大的无损检测图像进行试验,结果显示本文算法得到的分割图像视觉效果良好,而且指标G_mean也更高,进一步提升了基于灰度级模糊C均值算法适应能力。     

Low-loss light propagation in the chain of microcavities

According to the focusing property of the photonic nanojet, the coupling characteristics for the chain of microcavities have been studied. Low-loss light propagation in the chain of microcavities is proposed. The influence of different wavelengths on the intensity of the photonic nanojet and the location of the focal spot are given. Intensity distribution and transmission characteristics for the chain of coupled spherical microcavities are analysed. Simulation results and theoretical analysis show that the chain of spherical microcavities has a highly focused beam and low-loss characteristics because of the focusing effect and backscattering enhancement of the photonic nanojet. A low-loss waveguide can be designed by using these properties of the chain of spherical microcavities. Sub-wavelength focusing of the beam, high optical throughput, and broad spectral transmission properties make the chains of microspheres able to be used in a variety of biomedical applications including minimally invasive and ultraprecise laser surgery.     

A Cluster-Based Virtual Cooperative MIMO Transmission Scheme

基于分簇的虚拟协作MIMO传输策略研究

吕红芳

（上海电机学院 电气学院）

中文摘要：

无线传感器网络应用于环境状态监测时,最关键的问题是在无线通信时保障数据传输的可靠性、降低全网的能量消耗。本文提出了一种基于分簇的虚拟协作MIMO传输策略（CVCM）,该方案根据簇头节点到汇聚节点的距离动态配置最佳的协作节点数目和调制速率,保证每个虚拟MIMO单元和汇聚节点的通信能耗最小,从而降低整个网络的能力消耗。仿真实验结果表明,CVCM有效地降低了网络能耗,延长了网络的生命周期,提高了基站接收数据的有效性。

关键词：分簇;虚拟协作MIMO;动态配置;调制速率

     

基于功率分配的空间耦合数据传输性能分析

通过研究功率分配对空间耦合数据传输的性能影响,在空间耦合数据流间干扰完全消除的情况下,利用密度进化与势能函数证明了等功率传输性能最优.为了以较少的性能损失为代价,减少等功率传输引起的高检测复杂度,提出了一种功率分配算法实现非等功率传输.外信息转换图分析和仿真结果证实了理论分析的正确性和所提算法的可行性.     

快充纯电动BRT运营效果评价方法

针对快充纯电动快速公交(bus rapid transit,BRT)系统的运营效果评价问题,提出基于快充纯电动公交车辆运行特性与BRT运营特点的评价方法.首先,分析快充纯电动公交车辆在BRT中的适用性,从运营车辆性能、设施水平、乘客服务水平、经济效益等角度出发,建立包含燃油车辆替换率、动力电池衰减率、充电设施供需平衡关系等9项细化指标的快充纯电动BRT运营效果评价指标体系;其次,采用灰色聚类方法,构建快充纯电动BRT运营效果评价模型;最后,以金华市国内首条纯电动BRT线路为例,进行运营效果评价.与实际运营情况调查结果对比后发现,相比传统BRT运营效果评价方法,该方法更能真实、有效地反映纯电动BRT的实际运营效果.     

抗病毒类化合物结构与离解速率的关系

为了解决如何筛选出更加有效的抗病毒类化合物的问题,研究了离解速率(k_(off))与抗病毒类药物结构的关系,理论依据是k_(off)常用于评价药物在人体开放性系统中的活性.首先,应用分子描述符软件计算出每个抗病毒类化合物的分子描述符,并使用多元逐步回归分析法、偏最小二乘法和遗传算法3种方法对描述符进行筛选.然后,分别采用支持向量机和BP神经网络方法建立抗病毒类化合物的k_(off)的预测模型,并用测试集对模型进行了验证.结果表明:筛选出了具有良好预测能力的描述符,建立的2个预测模型经验证均合理,对未来抗病毒类药物的研制具有指导意义.     

基于手势的交互式三维场景并行光线跟踪绘制研究

利用光线跟踪可以绘制出高质量的真实感场景画面,但计算量很大,难以实现实时交互式绘制.为此,三维真实感场景的实时交互式绘制往往需要用到集群,但集群的绘制速度是由绘制速度最慢的节点决定的,如果节点之间的任务没有被划分均匀,集群的绘制效率就会下降.为了满足实时交互式绘制的要求、提高集群的绘制效率,设计了在粗粒度和细粒度两个层次上同时实现并行计算的集群绘制系统,并针对集群的负载均衡问题提出了基于像素递归深度的负载均衡方法.该方法首先统计出每个像素的递归深度从而得到整个场景的递归深度图,然后对上一帧按递归深度进行均衡划分.根据连续两帧所对应的场景具有的时空相关性,上一帧的划分结果可作为下一帧划分的依据.本算法的优势在于能够快速实现三维动态场景下的集群负载均衡.实验表明,该集群绘制系统可以高效地实现基于Kinect的实时交互式绘制.