行为时序逻辑中公平性的研究与完善

基于行为时序逻辑(TLA)的并发系统描述,就是对系统的初始状态、系统行为和行为的公平性进行规约和描述,但TLA中的公平性具有局限性,无法准确地描述某些系统的行为,从而限制了TLA的描述能力。通过研究TLA中公平性的推导过程,分析公平性的概念与定义方法,并以实际例子说明它的局限性。在此基础上,提出以加入两级新的公平性方式对其进行完善。最后,证明了新公平性等级之间的蕴涵关系。完善后的公平性具有更强的描述能力,能够对系统进行更完整的描述与规约。     

Variability of complementary relationship and its mechanism on different time scales

In the complementary relationship (CR) between actual and potential evapotranspiration, wet envi- ronment evapotranspiration (Ew) is usually calculated using the Priestley-Taylor (P-T) equation. Based on the data obtained from 38 catchments of the Haihe River basin and the Weishan experiment site in Shandong Province of China, this study has found variations in the Priestley-Taylor parameter α (e.g. increase in values with increase in the amount of precipitation on annual scale, seasonal fluctuation noted t...     

结合相位一致与全变差模型的高分辨率遥感图像边缘检测

目的边缘检测是有效利用遥感数据开展地物目标自动识别的重要步骤。高分辨率遥感图像地物类型复杂,细节信息过于丰富,使得基于相位一致的边缘检测结果中存在过多的噪声与伪边缘。为此提出了一种结合相位一致与全变差模型的高分辨率遥感图像边缘检测方法。方法根据相位一致原理,应用Log Gabor构造的2维相位一致模型,引入全变差去噪模型对基于相位一致的边缘强度图进行改进。结果借助有界变差空间对图像光滑性的约束,实现了高分辨率遥感图像噪声去除与伪边缘抑制,利用改进后的相位一致边缘强度图可有效检测高分辨率遥感图像的边缘。结论实验结果表明,与相位一致模型、Canny算法相比,该方法能消除了高分辨率遥感图像中同类地物内部细节特征形成的噪声,抑制相位一致边缘检测结果中的伪边缘,突出地物的真实边缘,并能正确地提取地物目标的整体轮廓信息,有助于后续地物目标的自动识别。     

任务级行动序列问题中的定性偏好研究

关注了一类典型行动序列,研究如何在动作集合上存在定性偏好,且偏好集合存在不一致性时开展规划。所考虑的行动序列问题称为任务级COA,以抽象层次的动作为基本要素,所考虑的定性偏好包括静态偏好和时序偏好,所讨论的规划目的是获得最大满意度的COA方案。首先建立了偏好与约束的归一化形式描述,在此基础上形成了COA方案设计算法;进一步,使用计算辩论技术排除偏好集合中的不一致性,形成用户接受度最高的COA方案。文中建立的以定性推理为基础的规划框架,实现了偏好解耦,能够适应不同的领域问题,是以定量计算为基础的传统规划算法的有效补充。通过快速响应卫星成像的COA案例,演示了算法的可行性。     

Adaptive variational models for image decomposition

Image decomposition refers to the splitting of an image into two or more components.In this paper,a clean image is separated into two parts:one is the cartoon component,consisting only of geometric structure,and the other is the oscillatory component,consisting of texture.Three parts for noisy image are considered:cartoon,texture,and noise.To better decompose an image,we propose two new variational models.In our models,two adaptive regularization terms are introduced.The two regularization terms are determined by an adaptive function which can discriminate the cartoon and texture of an image automatically.Experimental results illustrate the efectiveness of the proposed models for image decomposition.     

Temporal evolution of contacts and communities in networks of face-to-face human interactions

Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific coil- ferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze tile evolution of contacts and communities over time to consider the stability of the respective conmmnities. Furthermore, we assess different factors which have an influence on the quality of com- munity prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.     

Fractional partial differential equation denoising models for texture image

In this paper,a set of fractional partial differential equations based on fractional total variation and fractional steepest descent approach are proposed to address the problem of traditional drawbacks of PM and ROF multi-scale denoising for texture image.By extending Green,Gauss,Stokes and Euler-Lagrange formulas to fractional field,we can find that the integer formulas are just their special case of fractional ones.In order to improve the denoising capability,we proposed 4 fractional partial differential equation based multiscale denoising models,and then discussed their stabilities and convergence rate.Theoretic deduction and experimental evaluation demonstrate the stability and astringency of fractional steepest descent approach,and fractional nonlinearly multi-scale denoising capability and best value of parameters are discussed also.The experiments results prove that the ability for preserving high-frequency edge and complex texture information of the proposed denoising models are obviously superior to traditional integral based algorithms,especially for texture detail rich images.     

基于倒位变异的蜉蝣优化算法

蜉蝣算法(Mayfly Algorithm,MA)作为一种新型群智能优化算法,具有较好的寻优性能.但在高维非线性复杂问题上,蜉蝣算法依然容易出现早熟收敛现象.本文提出一种基于倒位变异的蜉蝣算法(Inversion Variation Mayfly Algorithm,IVMA),改变原算法在变异上的操作,随机选择个体的随机维度向全局最优个体的随机维度靠近,同时利用精英策略保留进化成果.利用倒位操作,将最优个体某一维度段内位置发生倒转,提高了算法跳出局部最优的能力.通过对10个测试函数的结果分析,表明本文所提出的算法具有较好的收敛精度,收敛性能得到了提高.     

多特征融合和尺度变化估计的船舶跟踪方法

传统的船舶视觉跟踪任务主要集中于单目标船舶跟踪,对多目标船舶跟踪研究相对较少。为解决该问题,提出一种多维特征融合机制和尺度变化估计的多目标船舶跟踪框架,该框架引入位置滤波器对输入的船舶训练样本进行学习,并将其应用于待跟踪的船舶图片序列,通过寻找最大响应的方法判定图像中的船舶位置。在此基础上,构建船舶尺度估计滤波器以确定待跟踪船舶的图像尺寸。通过和中值流跟踪算法和多示例学习跟踪算法对比分析,实验结果表明不同海事交通场景下的船舶跟踪误差均小于10像素,验证了算法的有效性和可靠性。     

深圳市降水变化特征及其未来变化情况预测

基于1998—2018年深圳市日降水资料,采用降水相对变率、集中度及集中期分析了月尺度、年尺度降水稳定性及其年内分配特征,运用Mann-Kendall非参数法和相关函数法对深圳市年降水量、降水稳定性和分配特征参数的变化趋势、突变特征及其未来降水变化特征进行分析与预估。结果表明:深圳市汛期降水稳定性比非汛期更好,年际降水分布稳定性较差,降水集中期为每年6—8月份,降水集中度处于0.23~0.72;降水量和降水分布特征参数均在2011—2013年发生突变,在发生突变后降水相对变率和集中度呈下降趋势,而年降水量和集中期呈上升趋势;深圳市未来降水序列变化趋势受过去变化特征的影响,其中未来年降水量有减少的趋势,降水集中程度逐渐增强,降水稳定性逐渐变差,年内降水分配均匀性变差,降水集中期持续向后推迟。研究成果为深圳市合理蓄水和用水提供科学依据,对水资源调度、利用以及防汛抗旱具有实际指导意义和重要的学术价值。