基于语义约束与Graph Cuts的稠密三维场景重建

本文提出一种新颖、有效的稠密三维场景重建算法.在城市建筑场景的重建中,为了快速恢复稠密、准确的深度信息,本文算法首先在视图中对建筑区域进行了语义分割以降低非重建区域(如天空、地面等)的干扰,在提高整体重建速度的同时也增强了采用平面模型对其进行重建的可靠性;然后,在通过基于DAISY特征的空间点扩散方法获取的初始深度图的基础上,针对传统算法难以重建的弱纹理、倾斜表面等区域,本文算法依据场景分段平滑的假设,在超像素级MRF能量优化框架中对其相应的空间平面进行了推断.由于能量函数融合了初始深度图的约束、空间平面先验及空间平面间的几何关系等信息,而且候选平面集通过平面拟合和已知平面约束下的多方向平面扫描两种方法构造,使得相应的两阶段迭代Graph Cuts对能量函数的求解更快速和精确.在标准数据集和真实数据上的实验表明,本文算法能有效克服光照变化、透视畸变、弱纹理区域等因素的影响,快速恢复建筑区域完整的深度图.     

探空火箭图像系统设计方案

作为一种理想的实验平台,探空火箭在微重力科学领域得到了广泛应用.利用探空火箭进行微重力实验,首要问题是如何将实验过程以图像形式记录下来,并实时传回地面台站.因此,箭上图像的实时采集、编码与传输对实验具有重要作用.根据探空火箭微重力实验图像的特点,提出了一种适合于探空火箭图像系统的设计方案.按照该方案,图像系统可根据微重力实验的科学需求.实时调节实验图像的分辨率、帧速率和压缩比.该系统中,箭上的小波压缩单是核心模块,它通过基于硬件级的小波自适应压缩算法实现图像的箭上实时压缩,解码和实时显示则由与之对应的地面小波解码单元完成.     

基于SOPC和CPCI总线的ARINC429数据接收卡

介绍了基于SOPC的ARINC429接收卡的硬件构建过程和软件设计流程.该系统使用嵌入式NiosII软核处理器作为整个系统的控制器;使用 ARINC429收发芯片HS3282,实现了两路高速ARINC429数据的接收;并将接收到的ARINC429数据通过CPCI总线上传至主控计算机.结果证明能够实时高效的接收和传输两路ARINC429数据.     

Solving Multivariate Polynomial Matrix Diophantine Equations with Gr?bner Basis Method

Different from previous viewpoints, multivariate polynomial matrix Diophantine equations are studied from the perspective of modules in this paper, that is, regarding the columns of matrices as elements in modules. A necessary and sufficient condition of the existence for the solution of equations is derived. Using powerful features and theoretical foundation of Gr?bner bases for modules, the problem for determining and computing the solution of matrix Diophantine equations can be solved. Meanwh...     

应急响应程序时效性评估方法研究

突发事件应急响应效果评估是提高应急响应能力和水平的重要手段。由于突发事件的突发性、机理复杂、信息不完全以及应对具有时间窗口等,多指标综合评估和仿真方法一般难以满足实际的评估需求。为此,采用构造时效性评估函数的方法,既能在不完全信息下借助专家经验进行评估,又能反映应急响应程序中一环扣一环的影响关系。基于前期研究,本研究构造了一种新型的时效性评估函数,构造算例验证了函数的有效性,且使得函数最优性质的分析更简便,为应急准备、应急响应相关决策问题提供理论依据。     

高斯杂波中距离扩展目标的模糊CFAR检测

为提高高分辨雷达的检测性能,提出了一种高斯杂波背景下距离扩展目标的模糊检测方法。该方法采用模糊恒虚警率(constant false alarm rate, CFAR)检测器代替传统的二进制CFAR检测器,将距离单元值转换成映射到虚警空间的模糊隶属函数值,然后采用模糊积累准则进行积累,得到检测统计量。研究了模糊代数积、模糊代数和两种积累准则,推导出这两种方法虚警概率的数学解析式。仿真结果表明,高斯杂波背景下模糊代数积积累较双门限二进制积累可获得近2 dB的性能改善。同时,模糊检测方法只采用单个检测门限,具有易于调节的优点。     

基于ACP方法的电子商务系统复杂性研究

运用复杂系统研究中的“人工系统＋计算实验＋平行执行（ACP）”方法,结合新兴电子商务的特点,提出了基于ACP方法的电子商务复杂性研究方法论。讨论了其中的代理建模、实验、决策和量化计算4个主要问题;设计了与真实电子商务系统平行执行的电子商务社会实验平台,为复杂性研究结果的评估、验证和修正提供了实验环境。该方法论与实验平台的研究,有助于加快新兴电子商务复杂性研究的步伐、提升企业的国际竞争能力。     

Interval Time Series Analysis with an Application to the Sterling-Dollar Exchange Rate

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.     

Preface

Different from traditional tax audit, supervision with self-audit is a combination of audit by the taxpayer himself and audit by the tax authority. This paper mainly studies a taxpayer’s optimal policy of tax evasion under supervision with self-audit and its related properties, in order to deduce some effective suggestions and theoretical bases to restrain tax evasion. Assuming that only a certain proportion of evaded tax can be discovered when the audit is executed, the authors first formulate a static model with self-audit. This model is divided into two stages. At the first stage, taxpayers declare their taxes, then the tax authority chooses audit objects, based on a known probability, and announces the result; if the taxpayer is chosen, he will enter the second stage, during which he has a chance to pay the evaded tax and the corresponding late fees and then is audited by the tax authority. The authors show the existence and uniqueness of the optimal amount of tax evasion at the first stage and the optimal proportion to self-expose at the second stage. The authors also discuss the related properties of the interior solution, and do elasticity analyses on some parameters. Besides, the authors extend the static model into the corresponding two-period model, and study the existence and uniqueness of the solution of the extended model. Finally, under the assumption that tax evasion can only be discovered with a certain probability when the audit is executed, the authors formulate another static model with self-audit and investigate its properties. This research is partially supported by the National Natural Science Foundation of China under Grant Nos. 60674082, 70221001, and 70731003.     

Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.