淮北平原主要农作物涝渍灾害 损失评估研究

针对淮北平原除涝减灾综合治理与规划中缺乏主要农作物受涝损失调查与评估基础资料的实 际问题,于2010年9月初区域降雨成灾之际,对受涝区进行主要农作物受涝经济损失调查。依据实 地调研成果,通过综合统计分析推求了不同耕地类型不同暴雨强度下的受涝损失率,在此基础上实现 了对淮北平原受涝经济损失的合理评估。     

水生态保护与建设的理论问题探讨——以武汉城市圈为视角

自武汉城市圈被评为两型社会综合配套改革实验区以来,面对水环境呈现多样性和复杂性的现状,将武汉城市圈建设成为富有滨水特色的现代化生态城市圈是两型社会建设主题中应有之义,也是推动城市圈生态化发展的必由之路。针对武汉城市圈水生态保护与建设的问题,在阐述了其必然性的基础上,对理论支撑及基本原则作了细致具体的分析,希望能对武汉城市圈水生态保护与建设的实践问题等相关研究有所帮助。     

武汉职院实训成绩评估系统的设计与实现

本文阐述了武汉职院实训效果评估系统的实现和设计过程。着重描述了在项目反映理论框架下实训任务分析模型的建立、问题的转换和算法实现的具体过程。将项目反映模型与极大似然算法结合,在极大似然算法的支持下的改进型双参数模型可以更高效、更客观的对技能水平做出预测。     

权重自适应模糊层次分析方法及应用

借鉴了误差反向修正理论和增量式的思想,结合层次分析法固有的优势,提出了具有自适应能力的权重设定层次分析法(adaptive weight setting method,AWS)新方法,使盾构法隧道施工风险识别模型能够利用较少的施工数据,完成进行权重调整,满足了实际隧道施工中风险评估的需求.该方法在上海地铁某区间隧道工程中进行了试用,取得了良好的效果,表明了该方法的有效性和实用性.     

基于量子粒子群优化的在线航迹规划

现代战场中,环境信息是变化的,飞行器很难预先获得精确的全局环境信息,因此要求无人飞行器具有实时的航迹规划能力,采用量子粒子群优化算法,将约束条件和搜索算法相结合,有效解决了简单粒子群算法在高维空间中易陷入局部最优点的问题;同时,根据地形障碍、敌方防御雷达、防空火力等威胁以及禁飞区的分布情况,引入最小威胁面的概念,利用B-Spline插值逼近最小威胁面中的三维航迹在二维水平面内的投影,从而将三维曲线的规划问题简化为二维平面中控制点的寻优问题,简化了问题复杂度,提高了计算效率.仿真结果表明该方法可以满足在线航迹规划的要求.     

无线传感器网络中视频质量评估方法

视频在无线传感器网络传输,由于网络的不稳定,可能使视频质量受到一定的影响。在人类视觉模型的基础上,提出一个改进的无参考质量评估算法,直接通过收到的图像,判断视频质量的好坏。较之当前广泛使用的视频评估算法一媒体延迟指标而言,大大提高在网络丢包时,对于视频质量评估的准确性。评估结果接近人眼观察的效果,并且可以大规模投入使用,具有较强现实意义和商业使用价值。     

Blind omnidirectional image quality assessment with representative features and viewport oriented statistical features

With the development of information technologies, various types of streaming images are generated, such as videos, graphics, Virtual Reality (VR)/omnidirectional images (OIs), etc. Among them, the OIs usually have a broader view and a higher resolution, which provides human an immersive visual experience in a head-mounted display. However, the current image quality assessment works cannot achieve good performance without considering representative human visual features and visual viewing characteristics of OIs, which limited OIs’ further development. Motivated by the above problem, this work proposes a blind omnidirectional image quality assessment (BOIQA) model based on representative features and viewport oriented statistical features. Specifically, we apply the local binary pattern operator to encoder the cross-channel color information, and apply the weighted LBP to extract the structural features. Then the local natural scene statistics (NSS) features are extracted by using the viewport sampling to boost the performance. Finally, we apply support vector regression to predict the OIs’ quality score, and experimental results on CVIQD2018 and OIQA2018 Databases prove that the proposed model achieves better performance than state-of-the-art OIQA models.     

Firm diversity and data breach risk: A longitudinal study

Research has extensively investigated the rationale of firm diversity from the economic perspective, but little is known about how such a strategy may affect information security. The present study is the first to examine how firm diversity is relevant to firms’ likelihood to experience data breaches (i.e., data breach risk). Drawing from the strands of literature on information security, diversification, and resource-based view, we propose hypotheses on the relationship between firm diversity and data breach risk, as well as the boundary conditions of this relationship. On the basis of a twelve-year sample of publicly-listed firms, our analysis provides evidence to support the negative association between firm diversity and data breach risk. Our analysis also delineates conditions under which the effects of firm diversity can intervene to reduce the data breach risk invoked, such as under related diversity and when managers are managerially capable. For academics, our research accentuates an intriguing but unexamined benefit of firm diversity because it relates to information security. For practicing professionals, this research highlights the significant impact of firms’ operational structure on information security.     

A level set method based on the Bayesian risk for medical image segmentation

This paper proposes an alternative criterion derived from the Bayesian risk classification error for image segmentation. The proposed model introduces a region-based force determined through the difference of the posterior image densities for the different classes, a term based on the prior probability derived from Kullback-Leibler information number, and a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. Compared with other level set methods, the proposed approach relies on the optimum decision of pixel classification and the estimates of prior probabilities; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach is able to extract the complicated shapes of targets and robust for various types of medical images. Moreover, the algorithm can be easily extendable for multiphase segmentation.     

Modular analysis and modelling of risk scenarios with dependencies

The risk analysis of critical infrastructures such as the electric power supply or telecommunications is complicated by the fact that such infrastructures are mutually dependent. We propose a modular approach to the modelling and analysis of risk scenarios with dependencies. Our approach may be used to deduce the risk level of an overall system from previous risk analyses of its constituent systems. A custom made assumption-guarantee style is put forward as a means to describe risk scenarios with external dependencies. We also define a set of deduction rules facilitating various kinds of reasoning, including the analysis of mutual dependencies between risk scenarios expressed in the assumption-guarantee style.