青藏高原东南部径流特征的空间规律及控制因子研究

采用青藏高原东南部的水文气象资料,对比分析了该地区23个(区间)流域的年径流深、集中度与集中期、基流系数、退水系数等径流特征及其空间分布规律,并进一步研究了这些径流特征的控制因子。结果表明:该地区径流特征的空间分布规律为年径流深从东南(700~1 300 mm)向西北(<400 mm)递减,而集中度则呈相反的空间格局(从<0.44增加到>0.59);退水系数及基流系数,在低海拔地区随高程增大而增大(分别为0.55~0.69、0.51~0.73),但在高海拔地区则随高程增大而减小(0.74~0.42、0.79~0.63)。本研究发现,青藏东南径流特征空间规律的控制因子,具有显著的区域分异:在低海拔流域(平均高程<3 000 m),降水是径流特征的主要影响因子;而在高海拔流域(平均高程>3 000 m),仅径流深和集中度受降水控制,其他特征则主要受温度、冻土、地形等条件的共同影响。可见,由于青藏高原东南部降水和冻土对气候变化敏感,该地区水资源时空分布格局将面临很大的不确定性,对此应予以充分重视。     

基于级差最大化组合赋权法的地下水功能评价

针对地下水功能评价中单独采用主观赋权法或客观赋权法存在的问题,结合二者的优点,采用基于主观赋权的层次分析法与客观赋权的熵权法相结合的级差最大化组合赋权法评价地下水功能,并通过计算不同赋权方法权重结果与平均值的相对偏差来衡量级差最大化组合赋权法的准确性。将该方法应用于伊犁河谷西北部平原区浅层地下水功能评价,评价结果基本符合评价区实际情况,验证了该评价方法在地下水功能评价中的可行性。     

滦河上游植被指数NDVI及EVI对SPEI的响应差异

在全球气候变化和人类活动的双重影响下,干旱呈广发、频发态势,使植被生长受到限制,进而影响生态系统结构,威胁区域生态安全。以滦河上游为研究区,基于2000-2019年归一化植被指数NDVI和增强型植被指数EVI,并结合标准化降水蒸散指数SPEI,分析滦河上游气象干旱和植被生长状况的变化趋势及空间分布特征,探讨植被变化对多时间尺度SPEI指数的响应差异。结果表明：近20年滦河上游SPEI呈波动式增大趋势,年尺度SPEI增大速率为0.040 1/a;干旱面积总体减少,减少速率为-1.430 4％/a;NDVI和EVI空间分布特征和变化趋势大体一致,植被覆盖度呈东南高西北低的特征,NDVI和EVI增大速率分别达0.027/a和0.026/a;大部分区域SPEI与植被指数呈正相关,其中NDVI对SPEI-3的响应最强,EVI对SPEI-6的响应最强,总体上EVI与SPEI的相关性强于NDVI;草地对气象干旱最敏感,农田次之,林地最弱。     

一种雷达和摄像机的空间标定方法

多传感器融合技术已经广泛应用在智能汽车环境感知领域中;雷达和摄像机的空间标定是伴随信息实时融合的道路检测技术的基础;结合智能汽车的实际应用,提出了针对激光雷达和摄像机的空间标定方法;通过特制的标定板来获得雷达数据和图像数据,选取激光雷达坐标系作为世界坐标系,通过参数拟合的方法来求取图像坐标系与雷达坐标系的变换关系,进而实现两种传感器的空间配准;该方法只需要标定板就能够完成激光雷达和摄像机的空间标定,标定精度高,实现了多个传感器世界坐标系的统一,避免了后续处理中数据解释的二义性;实验结果表明这种方法简单准确,满足系统要求。     

Practical Issues in Temporal Difference Learning

This paper examines whether temporal difference methods for training connectionist networks, such as Sutton's TD() algorithm, can be successfully applied to complex real-world problems. A number of important practical issues are identified and discussed from a general theoretical perspective. These practical issues are then examined in the context of a case study in which TD() is applied to learning the game of backgammon from the outcome of self-play. This is apparently the first application of this algorithm to a complex non-trivial task. It is found that, with zero knowledge built in, the network is able to learn from scratch to play the entire game at a fairly strong intermediate level of performance, which is clearly better than conventional commercial programs, and which in fact surpasses comparable networks trained on a massive human expert data set. This indicates that TD learning may work better in practice than one would expect based on current theory, and it suggests that further analysis of TD methods, as well as applications in other complex domains, may be worth investigating.     

一种简化的三维模糊制器

针对三维模糊控制器规则多,结构复杂,难以实现的问题,提出了一种简化的三维模糊控制器。该方法是把三个输入量分别为偏差、偏差变化、偏差的偏差变化的一维模糊控制器加权融合实现简化的三维模糊控制器。并提出了根据不同类型的被控对象设置三个加权系数。加权系数具有粗调、细调和微调的作用,类似于传统PID调节器的比例、积分和微分,实现对系统静差的消除。     

Weber定律下尺度空间的自适应构建

在Weber定律的启发下提出一种基于"恰可识别差异"的尺度空间自适应构建方法。Weber定律认为人类感知模式不仅与刺激的变化有关,而且与原刺激强度有关。基于此观点,在图像尺度空间的构建过程中,首先以Marr视觉理论的屋脊型边缘和阶梯型边缘的特征之和计算图像的信息量,再通过实验获得人类视觉能感知到"恰可识别差异"时的图像信息量变化,最后通过曲线拟合自适应构建图像的尺度空间。实验结果表明,该方法充分体现了人类视觉感知特性,与其他尺度空间构建方法相比,在目标匹配实验中,匹配数目提升了25%以上;在去噪实验中,本算法的去噪效果良好。     

人群头发元素分布及某些特征研究

本文介绍用堆中子活化分析法测定人群头发中I、Br、Mg、Mn、Cu、Na、V、Cl、Al、Ca、S等11种元素。研究人发中元素分布特征、相关性特征、性别年令分布特征。方法学中还从法庭科学应用角度介绍同一性识别中差异率和重合几率的双判据方法。     

Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

Efficiently performing high-resolution direction of arrival (DOA) estimation under low signal-to-noise ratio (SNR) conditions has always been a challenge task in the literatures. Obvi-ously, in order to address this problem, the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs. However, it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all. Therefore, this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators. Specifically, these basic spatial spec-trums are employed as the input of multi-source information fusion model. And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines, namely neural networks (NN), support vector machine (SVM), and random forests (RF). The final meta-spec-trum can be obtained by performing a final decision-making method. Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators. Even under low SNR conditions, promising DOA estimation performance can be achieved.