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。     

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.     

战略性新兴产业创新空间格局及影响因素研究——以湖南省为例

应用ArcGIS软件,选取2010年、2016年两个时间节点,用空间核密度、空间自相关、地理探测器等方法,分析了湖南省战略性新兴产业技术创新的空间分布与集聚特征,结果表明:湖南省战略性新兴产业创新活动整体呈现"东北多,西南少"的分布态势,空间集聚分布特征明显,长株潭城市群为空间热点与核心区域;专利数量空间核密度分布存在明显的"区域差异",具体表现为大面积的块状分布和小面积的零星核分布并存;湖南省战略性新兴产业创新空间分布主要受科学研究和技术服务业单位数、人均GDP、固定资产投资、农业机械总动力、消费品零售总额、卫生技术人员数等因素的影响。     

光热电站容量效益评估及影响因素研究

光热发电作为一种清洁能源,配置储热后具有良好的调节性能,近年得到大力发展。依据自身经济性,光热电站往往配备较大的储热装置,能够平移所吸收的光热能,且具有跨日调节能力,使得光热机组有一定替代常规机组带负荷的能力,即容量效益。本文提出一种基于等可靠性的光热电站容量效益评估方法,考虑了调峰方式、储热时长、新能源规模、光热电站规模对光热电站容量效益发挥的影响,采用基于数学优化的生产模拟仿真程序,以周为尺度,计算全年8760 h的系统运行状态,计及了机组启停、水电跨日调节、抽蓄跨日调节、光热电站的跨日调节等因素。最后通过对西北地区实际电网的仿真,验证了所提方法的有效性。     

石化企业循环水系统的节水运行与管理

围绕工业循环冷却水系统的工艺原理及水质特点，全面分析阐述了石化循环水系统开展节水运行管理的途径方法及其注意事项，天津石化公司乙烯厂在此方面取得了良好的效果。     

50年来辽宁省不同等级舒适日数演变及空间区划分析

根据1964—2015年辽宁省23个气象站点的逐日平均气温、相对湿度及平均风速资料,采用人体舒适度指数法计算人体舒适度指数,在季尺度上分析辽宁省不同等级舒适日数演变情况及典型旅游城市最佳旅游期;运用旋转经验正交函数分解法(REOF),分析人体舒适度指数季尺度空间变化特征,并应用ArcGIS软件进行舒适区域划分。结果表明:①辽宁省多年平均冷不舒适日数呈降低趋势,舒适日数呈增加趋势,热不舒适日数也呈微弱增加趋势;舒适程度夏季＞秋季＞春季＞冬季;②除冬季外5座典型旅游城市均适宜旅游,夏季为最佳旅游期;其中,沈阳、本溪更适宜春季旅游,大连更适宜秋季旅游。③辽宁省四季舒适度变化情况可大致划分为辽西、辽中、辽东三个区域,具体划分情况随季节有相应变化,总体舒适度由辽宁东部向西部逐渐递减。     

Peer-to-peer bichromatic reverse nearest neighbours in mobile ad-hoc networks

The increasing use of mobile communications has raised many issues of decision support and resource allocation. A crucial problem is how to solve queries of Reverse Nearest Neighbour (RNN). An RNN query returns all objects that consider the query object as their nearest neighbour. Existing methods mostly rely on a centralised base station. However, mobile P2P systems offer many benefits, including self-organisation, fault-tolerance and load-balancing. In this study, we propose and evaluate 3 distinct P2P algorithms focusing on bichromatic RNN queries, in which mobile query peers and static objects of interest are of two different categories, based on a time-out mechanism and a boundary polygon around the mobile query peers. The Brute-Force Search Algorithm provides a naive approach to exploit shared information among peers whereas two other Boundary Search Algorithms filter a number of peers involved in query processing. The algorithms are evaluated in the MiXiM simulation framework with both real and synthetic datasets. The results show the practical feasibility of the P2P approach for solving bichromatic RNN queries for mobile networks.     

Computational Problem Solving in Spatial Substrates -- A Cognitive Systems Engineering Approach

The ability to perform spatial tasks is crucial for everyday life and of great importance to cognitive agents such as humans, animals, and autonomous robots. A common artificial intelligence approach to accomplish spatial tasks is to represent spatial configurations and tasks in form of detailed knowledge about various aspects of space and time. Suitable algorithms then use the representations to compute solutions to spatial problems. In comparison, natural embodied and situated agents often solve spatial tasks without detailed knowledge about geometric, topological, or mechanical laws; they directly relate actions to effects that are due to spatio-temporal affordances in their bodies and environments. Accordingly, we propose a paradigm that makes the spatio-temporal substrate an integral part of the engine that drives spatial problem solving. We argue that spatial and temporal structures in body and environment can substantially support (and even replace) reasoning effort in computational processes: physical manipulation and perception in spatial environments substitute formal computation. While the approach is well known – for example, we employ diagrams as spatial substrate for geometric problem solving and maps for wayfinding – the underlying principle has not been systematically investigated or formally analyzed as a paradigm of cognitive processing. Topology, distance, and orientation constraints are all integrated and interdependent in truly 2- or 3-dimensional space. Exploiting this fact may not only help overcome the need for acquiring detailed knowledge about the interrelationships between different aspects of space; it also can point to a way of avoiding exploding computational complexity that occurs when we deal with these aspects of space in complex real-world scenarios. Our approach employs affordance-based object-level problem solving to complement knowledge-level formal approaches. We will assess strengths and weaknesses of the new cognitive systems paradigm.     

基于随机词汇迭代模型的POI分类检索

从特征选择、局部区域划分和词汇语义相似性计算入手,利用随机词汇迭代模型(random terms iterativemodel,RTIM)进行海量兴趣点(point of interest,POI)文本分类.通过词汇频度、集中度和离散度方法筛选出特征词汇;依据文本与各POI类别间的相似度进行局部区域划分;在每个局部区域内基于词汇在文本中的排列顺序构建词频向量,基于词频向量中词频的随机删除和重构,获取特征映射矩阵;通过特征映射矩阵将文本转为特征向量,并采用SVM分类器进行POI文本分类.实验证明,该方法有效提升了POI文本分类准确性和覆盖率.     

基于各向异性滤波和空间FCM的MRI图像分割方法

针对具复杂目标和边界模糊的MRI图像中多感兴趣区域的分割中分割MRI图像软组织难的问题, 提出了一种基于各向异性滤波和空间模糊C-均值聚类(SFCM)的MRI图像分割方法; 用新型各向异性滤波对图像进行预处理, 解决去噪平滑的同时弱化图像细节的问题; 用邻域空间信息设计空间函数, 改进传统FCM的目标函数; 用图像的空间信息实现图像各目标准确分类、有效解决孤立区域的正确归类问题, 进而使分割区域完整; 用直方图拟合曲线初始化分类数和初始聚类中心, 加快算法迭代到最优解, 进而减少运行时间。通过实验证实了各向异性滤波和空间FCM的MRI图像分割方法的综合应用显著提高了分割灰度重叠、目标不连续和目标边界模糊的MRI图像的分割效果。