应用剖分遥感影像模板快速提取作物种植面积

全球剖分理论为全球海量遥感影像数据的组织管理和多尺度遥感影像的作物提取和识别提供新的解决思路．结合基于地图分幅扩展的全球剖分模型及其剖分面片的几何特征,阐述剖分遥感影像模板的概念模型和数据模型,提出利用剖分遥感影像模板来提取作物种植面积的处理流程．并给出不同尺度范围提取作物面积适宜选取的剖分级别和影像分辨率。采用高分辨率遥感影像初步尝试对河南省许昌地区小麦种植面积进行提取,通用遥感影像处理软件相比,其精度和速度都有一定的提高．     

一种改进的粒子群算法稳定性证明及其应用

针对改进的粒子群算法缺乏理论上稳定性证明及其相应的参数选择问题．利用李雅普诺夫稳定性理论对个体决策粒子群算法给予稳定性证明,并给出相应的参数选择方式,改变传统粒子群算法只能从仿真角度说明该算法的稳定性。采用几个常用的测试函数进行仿真实验,与其他改进的粒子群算法相比,结果表明该算法具有更好的性能。     

基于树搜索算法的多用户MIMO系统的自由度分配

针对最大化多用户MIMO系统的和速率的问题,提出了将迭代式干扰对齐技术（Iterative Interference Alignment：ⅡA）和系统自由度（DoF）分配策略相结合的一种算法.系统的DoF分配是通过树搜索算法（Tree search）来实现的.理论分析及实验仿真结果表明,该算法获得的系统容量,接近遍历法（exhausting search：EX）的结果,但是在算法复杂性及收敛时间上明显优于遍历法.     

基于欧氏距离及向量内积的骨架提取算法

对骨架算法进行研究,提出一种骨架提取算法.通过对图像内部像素点进行距离变换得到其最近边界点的位置,将内部像素点到最近边界点的向量定义为边界向量,根据物体内部相邻边界向量的方向,计算每个像素点的内积值和其8邻域的最小内积值,得到的最小内积点,以确定的阈值从最小内积点中选取骨架种子点,再对骨架种子点进行处理,得到连通的骨架.试验证明这种算法能保证骨架具的完整性和连通性,正确反映物体的拓扑结构.     

Relay selection with feedback beamforming information through designed sector sweep report frame for mmWave WPANs

This paper proposes a novel relay selection strategy based on the feedback beamforming （BF） information through designed sector sweep （SSW） report frame for millimeter-wave （mmWave） wireless personal networks （WPANs）. First, an SSW report frame compatible with IEEE 802.11ad standard is designed. Second, an approach collecting instantaneous channel state information （CSI） overheard during BF is devised. Third, with the aim of minimizing the outage probability and maximizing the overall network throughput capacity, the optimal relay selection issue for non-line-of-sight （NLoS） links is formulated as a bipartite graph, and Kuhn Munkres （KM） algorithm is provided to resolve it. Both theoretical analysis and simulation results show, with CSI considering NLoS conditions and selected relays according to the overall network throughput capacity maximization principle, the improvements achieved over opportunistic relay selection strategy in terms of overall network throughput capacity and outage probability with minimal modifications to IEEE 802.1lad.     

A novel initial ranging algorithm for OFDMA systems using smart antenna

To improve the capacity of initial ranging （IR） users simultaneously accessing into network and enhance the performance of the IR, a novel IR algorithm is presented for the orthogonal frequency division multiple access systems with the smart antenna. The beamforming weight vectors provided by the antenna array are introduced for the IR users, and the procedures of the multiuser parameter estimation and the interference cancellation are designed. The proposed method can improve the accuracy of the channel estimation of active paths and efficiently reduce the residual multiple access interference. Simulation results show that the proposed method has a much better performance than the available successive multiuser detection and interference cancellation algorithm and accommodates more active ranging users simultaneously accessing into a cell.     

湛江电网故障统计分布及台风演化分析

研究湛江电网故障统计分布规律,发现湛江电网故障具有明显的幂律特性,去除故障极值日的数据呈指数分布.而故障极值日均伴有极端天气,表明湛江电网故障的幂律特性因极端天气引发.     

Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing

The proliferation of Building Information Modeling (BIM) applications, in tandem with the extensive variation of building products, pose new demands on design and engineering firms to efficiently manage and reuse BIM content (i.e., data-rich parametric model objects and assembly details). Tasks such as classifying BIM objects, indexing them with meta-data (e.g., category), and searching digital libraries to load objects into models still plague practice with inefficient manual workflows. This research aims to improve the productivity of BIM content management and retrieval by developing an AI-backed BIM content recommender system. Using data from a case-study firm, this research extracted content from over 30,000 technical BIM views (e.g., plans, sections, details) in historical projects to build an unsupervised machine-learning prototype with association rule mining. This prototype explicated the strength of relationships among co-occurring BIM objects. Using this prototype as the backbone AI-engine in live BIM sessions, this research developed a context-aware recommender system that dynamically provides BIM users with a set of objects associable with their modeling context (e.g., type of view, existing objects in the model) and human–computer interactions (e.g., objects selected by the user). By mining association data from hundreds of historical projects, this development marks a departure from the existing prototypes that rely on explicit coding, recurring user input, or subjective ratings to recommend BIM content to users. The simulation and experimental implementation of this recommender system yielded high efficacy in predicting content needs and achieved significant savings in the time spent on conventional BIM workflows.     

A joint introduction to Gaussian Processes and Relevance Vector Machines with connections to Kalman filtering and other kernel smoothers

The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and interpretability via uncertainty analysis. This article introduces and discusses two methods which straddle the areas of probabilistic Bayesian schemes and kernel methods for regression: Gaussian Processes and Relevance Vector Machines. Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering. Overall, we provide understanding of the mathematical concepts behind these models, and we summarize and discuss in depth different interpretations and highlight the relationship to other methods, such as linear kernel smoothers, Kalman filtering and Fourier approximations. Throughout, we provide numerous figures to promote understanding, and we make numerous recommendations to practitioners. Benefits and drawbacks of the different techniques are highlighted. To our knowledge, this is the most in-depth study of its kind to date focused on these two methods, and will be relevant to theoretical understanding and practitioners throughout the domains of data-science, signal processing, machine learning, and artificial intelligence in general.     

基于溶液图像时序特征的元素组分含量动态监测系统

针对稀土萃取过程中组分含量难以实时监测以及现有组分含量检测方法耗时、耗内存的现状,设计了一种基于溶液图像时序特征的元素组分含量动态监测系统。首先使用图像采集装置获取萃取槽体溶液的时序图像,考虑萃取液颜色特性和单一颜色空间的不全面性,采用主成分分析（PCA）方法在HSI和YUV融合的颜色空间提取图像的时序特征,并结合生产指标构造基于鲸鱼优化算法（WOA）的最小二乘支持向量机（LSSVM）分类器来对工况状态进行判断。然后当工况处于非最佳状态时,在HSV颜色空间对图像提取颜色直方图和颜色矩特征,并开发以溶液图像间的混合特征差值的线性加权值为相似度度量的图像检索系统,从而获取组分含量值。最后进行镨/钕萃取槽体混合溶液测试,结果表明该系统能够实现元素组分含量的动态监测。