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关键词： 交通机构   标准数据格式   公共交通   解读   Google   交通出行   数据结构   数据上传  

摘要： 如果你是公共交通从业者,又碰巧关注美国公共交通开放数据,那你很难没有听说过GTFS(General Transit Feed Specification)数据。简单来说,GTFS是一种用于分享公共交通车站、线路和时刻的标准数据格式。GTFS诞生于2005年,波特兰地区的大都市交通局(Tri-County Metropolitan Transportation District of Oregon)为了能让乘客在谷歌地图上计划公共交通出行,与谷歌合作制定了一种数据格式,用来与谷歌地图的数据结构融合,这便是GTFS的前身。那时GTFS还代表着Google Transit Feed Specification(谷歌公共交通数据上传规范)。

关键词： CATIA   船体模型   数据结构   OpenCasCade  

摘要： 以CATIA中建立的船体模型为数据源,分析其输出的STP标准格式文件数据结构,建立基于STP文件的不同实体对象的组织关系,并应用OpenCasCade开源包编写解析STP文件的程序,对STP文件进行解析,重构文件组织结构,实现文件数据的信息提取,生成验证效果良好的三维几何模型,为不同软件之间结构模型交换提供解决方案。

关键词： 数据结构   数据结构  

ISBN: (纸本)9787519821944

摘要： 本书作者强调实践知识和技能胜过理论，在书中为你展示了怎样使用数据结构实现有效的算法，并分析和测试了算法的性能。你将探索Java集合框架(JCF)中重要的类，它们是如何实现的，以及如何执行。书中的每一章都提供了动手练习及在线测试代码。

关键词： 室内定位   位置指纹定位   多映射数据结构   WKNN算法  

摘要： 在离线阶段对指纹库的构建采用多映射的数据结构,使每个采样点坐标对应同一AP的一个或多个RSSI信息值。在线匹配阶段,通过计算待定位点的RSSI信息与离线数据库的指纹信息的欧式距离,欧式距离值越小,说明两者的拟合程度越高,计算出两者拟合程度的最小值,以此找出与待定位点相关的指纹点。最后,使用WKNN匹配算法求出待定位点的估计坐标。

关键词： 双向重构   程序设计   数据结构   教学衔接  

摘要： 为实现程序设计与数据结构课程的良好衔接,对两门课程的知识点进行对比和分析,提出基于"双向重构"思想的数据结构、算法和程序的递进模型,介绍该模型的概念模型、逻辑模型和实现模型的设计,最后通过实例分析和总结实践结果。

关键词： 数据结构   软件工具  

ISBN: (纸本)9787115485779

摘要： 本书介绍了线性表，栈，队列，串，树和图等基本数据结构，以及这些数据结构的相关应用，还介绍了查找和排序的常用算法。本书介绍内容时理论和实现并重，并配有一定数量的上机实验和习题用于帮助读者巩固和加深对相关知识点的学习。

关键词： 数据结构  

ISBN: (纸本)9787563554362

摘要： 本书共分为绪论 ; 线性表 ; 堆栈和队列 ; 数组 ; 字符串 ; 广大义表 ; 树与二叉树 ; 图 ; 查找 ; 排序十章, 其主要内容包括: 基本术语 ; 算法的概念 ; 算法描述 ; 算法分析 ; 算法设计的基本步骤等。

关键词： Multi-player game playing   Adaptive data structures   Move ordering   Alpha-beta search  

摘要： This paper considers the problem of designing novel techniques for multi-player game playing, in a range of board games and configurations. Compared to the well-known case of two-player game playing, multi-player game playing is a more complex problem with unique requirements. To address the unique challenges of this domain, we examine the potential of employing techniques inspired by Adaptive Data Structures (ADSs) to rank opponents based on their relative threats, and using this information to achieve gains in move ordering and tree pruning. We name our new technique the Threat-ADS heuristic. We examine the Threat-ADS' performance within a range of game models, employing a number of different, well-understood update mechanisms for ADSs. We then extend our analysis to specifically consider intermediate board states, which are more interesting than the initial board state, as we do not assume the availability of "Opening book" moves, and where substantial variation can exist, in terms of available moves and threatening opponents. We expand this analysis to include an exploration of the Threat-ADS heuristic's performance in deeper ply game trees, to confirm that it maintains its benefits even when lookahead is greater, and with an expanded examination of how the number of players present in the game impacts the performance of the Threat-ADS heuristic. We find that in nearly all environments, the Threat-ADS heuristic is able to produce meaningful, statistically significant improvements in tree pruning, demonstrating that it serves as a very reliable move ordering heuristic for multi-player game playing under a wide range of configurations, thus motivating the use of ADS-based techniques within the field of game playing.

关键词： Multilinear algebra   principal component analysis (PCA)   tensor   tensor decomposition   tensor PCA  

摘要： The widespread use of multisensor technology and the emergence of big data sets have brought the necessity to develop more versatile tools to represent higher order data with multiple aspects and high dimensionality. Data in the form of multidimensional arrays, also referred to as tensors, arise in a variety of applications including chemometrics, hyperspectral imaging, high-resolution videos, neuroimaging, biometrics, and social network analysis. Early multiway data analysis approaches reformatted such tensor data as large vectors or matrices and then resorted to dimensionality reduction methods developed for classical two-way analysis such as principal component analysis (PCA). However, one cannot discover hidden components within multiway data using conventional PCA. To this end, tensor decomposition methods which are flexible in the choice of the constraints and that extract more general latent components have been proposed. In this paper, we review the major tensor decomposition methods with a focus on problems targeted by classical PCA. In particular, we present tensor methods that aim to solve three important challenges typically addressed by PCA: dimensionality reduction, i.e., low-rank tensor approximation;supervised learning, i.e., learning linear subspaces for feature extraction;and robust low-rank tensor recovery. We also provide experimental results to compare different tensor models for both dimensionality reduction and supervised learning applications.

关键词： C语言  

ISBN: (数字)9787115476036 ISBN: (纸本)9787115476036

摘要： 本书统一采用抽象数据类型ADT的设计、表示、实现、典型应用几个过程逐一描述和讨论各种数据结构和算法，使用了丰富的图示、增加了常见错误讲解、对重点概念和算法做了慕课讲解、每章后附带配套练习。主要内容包括：绪论、线性表、栈和队列、树及二叉树、图、查找、内排序。