天池大数据竞赛LSTM预测算法分享

美国NGSIM系统的使用手册，方便读者高效的利用NGSIM进行数据下载，完成交通领域的研究Technical Report Documentation Page1. Report No2. Government Accession no3. Recipients Catalog NoFHWA-HOP-06-0124. Title and subtitle5. Report DateNext Generation Simulation(NGSIM) Data Format Planly20046. Performing Organization Code7. Author(s8. Performing Organization report noVijay Kovvali, richard margiotta, Robert franc, vassiliAlexiadis9. Performing Organization Name and Address10. Work Unit NoCAMBRIDGE SYSTEMATIC INC150 CAMBRIDGE PARK DRIVE SUITE 400011. Contract or grant noCAMBRIDGE MA 02140DTFH61-02-C-0003612. Sponsoring Agency Name and Address13. Type of Report and Period CoveredDepartment of transportationFinal reportFederal Highway AdministrationJuly 2003-july 2004Office of Acquisition Management14. Sponsoring Agency Code400 Seventh Street SW, RM 4410Washington, DC 2059015. Supplementary notesFHWA COTR: John Halkias, Office of Operations, and James Colyar, Office of Operations r&d16. AbstractThe Next Generation Simulation Program(NGSIM) Data Format Plan was developed to define thestructure, documentation, and transfer requirements for data that will be collected for estimationcalibration, and validation of core behavioral algorithms. The development of the data Format Plan isbased on existing formats that are relevant to ngsim and augmented to fill in gaps. to this end, a reviewof existing data formats was undertaken and their relevance to NGSiM was assessed. The review includeddata standards developed for intelligent transportation systems(ITS), data formats developed specificallyfor traffic simulation models, and data formats developed for broader transportation applications. Thespecified data formats were developed with the objective of promoting efficient research by maintainingonsistency between data collection and research, and providing consistent storage and transmittalprotocols. On the other hand, this plan intentionally avoids over specification of data formats, so as tominimize unnecessary limitations to research. This document specifies the conceptual data model by meansof Unified Modeling Language UMl class diagrams; the data dictionary in the data standard prescribed bP1489-1999 format developed by the Institute of Electrical and Electronics Engineers(IEEE); the dataexchange structure for data transfer from user to user or from the database/repository to users; and theNGSIM metadata17. Key words1 8. Distribution StatementNext generation simulation, NGSIM, trafficNo restrictions. This document is available to thesimulation, high-level plan, traffic data collection, public through the National Technical Informationvehicle trajectory dataService, Springfield, VA2216119. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No of Pages22. PriceUnclassifiedUnclassifiedForm dot e1700.7(8-72)Reproduction of completed pages authorizedTABLE OF CONTENTSEXECUTIVE SUMMARY1.0 INTRODUCTION1.1High- Level plan context……垂垂垂垂·着垂垂垂垂非垂·非垂垂看垂音非非;·垂垂音看垂看垂1.2 Background1.3 Data Collection Types……1344581. 4 Data Conversion1.5 Data Formats·····.···············.··.···.·;···..·.··..·.···2.0 NGSIM DATA REQUIREMENTS22 Microsimulation Software Data format,…………172.1 NGSIM Data…192.3 Rcquirements for NgsiM data Collection..................193.0 RECOMMENDED NGSIM DATA FORMATS..m. 233.1 NGSIM Data model233.2 NGSIM Data Dictionary……………243.3 NGSIM Metadata.............................253.4 NGSIM Data Exchange Format273.5 File and Directory Naming Convention……293.6 Summary30REFERENCES31APPENDIX A-REVIEW OF EXISTING TRANSPORTATION DATA FORMATS3APPENDIX B-ACCURACY REQUIREMENTS FOR NGSIM DATACOLLECTION,45APPENDIX C-DATA MODEL∴….,,53APPENdIXD-DATA DICTIONARY.APPENDIX E-METADATA. ...........................................................................................99APPENdIX F-SYSTEM-STATE DATA看香音看音香n117List of FiguresFigure 1 Diagram. NGSIM task interdependencies4Figure2. Diagran. Data format classification relevant to ngsim1………Figure 3. Diagram. Top level data model of general traffic simulation55Figure4. Diagram. Influencing factors database packages………………56Figure 5. Diagram. Behavioral models packages57Figure6. Diagran. Facility type generalization…………18Figure 7. Diagram. Traffic management systems generalization......59Figure 8. Diagram. Transit management systems generalizationFigure9. Diagran. nvironment generalization.………………………0Figure 10. Diagram NTCIP Controller class diagram61Figure 11 Diagram Actuated traffic signal controller generalization2Figure12 Diagram. Generalized microsimulation data model………………63Figure 13 Diagram Data concept components and constructs(IEEE Std 1489-1999)66List of tablesTable 1. Example validation data by algorithm categoryTable 2. Summary of NGSiM categorizations for data formatsTable 3. Accuracy requirements for vehicle trajectory data. ..45Table 4. Accuracy requirements for instrumented vehicle data.........46Table 5. Accuracy requirements for wide-area detector data......... 47Table 6. Accuracy requirements for nctwork-rclated data48Table 7. Accuracy requirements for representative transportation managementsystems data52Table 8. Terminology for UMLmodeler54Table 9. Data dictionary for NgSim.67Table 10 processing documentation metadata for ngsimwwwwwm116Table1l. Requisite vehicle trajectory data…………………………117Table 12. requisite wide-area detector data requirements……118EXECUTIVE SUMMARYThe Next Generation Simulation Program(NGsim) Data Format plan was developed todefine the structure, documentation, and transfer requirements for data that will be col-lected for estimation, calibration, and validation of core behavioral algorithms. Thedevelopment of the data format plan is based on existing formats that are relevant toNGSIM and augmented to fill in gaps. To this end, a review of existing data formats wasundertaken and their relevance to ngsim was assessed. The review included data standards developed for intelligent transportation systems (ITS), data formats developed spe-cifically for traffic simulation models and data formats developed for broader transporta-tion applications. The specified data formats were developed with the objective of pro-moting efficient research by maintaining consistency between data collection andresearch, and providing consistent storage and transmittal protocols. On the other handthis plan intentionally avoids overspecification of data formats, so as to minimize unnecessary limitations to researchFour data format components were specified in this document, including: 1)data model,2)data dictionary, 3 )metadata, and 4) data exchange formatNGSIM Data Model- The conceptual data model for NGSIM data formats is pre-sented by means of Unified Modeling Language() class diagrams. Used in con-junction with the data dictionary, the data model allows for construction of a formaldatabase/repository for NGSIM validation dataNGSIM Data Dictionary This provides definition of individual data elementsrequired by NGsim. It follows the data standard prescribed by P1489-1999 formatdeveloped by the Institutc of Elcctrical and Electronics Engineers(ieee)NGSIM Data Exchange Format- The data cxchange structure dcfincs how datashould be transferred from user to user or from the database /repository to users. Thisdocument specifies the framework for developing data exchange formats by providingthe data model and the data dictionary; it also provides clear guidance on the formatstandards with which the data exchange format should conform Currently it doesnot provide specific schema for the data exchange formatsNGSIM Metadata- This includes both traditional metadata(definitions, specificationsand valid value lists for data elements and general information about the dataset andits availability); and processing metadata(what has happened to the data from data col-lection to data archival). Administrative metadata formats were adapted fromContent Standard for Digital Geospatial Metadata(FGDC-STD-001-1998), developedby the Federal Geographic Data Committee(FGDC). Recommendations for NGSiMprocessing metadata are based on the guidance provided in ASTM E2259-03, devel-oped by the American Society for Testing and Materials(ASTm)1.0 INTRODUCTIONThe objectives of the NGsim program include the followingDevelopment of a core set of open behavioral algorithms in support of traffic simulation with a primary focus on microscopic modelingCollection of extensive data that will be used for estimation calibration and validationof the core behavioral algorithms; and storing the data in a repository that can be uni-versally accessedThe High-Level Plan for DatasetsTask E3)identified different kinds of traffic data col-lection methods and technologies and recommended three kinds of data collection effortsfor ngsim, including vehicle trajectory data wide area detector data and instrumentedvehicle dataThis report Task F)presents the documentation, format structure, and transfer requirements for the ngsim data formats for these data collection efforts identified in task e3This report is organized as followsExecutive Summary -Provides an executive summary of this documentSection1.0-Provides an overview and introduction to this report, including the con-text of the data format plan within NGsIM, information on NGsim data collection anddata types, information on data conversion, general information on data formats, anda summary of available transportation data formats and their relevance for ngsimSection 2.0-Presents definitions and categorization for different data types, and pro-vides ngsim data requirementsSection 3.0-Presents data format recommendations for the NGsim program,including a data model, data elements for the data dictionary, metadata to describe thedata collection effort and data exchange formatsReferences-Presents references used in developing this data format planAppendix a-Presents a review of existing transportation data formatsAppendix B-Presents accuracy requirements for NGSiM data collectionAppendix C- Presents a UML representation of the ngsim data modelAppendix D-Presents a high-level NGSIM data dictionaryAppendix E- Presents metadata categories, dictionary, and recommended metadataformats for ngsim1.1 HIGH-LEVEL PLAN CONTEXTInterdependencies among NGSIM tasks are shown in figure 1. The High-Level Plan forDatasets(Task E. 3) presents an assessment of existing datasets of potential use for NGSIM,and makes recommendations on the focus for nGsim data collection methodologies. Thisreport on the data Format Plan task f) provides recommendations on the data exchangeformat(s) for NGSIM data collection efforts. The data formats are also influenced by theHigh-Level Verification and Validation Plan(task e 2)Task E 1-1Core algorithmAssessmentTask e,3Task e.1-2Task e2High-LevelCore AlgorithmHigh-Level Verificationlan for DatasetsPlanandⅤ alidation planTask eData format planFigure 1 Diagram. NGSIM task interdependencies.1.2 BACKGROUNDThe NGSiM field data collection effort pursues data required for developing, estimating,calibrating and validating traffic behavioral algorithms. Tactical route execution, opera-tional driving, and en-route strategic traveler behaviors were identified as the focus of theNGSIM core behavioral algorithm research in the identification and prioritization of coreAlgorithms Task D)report. The High-Level Verification and Validation Plan(task e2)provides an example of the data collection datasets for each algorithm category as shownin table 1. the table illustrates the extent over which data must be collected for each levelof algorithm. For example, for operational driving algorithms, a single stretch of roadwayon a freeway will likely be sufficient, while, for development of tactical driving algo-rithms, the data collection effort should be expanded to include the freeway section andmultiple entry and exit ramps that feed the freeway. The data formats developed in thisplan address the data, both static and dynamic, that are pertinent to the data collectionefforts necessary for developing and validating all three categories of driver behavioralalgorithms4

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包括PPT,及例题源码，及实验程序 绝对的正确，不对找我

异步FIFO的空、满、半满、将空、将满标志都有包含，代码通过modelsim验证

一个LABVIEW登陆界面程序，可实现到主界面的跳转

【实例简介】LSTM-DNN进行库存预测 修正LSTM模型的股价预测 它能做什么？ 它可以给出一只股票收盘价的预测值。 它不能预测离现在太远，而它所能做的就是预测明天的价格。 我们需要做的是提供一只股票过去的数据。 评估 我用MAPE（平均绝对百分比误差）评估模型。 通过该模型训练的某些股票的最佳性能可使MAPE达到2％-3％，这意味着精度（1-MAPE）可以达到97％-98％。 训练自己的数据 U首先从互联网上下载股票的csv数据，然后在代码中修改csv路线（在最后一栏中输入收盘价）。 学习性能良好的CPU模型需要花费数小时。 有用吗？ 尽管预测第二天的收盘价表现良好，但无法预测接下来的2,3,4 ...天的价格。 因此这可能会有所帮助，可能没有帮助。 未来 我将把我的股票价格预测系统放在github上，它具有许多新功能，包括预处理数据，训练模型，预测价格，评估模型以及推荐高收益股票。 同时

【第5部分：确定安全完整性等级的方法示例】GBT 20438.5-2017(IEC 61508-5-2010) 电气 电子 可编程电子安全相关系统的功能安全.pdf

主要研究矩形零件的排样方法，遗传算法的用途在此处体现的淋漓尽致本文算子的选择是有效的为进一步验证算法,对零件数量从16~97的不同算例进行试验,每类有3个例子,其最优排放图均已知,各个算例的基本试验数据(零件数量,板材尺寸)和本文试验最好结果见表2,表2算例2的基本试验数据及本文试验结果Tab 2 Dimension of second exampleand best result given by this paper问题零件最优高度原最优板材所得最低高度()SA+文算法结果(b)SA+最低水平线算法结果种类数量mm尺寸/mmmm2020×2020图1算例1的排放图40×15Fig 1 Layout of first example2860×3032表1本文算法与最低水平线算法排样结果对比C4496060×6064Tab. 1 Difference between our algorithm and7360×90the lowest outline algorithm9712080×120129最小高度最小高度最大高度平均高度运行时间图2给出了部分算例的最好排放结果。 Hopper算法/mm出现频次/mm/mm/ms8. Turton对以上规模不同的矩形件采用BL、BLFSA+最低水平线481/5053.716算法进行排放,允许零件旋转90°,GA、NE、SA、HCSA+本文方法483/505 I48.6等算法搜索排放顺序。文中指出采用BLF排放效果优于BL算法10%~30%,采用SA+BLF算法所得4.2算例2结果最优,见表3。(a)C11(b)C41(c)C61图2算例2采用本文算法所得的排放图ig 2 Best layout of second example with our algorithm表3各类别实例的相对距离百分比1表4各实例运行时间对比表Tab3 Relative distance of best solution toTab 4 Average elapsed time foroptimum height for six cases%six cases with different algorithm问题种类BIBLF SA+BLF本文算法问题A+BLFSA+本文算法174种类ms162.824126.7C41816132120C657.5注:1)表中值表示所得最好结果U与最优值lO)pt差值的白分比C61528189447(U-Op:)/lOpt。宇航材料工艺2007年第4期17对比表2、表3知,本文算法和文献[6]中采用图3表明:矩形排放耗时10ms,经人机交互调BLF解码的综合算法结果相近,并且在零件数量较整后材料利用率为86.4%,比人工排样提高约11少(如n=16)时能获得最优解,与埋论分析一致;由8%。表4知,本文算法的运行时间大大少于BLF算法,这5结论是因为在排放R;时只需搜索当前轮廓线段,比BLF实际算例表明最低轮廓线搜索算法能有效地进算法(搜索所有空域区域)搜索空间减少,因此效率明行矩形件排放,与模拟退火算法相结合,能在较短时显提高。由于文献[6的运行环境是:处理器奔腾间内获得与BLF算法相近的排放结果,并且在零件200MHκ,RAM65M, Windows nt4.0;而本文运行数量较少时能获得最优解,是解决大规模矩形件排放环境为:CPU2.8GHz,RAM512M,其速度大约是问题的有效方法200MHz处理器的15倍,因此表4所给BLF混合算参考文献法的运行时间做了相应处理。可见采用轮廓搜索法1张丽萍,张春丽,蒋寿伟.皮料优化排样的有效方法与BLF算法可获得相近的排放效果,但前者效率明软件学报,2005;16(2):316~323显高于后者。文献[7采用启发式递归(HR)算法对2曹炬,周济,余俊.矩形件排样优化的背包算法.中国以上算例进行求解,大大提高了运行效率,但在零件机械工程,1994;5(2):11~12数量较多时其速度也明显低于本文算法。因此最低3曹炬.二维异形切割件优化排样的拟合算法.中国机轮廓搜索法可用于求解大规模矩形件的排样问题。械工程,2000;11(4):438~4414.3应用举例1 Jakobs S On genetic algorithms for the packing of针对不规则复合材料铺层,采用矩形包络法求出 polygons,Eur. of oper,Res.,1996881):165-181其包络矩形,然后采用上述算法进行排放。图3是飞5贾志欣.面向发电设备制造的下料优化排样原理与关机坐舱罩顶棚的铺层展开数据采用以上策略获得的键技术,四川大学博士学位论文,2002排放图。6 Hopper E, Turton B C H. An empirical investigationof meta-heuristic and heuristic algorithms for a 2D packingproblem. EurJ of Oper Res, 2001; 128(1): 34577 Zhang Defu, Kang Yan, Deng Ansheng. A new heuristicrecursive algorithm for the strip rectangular packing problemComputers &. Operations Research, 2006; 33(8): 2209-2 217图3复合材料铺层排放实例(编辑李洪泉)ig. 3 Layout for composites plys18宇航材料工艺2007年第4期矩形件优化排样的研究旧万数据WANFANG DATA文献链接作者:邓冬梅,厝米水,安鲁陵,王桂宾, Deng Dongmei, Zhou laishui, An Luling,Wang guibin作者单位:南京航空航天大学机电学院,南京,210016刊名宇航材料工艺sTc|PKU英文刊名:AEROSPACe mATERIALS technology年,卷(期):2007,37(4)被引用次数4次惨考文献(条)1.张丽萍.张春丽.蒋寿伟皮料优化排样的有效方法[期刊论文]软件学报2005(02)2.曹炬.周济.余俊矩形件排样优化的背包算法[期刊论文]中国机械工程1994(02)3.曹炬二维异形切割件优化排样的拟合算法「期刊论文]中国机械工程2000(044.Jakobs S On geretic algorithms for the packing of polygons 1996 (05.贾志欣面向发电设备制造的下料优化排样原理与关键技术[学位论文]20026. Hopper E Turton B C H An empirical investigation of meta-heuristic and heuristic algorithms for a 2Dpacking problem 2001(01)7. Zhang Defu. Kang Yan. Deng Ansheng A new heuristic recursive algorithm for the strip rectangularpacking problem 2006 (08)相似文献(1条)1.学位论文邓冬梅复合材料铺层排样抆术硏究与开发2007复合材料因其比强度高、比模量大、材料的刚度和强度可设汁等一系列优点,在航空航天领域得到广泛应用,但高昂的价咯成了复合材料应用的最大壁垒。国外的硏究和应用成果表明数字化技术是降低复合材料构件制造成本、提高构件性能的有效途径。目前国内主要还以手工没计和手工制造为主、自动化程度不高,不仅浪费人力、物力,而且产品质量难以保证,因此有必要对复合材料数字化技术进行研究。优化排样是复合材料构件数字化生产过程中的重要环节。本文在研宄各种排样算法的基础上,提岀丁新的矩形件排样算法、优化算法以及不规则样片的排样算法,并与复合材料铺层排样的特点相结合开发了复合材料铺层排样软仁。主要研究内容和创新点如下矩形件排样不仅适用于矩形样片的排放,也是不规则样片排咩的基础。本文在建立矩形件排样数学模型的基础上,介绍了各种常见的定序列矩形件排样算法并分析其特点,提出了一种新的启发式排样算法——最低轮廓线搜索算法。该算法满足“最下最左”条件,克服了其他排样算法对某些排栏图不能给出排列的缺点,实验结果表明该算法排样效果好于最低水平线算法和最下最左(BL)算法。利用该算法实现了大量不同规格图纸的集中出图,省时省力,节约氏张2050%。矩形件排样问题具有图形运算和组合优化两方面的特性,单纯的排样算法只能解决图形运算问题,样片的排放顺序对排样结果同样重要。针对较小规模(一般少于100个图形)的矩形件排样问题,本文提岀了模拟退火与最低轮廓线搜索算法相结合的综合优化算法。对于十多个图形的排样,该算法可短时间内求得最优舾:对于近百个图形的排样,在排样效果相当旳情冮下,该亥算法比其基于模拟退火的综合优化算法效率提髙百以饣。针对大规模矩形件排样问题本文提出了蚁群笪法与最低轸廓线搜索算法相结合旳综合优化算法,该算法比模拟退火与最低轮廓线算法相结合的综合优化算法效率提高十倍以上。不规则图形排栏是所有排样研究中的热点和难点。本文将不规则样片简化成多边形进行排样,提出了两种不同的解法方法:一是基于矩形的排样方法,二是直接对多边形进行排样。基于矩形求解不规则样片排样时,将图形运算、矩形件排样算法及交互调整相结合,提出了基于矩形的多边形综合排样算法。通过各种优化组合策略,对单一样片和多种样片进行组合求其最小包络矩形,从而将不规则形状样片排样转化为矩形件排样进行求解。直接冄放多边形时,重点研究两个多边形的临界多边形(NFP)的求解。首先对基于倾斜图法的NFP求解法进行了改进和优化,完善了凹、凸两多边形NFP的求解,然后提出了适用于任意两多边形N求解的边界绕行法,该方法比基于倾斜图的求解方法适用范围广,计算简单、效率高。根据复合材料构件数宇化生产的主要过程,分析总结了复合材料铺层排样的特点,并将伉化排样算法与复合材料铺层排样的特点相结合,设计丌发了复合材料构件铺层排栏软件系统。引证文献(3条)1.卢远志杨建新.文桂林.周兵.钟志华基于排样思想的工程图坐标尺寸防干涉方法[期刊论文]中南大学学报(自然科学版)2010(2)2.张伟.安鲁陵.邵挠眀.郑盈一种矩形件分层排样算法[期刊论文]宇航材料工艺2010(1)3.陈婷.许超钣金零件排样技术及其发展[期刊论文]锻压装备与制造技术2008(4)本文链接http://d.wanfangdata.comcn/periodicAlyhclgy200704005.aspx授权使用:广东工业大学图书馆( gdgydxtsg),授权号:4flc88c5-bfdd-4dec-8ebf-9ec501113fe6下载时间:2011年4月14日

【实例简介】23种设计模式

FPGA有关的cordic讲解，xilinx公司ppt型的详细讲解，中文。从原理到实现（模型的建立等）。简介目前的具有许多乘法器和加法器。然而各种各样的通信技术和矩阵算法则需要三角函数、平方根等的运算如何在上执行这些运算可以使用查找表或是迭代法本节介绍了算法这是一个移位相加算法允许计算不同的三角函数例如0.0.0包括除法和对数酾数在内的其它函数。xⅫNX关于算法的细节问题,可参见下面的材料技术并不是什么新鲜的东西。事实上它可以追溯到年由发表的一篇文章。在上个世纪五十年代,在大型实际的计算机中的实行移位相加受到了当时技术上的限制,所以使用变得非常必要。到了七十年代,和其他公司出产了手持计算器,许多计算器使用一个内部单元来计算所有的三角函数(了解这件事的人们一定还记得,那时求一个角度的正切值需要延迟大约1秒中)二十世纪八十年代,随着高速度乘法器与带有大存储量的通用处理器的出现, CORDIC算法变得无关紧要了。然而在二十一世纪的今天,对于来说,定是在应用中(诸如多输入多输出(波束形成以及其他自适应系统)计算三角函数的备选技术。wwwsteepestascenCO1笛卡尔坐标平面旋转在坐标平面上将点(,)旋转0角度到点(,)的标准方法如下所示00这被称为是平面旋转、向量旋转或者线性矩阵代数中的旋转。xⅫNX上面的方程组同样可写成矩阵向量形式00例如一个相移为wwwsteepestascenCO1伪旋转通过提出因数,方程可写成下面的形式000(0)如果去除0项,我们得到伪旋转方程式0)6(0)即旋转的角度是正确的,但是与的值增加9倍由于所以模值变大。注意我们并不能通过适当的数学方法去除0项然而随后我们发现去除θ项可以简化坐标平面旋转的计算操作。xⅫNX在坐标平面中0因此经过伪旋转之后,向量的模值将增加0倍。向量旋转了正确的角度但模值出现错误。wwwsteepestascenCO1方法方法的核心是伪旋转角度θ,其中θ。故方程为下面的表格指出用于算法中每个迭代的旋转角度精确到位小数xⅫNX在这里,我们把变换改成了迭代算法。我们烀各种可能的旋转角度加以限制,使得对任意角度θ的旋转能够通过一系列连续小角度的旋转迭代来完成。旋转角度遵循法则:0,遵循这样的法则,乘以正切项变成了移位操作。前几次迭代的形式为第次迭代旋转第次迭代旋转第次迭代旋转等很显然,每次旋转的方向都影响到最终要旋转的累积角度。在≤日的范围内的任意角度都可以旋转。满足法则的所有角度的总和0为。对于该范围之外的角度,可使用三角恒等式转化成该范围内的角度。当然,角分辨率的数据位数与最终的精度有关。。因此,在次旋转以后,为了标定伪旋转的幅度,要求乘以一个系数角分辨率的数据位数对最终的旋转精度非常关键。wwwsteepestascenCO1