2015年美国数学建模竞赛A题
2015年数学建模国赛A题
二、 问题分析
问题一要建立直杆影子长度变化的数学模型， 首先需知道太阳影子长度计算 公式，故引入太阳高度角[1]这个概念。即若已知某时刻太阳高度角的大小和直 杆高度，根据其满足的三角函数关系便可得到此时太阳影子长度。太阳高度角与 观测地地理纬度、地方时角和太阳的赤纬[2]相关。其中太阳赤纬是太阳直射点 所在纬度，与日期有关；时角由当地经度及其所用时区时间决定，故根据影长、 太阳赤纬、时角计算公式可求得直杆影子长度变化模型，并根据模型分析影子长 度关于各参数的变化规律。将附件一中直杆的有关数据直杆影长变化模型中，可 求出该直杆的具体影长变化公式。根据所建立的模型，运用 MATLAB 软件便可得 到影子长度随时间的变化曲线。 问题二需根据某固定直杆在水平地面上的太阳影子顶点坐标数据， 建立数学 模型确定直杆所处的地点。首先由问题一可推测影子长度与时间的关系，故可将 太阳影子长度与对应时间进行拟合，得到影长与时间关系模型。当某个时刻影长 得到极小值时，该时刻为太阳与直杆距离最近，即地方时正午 12 时，结合当地 所使用的标准时间便可得到当地经度。 最后利用太阳高度角与直杆长度以及影长 满足的三角关系式，便可得到影长关于直杆高度、直杆所在地点的纬度的函数关 系式，即得到了有关太阳影子顶点坐标与直杆地点经纬度的模型。将附件一中影 子顶点坐标数据应用于该直杆位置模型，可得到直杆所在位置。用相对误差分析 法分析误差[3](168-169 页)，若所得的相对误差小于 2.5%，认为得到的模型合 理。 问题三可根据光照成影原理和太阳高度角计算公式建立影长与时间变 化模型，根据相关数据，运用 MATLAB 软件拟合可得到直杆所在位置的经纬 度。令年份均为 2015 年，根据太阳赤纬角计算公式，可求解具体的日期。 将附件 2 和附件 3 时间和对应直杆影长数据分别代入模型中，通过拟合计
2015年美国数学建模竞赛第二次模拟赛题
Problem A Warmer Days or Sour Grapes ?The high quality of wines（葡萄酒）produced in the Finger Lakes Region（五指湖区）of upstate （北部）New York is widely known. Proximity（接近）to lakes tempers the climate and makes it more suitable for growing several varieties of premium（独特）grapes: R iesling（雷司令）, G ewürztraminer（琼瑶浆）,C hardonnay（霞多丽）, M erlot（梅洛）, P inot Noir（黑比诺）, and CabernetF ranc（品丽珠）. (There are many more, but we will restrict（限制）the discussion to these six to simplify（简化）the modeling.) Each variety has its own preferred “average temperature” range but is also different in its susceptibility（感受性）to diseases and ability to withstand（抵抗）short periods of unusually cold temperature.As our local climate changes, the relative suitability of these varieties will be changing as well. A forward-looking winery（酒厂）has hired your team to help with the long-term planning. You will need to recommenda) the proportion（比例）of the total vineyard（葡萄园）to be used for growing each of the above six varieties;b) and when should these changes be implemented （实施）(based on observed temperatures and/or current market prices for each type of wine).Naturally, the winery is interested in maximizing its annual profit. But since the latter （后者）is weather-dependent, it might vary a lot year-to-year. You are also asked to evaluate the trade-offs （权衡）between optimizing the expected/average case versus the worst(-realistic-)scenario（情景）.Things to keep in mind:Climate modeling is complicated（复杂）and predicting the rate of “global warming” is a hotly debated area. For the purposes of this problem, assume that the annual average temperature in Ithaca（伊萨卡）, NY will increase by no more than 4°C by the end of this century.It is not all about the average temperature – a short snap（临时）of sub- zero（零度）temperature in late Ferburay or early March (after the vines already started getting used to warmer weather) is far more damaging than the same low temperature would be in the middle of the winter.It takes at least 3 years for a newly planted vine to start producing grapes suitable for winemaking.Problem B Outlook of Car-to-Car TechSAN FRANCISCO -- After more than a decade of research into car-to-car communications, U.S. auto safety regulators took a step forward today by unveiling their plan for requiring cars to have wireless gear that will enable them to warn drivers of danger.These vehicle-to-vehicle (V2V) transmitters and software could save thousands of lives and prevent hundreds of thousands of crashes each year by providing cars with information they never will be able to gather simply from cameras and sensors. “Safety is our top priority, and V2V technology represents the next great advance in saving lives,” Transportation Secretary Anthony Foxx said in an announcement. “This technology could move us from helping people survive crashes to helping them avoid crashes altogether.”Requirement 1: Present a mathematical model to discuss the reduction of the number of traffic accidents and road fatalities/injuries in San Francisco by V2V technology. Requirement 2: Determine the maximum number of cars in San Francisco due to the V2V technology.Requirement 3: Discuss the benefits of V2V technology to alleviate road congestion. Requirement 4: Provide your recommendation to the government.Prblem C Forest FiresOne major environmental concern is the occurrence of forest fires (also called wildfires), which affect forest preservation, bring economical and ecological damage and endanger human lives. Such phenomenon is due to multiple causes (e.g. human negligence and lightnings). Despite an increasing of state expenses to control this disaster, each year millions of forest hectares (ha) are destroyed all around the world.Fast detection is an important element for successful firefighting. Traditional human surveillance is expensive and affected by subjective factors, there has been an emphasis to develop automatic solutions, such as satellite-based, infrared/smoke scanners and local sensors (e.g. meteorological). Propagation models try to describe the future evolution of the forest fire given an initial scenario and certain input parameters. Modeling the dynamical behavior of fire propagation in a forest is helpful for creating scheme to control and fight fire.Requirement 1 Describe several different metrics that could be used to evaluate the effectiveness of fire detection. Could you combine your metrics to make them even more useful for measuring quality?Requirement 2 Model the dynamical behavior of fire spread in a forest. Requirement 3 Discuss the factors to affect fire occurrence. Which factors are the most critical in causing fires. Build mathematical models to predict the burned area of fires using Meteorological Data.Requirement 4 Give y our suggestion for preventing from forest fire and fighting against it.Problem D Wearable Activity RecognitionThe percentage of EU citizens aged 65 years or over is projected to increase from 17.1% in 2008 to 30.0% in 2060. In particular, the number of 65 years old is projected to rise from 84.6 million to 151.5 million, while the number of people aged 80 or over is projected to almost triple from 21.8 million to 61.4 million (EUROSTAT: New European Population projections 2008–2060). It has been calculated that the purely demographic effect of an ageing population will push up health-care spending by between 1% and 2% of the gross domestic product (GDP) of most member states. At first sight this may not appear to be very much when extended over several decades, but on average it would in fact amount to approximately a 25% increase in spending on health care, as a share of GDP, in the next 50 years (European Economy Commission, 2006). The effective incorporation of technology into health-care systems could therefore be decisive in helping to decrease overall public spending on health. One of these emerging health-care systems is daily living physical activity recognition.Daily living physical activity recognition is currently being applied in chronic disease management (Amft & Troter, 2008; Zwartjes, Heida, van Vugt, Geelen, & Veltink, 2010), rehabilitation systems (Sazonov, Fulk, Sazonova, & Schuckers, 2009) and disease prevention (Sazonov, Fulk, Hill, Schutz, & Browning, 2011; Warren et al., 2010), as well as being a personal indicator to health status (Arcelus et al., 2009). One of the principal subjects of the health related applications being mooted is the monitoring of the elderly. For example, falls represent one of the major risks and obstacles to old people’s independence (Najafi, Aminian, Loew, Blanc, & Robert, 2002; Yu, 2008). This risk is increased when some kind of degenerative disease affects them. Most Alzheimer’s patients, for exa mple, spend a long time every day either sitting or lying down since they would otherwise need continuous vigilance and attention to avoid a fall.The registration of daily events, an important task in anticipating and/or detecting anomalous behavior patterns and a primary step towards carrying out proactive management and personalized treatment, is normally poorly accomplished by patients’ families, healthcare units or auxiliary assistants because of limitations in time and resources. Automatic activity-recognition systems could allow us to conduct a completely detailed monitoring and assessment of the individual, thus significantly reducing current human supervision requirements.Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements.Your task is as follows.(1) Build models to recognize daily living activities.(2) Explore the effects of sensor displacement induced by both the intentionalmisplacement of sensors and self-placement by the user.(3) Verify your recognition models’ toleranc e to sensor displacement.Data Set Information:The REALDISP (REAListic sensor DISPlacement) dataset has been originally collected to investigate the effects of sensor displacement in the activity recognition process in real-world settings. It builds on the concept of ideal-placement, self-placement and induced- displacement. The ideal and mutual-displacement conditions represent extreme displacement variants and thus could represent boundary conditions for recognition algorithms. In contrast, self-placement reflects a users perception of how sensors could be attached, e.g., in a sports or lifestyle application. The dataset includes a wide range of physical activities (warm up, cool down and fitness exercises), sensor modalities (acceleration, rate of turn, magnetic field and quaternions) and participants (17 subjects). Apart from investigating sensor displacement, the dataset lend itself for benchmarking activity recognition techniques in ideal conditions.Dataset summary:#Activities: 33#Sensors: 9#Subjects: 17#Scenarios: 3ACTIVITY SET:A1: WalkingA2: JoggingA3: RunningA4: Jump upA5: Jump front & backA6: Jump sidewaysA7: Jump leg/arms open/closedA8: Jump ropeA9: Trunk twist (arms outstretched)A10: Trunk twist (elbows bent)A11: Waist bends forwardA12: Waist rotationA13: Waist bends (reach foot with opposite hand)A14: Reach heels backwardsA15: Lateral bend (10_ to the left + 10_ to the right)A16: Lateral bend with arm up (10_ to the left + 10_ to the right)A17: Repetitive forward stretchingA18: Upper trunk and lower body opposite twistA19: Lateral elevation of armsA20: Frontal elevation of armsA21: Frontal hand clapsA22: Frontal crossing of armsA23: Shoulders high-amplitude rotationA24: Shoulders low-amplitude rotationA25: Arms inner rotationA26: Knees (alternating) to the breastA27: Heels (alternating) to the backsideA28: Knees bending (crouching)A29: Knees (alternating) bending forwardA30: Rotation on the kneesA31: RowingA32: Elliptical bikeA33: CyclingSENSOR SETUP:Each sensor provides 3D acceleration (accX,accY,accZ), 3D gyro (gyrX,gyrY,gyrZ), 3D magnetic field orientation (magX,magY,magZ) and 4D quaternions (Q1,Q2,Q3,Q4). The sensors are identified according to the body part on which is placed respectively:。
(最新整理)数学建模美赛试题
2015数学建模美赛试题编辑整理：尊敬的读者朋友们：这里是精品文档编辑中心，本文档内容是由我和我的同事精心编辑整理后发布的，发布之前我们对文中内容进行仔细校对，但是难免会有疏漏的地方，但是任然希望（2015数学建模美赛试题）的内容能够给您的工作和学习带来便利。
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地球资源的消耗速度快,越来越多的人关注人类社会的未来.自1960年以来，已经有许多专家研究可持续发展.然而大多数人的研究对象是整个世界，一个国家或一个地区。
几乎没有人选择48个最不发达国家(LDC)在联合国为研究对象列表。
然而,LDC国家集团共享许多相同的点。
他们的发展道路也有法律的内涵。
本文选择这些国家为研究对象针对发现常规的可持续发展道路。
本文组织如下.第二部分介绍研究的背景和本研究的意义。
第三节描述了我们对可持续发展的理解细节和显示我们的评估系统的建立过程和原理，那么我们估计每一个国家的LDC和获得可持续发展的能力和等级。
第四节提供了一个最糟糕的国家毛里塔尼亚计划指数在第三节。
第五节演示了在第四节的合理性和可用性计划。
最后在第六节总结本文的主要结论和讨论的力量和潜在的弱点。
地球上的资源是有限的。
三大能源石油、天然气和煤炭可再生。
如何避免人类的发展了资源枯竭和实现可持续发展目标是现在的一个热门话题.在过去的两个世纪,发达国家已经路上,先污染，再控制和达到高水平的可持续发展。
发展中国家希望发展和丰富。
然而,因为他们的技术力量和低水平的经济基础薄弱,浪费和低效率的发展在这些国家是正常的.所以本文主要关注如何帮助发展中国家特别是48在联合国最不发达国家实现可持续发展是列表可持续发展的理解是解决问题的关键.可持续发展的定义经历了一个长期发展的过程.在这里，布伦特兰可持续发展委员会的简短定义的"能力发展可持续- — - — - -以确保它既满足现代人的需求又不损害未来的能力代来满足自己的需求"［1]无疑是最被广泛接受的一个在各种内吗定义.这个定义方面发挥了重要作用在很多国家的政策制定的过程.然而，为了证明一个国家的现状是否可持续不可持续的,更具体的定义是必要的更具体的概念,我们认为,如果一个国家的发展是可持续的,它应该有一个基本的目前的发展水平，一个平衡的国家结构和一个光明的未来。
2015数学建模A题论文介绍
2015数学建模A题论⽂介绍A题太阳影⼦定位摘要本⽂⾸先确定了不同地点不同⽇期的直杆影长的模型，利⽤该模型解决了不同地点不同⽇期直杆影⼦变化和时间的的关系，为分析视频的拍摄地点和⽇期提供了模型上的基础。
对于问题⼀，为了确⽴直杆的影长与时间的关系，建⽴了地球坐标系和天球坐标系，引⼊太阳⾼度⾓、⾚纬、太阳时⾓、时差等参数变量。
利⽤太阳⾼度⾓和时间的关系建⽴了影长和时间的关系模型。
利⽤MATLAB软件求得影长关于时间的变化曲线，从9点到15点影⼦长度先减⼩后增⼤，在北京时间12点14分直杆影长最短，最短为3.5⽶，在北京时间9点直杆影长最长，长度为7.3⽶。
对于问题⼆，结合问题⼀中各参数变量之间的关系，使⽤Bourges算法和太阳⽅位⾓与时间的关系，得到确定直杆所在地点的数学模型，将附件1所给数据带⼊模型，利⽤excel和MATLAB软件进⾏求参数和拟合函数图像，求得直杆所处的可能地点为北纬19.21，东经108.43。
该地点在海南。
或者为南纬3.9412度，东经137.3度。
该地点在为印度尼西亚纳⽐雷附近。
对于问题三，由所给影⼦顶点坐标数据计算出各时间点的太阳⽅位⾓，利⽤excel 软件拟合出太阳⽅位⾓与时间的关系，进⽽确定直杆点的经度，结合问题⼆的数学模型得到直杆地点和⽇期求法的数学模型。
再次通过MATLAB进⾏求参数和拟合函数图像，求出了附件2地点可能为北纬39.88，东经79.7925或南纬39.88，东经79.7925，可能⽇期为：5⽉25号和7⽉20号或1⽉17号和1⽉26号。
对于问题四，提取出视频所有的帧数，等差得选取其中的20张进⾏模拟，利⽤3DMAX 软件仿真出视频的场景，通过测量所建模型中影⼦长度，确定出20组影⼦顶点坐标数据，再⽤问题⼆中所⽤到的模型进⾏求解，得到经纬度为北纬15.2，东经113.9.拍摄地点在海南省的三沙市。
⽤问题三中的模型求解得到拍摄地点纬度为0，东经123.8度在印度尼西亚，⽇期为3⽉21号或10⽉23号。
2015年美赛O奖论文A题Problem_A_32150
Team Control Number
For office use only F1 ________________ F2 ________________
32150
Problem Chosen
A
F3 ________________ F4 ________________
2015 Mathematical Contest iow to Eradicate Ebola? The breakout of Ebola in 2014 triggered global panic. How to control and eradicate Ebola has become a universal concern ever since. Firstly, we build up an epidemic model SEIHCR (CT) which takes the special features of Ebola into consideration. These are treatment from hospital, infectious corpses and intensified contact tracing. This model is developed from the traditional SEIR model. The model’s results (Fig.4,5,6), whose parameters are decided using computer simulation, match perfectly with the data reported by WHO, suggesting the validity of our improved model. Secondly, pharmaceutical intervention is studied thoroughly. The total quantity of the medicine needed is based on the cumulative number of individuals CUM (Fig.7). Results calculated from the WHO statistics and from the SEIHCR (CT) model show only minor discrepancy, further indicating the feasibility of our model. In designing the delivery system, we apply the weighted Fuzzy c- Means Clustering Algorithm and select 6 locations (Fig.10, Table.2) that should serve as the delivery centers for other cities. We optimize the delivery locations by each city’s location and needed medicine. The percentage each location shares is also figured out to facilitate future allocation (Table.3,4). The average speed of manufacturing should be no less than 106.2 unit dose per day and an increase in the manufacturing speed and the efficacy of medicine will reinforce the intervention effect. Thirdly, other critical factors besides those discussed early in the model, safer treatment of corpses, and earlier identification/isolation also prove to be relevant. By varying the value of parameters, we can project the future CUM . Results (Fig.12,13) show that these interventions will help reduce CUM to a lower plateau at a faster speed. We then analyze the factors for controlling and the time of eradication of Ebola. For example, when the rate of the infectious being isolated is 33% - 40%, the disease can be successfully controlled (Table.5). When the introduction time for treatment decreases from 210 to 145 days, the eradication of Ebola arrives over 200 days earlier. Finally, we select three parameters: the transmission rate, the incubation period and the fatality rate for sensitivity analysis. Key words: Ebola, epidemic model, cumulative cases, Clustering Algorithm
美国数学学会中学生数学竞赛真题和答案解析2015AMC8 Solutions
This Solutions Pamphlet gives at least one solution for each problem on this year’s exam and shows that all the problems can be solved using material normally associated with the mathematics curriculum for students in eighth grade or below. These solutions are by no means the only ones possible, nor are they necessarily superior to others the reader may devise.We hope that teachers will share these solutions with their students. However, the publication, reproduction, or communication of the problems or solutions of the AMC 8 during the period when students are eligible to participate seriously jeopardizes the integrity of the results. Dissemination at any time via copier, telephone, email, internet or media of any type is a violation of the competition rules.Correspondence about the problems and solutions should be addressed to:Prof. Norbert Kuenzi, AMC 8 Chair934 Nicolet AveOshkosh, WI 54901-1634Orders for prior year exam questions and solutions pamphlets should be addressed to:MAA American Mathematics CompetitionsAttn: PublicationsPO Box 471Annapolis Junction, MD 20701© 2015 Mathematical Association of AmericaWe thank the following donors for their generous support of the MAA American Mathematics Competitions, MOSP and the IMOPatron’s CircleAkamai FoundationSimons FoundationWinner’s CircleAmerican Mathematical SocietyThe D.E. Shaw GroupDropboxMathWorksTwo SigmaTudor Investment CorporationAchiever’s CircleArt of Problem SolvingJane Street CapitalMath for AmericaSustainer’s circleAcademy of Applied ScienceArmy Educational Outreach ProgramCollaborator’s CircleAmerican Statistical AssociationCasualty Actuarial SocietyConference Board of the Mathematical SciencesExpii, Inc.IDEA MATHMu Alpha ThetaNational Council of Teachers of MathematicsSociety for Industrial and Applied MathematicsStar League。
2015年数模美赛ABCD题目
A题The world medical association has announced that their new medication could stop Ebola and cure patients whose disease is not advanced. Build a realistic, sensible, and useful model that considers not only the spread of the disease, the quantity of the medicine needed, possible feasible delivery systems, locations of delivery, speed of manufacturing of the vaccine or drug, but also any other critical factors your team considers necessary as part of the model to optimize the eradication of Ebola, or at least its current strain. In addition to your modeling approach for the contest, prepare a 1-2 page non-technical letter for the world medical association to use in their announcement.世界医学协会日前宣布，其新的药物可以阻止埃博拉病毒和治愈患者的疾病，谁的病没有进入晚期。
因此，建立一个现实的、合理的，并且有用的模型是认为制造的疫苗或药物的不仅是这种疾病的传播、所述药物的所需要的数量、可能的可行交付系统、交付地点、制造的疫苗或药物的速度，但也可以是任何你的团队认为有必要为模型做贡献的其他关键因素，以便优化消灭埃博拉病毒或者至少抑制其目前的压力。
2015美赛A题优秀论文
2.4 2.5 2.6
Model Modeling Objectives . . . . . . . . . . . . . . . . . . . . Problem Space . . . . . . . . . . . . . . . . . . . . . . . The Multi-Layer State Based Stochastic Epidemic Model 2.3.1 Individual Layer - Stochastic State Based Model . 2.3.2 Inter-Region Layer modeling . . . . . . . . . . . . 2.3.3 Human Mobility Model . . . . . . . . . . . . . . . 2.3.4 Supply Distribution Model . . . . . . . . . . . . . 2.3.5 A note on GLEAM . . . . . . . . . . . . . . . . . Implementation . . . . . . . . . . . . . . . . . . . . . . . Additional Considerations . . . . . . . . . . . . . . . . . 2.5.1 Modeling of Hospitals . . . . . . . . . . . . . . . Consequences of Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .