优化估计:球面中超曲面上Jacobi算子第二特征值

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"关于球面中超曲面上的Jacobi算子的第二特征值的估计的一个注记" 这篇学术论文是对球面中超曲面上雅可比算子第二特征值估计的进一步探讨。雅可比算子(Jacobi operator)在微分几何中扮演着重要角色,特别是在研究曲面的稳定性时。在球面背景中,它与曲面的几何性质紧密相关,特别是与曲率和特征值有关。 文章提到的李-王研究是指之前由两位学者进行的工作,他们关注的是具有常数量曲率的超曲面。量曲率是描述超曲面曲率的一种方式,常量曲率意味着曲率在整个超曲面上保持不变。在这种情况下,他们研究了雅可比算子的第二特征值,这是一个关键的数值,因为它与超曲面的几何性质如面积、体积和稳定性紧密相关。 陈-王的后续工作则扩展到了球面中的魏恩加滕(Weingarten)超曲面,这是一种特殊的超曲面,其几何特性可以通过它的平均曲率函数来描述。他们考虑的是保体积变分问题,即在保持超曲面体积不变的情况下,研究超曲面的形状变化如何影响雅可比算子的第二特征值。 在原定理中,李-王和陈-王都假设了超曲面的维度"n"至少为5。然而,本文作者陈航和王险峰证明了一个更强的不等式,表明这个假设可以放宽到"n≥4"。这一改进意味着在更低的维度下,也可以得到关于雅可比算子第二特征值的精确估计,这对于理解低维超曲面的几何特性具有重要意义。 关键词涉及到的基础数学领域,特别是雅可比算子和第二特征值,是微分几何的核心概念。中图分类号"O186.1"表明这是数学领域中的理论研究。论文的贡献在于提供了一个最优的不等式估计,这不仅改进了先前理论的适用范围,也为未来在这个领域的研究提供了新的方向和工具。
2023-05-19 上传

With the rapid development of China's economy, the per capita share of cars has rapidly increased, bringing great convenience to people's lives. However, with it came a huge number of traffic accidents. A statistical data from Europe shows that if a warning can be issued to drivers 0.5 seconds before an accident occurs, 70% of traffic accidents can be avoided. Therefore, it is particularly important to promptly remind drivers of potential dangers to prevent traffic accidents from occurring. The purpose of this question is to construct a machine vision based driving assistance system based on machine vision, providing driving assistance for drivers during daytime driving. The main function of the system is to achieve visual recognition of pedestrians and traffic signs, estimate the distance from the vehicle in front, and issue a warning to the driver when needed. This driving assistance system can effectively reduce the probability of traffic accidents and ensure the safety of drivers' lives and property. The main research content of this article includes the following aspects: 1. Implement object detection based on the YOLOv5 model. Conduct research on convolutional neural networks and YOLOv5 algorithm, and develop an object detection algorithm based on YOLO5. Detect the algorithm through road images, and analyze the target detection algorithm based on the data returned after training. 2. Estimate the distance from the front vehicle based on a monocular camera. Study the principle of estimating distance with a monocular camera, combined with parameters fed back by object detection algorithms, to achieve distance estimation for vehicles ahead. Finally, the distance estimation function was tested and the error in the system's distance estimation was analyzed. 3. Design and implementation of a driving assistance system. Based on the results of two parts: target detection and distance estimation, an intelligent driving assistance system is constructed. The system is tested through actual road images, and the operational effectiveness of the intelligent driving assistance system is analyzed. Finally, the driving assistance system is analyzed and summarized.

2023-06-03 上传

(a) Consider the case of a European Vanilla Call option which is path independent. Examine the convergence of the Monte Carlo Method using the programme given in ‘MC Call.m’. How does the error vary with the number of paths nP aths? The current time is t = 0 and the Expiry date of the option is t = T = 0.5. Suppose that the current value of the underlying asset is S(t = 0) = 100 and the Exercise price is E = 100, with a risk free interest rate of r = 0.04 and a volatility of σ = 0.5. (b) Now repeat part (a) above but assume that the volatility is σ = 0.05. Does the change in the volatility σ influence the convergence of the Monte Carlo Method? (c) Now repeat part (a) but instead of taking one big step from t = 0 to t = T divide the interval into nSteps discrete time steps by using the programme given in ‘MC Call Small Steps.m’. Confirm that for path independent options, the value of nP aths determines the rate of convergence and that the value of nSteps can be set to 1. (d) Now let us consider path dependent options. The programme given in ‘MC Call Small Steps.m’ is the obvious starting point here. We assume that the current time is t = 0 and the expiry date of the option is t = T = 0.5. The current value of the underlying asset is S(t = 0) = 100 and the risk free interest rate is r = 0.05 and the volatility is σ = 0.3. (i) Use the Monte Carlo Method to estimate the value of an Arithematic Average Asian Strike Call option with Payoff given by max(S(T) − S, ¯ 0). (ii) Use the Monte Carlo Method to estimate the value of an Up and Out Call option with Exercise Price E = 100 and a barrier X = 150. (iii) Comment on the the rate of convergence for part (i) and (ii) above with respect to the parameters nP aths and nP aths使用matlab编程

2023-06-11 上传

基于这些Budget aims/goals The current budget aim is to produce a solution while staying well under our project prototyping budget of $350. We aim to keep our budget below 50% of the $350, however this will only be achievable in the development phase because of savings of borrowed items from Shoalhaven Water. Final costings will be dependent on housing construction and will be decided after further discussion with Shoalhaven Water. Budget Estimate Most hardware components in the design are finalised for both the power system and housing, compiled in a full list, sourced, and budgeted. Changes to Previous Estimate: Updated the selected battery from the 6000mAh LIPO ($31) to 2x parallel 18650 2600mAh with battery holders ($46 + $7). Updated solar power manager and separate solar panel cost ($8 and $12) to a combined solar panel and power manager ($50) This solar power manager added an optional 5v input and removed the need for the voltage regulator ($5). Added two Stainless Steel U Bolts ($10) and O-ring gasket material ($5) for housing. The estimated total was updated from $273 to $340. Project Costing Hardware The supplied components include the mDot LoRaWAN device ($74), the voltage pulse reed switch ($93) (water meter and standpipe are already used/in field). The device cost will only consider the hardware involved. It will utilise the Shoalhaven Water pre-existing LoRaWAN network as well as their backend infrastructure for data storage and analysis which are additional costs that will not be accounted for in the budget of this project. The estimated cost of the device hardware per unit is $340. The full list of device hardware expenses is detailed below in table VIII. For the project prototype, Shoalhaven Water are supplying the LoRaWAN mDot, voltage pulse reed switch, standpipe and water meter, making the actual hardware expenses during the project just $173. This means that our design is $177 below our required project budget.

2023-06-03 上传