基于Downhill-Simplex算法的观测数据与作物生长模型同化方法研究

以夏玉米叶面积指数(LAI)、贮存器官干重(WSO)、地上总干重(TAGP)以及土壤水分含量(SM)为结合点,建立了基于Downhill-Simplex算法的作物生长模型WOFOST同化多种地面观测数据的一般方法或流程:开展观测数据与作物生长模型同化方法的正确性验证→利用Downhill-Simplex算法进行WOFOST模型的敏感性分析→选择敏感参数组合→通过优化效果确定待优化参数→利用新的观测数据对待优化参数进行优化,从而实现了观测数据与作物生长模型的同化,提升了模型的模拟能力。同化过程中遴选出的WOFOST模型的待优化参数主要包括比叶面积、最大CO2同化速率、初始地上部总干物重、根深最大日增量和初始土壤有效水等。

关于Uphill和Downhill悖论的解释

近年来，国际资本流动发生了逆转，由之前的发达国家流向发展中国家转为由发展中国家流向发达国家。资本流动方向与传统的经济学理论相悖，有关悖论的解释成为国际经济学领域一大挑战。本文拟从制度环境、人口结构、资本市场完善程度、外资利用水平、国际金融秩序领域进行悖论解释。

Downhill Zagreb Topological Indices and <i>M<SUB>dn</SUB></i>-Polynomial of Some Chemical Structures Applied for the Treatment of COVID-19 Patients

A graphical index is a numeric value corresponding to a graph which is structurally invariant and in molecular graph theory these invariants are known as topological indices. In the field of Chemical and Medical Sciences, the topological indices are used to study the chemical, biological, medical and pharmaceutical features of drugs. With reference to the previous deadly diseases, the COVID-19 pandemic has considered to be the biggest life threatening issue that modern medicines have ever tackled. COVID-19 is immedicable and even the existing treatments are only helping the certain group of sufferers. Scientists have tested available antiviral agents and got a favorable impact on recovering from pandemic. Some of these antiviral agents are remdesivir, chloroquine, hydroxychloroquine, theaflavin and dexamethasone. Keeping in view of the importance of topological indices in the study of pharmaceutical and chemical drugs, in this paper, we calculate the Mdn-Polynomial, some downhill Zagreb topological indices and some downhill Zagreb polynomials of some of the anti viral agents remdesivir, chloroquine, hydroxychloroquine, theaflavin and dexamethasone. The results thus obtained may be useful for the finding new medicine and vaccine for the treatment of COVID-19.

Optimal Energy-Efficient Operation of a Metro Train on a Long and Steep Downhill Segment

Because the standard four-stage operation and con-trol strategy cannot fully utilize the gravitational potential energy of a train operating on a long and steep downhill segment,this paper further improves the method for train operation and control strategy.The improved operation includes three stages of acceleration,coasting-speed limit cruising,and brak-ing.Taking the speed limit,time limit,and distance limit as the constraints,the coasting condition switching point,braking condition switching point,traction coefficient,and braking force coefficient are used as the decision variables.Then,an improved train traction energy consumption model is constructed,and an improved differential evolution algorithm is designed to solve this model.The improved method is used to simulate two long and steep downhill segments of the Nanning metro.The results show that the improved method can meet the requirements of speed limit,time limit,and distance limit.Compared with the standard four-stage operation,the improved train operation and control strategy can reduce train energy consumption by more than 40%on the two long and steep downhill segments;compared with other similar algorithms,the improved algorithm is more suitable for solving the model.

Application of Neural Network to Downhill Shift Strategy for Automatic Transmission

<Abstract>A method is proposed to estimate the longitudinal road gradient with a concept"general gradient force(GGF)",in which uncertain factors such as additional vertical load,road surface change,and strong wind are also taken into account.An adaptive downhill shift control system is then developed to help driver to use the engine brake with lower gears while downhill driving.In the adaptive system,a three-layer neural network is built to evaluate the necessity to make use of engine brake capability in current downhill situation,and the neural network is trained with samples from experienced drivers.Field test results of the adaptive system are introduced to verify the effectiveness of the approach mentioned above.

Optimization of Linear Filtering Model to Predict Post-LASIK Corneal Smoothing Based on Training Data Sets

Laser vision correction is a rapidly growing field for correcting nearsightedness, farsightedness as well as astigmatism with dominating laser-assisted in situ keratomileusis (LASIK) procedures. While the technique works well for correcting spherocylindrical aberrations, it does not fully correct high order aberrations (HOAs), in particular spherical aberration (SA), due to unexpected induction of HOAs post-surgery. Corneal epithelial remodeling was proposed as one source to account for such HOA induction process. This work proposes a dual-scale linear filtering kernel to model such a process. Several retrospective clinical data sets were used as training data sets to construct the model, with a downhill simplex algorithm to optimize the two free parameters of the kernel. The performance of the optimized kernel was testedon new clinical data sets that were not previously used for the optimization.

Numerical Efficient Thermal Network for Calculating the Brake Disc Temperature

The simulation of the brake disc temperature is an important tool in the development of passenger cars.Nowadays thermal models of brake discs are real-time applications,running on electronic control units(ECUs)of cars to improve the vehicle safety in several ways.These models are often working with full empirical methods,leading to large deviations between calculation and measurement.To meet the requirements of automotive safety integrity levels(ASILs),these thermal models cannot rely on the state of the art ambient air temperature sensors,which causes unacceptable deviations.Focusing on numerical efficient thermal simulations,a new approach of a semi-analytical thermal network for simulating the brake disc temperature with minimal effort is proposed.The thermal network is based on lumped parameters,using two thermal capacity nodes and a constant ambient temperature.Using semi-analytical correlations,the model can be adapted to different geometries and car lines effortlessly.The empirical parameters of the model result only from two standardized tests.These parameters are used to evaluate the estimation accuracy in real driving situations.Additionally,the adaptability is tested for two different car lines and four brake disc dimensions.These tests are initially performed with unchanged parameters and afterwards with refitted parameters.The model shows a good estimation for the tested load cases.Compared to the state of the art,the proposed model is less accurate than complex finite element method(FEM)models and computational fluid dynamic(CFD)approaches,but shows a higher accuracy and better adaptability than other lumped parameter models with comparable numerical effort.Hence,possible applications can be dimensioning the brake system in the development process of new car lines or a real-time simulation on the latest ECU in the vehicle.

Adaptive hybrid global inversion algorithm

Most geophysical inversions can be regarded as multiparameter, nonlinear, and multiminimum discontinuous optimization problems. An adaptive hybrid global inversion algorithm based on simulated annealing, downhill simplex method, uniform design, and adaptive annealing rule is formulated. Numeral test and model computation show that this algorithm has very fast speed and high efficiency in searching for global minimum.