Cellular automata spatial temporal data model and forecasting approach

The standard cellular automata(CA) model is expanded to meet requests of space time dynamic simulation and forecast under the platform of geographic information system(GIS). Taking power load forecasting of the electric power industry as the specific application, the relations between dynamic model of the land use and power load space are established. The data and attribute data interactive discrete in spatial temporal data management have been solved. The CA theory is practically used to simulate the process of urban land use dynamic development, to forecast future land use types of each small area, to establish spatial load forecasting model. It breaks through the localization of all kinds of forecasting methods of traditional space time separation power prediction. The effectiveness of the prediction method is verified by example.     

Key parameters analysis of surface subsidence induced by tunneling construction

In order to identify the key factors inducing the surface subsidence while shield tunneling,a key parameter selection model and solving method based on RS-SVM is proposed. The information entropy rules were used to discretize seven continuous variables including Internal Friction Angle(IFA),Cohesive Force(CF)etc. Genetic Algorithm and Rough Set for attribute reduction were combined to obtain several collections that significantly affect the surface subsidence; Using the statistical learning of RVM to select the best collection which optimally reflects the relationship between parameters and surface subsidence,we get four parameters: Single Ring Grouting Pressure,Internal Friction Angle,Specific Torque(ST),Incision of Slurry Pressure and each of the collections was the critical parameter that should be considered in construction. The method was applied in a completed metro tunnel in Wuhan,China and the results indicated the feasibility of the method.     

基于多模型的农产品供求信息预测系统研究

文章介绍了基于多模型的农产品供求信息预测系统的预测模型理论基础、系统总体平台结构、系统功能结构,并重点介绍其主要功能模块,同时,指出了系统实现中解决的关键问题,最后进行总结并提出改进方法。     

Application and research of improved artificial immune network to power short-term load forecasting

According to the deficiencies of load forecasting model at present,a short-term load forecasting model based on optimized clone immune and BP neural network (BPNN) is presented. In the design of artificial immune network (AIN),the principle of immune network regulation is used in a creative way and the method of immune programming is used to evolve the network structure. The probability of selective antibody concentration,a new fitness function of neurons,a new mutation operator and a new self-adaptive chaos mutation operator are adopted in the AIN. The excitation function controls the BP algorithm which greatly accelerates convergence of BP training,the self adaptable strategy based on clone immune optimizes the controlled BP algorithm,and it improves its global searching ability better than the BP algorithm optimized by chaos and avoids the algorithm to be trapped in local minimum and improves the prediction accuracy.     

A multi model composition framework based on bayesian model comparision and its application in soft sensor modeling

In order to improve the prediction performance of single model based soft sensor, the features of the current model combination frameworksby analynizing, a new multi model combination framework based on the bayesian model comparison is proposed. In this framework, fuzzy c means clustering to the historial data is used to analyze the production states, then the prediction performance of sub models at different states are compared based on bayesian model comparison. The comparing results are the basis of the model combination stratery at different states. With adapting cross validation predictive distribution, the samples got from the trained models are used to successfully reduce computation load of model comparion.The framework has obtained good results in the practical application.     

Numerical implementation and applications on subloading surface model with improved overconsolidation state variable

为合理描述超固结土复杂的弹塑性力学行为,对现有Hashiguchi次加载面模型中的超固结状态参量R进行修正,在硬化方程中,考虑塑性体应变与塑性剪应变的综合作用,提出了修正超固结状态参量的次加载面模型。同时,着重介绍了该模型的隐式积分算法及数值实现过程,编制了对应的接口子程序,实现了该模型在有限元软件ABAQUS中的应用。通过不同工况和加载方式下的数值模拟验证了程序的合理性,最后应用模型研究了Fujinomori 黏土的三轴压缩力学特性并与UH模型的模拟结果、室内试验研究进行对比。结果表明,子程序具有较高的计算精度和可靠性,模型能够准确地模拟黏土的超固结特性。     

社会网络分析法在城镇工程基础设施健康评价中的应用

针对工程性基础设施网络拓扑结构不合理导致的全局性破坏问题,将社会网络分析(SNA)方法的密度、凝聚子群、K-核、Lambda集合、切点和中心度等计算概念引入基础设施网络拓扑结构评价,提出了由网络完备度、层级边关联度、局部稳定成份比例、节点脆弱性、荷载运行均衡度等指标构成的评价体系。通过对重庆长寿城区的电力基础设施实例研究发现,社会网络分析方法能够对设施网络拓扑结构与电网运行过程中的稳定性进行客观评价,能够找出电网过载点、对设施网络拓扑结构的紧凑程度和完整程度进行评价以及对网络整体的性能做出评价。     

农村公共产品的供给与需求——基于安徽省364份调查问卷的分析

农村公共产品的供给对新农村建设具有重大意义,农村公共产品的需求决定了农村公共产品供给的优先顺序。本文运用安徽省的抽样调查资料,对农村公共产品的供给状况和需求进行了统计描述和分析。研究表明,安徽省农村的基础设施条件较差,公共产品供给的缺口较大。农民对农村公共产品投资需求顺序依次为农村道路、农田水利、技术服务和培训、村庄改造、搞好村建设规划、幼儿园及小学建设、合作医疗及统筹养老保险等。因此,政府在进行公共产品投资决策时,应根据农民需求考虑投资优先顺序,有所侧重并逐步推进。     

Urban Water Demand Forecasting by Combining Improved BP Neural Network and Grey Model with Optimum Weight

Prediction of water demand is a complex system affected by a mass of data and influencing factors together. Nowadays, most of the forecasting methods are single model ones. They reflect only part of the system. In view of this situation, a combined model     

Finite element model of coupled systems for vibration reduction plates with smart constrained layer damping

Considering the relationships of coupling movement and compatible displacement among bonding layer, piezoelectric layer, damping layer, and base structure, a finite element dynamic model of coupled systems for vibration reduction plates is established. The model combined with piezoelectric theory, finite element theory and ADF model of viscoelastic damping material can be suitable for vibration model of complex SCLD structure. The dynamics parameters for clamped-clamped steel plate with partially treated smart constrained layer damping are obtained by theoretical calculation, ANSYS modal analysis and modal experiment. The results show that theoretical values are closer to the test results and ANSYS analysis, and the methods proposed is more accurate and effective.     

考虑预估驾驶行为的跟驰模型及其稳定性分析

在OV模型的基础上,进一步考虑预估驾驶行为对车流的影响,提出一个新的跟驰模型以获得预估驾驶行为与交通拥堵的关系。通过对新模型进行稳定性分析得到了系统的临界稳定条件。数值仿真结果表明：新模型能够模拟诸如时停时走、系统临界相变等实际交通现象,较OV模型更贴近于实际。同时,预估驾驶行为增强了交通流稳定性能,提高了车流陷入交通阻塞状态的阈值。最后以车速的平滑度和波动幅度最小为评价指标得到了新模型中预估参数的最优取值范围。     

The improved geometrical constraint model of open-pit mine boundary optimum and its application

This paper develops the improved geometric constraint model (IGCM) based on the 3D block model. The variable-slope excavation cones of each block have been abstracted to collections of nodes and directed edges, and the optimization method has been introduced using the set theory. The IGCM consists of the optimized collection nodes and directed edges. The algorithm based on IGCM has been actualized in DIMINE digital mine system, and is applied to an actual open pit mine boundary optimization engineering. The results shows that IGCM can accurately simulate variable slope angles in any azimuth and elevation changes, and adapt to all open pit mine boundary optimum problems in any complex geological conditions, calculation speed and accurate. It provides a new and effective method for the variable-slope open pit mine boundary optimization problem in complex geological conditions.     

MecaNiCAL, a supply–demand model of carbon and nitrogen partitioning applied to defoliated grass: 1. Model description and analysis

A model for the dynamic simulation of dry matter distribution between shoots and roots of a grass forage plant is presented. The objective of this work was to develop a relatively simple mechanistic model of grass growth to simulate the response of assimilate partitioning to variations in light and nitrogen supply based on an original theoretical scheme [Gillet, M., Lemaire, G., Gosse, G., 1984. Essai d'élaboration d'un schéma global de la croissance des gramineés forragères. Agronomie 4 (1), 75–82]. In the model, called MecaNiCAL, C and N assimilates are partitioned between shoots and roots according to the availability of carbohydrate substrate, organ demands and a fixed priority between each demand. Demand related to organ synthesis is described as a function of the plant concentration in the organic nitrogen substrate and temperature. To stay within an acceptable range of complexity, the model is applied only in one vegetative regrowth performed on a hydroponic system with different light and nitrogen nutrition. Carbon and nitrogen acquisition per day are computed by independent submodels, which makes it possible to connect simple and robust functions of carbon assimilation and nitrogen uptake to the partitioning model. Carbon assimilation is simulated for a plant in a canopy, and nitrogen uptake is simulated by a function which depends on nitrogen plant requirements directly related to plant net photosynthesis and the amount of nitrogen available in the nutritive solution. All the assumptions used to describe assimilate partitioning are discussed in relation to knowledge of physiological processes in order to examine closely the limits of the model.     

Pulse Signal Analysis of Druggers with the Wavelet and Neural Network

The most significant difference between the human pulse signals collected from heroin druggers and healthy persons is at their amplitude waveforms as time functions. That is, the amplitude values and change rates of two types of signals, within a particular time range, appear different features. However, the partial components of the scaling and wavelet coefficients of the pulse signals obtained by using wavelet transform can reveal such key features. The pulse signals of 15 heroin druggers and 15 healthy persons are analyzed through using the muhiresolution analysis of wavelet transform. By using db2 orthogonal wavelet, every pulse signal is decomposed into three levels and the absolute values of the sixth component of scaling coefficients and the second component of the wavelet coefficients in the third level are combined to form a feature vector. A probabilistic neural network with good detection performance is successfully designed for automatically detecting 30 feature vectors. During the network design, 20 feature vectors are used as training samples. The remained 10 feature vectors are used as testing samples. Based on these steps, 15 heroin druggers and 15 healthy persons are all correctly identified. In other words, the detection rate arrives at 100%. druggers.     

MecaNiCAL, a supply–demand model of carbon and nitrogen partitioning applied to defoliated grass: 2. Parameter estimation and model evaluation

The MecaNiCAL model of carbon and nitrogen partitioning over a period of regrowth for a mean grass plant as described in Part 1 was fitted and evaluated against data from an experiment under controlled conditions with contrasting light and nitrogen nutrition. In the first investigation, the rate of nitrogen uptake was imposed to limit the compensation of errors between process. The root:shoot ratio was simulated well, with a good estimation of the nitrogen allocation to shoots. Some underestimation of the root weight after the cut and the final shoot weight under the low nitrogen treatment were consequences of both the lack of remobilisation and the high priority given to shoots. The good agreement obtained with the expected trend in simulations conducted with the simple nitrogen aborption submodel in extended conditions (contrasting initial content of nitrogen substrate and temperature change in regrowth) ensures the general consistency of the model in the limit of its area of validity. Partitioning to shoots and forage production seem to be sensitive mainly to the size of the carbohydrate substrate pool and its availibility, particularly under conditions of low light. Estimating the initial carbohydrate substrate pool by biochemical analysis may improve the accuracy of the model. Compared with more simple assumptions of partitioning, MecaNiCAL shows a higher level of agreement with the partitioning response to a wide range of environmental conditions. A simple assumption relating partitioning to plant nitrogen content does not seem to be sufficient to simulate the decrease in the root:shoot ratio observed under conditions of low light. Nevertheless, more data from experiments conducted under a wider range of contrasting conditions are required to estimate the validity of MecaNiCAL attached to the nitrogen absorption submodel and the advantage of its more complex assumptions with even greater accuracy.     

Establishment and analysis of dynamic mathematical model for coal gasifier

Based on the analysis of flowing combustion, gasification reaction, and lumped parameter, a three region model is established to predict the temperature and gas components of Texaco coal gasifier. Using mass balance and energy balance equations to each zone, the model takes account of gas solid two phase flow, coal pyrolysis, radiation heat transfer, and heterogeneous and homogeneous kinetic chemical reactions. Based on the model, a program is developed for dynamic and static simulation. After parameterization and simulation analysis, some important trends and conclusions are attained. Because the model can accurately predict the temperature and gas components in jet region, it has widespread engineering application value.     

基于高斯函数的春玉米叶片功能期模型构建与应用

叶片功能期是影响光合生产能力的关键因素,冠层叶片功能期的量化评估对玉米植株生长和产量形成具有重要意义。本研究于2015—2017年在中国农业科学院作物科学研究所吉林公主岭试验站进行,定株观测先玉335和郑单958两个品种各个叶位叶片展开时间和衰老时间,基于2015年和2016年试验数据,以高斯函数(y =a+b×e-(x-c)^2/2d^2)模拟玉米各叶位叶片功能期的动态变化,并用2017年数据验证,在此基础上进一步明确了模型特征参数的生理学意义,简化了叶片功能期模型构建的方法。研究条件下利用高斯函数构建的玉米叶片功能期模型年际间稳定性好、品种间区分度大。进一步解析利用一阶导(功能期最大值)、二阶导(功能期变化速率最大的点)、三阶导(功能期开始快速增大的点)等于零取整后的叶位并配合最顶部叶位(n)和基部第1叶这5个转折叶位叶片功能期构建的模型拟合度良好,极大地简化了该模型参数拟合的数据需求,并探讨了利用该模型函数对玉米叶片功能分组的可能性。本研究为玉米生产能力的量化分析提供了思路和方法,对各类玉米生长模型的完善和其他相关研究也有借鉴意义。     

不同地层比功阈值优化的统计方法及其应用

采用在线监测系统监测进行随钻参数的采样与分析,建立了派生参数与直测参数的理论表达式,提出了多因素组合的地层比功模型,实现了地层地质条件与随钻参数的有机结合。以重庆市某高压变电站施工场地为依托,建立了不同地层如素填土层、粉质粘土层、砂岩层及泥岩层的地层比功演化曲线,对比地勘信息可知,不同地层的比功值存在显著差异。可见,合理的地层比功阈值是辨识地层类型的重要参量。运用统计学原理对勘测孔附近不同地层的比功值阈值进行保值优化,通过地勘获得的地层信息来验证阈值区间的可靠性。进一步采用如上地层比功阈值对该区域的其他桩位所在地层进行分析验证,说明了地层比功阈值优化的统计方法可行,其阈值区间合理,可用于其他赋存环境的地层比功阈值确定及地层识别。     

Modeling and simulation of the upper limb artery system using three section electric network model

A three section electric network model is built for human upper limb arterial system according to the Windkessel model. Blood pressure waveform of brachial artery is inputted into the electric network model. The state space equations of the electric network model are refined by using the state space analysis and solved numerically by the fourth order Runge Kutta method to obtain the blood pressure and flow waveform of radial artery. Effects of changes of the electric network model’s parameters on the blood pressure and flow waveform are also analyzed in normal and different pathological conditions(arteriosc lerosis and hypertension). The results of simulation show that the parameters of the model can well reflect the physiological significations of the upper limb arterial system; the analytic and computational methods are of fairly high precision and fast convergence for the model; and the model can simulate effectively the hemodynamic characteristics of upper limb arteries under normal and different pathological conditions.     

Mechanical property prediction model of AZ31 magnesium alloys based on an artificial neural network with parameter optimization

The tensile strength, yield strength and elongation of AZ31 magnesium alloys on different annealed conditions are tested by mechanical properties experiments. A model of corresponding mechanical properties is built by applying artificial neural network, and it is optimized by a new method,namely all permutations and combinations training of parameters. The results show that the network model has an excellent performance, which is based on optimal parameters obtained from all permutations and combinations training. Compared with traditional model, whose parameters are obtained from conventional heuristic, the improved model has higher average correlation coefficient and lower average error. Therefore, it can predict the mechanical properties of AZ31 magnesium alloy on different annealed conditions more accurately.