基于改进PSO-SVM的噪声稳健建筑物变形监测方法

对高层、超高层建筑物进行实时,高精度的变形监测对提前预防安全隐患,保证人民生命财产安全具有重要意义。建筑物变形作为一种典型的随机性和微弱性过程,噪声等误差的存在会影响从中提取有用的变形信息。针对该问题,提出一种改进粒子群(Particle Swarm Optimization,PSO)算法优化支持向量机(Support Vector Machine,SVM)的噪声稳健建筑物变形监测方法,利用改进PSO算法的全局搜索能力对SVM的核参数进行优化,提升预测精度的同时增强算法的噪声稳健性。基于实测数据的试验结果表明,相对于传统交叉验证SVM和小波方法,所提方法可以获得更高的变形预测精度,并且在低信噪比条件下优势更加明显。     

Optimization of nonconventional wells under uncertainty using statistical proxies

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained.     

Optimization of SAGD Well Elevation

The application of steam-assisted gravity drainage (SAGD) to recover heavy oil sands is becoming increasingly important in the northern Alberta McMurray Formation because of the vast resources/reserves accessible with this mechanism. Selecting the stratigraphic elevations of SAGD well pairs is a vital decision for reservoir evaluation and planning. The inherent uncertainty in the distribution of geological variables significantly influences this decision. Geostatistical simulation is used to capture geological uncertainty, which is used can be transformed into a distribution of the best possible well pair elevations. A simple exhaustive calculation scheme is used to determine the optimum stratigraphic location of a SAGD well pair where the recovery R is maximized. There are three basic steps to the methodology: (1) model the uncertainty in the top continuous bitumen (TCB) and bottom continuous bitumen (BCB) surfaces, (2) calculate the recovery at all possible elevation increments within the TCB and BCB interval, and (3) identify the elevation that maximizes R. This is repeated for multiple TCB/BCB pairs of surfaces to assess uncertainty. The methodology is described and implemented on a subset of data from the Athabasca Oilsands in Fort McMurray, Alberta.     

数据备份服务器备份性能分析

针对数据备份服务器备份性能优化问题，分别对实时数据存储与历史数据备份之间的关系、数据备份服务器所备份的数据类型非唯一性、客户数量对备份服务器备份效率的影响三方面进行初步的测试与分析，提出了通过优化数据块的选取、优化数据采集方式、优化备份策略提高备份服务器备份效率的基本方法。     

Combination of Dependent Realizations Within the Gradual Deformation Method

Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters.     

Melting of β-quartz up to 2.0 GPa and thermodynamic optimization of the silica liquidus up to 6.0 GPa

Melting relations of β-quartz were experimentally determined at 1.0 GPa (1900±20 °C), 1.5 GPa (2033±20 °C), and 2.0 GPa (2145±20 °C) using a new high-pressure assembly in a piston–cylinder apparatus and substantial differences were found with data previously reported. The new melting data of β-quartz were combined and optimized with all available thermodynamic, volumetric, and phase equilibria data for β-cristobalite, β-quartz and coesite to produce a P–T liquidus diagram for silica valid up to 6.0 GPa. Using the new optimized thermodynamic parameters, the invariant point β-cristobalite+β-quartz+liquid and β-quartz+coesite+liquid were determined to lie at 1687±17 °C and 0.457 GPa, and 2425±25 °C and 5.00 GPa, respectively.     

海洋平台结构振动的AMD主动控制参数优化分析

本文针对海洋平台结构的冰激振动和地震反应控制问题，提出了采用AMD主动控制的控制策略，结合JZ20－2MUQ平台结构进行了AMD控制系统的硬参数和软参数的优化分析，并就相应于最优参数下的AMD控制海洋平台结构冰激振动和地震反应的几种代表性工况进行了时程分析，得到了一些定性和定量的结论，为实际工程的控制设计提供了基础。本文提出的AMD主动控制方法对类似的海洋平台结构的控制问题也有参考价值。     

The water level simulation for crane habitat optimization in Xianghai Nature Reserve

The probability of crane living in reedy wetlands can reach 100%, at the same time, the area of reed, the water level and adjacent water area are main factors which control the crane's habitat selection. We all know that all these factors are spatially heterogeneous. For the Xianghai wetland safety and to protect the Xianghai wetland habitat of crane, this paper has mainly identified a solution to these problems. The wetland information is extracted from the TM images, which reflect the whole wetland landscape and are very important for both quantitative analysis of remote sensing observation of the earth system and positioning analysis in GIS database that is automatically extracted from DEM. The DEM for Xianghai characteristics of topography is created. On the basis of the GRID SUBMODULE, applying the GIS spatial overlay analysis, the relationship between the water level and the reed area below the water level and the rating distribution maps of reed area above water level is established. When the water level reaches the altitude of 165 m, the reed area, 981.2 ha is maximum, i.e., the water level of 165 m is the optimal.     

灰色GM(1,1)模型最优维数的研究

灰色GM(1,1)模型的维数对模型的预测精度有很大的影响,本文通过自编的FORTRAN程序,在合适的范围内对GM(1,1)模型的维数进行自动优化,通过对宁波市地面沉降数据的计算分析,得到了C和P的定性关系,确定了优化的评价标准,维数最优化后的模型能反映预测数列变化幅度的大小。