利用谱分解技术进行薄储层预测

采用短时窗离散傅里叶变换的频谱分解技术,实现了在频率域内通过调谐振幅的成像特征来研究储层横向变化规律的目标,最大限度地挖掘了地震资料主频至高频端的地震分辨能力。本文介绍了谱分解技术的基本原理、谱分解数据体的类型及其解释方法,充分利用谱分解得到的一系列单一频率的调谐振幅数据体研究辽河盆地东部凹陷西斜坡铁匠炉地区近源沉积砂砾岩储层,查清了该套储层沉积相带的平面分布和有效储层横向展布特征,预测结果已被钻井所证实。     

声光效应拉曼-内斯方程矩阵级数解法及声光调制器优化设计

提出一种求解正常声光相互作用拉曼 内斯 (Raman Nath)方程的矩阵级数解法 ,该解法直观方便且具有普遍性。计算结果表明 ,对Q =4 1π ,Bragg衍射的效率只有 97 5 % ;对非对称入射 ,以往的Raman Nath近似解误差较大 ;指出提高Bragg衍射效率的有效途径在于提高声光频率比并给出计算声光器件最优长度的计算公式。     

VG7S变频器及其在电梯拖动系统中的应用

指出了由VG7S变频器拖动的电梯系统运转停止时序分析中的几个主要问题,探讨了使用中的抗干扰技术,并对常用控制端子的设定方法和部分调试要点作了较为详尽的说明。     

用于机器人轨迹定位纠正系统的回声状态神经网络研究

循环神经网络(RecurrentNeuralNetworks)是人工神经网络(ArtificialNeuralNetworks)中重要的分支,与前馈神经网络(ForwardNeuralNetworks)相比具有更好的时间序列学习能力。但长期以来其学习法一直不能脱离前馈神经网络而自成一体,回声状态神经网络(EchoStateNetworks(ESN))是打破这一局面的全新学习方法。其独特的结构,良好的短期记忆能力,方便的学习方法,不俗的非线性特性是以前循环神经网络所不可比的。本文在介绍了回声状态神经网络之后将其用于四轮机器人的位置测量系统中,有良好的表现。     

A time series bootstrap procedure for interpolation intervals

A sieve bootstrap procedure for constructing interpolation intervals for a general class of linear processes is proposed. This sieve bootstrap provides consistent estimators of the conditional distribution of the missing values, given the observed data. A Monte Carlo experiment is used to show the finite sample properties of the sieve bootstrap and finally, the performance of the proposed method is illustrated with a real data example.     

小波包变换压缩与地震数据处理

探讨了地震数据经小波包变换压缩后对后续处理过程的影响,以及经过某些处理后的数据会对小波包变换压缩产生什么影响等问题。将小波包变换压缩地震数据的结果与快速傅里叶变换压缩地震数据的结果进行了对比,其结果表明:应用小波包变换压缩地震数据,有效信号的损失非常小,压缩比大于快速傅里叶变换的压缩比。在大批量地震数据处理中可以使用小波包变换压缩地震数据。     

Quasi-Chun-Ching Shih''''s Fractional Fourier Transform with Periodicity of 2,3 and M

Based on Chun-Ching Shih's idea, the basic transform was substituted and the quasi-ChunChing Shih's fractional Fourier transform with periodicity of 2, 3 and M was deduced. The two former transforms and the Chun-Ching Shih's fractional Fourier transform were only the particular cases of quasiChun-Ching Shih's fractional Fourier transform with periodicity of M.     

Developing Cost Response Models for Company-Level Cost Flow Forecasting of Project-Based Corporations

The difficulty in applying the standard curve (S-curve) and cost-schedule integration (CSI) techniques for company-level cost flow forecasting in a project-based industry is the prerequisite of forecasting future unknown individual projects and contract classifications. By analyzing cost flows at the company level through a pool of macroeconomic and internal financial data, this paper proposes an innovative approach to firm-specific model estimation. First, a series of data transformations introduce linear relationships between cost, macroeconomic, and internal financial variables. Second, multivariate regression analysis is employed for initial model building. Third, for the purposes of model restructuring, a subsequent application of Yule–Walker estimates and incomplete principal component analysis is used. This paper uses a sample of four project-based construction firms to demonstrate model performance. Using this methodology, mean absolute percentage error (MAPE) values of the forecasting models range from 0.27 to 0.60%. As such, the transformed cost, macroeconomic, internal financial data could strongly predict company-level cost flow forecasting. While converting the predicted cumulative cost data to periodic cost flows, the MAPE values were augmented, ranging from 7.04 to 17.55%, thus, requiring future research.     

两相邻边固定另两边任意支承矩形厚板问题

在Ｒｅｉｓｓｎｅｒ厚板理论中，利用分离变量法得到挠度函数Ｗ（ｘ，ｙ）和应力函数ψ（ｘ，ｙ）的解。在这些解中．选择一些三角级数和多项式作为该问题的挠度函数Ｗ（ｘ，ｙ）和应力函数ψ（ｘ，ｙ），从而得到了两相邻边固定另两边任意支承矩形厚板弯曲问题的精确解．这里不需要繁琐地叠加．     

A neural network model for fault detection in conjunction with a programmable logic controller

This paper discusses the feasibility of using neural networks as a tool in the fault detection process. A neural network is integrated with a state language programmable logic controller, an important device in an automatic control system. Time series data related to time spent in a state is gathered and used as input into a neural network, for the purpose of identifying when a fault has occurred. A feedforward neural network is used to identify which (if any) of three types of faults may have occurred. Experimental results related to sensitivity and accuracy measures are presented. A brief review of related applications and research is also presented.