储层识别模式的研究

用数学方法建立气测井油气储层识别模式,以解决录井中实时软件在判别依据少的条件下,及时识别油气显示假异常的难题。该判别式经实用,正到率达90％。     

TRANSFER FUNCTION MODEL ORDER AND PARAMETER ESTIMATION

Abstract. A distributed lag model with a rational transfer function is considered. We have demonstrated that the C -table can be used to determine the orders of the rational transfer function and the Padé table can be used to estimate the parameters of an identifiable transfer function. In case the model is not uniquely identified, the C -table reduces possible competing models to only a few for further examination. Padé approximant gives consistent initial values for possibly more efficient iterative procedures to estimate the coefficients of the identified model. Some asymptotic results on the estimation of the C -table and the Padé table are given. A few examples are presented to demonstrate the effectiveness of the method.     

Sensorless control of synchronous reluctance motors using on‐line parameter identification method not affected by position estimation accuracy

This paper presents a novel on‐line parameter identification method for sensorless control of Synchronous Reluctance Motors (SynRMs). Although conventional sensorless control methods based on mathematical models usually need some complex measurements of motor parameters in advance, the proposed identification method does not require them and can be realized on‐line. The proposed method identifies motor parameters under sensorless control, so rotor position and velocity cannot be used to identify these parameters. However, the proposed method does not need rotor position and velocity, and identified parameters are not affected by these estimation errors. The sensorless control using identified motor parameters is realized, and the effectiveness of the proposed method is verified by experimental results. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 62–69, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20258     

测井相识别模型及其在东海油气勘探中的应用

文章提出一种测井相识别模型,该模型用常规测井信息,定量地确定测井相在钻遇地层中的空间位置,建立测井相。配合东海油气勘探工作,用该识别模型对苏堤构造带的钻遇地层进行了测井相分析。     

A study on the critical success factors for corporations embarking on knowledge community-based e-learning

Corporations have felt the pressure for fast-paced innovations and knowledge transfer as major driving forces in raising their sustainable competitive advantage and organizational total productivity. Thus, the development of the knowledge community through e-learning is an important strategy in implementing knowledge management policy. This paper illustrates an empirical study with which to conduct qualitative research of learning organization. From the case company’s real progress and practical experience, some core principles were extracted from four managerial aspects, such as: strategy, technology, process, and personnel. These aspects were analyzed in order to understand the value chain framework and the major concerns of knowledge community development. Further, the main benefits are addressed and certain critical factors are proposed and shown in this paper to ensure the success of corporations embarking on knowledge community-based e-learning.     

Are Samples in Randomized Controlled Trials of Psychotherapy Representative of Community Outpatients? A New Methodology and Initial Findings.

To determine the extent to which published randomized controlled trials (RCTs) of psychotherapy can be generalized to a sample of community outpatients, the authors used a method of matching information obtained from outpatient charts to inclusion and exclusion criteria from published RCT studies. They found that 80% of the patients in their sample who had diagnoses represented in the RCT literature were judged eligible for at least 1 published RCT; however, 58% of the patients had primary diagnoses such as adjustment disorder or dysthymia, which were not represented in the existing psychotherapy outcome literature. The most common reasons that patients in their sample did not match with published RCTs for psychotherapy are listed, and the implications of these findings for research and practice are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Step response classification for model-based autotuning via polygonal curve approximation

A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results.     

Parameter identification for nonlinear systems: Guaranteed confidence regions through LSCR

In this paper we consider the problem of constructing confidence regions for the parameters of nonlinear dynamical systems. The proposed method uses higher order statistics and extends the LSCR (leave-out sign-dominant correlation regions) algorithm for linear systems introduced in Campi and Weyer [2005, Guaranteed non-asymptotic confidence regions in system identification. Automatica 41(10), 1751-1764. Extended version available at 〈http://www.ing.unibs.it/∼campi〉]. The confidence regions contain the true parameter value with a guaranteed probability for any finite number of data points. Moreover, the confidence regions shrink around the true parameter value as the number of data points increases. The usefulness of the proposed approach is illustrated on some simple examples.     

Evaluating Community Safety Programmes and Community Engagement: The Role of Qualitative Methods and Collaborative Approaches to Policy Research

This article explores the role of qualitative and collaborative methods when undertaking evaluation research into local community safety partnerships and neighbourhood strategies. It draws on recent examples of research and evaluation studies of regeneration and community safety programmes in a number of cities and towns in England. The discussion is set against the background of the major changes in policy relating to crime and community safety, particularly the introduction of Crime and Disorder Partnerships and their engagement with local communities. The value of qualitative methods in exploring some key questions is discussed, particularly within a collaborative approach to policy research.     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.