陆相碎屑岩储层隔夹层成因、特征及其识别

陆相储层隔夹层是形成储层流体流动非均质的主要因素之一，也是储层精细表征的重要内容，油田勘探开发实践表明，陆相储层隔夹层主要可分为泥质隔夹层、钙质隔夹层和物性隔夹层三大类。根据这种分类，深入分析和总结了这三类隔夹层成因、岩性特征、分布特征和测井识别特征，并比较其区别，这对以后新老区块隔夹层研究有参考和适用价值。     

陆相断陷湖盆中层序边界的形成机理及其识别特征研究

陆相断陷湖盆中层序边界是由于沉积物表面高出沉积基准面或盆地基准面、沉积物发生剥蚀或无沉积作用所形成的，其产生机理主要有边界断层停止活动、断块翘倾活动、湖盆整体抬升和湖平面下降。层序边界在地震特征上、测井曲线上、钻井剖面中的岩性、岩相特征、古生物组合、微量元素的变化上均有明显的特征。利用这些特征，在济阳坳陷下第三系中识别出9个三级层序。     

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.     

致密砂岩储集层裂缝综合录井识别技术研究

四川盆地油气勘探结果表明，盆地油气储集层普遍为致密砂岩非常规储集层，有效性主要取决于孔、洞、缝发育程度，因此在油气钻井中如何识别致密储集层的孔、洞、缝并确定其所在井段尤其重要。针对综合录井仪的0．1m钻时及相关参数的资料采集技术要求，通过对大量录井数据资料的统计和演算，建立起储集层裂缝发育程度及裂缝发育位置的3种储集层裂缝识别模型，即直接识别模型、地层可钻性（A值）识别模型、钻时回归（RWSR）值识别模型。系统阐述了致密砂岩储集层裂缝综合录井识别技术方法原理，详细介绍了致密砂岩储集层裂缝识别的3个模型在实际生产中的应用情况。该技术方法的研究在国内尚属首次，为油气勘探中储集层评价提供了一个新的方法和手段，可最大限度地发挥现代录井技术在油气勘探开发过程中的作用。     

An intelligent sensor for detection of milling chatter

In this paper, a new real-time sensor system has been developed to detect chatter in milling operations. In the developed sensor system, a pattern recognition technique based on an unsupervised neural network using the adaptive resonance theory (ART) is adopted for detection of milling chatter. The features on the cutting force spectrum are fed into the sensor system to classify the milling process with or without chatter. The experimental results indicate that the proposed sensor system can accurately detect milling chatter regardless of the variation in cutting conditions.     

Discrete-time adaptive sliding mode power system stabilizer with only input/output measurements

In this paper a discrete-time adaptive sliding mode control method is newly developed and applied to the power system stabilization problem. A controllable canonical form of state space realization is constructed using the parameters identified by the on-line recursive least squares method and the system state is estimated from the input/output measurements and the simple state transformations. The identified parameters and the estimated state are then used by the discrete-time sliding mode control, which is suitable for the digital equipment. The most important advantage of the proposed power system stabilizer (PSS) is that it is able to maintain its regulating performance with a slower sampling period than that of the conventional sliding mode PSS because it is developed in a pure discrete-time domain. Another advantage of the proposed PSS is that it needs neither a mathematical model of the power system nor the full-state measurements because they are identified through on-line identifications. Several computer simulations for the linear power system are performed to verify the performance of the proposed PSS. In the computer simulations for various circumstances which are probable in a power system are considered, such as transitions of the active and reactive powers, change of parameters of the synchronous machine, line-to-ground faults and measurement noise. As a result, a new power system stabilizer which can operate in a wide range of operating conditions and can overcome various disturbances and measurement noises is proposed.     

基于神经网络非线性系统辨识和控制的研究

本文提出了由静态的前馈网络和稳定的滤波器构成的非线性系统的辨识模型，在神经网络固有的逼近误差存在的情况下，从理论上讨论了神经网络应用于辨识控制过程中系统的稳定性问题，最后研究了在非线性系统的轨迹跟踪过程中增加滑动控制来偿神经网络的逼近误差，从而提高系统跟踪性能。     

全球化进程中建筑的趋同

对全球化进程中建筑趋同的原因进行了分析,探讨了全球化对中国建筑的影响,并对我国当代建筑创作问题提出了见解,以使人们对建筑全球化的趋同有一个较为清晰的认识。     

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.     

Combinatorial properties of noninteger vertices of a polytope in a three-index axial assignment problem

It is proved that, for any r ∈ { 2n, 2n + 1,…, 3n−2} and only for such r, the polytope of a three-index axial assignment problem of order n, n ≥ 2, contains completely r-noninteger vertices (r-CNVs), i.e., vertices such that all their positive components are fractional and their number equals r. For each r ∈ {2n, 2n + 1,…, 3n −2}, all the types of r-CNVs are characterized and the combinatorial properties of completely r-noninteger vertices of the polytope are studied. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 33–44, January–February 2007.