川西新场气田JS_2~1气藏测井多参数评价方法

针对川西新场气田JS12气藏开展了多参数的综合评价研究,多参数包括储层参数、裂缝参数、储层分类厚度参数。首先,开展了储层参数及裂缝参数的计算,以岩心分析结果为依据,建立了孔隙度、渗透率、含水饱和度计算模型。利用铸体薄片进行了微裂缝识别,对样品的裂缝参数和测井曲线进行相关分析,建立了测井计算裂缝参数的数学模型;其次,对储层进行聚类分析,通过聚类将储层分为三类;最后,将计算的裂缝参数、储层分类中的Ⅰ类储层厚度、孔渗饱等参数进行正态化处理,拟和测试的无阻流量与这些参数建立多元回归计算模型,即综合含气指标的数学计算模型。以综合含气指标为主要依据,进行储层综合分类和评价,指出了有利的含气区域和可供挖潜井的建议。该方法在指导气藏开发中获得了良好效果。     

低温脂肪酶产生菌的筛选、鉴定及其部分酶学性质

从南极乔治王岛冻土来源的76株低温细菌中筛选到13株低温脂肪酶产生菌，对其中的BTsl0022菌株进行鉴定。通过生理生化特征、16s rDNA基因序列的同源性和系统发育分析发现，菌株RTsl0022属于假单胞菌属(Pseudomonas)，但与已定名的假单胞菌有一定的差异，与未定名的Pseudomonas sp．PsB的亲缘关系最接近，故将其暂定名为Pseudomonas sp．BTsl0022。对该菌脂肪酶的酶学性质初步研究表明，酶的最适作用温度为24℃，对热敏感，60℃处理30min仅残留25％酶活性，酶的适宜作用pH范围在7．0～9．0，最适pH为8．0。     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

海杂波中基于混沌预测的目标检测方法改进

基于记忆库非线性预测的NP-CFAR方法是目前混沌海杂波背景中目标检测的一种典型而先进的方法.考虑到海杂波功率与特征的时变不稳定性,本文提出运用旋转超盒分类取代这一方法中的NP-CFAR进行目标检测,并探讨了运用盒维数特征提取进行预处理以节省运算开销的问题.仿真实验验证了本文所提改进方法的有效性.     

油气圈闭地质评价方法及应用

本从含油气系统理论出发，对圈闭成藏的油源，储层，圈闭，保存，配套史制定分级评价标准，并赋予相应的评价系数，在此基础上应用地质风险概率理论与模糊数学的方法，在微机上定量评价圈闭含油气性。     

The new intra-urban dynamics: Suburbanisation and functional specialisation in French cities

This article examines the relationships between the employment suburbanisation from central cities towards their suburbs, and the process of intra-urban specialisation that occurred simultaneously in the fifty largest French metropolitan areas. A methodology is proposed to identify urban subcentres and to analyse the effects of the intra-urban specialisations on suburbanisation patterns. We conclude that the specialisation of both subcentres and central cities has a significant effect on suburbanisation rates. Lastly, an intra-metropolitan shift/share analysis provides additional insights into the employment dynamics of central cities and suburbs during the last twenty years. Received: 25 July 2000 / Accepted: 29 May 2001     

Classification of hand postures against complex backgrounds using elastic graph matching

A system for person-independent classification of hand postures against complex backgrounds in video images is presented. The system employs elastic graph matching, which has already been successfully applied for object and face recognition. We use the bunch graph technique to model variance in hand posture appearance between different subjects and variance in backgrounds. Our system does not need a separate segmentation stage but closely integrates finding the object boundaries with posture classification.     

The availability of daylight from tropical skies—a case study of Malaysia

In Malaysia, no long-term daylight data are measured. It was only recently that the need to measure the availability of daylight became urgent when the importance of daylighting in buildings was rediscovered. The hourly daylight availability has been simulated for the Malaysian sky using daylight modelling techniques based on empirical and measured solar irradiation and cloud cover data. This paper presents the techniques involved in producing exterior illuminance data. These data were then compared with measured illuminance at Shah Alam and Bangi, Malaysia. The global illuminance levels are generally high, with values exceeding 80,000 lux at noon during the months when solar irradiation is highest. Even during the months when the ground receives less solar irradiation, the peak illuminance can reach 60,000 lux. Applications and uses of such data are in daylighting design, both for visual and thermal comfort, task illuminance and energy-conscious design of buildings. Recommendations are made at the end of the paper on the various climatic data that are required to be measured for overall daylighting design applications.     

Extending boosting for large scale spoken language understanding

We propose three methods for extending the Boosting family of classifiers motivated by the real-life problems we have encountered. First, we propose a semisupervised learning method for exploiting the unlabeled data in Boosting. We then present a novel classification model adaptation method. The goal of adaptation is optimizing an existing model for a new target application, which is similar to the previous one but may have different classes or class distributions. Finally, we present an efficient and effective cost-sensitive classification method that extends Boosting to allow for weighted classes. We evaluated these methods for call classification in the AT&;T VoiceTone^® spoken language understanding system. Our results indicate that it is possible to obtain the same classification performance by using 30% less labeled data when the unlabeled data is utilized through semisupervised learning. Using model adaptation we can achieve the same classification accuracy using less than half of the labeled data from the new application. Finally, we present significant improvements in the “important” (i.e., higher weighted) classes without a significant loss in overall performance using the proposed cost-sensitive classification method.     

Finite sample properties of estimators of spatial autoregressive models with autoregressive disturbances

The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investigate the finite sample behavior of the feasible generalized spatial two-stage least squares (FGS2SLS) estimator introduced by Kelejian and Prucha (1998), the maximum likelihood (ML) estimator, as well as that of several other estimators. We find that the FGS2SLS estimator is virtually as efficient as the ML estimator. This is important because the ML estimator is computationally burdensome, and may even be forbidding in large samples, while the FGS2SLS estimator remains computationally feasible in large samples. Received: 20 January 2001 / Accepted: 31 August 2001