注水开发后期提高油砂体采收率方法探讨

注水开发后期，研究油砂体剩余油分布规律，实施油砂体综合治理，完善油砂体注采关系是提高最终采收率的重要途径之一。按照含油面积、采出程度及生产动态特点将油砂体分为3类。根据每类砂体的不同特点，采取相应的治理措施，取得了较好效果，累积增油18．5kt，增加水驱储量1．08Mt。注水开发后期提高油砂体采收率必须以剩余油的分析研究为指导，以井网对剩余可采储量的控制为前提，以油水井措施的联动为手段，从而达到提高最终采收率的目的。     

Solving the small sample size problem in face recognition using generalized discriminant analysis

The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.     

Stream Classification System Based on Susceptibility to Algal Growth in Response to Nutrients

We developed a stream classification system that is based on stream’s susceptibility to algal growth using a two-step approach. The model portrays algal biomass as a result of stream’s response to nutrient concentrations and the response is governed by various stream factors. In the first step, a nutrient-chlorophyll a relationship was developed to characterize nutrient’s effects on algal biomass. Residuals of the relationship were attributed to stream’s susceptibility to algal growth in response to nutrients and referred to as “observed” susceptibility. In the second step, conditions of other contributing factors were used to explain the variation in the residuals and the developed relationship was used to generate “predicted” susceptibility. Existing data compiled from various monitoring projects of Illinois streams and rivers were used to illustrate the approach. Streams were classified into three (high, medium, and low) categories based on their observed and predicted susceptibility values, respectively. With the available data, the model showed a 40-50% success rate for classifying the streams based on three observed and predicted susceptibility categories. Model entropy also was calculated for selecting the best model. The results show the important role of both nutrients and other contributing factors in explaining the variation of algal biomass. The study also suggests ways to fine tune the model and improve its accuracy, which would make the presented model a more viable tool for stream classification for establishing nutrient criteria to prevent surface streams from eutrophication.     

Generation and selection of likely interpretations during plan recognition in task-oriented consultation systems

This paper presents a mechanism which infers a user's plans from his/her utterances by directing the inference process towards the more likely interpretations of a speaker's statements among many possible interpretations. Our mechanism uses Bayesian theory of probability to assess the likelihood of an interpretation, and it complements this assessment by taking into consideration two aspects of an interpretation: its coherence and its information content. The coherence of an interpretation is determined by the relationships between the different statements in the discourse. The information content of an interpretation is a measure of how well defined the interpretation is in terms of the actions to be performed on the basis of this interpretation. This measure is used to guide the inference process towards interpretations with higher information content. The information content of an interpretation depends on the specificity and the certainty of the inferences in it, where the certainty of an inference depends on the knowledge on which the inference is based. Our mechanism has been developed for use in task-oriented consultation systems. The particular domain that we have chosen for exploration is that of travel booking.     

Successive pattern classification based on test feature classifier and its application to defect image classification

A novel successive learning algorithm based on a Test Feature Classifier is proposed for efficient handling of sequentially provided training data. The fundamental characteristics of the successive learning are considered. In the learning, after recognition of a set of unknown data by a classifier, they are fed into the classifier in order to obtain a modified performance. An efficient algorithm is proposed for the incremental definition of prime tests which are irreducible combinations of features and capable of classifying training patterns into correct classes. Four strategies for addition of training patterns are investigated with respect to their precision and performance using real pattern data. A real-world problem of classification of defects on wafer images has been dealt with by the proposed classifier, obtaining excellent performance even through efficient addition strategies.     

Data mining of resilience indicators

In recent years, the Asia-Pacific region has experienced several financial setbacks, including speculative attacks in 1998 and the SARS outbreak in 2003. Financial stresses of this nature are unanticipated, and not all of the dangers can be predicted by the examination of market information and macroeconomic indicators. The Early Warning System (EWS) that has been adopted by the International Monetary Fund may not be able to predict future financial crises for all possible scenarios, because shocks come in many different forms. To supplement the EWS, this paper proposes a data mining framework to measure the resilience of an economy. The resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand a financial shock should one occur. The framework is based on a feedback system consisting of two stages. The first stage assigns a resilience score to each economy based on a fuzzy logic scoring scheme that is built on the ambiguous reasoning of experts. The second stage uses the classification tree approach to estimate thresholds for each economic indicator, and examines the quality of the fuzzy score. The result from the second stage is then passed back to the first stage as feedback. The final result is obtained when the feedback system reaches its equilibrium state. The proposed resilience framework is applied to the external-sector and the public-sector economies of several countries to illustrate its applicability.     

一种协调的科技文献分类方法

科技文献之间的相互引证关系反映了一种科学交流活动,显示了科学文献之间(甚至是学科之间)的内在联系,而通过追溯文献之间的这种关系,可以改善和提高传统的基于内容的科技文献的分类的方法。论文利用有相互引证关系,有同引关系,以及有耦合关系的两篇文献一般是属于同一类的这一特点,提出了文献之间的引用相似度,同引相似度,耦合相似度这三个概念,再利用这三个概念生成了文献之间的“结构相似度”,并将它用于K-NN分类法中得出一种基于结构的分类法。最后,论文将这种基于结构的分类法和基于内容的NaveBayes分类法结合起来提出了一种新的协调分类法。     

Experiments of Fast Learning with High Order Boltzmann Machines

This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.