中原高温高盐油藏疏水缔合聚合物凝胶调剖剂研究

针对中原油田高温(>80℃)、高盐度(>50 g/L)、高硬度(>1 g/L)砂岩油藏,研制了地下成胶的高热稳定性聚合物凝胶调剖剂。所用聚合物为疏水缔合聚合物AP-P4,M=9.0×106,HD=27%,疏水基摩尔分数0.2%;交联剂为可生成酚醛树脂的3种化合物;调剖剂胶液用矿化度160 g/L的马寨油田污水配制;实验温度95℃。根据形成的凝胶黏度(95℃,1.1 s-1)确定交联剂各组分用量为:MZ-YL 0.429%,MZ-BE 0.060%,MZ-XS 0.012%;酸度调整剂用量0.12%;成胶时间10~15小时。2.5、3.0、3.5 g/L AP-P4的凝胶在95℃老化100天后黏度保持在~40 Pa.s及以上。初配制胶液在3000 r/min下剪切15分钟后黏度降低87.5%~89.0%,但成胶后和老化过程中凝胶黏度只比未剪切样降低17.1%~6.6%。3.5 g/L AP-P4的调剖剂对~1μm2的6支储层岩心的堵塞率在88.1%~95.8%范围,平均93.3%,造成的残余阻力系数在15.2~28.6范围,平均19.8,使渗透率级差2.3~8.0的4组双人造岩心的注水流量比发生不同程度的反转。认为该凝胶调剖剂高温下稳定性好的原因,是强化学交联密度低,因而凝胶脱水收缩作用弱。图2表5参5。     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

Evolving Fault-Tolerant Neural Networks

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Passivity Analysis of Dynamic Neural Networks with Different Time-scales

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.     

Improving Neural Networks for Mechanism Kinematic Chain Isomorphism Identification

Detection of isomorphism among kinematic chains is essential in mechanical design, but difficult and computationally expensive. It has been shown that both traditional methods and previously presented neural networks still have a lot to be desired in aspects such as simplifying procedure of identification and adapting automatic computation. Therefore, a new algorithm based on a competitive Hopfield network is developed for automatic computation in the kinematic chain isomorphism problem. The neural approach provides directly interpretable solutions and does not demand tuning of parameters. We have tested the algorithm by solving problems reported in the recent mechanical literature. Simulation results show the effectiveness of the network that rapidly identifies isomorphic kinematic chains.     

股票期权的会计处理方法研究

经理人股票期权是对管理者实行的长期激励制度，由于我国制度上的缺陷，目前在我国没有统一的处理方法，本文讨论的主要是在国际上股票期权的确认、计量以及在会计报表中披露的会计方法。     

改进的BP神经网络在大坝安全监控中的应用

针对经典BP神经网络运行中存在的缺陷，提出了改进的BP神经网络，不仅解决了经典BP网络易陷入局部最小的弊端，而且应用的0.618分割选取法能使网络快速找到较优隐含层节点数，初始权值的自相关修正进一步提高了网络的稳定性。实际应用证明，改进的BP神经网络有效提高了网络质量，适合大型网络的构建与训练。     

Implicit, long-term spatial contextual memory.

Learning and memory of novel spatial configurations aids behaviors such as visual search through an implicit process called contextual cuing (M. M. Chun & Y. Jiang, 1998). The present study provides rigorous tests of the implicit nature of contextual cuing. Experiment 1 used a recognition test that closely matched the learning task, confirming that memory traces of predictive spatial context were not accessible to conscious retrieval. Experiment 2 gave explicit instructions to encode visual context during learning, but learning was not improved and conscious memory remained undetectable. Experiment 3 illustrates that memory traces for spatial context may persist for at least 1 week, suggesting a long-term component of contextual cuing. These experiments indicate that the learning and memory of spatial context in the contextual cuing task are indeed implicit. The results have implications for understanding the neural substrate of spatial contextual learning, which may depend on an intact medial temporal lobe system that includes the hippocampus (M. M. Chun & E. A. Phelps, 1999). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A connectionist implementation of the theory of planned behavior: Association of beliefs with exercise intention.

The theory of planned behavior suggests attitudes are a product of salient beliefs. This study examined whether aggregating salient beliefs was plausible within a more biologically centered information-processing environment. A neural network was used to examine associations among beliefs relating to exercise intention. Data on intentions and behavioral, normative, and control beliefs from 114 respondents were used to train (by error backpropagation) a neural network to associate beliefs with intention. The R2 between the network's estimated and self-reported intention was .66. The network's representation comprised 6 belief profiles associated with high, moderate, or low behavioral intention. The neural network accommodated complex relationships among beliefs and belief-intention associations and indicated how high-level constructs such as attitudes may be viewed as the best fit (compromise state) between aroused beliefs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)