大洋多金属结核资源评价的基本理论与方法

根据大量工作实践和研究 ,系统扼要地介绍了大洋多金属结核资源评价的基本概念、理论基础、评价方法及国内外概况 ;论述了多元统计分析 (聚类分析、因子分析、趋势面分析 )、地质统计学 (克立格法 )、神经元网络分析进行多金属结核资源评价的方法原理 ;以及实现矿区边界指标—资源量—面积动态分析的《大洋多金属结核资源动态评价系统》

Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks

An artificial neural network model was developed to predict the oxidation of refractory gold concentrate （RGC） by ozone and ferric ions. The concentration of ozone and ferric ions, pulp density, oxygen amount, leaching time and temperature were employed as inputs to the network; the output of the network was the percentage of the ferric extraction iron from RGC. The multilayered feed-forward networks were trained by 33 sets of input-output patterns using a back propagation algorithm; a three-layer network with 8 neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient （R2=0.966）. The predictions by ANN are more accurate when compared with conventional multivariate regression analysis （MVRA）. In addition, calculation with ANN model indicates that temperature is the predominant parameter and ozone concentration is the lesser influential parameter in the pre-oxidation process of refractory gold ore. The ANN neural network model accurately estimates the ferric extraction during pretreatment process of RGC in gold smelter plants and can be used to optimize the process parameters.

基于多频超声波原理的变压器故障检测方法探究

变压器的运行状态直接影响到整个电网的运行安全,如何实现对变压器多种故障实时监测、保障电网的正常运行至关重要。本文提出基于多频超声波的变压器故障检测技术,即同时利用上百个频率不同的超声波对变压器油不间断扫描检测,并对接收到的超声波的各项参数采用多元统计分析技术和复数人工神经元网络数据分析技术进行处理,得到不同超声波的特征值,经过与变压器故障特征值比对可得到变压器的运行工况和故障名称,从而实现高效、准确、同时检测出多种故障。

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.

Experimental study of fatigue degree quantification for multi-feature fusion identification

A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the reaction and operation abilities of drivers about traffic signals.By comparison experiment with that EEG signal based,multivariate statistical analysis and fusion identification based on BP neural network(BPNN) results show that the experimental procedure is simple and practical,and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory,and also can achieve accurate and reliable quantification of fatigue degree,especially under the associated action of multiple fatigue feature parameters.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Bistability Analysis of Excitatory-Inhibitory Neural Networks in Limited-Sustained-Activity Regime

Bistable behavior of neuronal complex networks is investigated in the limited-sustained-activity regime when the network is composed of excitatory and inhibitory neurons.The standard stability analysis is performed on the two metastable states separately.Both theoretical analysis and numerical simulations show consistently that the difference between time scales of excitatory and inhibitory populations can influence the dynamical behaviors of the neuronal networks dramatically,leading to the transition from bistable behaviors with memory effects to the collapse of bistable behaviors.These results may suggest one possible neuronal information processing by only tuning time scales.