智能高效小型一体化泥浆不落地系统的研究与应用

针对固体矿产勘查钻探工程泥浆性能维护和固控装备自动化的需求,开展小型一体化泥浆不落地循环处理工艺研究和自动化智能化控制装备研制。装备主要由循环系统、净化系统、控制系统及循环管汇组成。系统引入PID控制和物联网技术,实现过渡泵的自反馈控制和远程可视化控制。现场应用结果表明,装备运行安全、可靠、平稳,净化效率高、自动化程度高,在技术、成本和环保等方面都取得了良好的效果,符合绿色勘查需求。通过自动化智能化控制技术,减少现场作业人员,降低工人劳动强度,一定程度上提升了岩心钻探设备自动化智能化水平,为实现岩心钻探设备自动化、智能化目标提供技术支撑。     

Artificial neural networks approach for zeta potential of Montmorillonite in the presence of different cations

In this study, the zeta potential of montmorillonite in the presence of different chemical solutions was modeled by means of artificial neural networks (ANNs). Zeta potential of the montmorillonite was measured in the presence of salt cations, Na⁺, Li⁺ and Ca²⁺ and metals Zn²⁺, Pb²⁺, Cu²⁺, and Al³⁺ at different pH values, and observed values pointed to a different behavior for this mineral in the presence of salt and heavy metal cations. Artificial neural networks were successfully developed for the prediction of the zeta potential of montmorillonite in the presence of salt and heavy metal cations at different pH values and ionic strengths. Resulting zeta potential of montmorillonite shows different behavior in the presence of salt and heavy metal cations, and two ANN models were developed in order to be compared with experimental results. The ANNs results were found to be close to experimentally measured zeta potential values. The performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance account for were used to control the performance of the prediction capacity of the models developed in this study. These indices obtained make it clear that the predictive models constructed are quite powerful. The constructed ANN models exhibited a high performance according to the performance indices. This performance has also shown that the ANNs seem to be a useful tool to minimize the uncertainties encountered during the soil engineering projects. For this reason, the use of ANNs may provide new approaches and methodologies.     

一个圈闭评价专家系统的设计与实现

本文详细论述一个圈闭评价专家系统的设计与实现技术,该系统能够接收用户提供的圈闭信息,根据知识库中的专家知识给出圈闭的含油气资源评价;系统采用了基于黑扳的控制结构和基于规则的知识表达以及演绎推理、模糊推理、模糊综合评判等一系列技术在IBM-PC机上用C语言及少量汇编语言实现。系统已通过专家鉴定。     

城市土地利用演变信息的数据挖掘——以上海市为例

城市土地利用变化 ,具有非线性特征 ,一般的数据挖掘方法基本上失效。本文研究了利用马尔可夫链和神经网络两种方法 ,基于地理信息系统、遥感图像、电子地图 ,预测了上海市中心城区、 2 0 0 2年和 2 0 0 5年的土地利用总量和土地利用类型结构的变化 ,从而研究了城市土地利用状况演变预测的地学数据挖掘技术。     

应用人工神经网络评价长春南湖水的营养状态

根据水质分析资料,以化学需氧量,总氮和总磷作为评价参数,经过反复的尝试,构建了具有４层结构用于评价湖泊的营养状态的误差逆传播网络,其输入层有３个神经元,２个隐含层各有４个神经元,输出层有１个神经元,将湖泊营养状态评价标准作为样本模式提供给网络,按照误差逆传播网络的学习规则对网络进行训练,经过３９９２５次学习后网络达到预先给定的收敛标准,应用该网络对长春南湖水的营养状态进行了评价,操作过程简便易行,     

东莞地区土地利用变化预测的CBR和CA方法对比研究(英文)

Many studies on land use change(LUC),using different approaches and models,have yielded good results.Applications of these methods have revealed both advantages and limitations.However,LUC is a complex problem due to influences of many factors,and variations in policy and natural conditions.Hence,the characteristics and regional suitability of different methods require further research,and comparison of typical approaches is re-quired.Since the late 1980s,CA has been used to simulate urban growth,urban sprawl and land use evolution successfully.Nowadays it is very popular in resolving the LUC estimating problem.Case-based reasoning(CBR),as an artificial intelligence technology,has also been employed to study LUC by some researchers since the 2000s.More and more researchers used the CBR method in the study of LUC.The CA approach is a mathematical system con-structed from many typical simple components,which together are capable of simulating complex behavior,while CBR is a problem-oriented analysis method to solve geographic problems,particularly when the driving mechanisms of geographic processes are not yet understood fully.These two methods were completely different in the LUC research.Thus,in this paper,based on the enhanced CBR model,which is proposed in our previous research(Du et al.2009),a comparison between the CBR and CA approaches to assessing LUC is presented.LUC in Dongguan coastal region,China is investigated.Applications of the im-proved CBR and the cellular automata(CA) to the study area,produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR,and 70.7% accuracy from the CA.From the results,we can see that the accuracies of the CA and CBR approaches are both >70%.Although CA method has the distinct advantage in predicting the urban type,CBR method has the obvious tendency in predicting non-urban type.Considering the entire ana-lytical process,the preprocessing workload in CBR is less than that of the CA approach.As such,it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.     

浅层人工地震探测在八钢-石化隐伏断裂中的应用

以八钢—石化隐伏断裂为例,介绍了在乌鲁木齐地区第四系较厚的河流相卵砾石覆盖层中利用浅层人工地震探测隐伏活动断裂的具体方法。首先进行观测系统参数的确定,其次实施多次覆盖的人工地震探测,从而获得较好的多层反射界面剖面,在此基础上结合相关资料进行地质解译,最后通过钻孔联合剖面验证。分析结果表明,八钢—石化隐伏断裂为隐伏的逆断层—背斜构造,宽度达150~300 m,主断层位于背斜北侧,有2条,为倾向S或SE的逆断层,错断了中更新统—上更新统砾石层,上盘形成背斜构造,在背斜南翼发育次级逆断层或正断层,主逆断层带的总垂直位移为21~23 m。     

人工蜂群算法优化SVR的叶面积指数反演

支持向量机回归SVR (Support Vector Regression)方法作为叶面积指数反演的一种新思路,在LAI反演中具有一定的应用价值和前景,但SVR算法中惩罚系数C、核函数宽度参数g、不敏感损失函数参数ε的取值对回归精度有显著的影响。本文提出了一种基于人工蜂群算法ABC (Artificial Bee Colony)优化SVR参数的遥感影像叶面积指数反演方法。研究数据为美国土壤水分实验（SMEX02） 2002年LAI实测数据和同期的Landsat 7ETM+地表反射率数据,为了验证ABC算法优化SVR各个参数对反演精度的影响,建立了未优化参数（SVR）、优化单个参数（ABC-SVR-C,ABC-SVR-g,ABC-SVR-ε）、优化3个参数（ABC-SVR）的3类LAI反演模型,并比较了其回归拟合精度。在此基础上,分析了3个关键参数对LAI反演模型精度的敏感性,并对ABC算法优化SVR模型的精度进行显著性检验。研究表明：（1）相比未优化参数模型,ABC算法优化模型具有更高的反演精度,优化3个参数优于优化单个参数,回归直线斜率k达到0.797、决定系数r2达到0.775。（...     

基于神经网络和开窗技术的储层渗透率的预测方法——以大庆萨尔图油田葡萄花油层组PⅠ1-PⅠ4小层砂岩为例

渗透率是油藏描述和油藏工程中较为关键性的参数,因而如何求取较为精确的地层渗透率参数值显得格外重要.本文在岩心分析化验数据和相关测井曲线数据归一化的基础上,利用改进的开窗技术,借助反馈的神经网络方法逐点计算地层的渗透率.以往在利用遗传算法预测渗透率的时候,因为只考虑了单一的数据点,没有把邻近层位的数据加入学习过程中来,故影响了预测模型的精度和可信度.笔者经过系统的研究,用相邻5个层位的数据点进行学习,建立储层渗透率的预测模型.大庆萨尔图油田葡萄花油层组PⅠ1-PⅠ4小层砂岩的油气勘探实践证明,预测的渗透率与实测的渗透率的值符合较好.     

Optimized support vector regression algorithm-based modeling of ship dynamics

This study contributes to solving the problem of how to derive a simplistic model feasible for describing dynamics of different types of ships for maneuvering simulation employed to study maritime traffic and furthermore to provide ship models for simulation-based engineering test-beds. The problem is first addressed with the modification and simplification of a complicated and nonlinearly coupling vectorial representation in 6 degrees of freedom (DOF) to a 3 DOF model in a simple form for simultaneously capturing surge motions and steering motions based on several pieces of reasonable assumptions. The created simple dynamic model is aiming to be useful for different types of ships only with minor modifications on the experiment setup. Another issue concerning the proposed problem is the estimation of parameters in the model through a suitable technique, which is investigated by using the system identification in combination with full-scale ship trail tests, e.g., standard zigzag maneuvers. To improve the global optimization ability of support vector regression algorithm (SVR) based identification method, the artificial bee colony algorithm (ABC) presenting superior optimization performance with the advantage of few control parameters is used to optimize and assign the particular settings for structural parameters of SVR. Afterward, the simulation study on identifying a simplified dynamic model for a large container ship verifies the effectiveness of the optimized identification method at the same time inspires special considerations on further simplification of the initially simplified dynamic model. Finally, the further simplified dynamic model is validated through not only the simulation study on a container ship but also the experimental study on an unmanned surface vessel so-called I-Nav-II vessel. Either simulation study results or experimental study results demonstrate a valid model in a simple form for describing the dynamics of different types’ ships and also validate the performance of the proposed parameter estimation method.