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.     

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,     

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。（...     

基于生态通道模型的我国渔业资源生态容量研究进展

随着人类活动对于海洋资源的过度开发,渔业资源的衰退趋势愈发显著。因此,渔业资源亟待科学合理的理论指导来保护和利用有限的渔业资源。Ecopath生态通道模型是一种研究生态系统特征与变化的经典模型,除了可用于分析生态系统结构与功能以及评估生态系统的成熟稳定程度外,该模型还可以评估物种的生态容量,为增殖放流活动等渔业资源提供保护措施,因此被广泛应用于渔业资源生态容量研究。本文综述了生态容量的概念以及目前国内研究生态容量的常见方法,对Ecopath模型的发展历史做了简要概述,介绍了模型的原理、建立、调试方法以及使用Ecopath模型评估物种生态容量的操作方法,随后对模型中的各项参数指标做了详细的介绍。本文根据Ecopath模型在我国渔业资源研究中的应用方向进行了分类讨论,并对我国当前基于Ecopath的水域生态容量研究进展进行了汇总,对比了不同类型生态系统之间生态特征参数的异同,重点归纳了不同水域生态系统下的物种生态容量评估研究,最后提出了模型的限制性以及发展前景。     

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.     

Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network

With the support of big data and GPU acceleration training, the artificial intelligence technology with deep learning as its core is developing rapidly and has been widely used in many fields. At the same time, feature extraction operations are required by the current image-based corrosion damage detection method in the field of ships, with little effect but consuming the large amount of manpower and financial resources. Therefore, a new method for hull structural plate corrosion damage detection and recognition based on artificial intelligence using convolutional neural network is proposed. The convolutional neural network (CNN) model is trained through a large number of classified corrosion damage images to obtain a classifier model. Then the classifier model is used with overlap-scanning sliding window algorithm to recognize and position the location of corrosion damage. Finally, the damage detection pattern for hull structural plate corrosion damage as well as other types of superficial structural damage using convolutional neural network is proposed, which can accelerate the application of artificial intelligence technology into the field of naval architecture & ocean engineering.     

丹顶鹤越冬期在人工湿地的夜栖分布

在盐城国家级自然保护区人工湿地内,通过对丹顶鹤越冬期间夜栖地的调查,得出鹤群夜栖分布及其动态规律,并提出对该种群与夜栖地的管理措施。该项工作主要借助望远镜、GPS.照相机,采用直接计数法统计数量和利用文献资料数据。结果表明:人工湿地中丹顶鹤的夜栖分布随着环境的改变而改变,其中安全度和水位为其主要限制因子。因此控制人工湿地冬季水位,保护栖息环境不受人为影响是丹顶鹤安全越冬的主要保护措施。