A brief catalogue of failures: Framing evaluation and learning in fisheries resource management

We propose a framework for enabling a systematic evaluation of a fisheries resource management system, which we define as a feed-back mechanism coupled to a fishery. The resource management system includes four basic functions: diagnostics, intervention, goal setting, and decision making. This model allows for the development of an evaluation framework for fisheries resource management by facilitating a typology of failures. We suggest that the potential for systemic and interdisciplinary learning will be significantly enhanced through the process of developing such a framework.     

空间智能:地理信息科学的新进展

在总结多年来研究GIS智能计算的理论与实践基础上,提出地理信息科学发展的新方向:空间智能.空间智能强调发现与应用空间模式,以增强GIS处理复杂数据和解决复杂问题的能力.空间智能主要的技术体系由空间分析、空间优化和空间模拟三大模块构成,其技术基础包括空间统计与索引、智能代理、高级启发式,以及数学规划等系列智能技术.由于空间智能融合了机器学习、统计分析和人工智能等多个学科理论,面向解决实际工程需求中大量存在的复杂时空问题,因此理论上具有广阔的发展空间,实践上也有重大的应用需求.随着空间智能体系的完善和技术的进一步成熟,它将在实际应用中具有巨大的价值.     

基于光谱和纹理的SVM矿化蚀变信息提取研究

针对传统矿化信息提取方法单一,利用光谱或纹理、信息量相对较少、需要大量样本的缺陷,利用基于光谱和纹理的支持向量机(SVM)原理,建立矿化信息提取模型.选择青海泽库县析界日地区作为典型研究区.首先提取研究区光谱和纹理信息,选取训练样本;然后求解最优超平面,进而确定决策函数;最后泛化推广识别其他待识别的样本.通过所提取的遥感蚀变异常信息与重砂异常点叠加分析,叠加基本吻合;从野外实地验证来看,均发现了不同程度的矿化现象,并指出了5个重点异常区.     

区域似大地水准面确定的最小二乘支持向量机方法

支持向量机(SVM)是近年来发展起来的机器学习的新方法,它较好地解决小样本、非线性、高维数、局部极小点等实际问题.文中研究支持向量机的拓展算法--最小二乘支持向量机(LSSVM),并将其应用于确定大面积复杂似大地水准面.通过工程实例并与神经网络模型和二次曲面多项式拟合模型相比较,验证确定区域似大地水准面的LSSVM方法的有效性.     

动态最小二乘支持向量机学习算法

针对大样本集的训练问题和动态训练样本的模型更新问题,提出了动态最小二乘支持向量机学习算法.该算法充分利用已建好的模型,逐渐加入新样本,并可删除位于任何位置的非支持向量,避免了矩阵求逆运算,保证了算法的高效率.大坝变形及电离层延迟两个时间序列的预报实例表明,该算法具有计算时间短、预报精度高的特点.     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.     

基于改进PSO-SVM的噪声稳健建筑物变形监测方法

对高层、超高层建筑物进行实时,高精度的变形监测对提前预防安全隐患,保证人民生命财产安全具有重要意义。建筑物变形作为一种典型的随机性和微弱性过程,噪声等误差的存在会影响从中提取有用的变形信息。针对该问题,提出一种改进粒子群(Particle Swarm Optimization,PSO)算法优化支持向量机(Support Vector Machine,SVM)的噪声稳健建筑物变形监测方法,利用改进PSO算法的全局搜索能力对SVM的核参数进行优化,提升预测精度的同时增强算法的噪声稳健性。基于实测数据的试验结果表明,相对于传统交叉验证SVM和小波方法,所提方法可以获得更高的变形预测精度,并且在低信噪比条件下优势更加明显。     

一种基于核学习的储集层渗透率预测新方法

基于核学习的支持向量机,是一种采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法,具有完备的理论基础。这里提出了核学习技术在储集层非均质特性描述中渗透率参数预测的新用途。在复杂地层中,基于支持向量机的智能和自适应模式识别能力而建立了常规测井多参数信息输入的渗透率预测模型,然后对实际油田储集层渗透率进行了预测。与常规线性回归模型预测结果相对比,所提出的方法更易于使用,很少受不确定因素的影响,并具有较强的信息整合能力以及更高的预测准确性和可信度。     

对目前岩石“全曲线”测试的若干看法

从电液伺服岩石压力试验机工作机理的角度,对目前岩石应力-应变全过程曲线(以下简称“全曲线”)测试中的一些问题进行了分析,提出了自己的看法,目的是为了解决“全曲线”测试的2个问题,即如何测出“全曲线”;如何使测出的“全曲线”真实反映岩石的力学特性,并将其用到工程中。     

Computer-assisted teaching and learning in photogrammetry

The advanced personal computer models, new storage media such as CD-ROMs, new authoring tools and especially the Internet will influence the production of learning software for photogrammetry. Some examples of learning programs are presented, including experience from the development and use of these programs in the education of land surveyors. They demonstrate the progress made over the years as well as the potential and possibilities of computer-assisted teaching and learning in photogrammetry.