生物多样性维护功能评估及其空间尺度效应分析——以陕西省为例

生物多样性维护功能评估是生态系统综合评估的重要内容。基于物种丰富度的海拔梯度格局规律来修正生物多样性维护服务能力指数模型,并探讨该模型在省级、市级和生物多样性优先区三种不同尺度水平的评估响应。结果显示：（1）三种尺度识别的生物多样性极重要区面积和空间分布差异较大,省级尺度评估的极重要区集中于秦巴山地,市级尺度评估时极重要区分布广泛且布局分散,优先区尺度评估时极重要区与自然保护区重叠面积最大。（2）三种尺度评估识别的精准度由大到小依次为：优先区尺度（19.5%）>省级尺度（11.7%）>市级尺度（11.2%）,优先区尺度评估识别的精准度明显高于行政区尺度的评估。（3）优先区尺度评估识别的极重要区面积为28032.3 km²,占陕西省国土面积的13.7%;极重要区内土地利用结构和物种重要性综合评估结果优于行政区尺度的评估结果。     

基于Multi-class SVM的车辆换道行为识别模型研究

自动安全换道是车辆实现无人驾驶的关键,为精确识别行驶车辆换道状态,保证行车安全,设计了一种基于多分类支持向量机(Multi-class Support Vector Machine,Multiclass SVM)的车辆换道识别模型。从NGSIM数据集中选取美国101公路车辆轨迹数据进行分类处理,并将车辆换道过程划分为车辆跟驰阶段、车辆换道准备阶段和车辆换道执行阶段。采用网格搜索结合粒子群优化算法(Grid Search-PSO)对SVM模型中惩罚参数C和核参数g进行寻优标定,利用多分类支持向量机换道识别模型对样本数据进行训练和测试,模型测试精度达97.68%。研究表明,模型能够很好地识别车辆在换道过程中的行为状态,为车辆换道阶段的研究提供支持。     

论金属矿山地质灾害模式识别模型

从崩塌与滑坡、泥石流、采空塌陷3个方面探讨了金属矿山地质灾害模式,运用层次分析法确定了金属矿山地质灾害模式识别指标的权重。考虑到影响金属矿山地质灾害因素的不确定性,引入SPA,并与最优化原理相结合,提出了同异反最优模式识别模型,将其与现有的最大联系测度方法应用于某金属矿山地质灾害模式识别中。实例应用表明,同异反最优模式识别模型从整体上判断金属矿山地质灾害模式,较最大联系测度方法更为科学、合理。     

硫化矿石自燃倾向性评价的属性区间识别模型

为合理判定硫化矿石的自燃倾向性大小,将属性区间识别理论与信息熵理论结合,建立硫化矿石自燃倾向性综合评价的熵权属性区间识别模型。选取矿样的吸氧速度常数、自热点、着火点3项指标作为属性区间识别模型的基本判别因子,依据信息熵理论获得各判别指标的权重。给出矿样自燃倾向性的属性空间矩阵,采用均化系数将矿样的属性测度区间转化为综合属性测度;利用置信度识别准则和分级标准判别各个矿样的自燃倾向性。对采自国内典型硫化矿山的14个代表性矿样的室内实测值进行综合判定;结果表明,基于熵权的属性区间识别模型能更好地评价硫化矿石的自燃倾向性大小,如实反映出矿样的自燃特性。     

石化企业火灾危险性模式识别模型研究

石化企业具有易燃、易爆危险性等特点,一旦发生事故,后果相当严重.考虑到影响石化企业火灾危险性因素较多,从生产过程、储存过程、运输过程、消防设施与安全管理五个方面探讨了石化企业火灾危险性模式,运用层次分析法确定了石化企业火灾危险性模式识别指标的权重.将SPA理论与最优化原理相结合,提出了同异反最优模式识别模型,并将其与现有的最大联系测度方法应用于某石化企业危险性模式识别中.实例应用表明,同异反最优模式识别模型从整体上判断石化企业危险性模式,较最大联系测度方法更为科学、合理.     

基于解释图的概率多规划识别事故分析方法研究

基于多规划识别在行为分析的多目标性、过程性与结果解释性方面的优势,提出了一种改进的概率多规划识别事故分析法,并将其应用于井下电机车撞、压人事故行为分析。通过构建事故规划库和基本事件解释图,找出顶层事故致因的所有解释集合,并定义解释集合重要度对解释集合路径进行重要程度排序,从而找到导致顶层事件发生的最佳解释集合;通过临界重要度系数验算,对解释集合重要度的排序结果进行论证,两者计算结果基本一致且后者优势明显,说明了基于解释图的多规划识别算法应用于事故分析的可行性和优越性;最后指出了此算法对处理逻辑结构复杂的大样本事故分析存在的不足及拟解决办法。     

Exploring human–nature interactions in national parks with social media photographs and computer vision

Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user-generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state-of-the-art computer-vision methods to studying human–nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human–nature interactions can be extracted from user-generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer-vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.     

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

• UV-vis absorption analyzer was applied in drainage type online recognition. • The UV-vis spectrum of four drainage types were collected and evaluated. • A convolutional neural network with multiple derivative inputs was established. • Effects of different network structures and input contents were compared. Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%.     

2种基于模式识别的环状燃气管网泄漏检测方法

为了将模式识别技术应用于环状燃气管网泄漏检测并找到合适的特征提取方法,以天津城建大学实验室环状燃气管网泄漏为例,将实验的28种工况作为测试样本,与之对应的模拟工况作为训练样本,采用提取压力图像特征向量法和节点压力矩阵法分别进行环状燃气管网的泄漏检测,采用支持向量机分类器将2种方法获得的特征向量进行训练与分类检验,进而将其分类准确率进行对比分析。研究结果表明:该2种方法均可用于环状燃气管网泄漏检测,提取压力图像特征向量法因有效地降低了特征向量的维度和数据波动的干扰,其结果更优。结合SCADA和GIS系统,可将该法应用于实际水、气、油管网泄漏检测和定位,有助于降低成本,提高检测效果。     

基于多因素模式识别的煤与瓦斯突出预测研究

采煤工作面煤与瓦斯突出是由煤层自然条件和工程扰动共同作用决定的,充分考虑煤层原始赋存条件和人类工程活动对煤与瓦斯突出的影响,建立多因素模式识别准则和方法,应用VBA技术完成了工作面煤与瓦斯突出危险性动态预测系统开发。以平顶山十矿己15-24080工作面为研究对象,将瓦斯含量、瓦斯压力、采动应力等因素作为工作面煤与瓦斯突出的主要影响因素,运用多因素模式识别方法实现了对工作面煤与瓦斯突出危险性分单元概率预测,且能够随着工作面不断推进进行动态预测和分级管理。研究结果表明:突出危险性预测结果与现场实况有较好的一致性,对煤矿安全开采具有良好的指导作用。