基于改进PSO-SVM的噪声稳健建筑物变形监测方法     

张建奇 《北京测绘》2021,35(2):166-171

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。  相似文献   

Flotation and depression control of arsenopyrite through pH and pulp redox potential using xanthate as the collector     

A. López Valdivieso  A.A. Sánchez López  C. Ojeda Escamilla  M.C. Fuerstenau 《International Journal of Mineral Processing》2006

The role of pH and pulp redox potential (E_H) to control the flotation and depression of arsenopyrite has been investigated through studies on microflotation of arsenopyrite crystals and batch flotation of an arsenopyritic ore using isopropyl xanthate as collector. The transition between flotation and depression of arsenopyrite is established by the reversible potential of the xanthate/dixanthogen couple. Adsorption of arsenate ions on ferric hydroxide has been studied through electrokinetics to delineate mechanisms involved in the depression of arsenopyrite using oxidants. Chemical binding between arsenate species and ferric hydroxide sites on arsenopyrite is suggested as the mechanism responsible for depression of arsenopyrite. E_H conditions are given for the flotation and depression of arsenopyite at various pH values for the arsenopyritic ore.  相似文献   

Continuous air rate measurement in flotation cells: Some fundamental considerations     

J.A. Torrealba-Vargas  J.A. Finch 《International Journal of Mineral Processing》2006

A robust and reliable sensor to measure gas (air) superficial velocity (J_g) continuously in flotation systems is introduced. It is based on the sampling of bubbles by buoyancy with the accumulating air allowed to exit through an orifice. At steady state, pressure drop is measured and related to the J_g by prior calibration. The continuous device is readily automated and extended to a multi-unit set-up. The sensor and data collections are described. The governing gas flow equation and models of the dynamic response to air flow rate set point change and fluctuations in froth depth are derived. Model predictions are confirmed against plant data. By taking a moving average, the J_g remains valid in the face of typical plant disturbances.  相似文献   

一种基于核学习的储集层渗透率预测新方法   总被引：2，自引：1，他引：2  

杨斌  匡立春  施泽进  孙中春 《物探化探计算技术》2005,27(2):119-123

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。  相似文献   

氯化钠浮选剂概述   总被引：2，自引：0，他引：2       下载免费PDF全文

王爱丽  张全有  李海民 《盐湖研究》2003,11(4):42-46

综述了各类氯化钠浮选剂对氯化钠的浮选。并对各类浮选剂的浮选性能进行了比较。  相似文献   

对目前岩石“全曲线”测试的若干看法   总被引：1，自引：0，他引：1  

马世民  马宇博 《岩土力学》2003,(Z1)

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。  相似文献   

一种新型自动开槽机及其控制     

靳建刚  王丽红 《中国煤田地质》2004,16(5):60-62

针对棒、管料开槽的问题，提出了一种新型全自动开槽机。介绍了该开槽机的结构和工作原理，建立全自动开槽机相适应的液压系统，搭建了开槽机继电器——控制器控制电路，实现了该设备的全自动过程控制。  相似文献   

The Effect of Machine Behaviour and Mechanical Properties of Intact Sandstone Under Static and Dynamic Uniaxial Cyclic Loading   总被引：5，自引：0，他引：5  

M. N. Bagde  V. Petroš 《Rock Mechanics and Rock Engineering》2005,38(1):59-67

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Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China     

Mingjuan Xie  Geping Luo  Olaf Hellwich  Amaury Frankl  Wenqiang Zhang  Chunbo Chen  Chen Zhang  Philippe De Maeyer 《水文研究》2021,35(12):e14444

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.  相似文献   

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)     

Pijush Samui  T. G. Sitharam 《国际地质力学数值与分析法杂志》2010,34(7):755-770

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献