红板岩材料隐式本构模型的建立及其应用研究

针对红板岩材料在岩土工程中所表现的大量模糊的和不确定的因素等特点,基于人工神经网络的学习能力,借助于室内岩石力学试验,进行了对该材料的力学本构特性进行了神经网络模拟研究,提出了隐式本构模型的思想和方法,并通过该方法对该岩石的流变试验结果进行学习,获得了以网络权值结构保存的力学特性知识,由此得到了表征红板岩应力应变本构关系的隐式本构模型。应用结果表明,该方法对岩土类材料本构关系的模拟研究具有很好的应用前景。     

辽宁省“十五”数字化地磁数据分析系统

依据台站“十五”数字化地磁工作需求,结合地磁学科组运行管理的相关技术要求,研发辽宁省“十五”数字化地磁数据分析系统.该软件采用IDL作为开发语言,Oracle 10g数据库管理数据,主要包含用户管理模块、日志管理模块和数据管理模块3部分,具备数据实时处理、快速成像、操作简便、安全稳定等特点,可以有效解决目前台站地磁数据...     

基于贝叶斯正则化BP神经网络的DEM趋势面逼近

趋势面从宏观上揭示了研究对象的特性,在各领域发挥着重要作用。BP神经网络可以对复杂系统进行无限逼近,进而进行预测。建立了基于贝叶斯正则化BP神经网络的数字高程模型趋势面,与二次多项式建立的数字高程模型趋势面进行比较分析,证明了该方法的可行性和有效性。     

Improving the efficiency of ant algorithms using adaptive refinement: Application to storm water network design

The ant algorithm is a new evolutionary optimization method proposed for the solution of discrete combinatorial optimization problems. Many engineering optimization problems involve decision variables of continuous nature. Application of the ant algorithm to the optimization of these continuous problems requires discretization of the continuous search space, thereby reducing the underlying continuous problem to a discrete optimization problem. The level of discretization of the continuous search space, however, could present some problems. Generally, coarse discretization of the continuous design variables could adversely affect the quality of the final solution while finer discretization would enlarge the scale of the problem leading to higher computation cost and, occasionally, to low quality solutions. An adaptive refinement procedure is introduced in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is discretized and a completely new search is conducted to find a better solution. The procedure is continued until no improvement can be made by further refinement. The method is applied to a benchmark problem in storm water network design discipline and the results are compared with those of existing methods. The method is shown to be very effective and efficient regarding the optimality of the solution, and the convergence characteristics of the resulting ant algorithm. Furthermore, the method proves itself capable of finding an optimal, or near-optimal solution, independent of the discretization level and the size of the colony used.     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.     

Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.     

山东省三级地下水监测站网建设思考

随着国家地下水监测工程的启动,国土资源部提出了国家、省、市新的三级地下水监测站网建设要求。该文介绍了各级地下水监测站网的基本概念、主要建设内容,各级地下水监测站网建设服务对象、基本要求,细化了各级地下水监测站网建设要求,规范了建设行动。     

浅谈高分辨率卫星影像数据在城市污水行业的应用

随着遥感技术的发展和卫星影像分辨率的不断提高,高分辨率卫星影像广泛应用于各个行业。本文介绍了对卫星影像进行DOM(数字正射影像)制作、河涌排放口定位、污水等级监督分类及变化监测等在城市污水行业中的应用。     

基于网络药理学和分子对接探讨左金丸治疗肝癌的作用机制

目的：运用网络药理学和分子对接方法探讨左金丸治疗肝癌的作用机制。方法：从TCMSP和BATMAN-TCM数据库获取左金丸的化合物及其相应靶点,检索GeneCards、OMIM和TTD 3个数据库获得肝癌的相关靶基因,取两者靶基因交集得到左金丸治疗肝癌的预测靶基因;运用Cytoscape 3.7.1软件构建左金丸-化合物-靶点-肝癌和PPI网络图;运用R软件对预测靶基因进行GO和KEGG富集分析;最后运用分子对接技术对关键化合物和靶点进行验证。结果：共获取左金丸35个化合物及173个相应靶点,左金丸与肝癌的共同靶点有103个。PPI结果表明AKT1、MAPK1、TP53、JUN和RELA可能为左金丸治疗肝癌的关键靶点。富集分析示左金丸可能通过乙型肝炎、卡波西肉瘤相关疱疹病毒感染、人巨细胞病毒感染、丙型肝炎、MAPK信号通路、肝癌等信号通路抗肝癌。分子对接结果示槲皮素和黄连素均能与AKT1和MAPK1稳定结合。结论：本研究初步揭示了左金丸通过多成分、多靶点、多通路治疗肝癌的作用机制,为后续左金丸治疗肝癌提供理论参考。     

面向LiDAR/Radar松组合的迭代加权IEKF-BP组合算法精度分析

为了验证目前高精度定位中多传感器组合定位模型性能的优越性,以更好地解决自动驾驶场景下自主定位中出现的预测精度标准不一致、预测不及时及误预测率高等问题,本文利用LiDAR与Radar数据,建立了一种基于迭代加权的IEKF-BP组合算法的松组合模型,并对两种传感器组合定位结果精度进行了分析.试验表明,迭代加权的IEKF-B...