基于移动网络的端到端密钥协商协议

虽然3G网络的安全机制扩展到核心网络,但是并没有实现端到端安全.基于自生成证书公钥密码体制,设计一种端到端密钥协商协议,只需要一个消息交换就可以建立安全的三方会话密钥.安全性分析结果表明:基于移动网络的端到端密钥协商协议能够解决密钥管理及第三方无举证窃听,与其他密钥协商协议相比,具有较好的安全性,为端到端加密在移动通信系统中的大规模应用提供基础.     

浅谈提高空中三角测量数据成果质量的3个关键环节

空中三角测量数据主要是为摄影测量提供定向框架数据,其质量决定最终产品的质量。笔者结合生产实践中遇到的实际问题,对提高空中三角测量数据成果质量的3个关键环节——航外控制测量,航内控制点量测,外业控制点的残差分析、空中三角测量成果检查及评定的重要性进行了阐述。     

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.     

Prediction of the dynamic response of a slender marine structure under an irregular ocean wave using the NARX-based quadratic Volterra series

The time series of the dynamic response of a slender marine structure was predicted in approximate sense using a truncated quadratic Volterra series. The wave-structure interaction system was identified using the NARX (Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through supervised training using prepared datasets. The dataset used for network training was obtained by nonlinear finite element analysis of the slender marine structure under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of the relative velocity between the water particle and structure in the Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of the response of the structure was predicted using the quadratic Volterra series. To check the applicability of the method, the response of a slender marine structure under a realistic ocean wave environment with a given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. The predicted time series of the response of structure with quadratic Volterra series successfully captured the slowly varying response with reasonably good accuracy. This method can be used to predict the response of the slender offshore structure exposed to a Morison type load without relying on the computationally expensive time domain analysis, especially for screening purposes.     

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。