中等传输距离下的无线功率传输链路第1部分:实现工作状态最优化

中程无线功率传输（WPT）可以采用几种不同的方式实现,如通过电感或电容耦合、谐振或非谐振网络实现.本文主要研究了通过感应耦合谐振器实现的WPT链路,而且只着重研究了利用2个谐振器的链路（直接链路）并工作在主谐振频率下的情况.研究结果表明,当工作在主谐振频率下,可以根据网络参数来对传输效率或负载功率进行优化.     

中国县域城镇化的道路交通影响因素识别及空间协同性解析

本文基于人口普查数据和路网数据,利用空间滞后回归模型和耦合协调度模型等,对中国县域城镇化率的道路交通影响及其空间耦合协同性进行分析,研究表明：①中国县域低城镇化水平的县(区)主要集中在传统农区、集中连片贫困山区、高寒经济欠发达区。经济发展水平与人力资源空间上的错位,致使经济欠发达地区的人口倒挂,异地城市化特征显著;②中国县(区)道路交通优势度宏观上呈现出东西部区域差异和交通枢纽与外围区域的差异,“点—轴”地域结构特征显著,县域道路交通优势度数值呈现偏正态分布;③道路交通是城镇间和城镇与区域间联系的核心纽带和产业转型升级发展的传输廊道,开放式的道路系统增强了农村生产要素非农化转型的市场可介入性,对县域城镇化发展有积极作用。高速公路出口、火车站的布局对城镇化影响和带动农村要素非农化作用较为明显;④中国县域交通优势度与城镇化率耦合协调度分级分布为偏正态分布,城镇化发展与交通优势度相互影响显著,呈一定的双向耦合性。     

LBS导航认知地图服务系统设计与实现

科技发展改变了电子地图制作和使用环境。本文在分析传统大数据量地图形式在LBS中的应用缺陷后,提出了符合用户需求和移动终端条件的认知地图可视化方案,这种不同于传统制图综合原则制作的认知地图能有效缓解目前在LBS应用中存在的地图数据与屏幕大小的矛盾,适应了移动环境下用户的多行为状态和终端硬件特点。     

中国区域BP-Adaboost强预测器对流层天顶延迟建模研究

选取中国大陆构造环境监测网（陆态网）提供的155个测站2014~2018年对流层延迟产品,基于BP-Adaboost算法将多个弱神经网络预测器集成为强预测器,建立新的无气象参数对流层延迟计算模型。利用陆态网2019年参与建模的141个建模测站、未参与建模的62个测站的对流层延迟产品和中国区域86个无线电探空站解算出的对流层延迟精确值对BP-Adaboost模型进行精度评定,结果表明,新模型的平均偏差分别为0.62 mm、-1.16 mm和12.32 mm,均方根误差分别为25.30 mm、26.72 mm和46.29 mm,优于常见的无气象参数模型;BP-Adaboost模型在内陆地区或海拔2 km以上地区具有更高的精度,能够满足中国大陆区域卫星导航用户实时对流层延迟改正的需求。     

On the identification of ship coupled heave–pitch motions using neural networks

This paper outlines a procedure for the derivation of the differential equations describing the free response of a heaving and pitching ship from its stationary response to random waves. The coupled heave–pitch motion of a ship in random seas is modelled as a multi-dimensional Markov process. The partial differential equation describing the transition probability density function, known as the Fokker-Planck equation, for this process is derived. The Fokker-Planck equation is used to derive the random decrement equations for the coupled heave–pitch motion. The parameters in these equations are then identified using a neural network approach. The method is validated using numerical simulations and experimental results. The experimental data was obtained using an icebreaker ship model heaving and pitching in random waves. It is shown that the method produces good results when the system is lightly damped. An extension for using this method to identify couple heave–pitch motion in realistic seas is suggested.     

Crust and upper mantle heterogeneities in the southwest Pacific from surface wave phase velocity analysis

Direct earthquake-to-station Rayleigh and Love wave data observed on high gain broadband records are analyzed in order to improve the lateral resolution of the uppermost mantle in the southwest Pacific region. We used data of nine permanent Geoscope and Iris stations located in the southern hemisphere and nine other stations as part of two temporary networks, the first one installed in New Caledonia and Vanuatu (hereafter named Cavascope network) by ORSTOM and the EOST from Louis Pasteur University in Strasbourg (France) and the second one installed in the Fiji, Tonga and Niue islands (hereafter named Spase network) by Washington University in St. Louis (USA). In order to collect more significant details on the surficial structures, we included the analysis of short period waves down to 8 s. A multiple frequency filtering technique has been used to recover phase velocities of Rayleigh and Love waves for selected earthquakes with magnitude greater than 5.5 and with known centroid moment tensor (CMT). About 1100 well-distributed seismograms have been processed in the period range 8–100 s and corrections for topography and water depth have been applied to the observed phase velocities. The geographical distribution of phase velocity anomalies have then been computed using the tomographic method developed by Montagner [Montagner, J.P., 1986a. Regional three-dimensional structures using long-period surface waves. Ann. Geophys. 4 (B3), 283–294]. Due to a poor knowledge of dense, well-distributed, crustal thickness values and corresponding velocity models, we did not perform or speculate on the construction of an S-wave 3D velocity model; therefore, we limited this study to the interpretation of the phase velocity distribution. The location of phase velocity anomalies are well determined and the deviations are discussed within the framework of the geological context and compared with other tomographic models. At long periods, from 40 s to 100 s, our results agree well with most of previous studies: the tomographic imaging shows a large contrast between low and high phase velocities along the Solomon, New Hebrides and Fiji–Tonga trenches. The lowest phase velocity anomalies are distributed beneath northern and southern Fiji basins and the Lau basin (corresponding to the volume situated just above the dipping slabs), whereas the highest values are displayed beneath the Pacific plate and the eastern part of Indian plate downgoing under the North Fiji basin. At shorter periods, our results show that the phase velocity distributions are well correlated with the large structural crustal domains. The use of local temporary broadband stations in the central part of the studied area gives us the opportunity to observe surface waves showing well-dispersed trains, allowing extended velocity measurements down to 8 s although aliasing due to multipaths become important. The continental regions (Eastern Australia, New Guinea, Fiji islands and New Zealand) show low velocities which are likely due to thick continental crust, whereas the Tasmanian, D'Entrecasteaux, and the Northern and Southern Fiji basins are characterized by higher velocities suggesting thinner oceanic crust. Additional analysis including the anisotropic case and S-wave velocity inversion with depth is in progress.     

裂隙网络分维及其与渗透率的关系

在前人裂隙网络水实验室试验的基础上,依据分形理论对裂隙网络复杂系统进行简单的统计分析,获得定量描述这一复杂体系的特征值——分维,并查明它与裂隙网络渗透率之间的关系。从计算结果可以看出,裂隙网络(指有效裂隙网络)的分维值与渗透率之间存在明显的正相关关系,这有可能为裂隙网络渗透率的定量评价提供一个简单而又实用的方法。      

Improving neural network performance on the classification of complex geographic datasets

Neural Networks are now established computational tools used for search minimisation and data classification. They offer some highly desirable features for landuse classification problems since they are able to take in a variety of data types, recorded on different statistical scales, and combine them. As such, neural networks should offer advantages of increased accuracy. However, a barrier to their general acceptance and use by all but `experts' is the difficulty of configuring the network initially.  This paper describes the architectural problems of applying neural networks to landcover classification exercises in geography and details some of the latest developments from an ongoing research project aimed at overcoming these problems. A comprehensive strategy for the configuration of neural networks is presented, whereby the network is automatically constructed by a process involving initial analysis of the training data. By careful study of the functioning of each part of the network it is possible to select the architecture and initial weights on the node connections so the constructed network is `right first time'. Further adaptations are described to control network behaviour, to optimise functioning from the perspective of landcover classification. The entire configuration process is encapsulated by a single application which may be treated by the user as a `black box', allowing the network to the applied in much the same way as a maximum likelihood classifier, with no further effort being required of the user.     

Using computational intelligence techniques to model subglacial water systems

Measurements of water pressure beneath Trapridge Glacier, Yukon Territory, Canada show that the basal water system is highly heterogeneous. Three types of behaviour were recorded: pressure records which are strongly correlated, records which are strongly anticorrelated, and records which alternate between strong correlation and strong anticorrelation. We take the pressure in bore-holes that are connected to the evacuation route for basal water as the forcing, and the other pressures as the response to this forcing. Previous work (Murray and Clarke 1995) has shown that these relationships can be modelled using low-order nonlinear differential equations optimized by inversion. However, despite optimizing the model parameters we cannot be sure that the final model forms are themselves optimal. Computational intelligence techniques provide alternative methods for fitting models and are robust to missing or noisy data, applicable to non-smooth models, and attempt to derive optimal model forms as well as optimal model parameters. Four computational intelligence techniques have been used and the results compared with the more conventional mathematical model. These methods were genetic programming, artificial neural networks, fuzzy logic and self-organizing maps. We compare each technique and offer an evaluation of their suitability for modelling the pressure data. The evaluation criteria are threefold: (1) goodness of fit and an ability to predict subsequent data under different surface weather conditions; (2) interpretability, and the extent and significance of any new insights offered into the physics of the glacier; (3) computation time. The results suggest that the suitability of the computational intelligence techniques to model these data increases with the complexity of the system to be modelled.     

铁路几种类型的便线设计及测量要素的计算方法

本文在介绍铁路便线的设计过程中,详细地讨论了各类曲线测量要素的计算方法,掌握了这些方法,铁路便线施工放样的困难就可迎刃而解。