卫星激光测距的新进展

扼要综述了近几年国际上卫星激光测距的进展，介绍了国内激光测距网的现状，展望了未来卫星激光测距的发展。     

山东省城镇网络结构与城镇网络角色识别——基于民国时期土货／洋货流通网络的分析

本文运用复杂网络分析方法,基于1932年的山东省城镇间土洋货流通的基础数据,建立了城镇间的关系矩阵,讨论了1932年时山东省城镇间土洋货流通网络的全网结构特征,并识别出网络中的城镇角色和关键链接。研究发现：第一,城镇度符合幂律分布,绝大多数城镇度值较低,少数几个城镇度值较高。网络连通性差,城镇节点度与聚集系数为负相关关...     

西霞院反调节水库控制网的布设及优化

采取平面网与高程网相结合的方法布设了施工控制网，并引用工程控制网优化设计的有关理论，结合实际对平面网的网形进行了机助优化。在网形确定的基础上，文中采用可靠因子为质量指标对网中的观测量进行优化，最终得到了满足精度和可靠性要求的最优布设方案。     

江苏地区介质非弹性衰减和场地响应研究

选取江苏数字地震台网在2001年1月-2010年7月记录到43次高质量数字地震波形,采用Atkinson(1992)和Moya(2000)的方法,反演得到江苏地区的介质非弹性衰减特性和场地响应.     

Adaptive Predictive Inverse Control of Offshore Jacket Platform Based on Rough Neural Network

The offshore jacket platform is a complex and time-varying nonlinear system,which can be excited of harmful vibration by external loads.It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model.In this paper,an adaptive inverse control method is proposed on the basis of novel rough neural networks (RNN) to control the harmful vibration of the offshore jacket platform,and the offshore jacket platform model is established by dynamic stiffness matrix (DSM) method.Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory,RNN is utilized to identify the predictive inverse model of the offshore jacket platform system.Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads,and to deal with the delay problem caused by signal transmission in the control process.The numerical results show that the constructed novel RNN has advantages such as clear structure,fast training speed and strong error-tolerance ability,and the proposed method based on RNN can effectively control the harmfid vibration of the offshore jacket platform.     

环渤海地区农村空心化程度与耕地利用集约度的时空变化及其耦合关系

利用环渤海地区分县社会经济数据和土地利用数据, 采用神经网络确定权重的方法, 对该区分县农村空心化程度和耕地利用集约度进行综合测评, 并以山东省为例, 进行两者耦合规律探索。结果表明:① 环渤海地区农村空心化区域差异明显。高度空心化且进入稳定期的地区主要集中在“C”型沿海地区;平原传统农区, 农村空心化正处空心化的成长、兴盛期;农村空心化低值区主要集中落后山区。② 耕地利用集约度时空差异明显。辽中平原粮食主产区、冀中南粮食主产区、鲁西鲁南粮食主产区集约度较高;低集约度主要集中在太行山区、长白山区、沂蒙山区、坝上高原等落后地区。③ 农村空心化与耕地利用集约度变化存在着耦合联动关系。空心化出现、成长期, 农村劳动力向城镇转移, 农村地区隐性失业问题得以解决, 耕地利用集约度不断提升;农村空心化发展至兴盛期, 农村地域发展面临主体弱化, 耕地利用集约度有所下降;至空心化稳定期, 健全的土地流转机制和农业现代化的持续推进, 耕地利用集约度将反弹上升, 最终趋于波动稳定。     

南水北调中线降水丰枯遭遇风险分析

受降水丰枯变化不确定性和差异性的影响,南水北调中线工程水源区与受水区降水的丰枯遭遇状态各不相同,给南水北调工程水资源调度运行带来风险。联合copula函数和贝叶斯网络理论,建立了南水北调中线工程水源区和受水区降水丰枯遭遇风险分析模型,对南水北调中线工程调水最不利的丰枯遭遇风险概率进行了研究。利用copula函数建立了水源区和受水区年降水量联合分布函数,计算条件概率,结合贝叶斯网络进行丰枯遭遇风险分析。结果表明南水北调中线4个受水区调水风险的概率均在25%以下,并对不同情景的调水风险进行了仿真分析。     

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.     

Retrieval of water vapor profiles with radio occultation measurements using an artificial neural network

A new method applying an artificial neural network （ANN） to retrieve water vapor profiles in the troposphere is presented. In this paper, a fully-connected, three-layer network based on the backpropagation algorithm is constructed. Month, latitude, altitude and bending angle are chosen as the input vectors and water vapor pressure as the output vector. There are 130 groups of occultation measurements from June to November 2002 in the dataset. Seventy pairs of bending angles and water vapor pressure profiles are used to train the ANN, and the sixty remaining pairs of profiles are applied to the validation of the retrieval. By comparing the retrieved profiles with the corresponding ones from the Information System and Data Center of the Challenging Mini-Satellite Payload for Geoscientific Research and Application （CHAMP-ISDC）, it can be concluded that the ANN is relatively convenient and accurate. Its results can be provided as the first guess for the iterative methods or the non-linear optimal estimation inverse method.