Defending against wormhole attack in OLSR

OLSR (optimal link state routing) is one of the four basic routing protocols used in mobile ad hoc Networks by the MANET working group of IETF (Internet engineering task force). OLSR, a proactive routing protocol, is based on a multipoint relaying flooding technique to reduce the number of topology broadcast. OLSR uses periodic HELLO packets to neighbor detection. As introduced in Reference [1], the wormhole attack can form a serious threat in wireless Networks, especially against many ad hoc Network routing protocols and location-based wireless security systems. Here, a trust model to handle this attack in OLSR is provided and simulated in NS2.     

长短期记忆神经网络(LSTM)模型在低能见度预报中的应用

利用浙江省义乌市2015—2019年逐小时气象观测数据(相对湿度、风速、地气温差、能见度)和空气质量指数(Air Quality Index, AQI)数据, 分析了义乌地区低能见度天气(观测能见度lt; 10 km)的分布特征和气象要素条件。利用长短期记忆神经网络(Long Short Term Memory Neural Network, LSTM)模型对逐小时能见度进行模拟, 分别对比了观测能见度作为输入变量与否的模拟效果; 根据义乌地区低能见度天气条件的特征, 将模拟时段分为三个时期(11月至翌年2月, 3—6月, 7—10月), 对比了分时期模拟的效果; 以及评估了模型的预报步长。结果表明: 高湿、高污染、气温高于地温和低风速是义乌地区低能见度天气的主要特征。LSTM模型对单站能见度有较好的模拟效果, 当输入参数中加入历史观测能见度时, 能大幅提高模拟准确度, 日均能见度模拟结果均方根误差RMSE=0.63 km, 平均绝对误差MAE=0.51 km, 拟合优度R²=0.99;分时期进行模拟能得到更精准的模拟结果。本研究中选用的输入要素在冬季(11月至翌年2月)模拟效果最好, RMSE=2.35 km, MAE=1.46 km, 低能见度均方根误差RMSE_10 km=1.81 km, 低能见度平均绝对误差MAE_10 km=1.13 km, R²=0.83; 3—6月的模拟中, 输入变量中不加AQI模拟效果更好, 这意味着3—6月义乌地区的低能见度天气以雾天气为主导, 加入过多变量并不一定能提高模型准确度; 随着预报步长增大, 模型预报效果变差, 预测步长等于3 h, R²=0.71, 预测结果已不具备实际应用意义。     

斜坡地质灾害气象预报预警空间数据库的设计与建立

斜坡地质灾害的频发常常带来重大的经济损失,尤其是降雨诱发的斜坡地质灾害问题日益严重。鉴于传统数据库不能有效地管理空间数据的局限性,采用ArcSDE Geodatabase和SQL Server空间数据库技术有利于对斜坡地质灾害气象预报预警的各种空间数据进行高效、统一、科学地管理。从数据准备、空间数据库的设计、空间数据库的建立三大方面介绍了斜坡地质灾害气象预报预警空间数据库的设计与建立;最后以云南省怒江州为例,对该数据库进行实例验证,充分地论证了该数据库具有一定的可行性。研究成果可为斜坡地质灾害气象预报预警和防治提供科学的依据。     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

Using snakes for the registration of topographic road database objects to ALS features

For historical reasons many national mapping agencies store their topographic data in a dual system consisting of a Digital Landscape Model (DLM) and a Digital Terrain Model (DTM). The DLM contains 2D vector data representing objects on the Earth’s surface, such as roads and rivers, whereas the DTM is a 2.5D representation of the related height information, often acquired by Airborne Laser Scanning (ALS). Today, many applications require reliable 3D topographic data. Therefore, it is advantageous to convert the dual system into a 3D DLM. However, as a result of different methods of acquisition, processing, and modelling, the registration of the two data sets often presents difficulties. Thus, a straightforward integration of the DTM and DLM might lead to inaccurate and semantically incorrect 3D objects.In this paper we propose a new method for the fusion of the two data sets that exploits parametric active contours (also called snakes), focusing on road networks. For that purpose, the roads from a DLM initialise the snakes, defining their topology and their internal energy, whereas ALS features exert external forces to the snake via the image energy. After the optimisation process the shape and position of the snakes should coincide with the ALS features. With respect to the robustness of the method several known modifications of snakes are combined in a consistent framework for DLM road network adaptation. One important modification redefines the standard internal energy and thus the geometrical model of the snake in order to prevent changes in shape or position not caused by significant features in the image energy. For this purpose, the initial shape is utilized creating template-like snakes with the ability of local adaptation. This is one crucial point towards the applicability of the entire method considering the strongly varying significance of the ALS features. Other concepts related to snakes are integrated which enable our method to model network and ribbon-like characteristics simultaneously. Additionally, besides ALS road features information about context objects, such as bridges and buildings, is introduced as part of the image energy to support the optimisation process. Meaningful examples are presented that emphasize and evaluate the applicability of the proposed method.     

ArcGIS在公众地理信息服务平台建设中的应用

在目前的GIS领域中,为用户提供及时、丰富、便捷的信息服务成为研究的主要课题之一。针对这种情况,本文主要阐述了怎样利用ArcGIS软件建立公众地理信息服务平台以体现地理信息的实效特征。     

浅谈新疆测震台网传输链路的改造

要做到IP到台站有很多种方式可选,根据新疆测震台网各台站的现状列举了7类不同的数据传输方式,这些数据传输方式在新疆测震台网中均得到应用,也都达到IP到台站的目的.分别对这些数据传输链路的原理及优缺点进行阐述,并结合目前各台站具体情况作链路适用性的相关分析,希望能够对其他省局台网的台站通信链路设计、改造起到参考.     

大凌河流域MIKE BASIN水资源模型

Mike Basin模型是一个集总式综合河网模拟系统,与GIS系统全面链接,具备清楚的数据与模型结构,支持水资源综合管理的参与式对话和矛盾解决方案。它由两个模型单元组成:水文模型(NAM)和水资源分配模型(MIKE BASIN)。它是认识和分析流域水资源状况,进行流域水资源综合管理规划十分重要的工具。本文从模型数据的需求、分析与应用以及参数的率定,较详细地阐述了大凌河流域MIKE BASIN水资源模型的建立过程,为其他用户进行模型的建立提供了借鉴。     

基于GIS和BP神经网络的区域农村贫困空间模拟分析——一种区域贫困程度测度新方法

基于区域农村贫困程度测定方式不完善及缺乏地理视角的现状,选取四川省36个国家级扶贫县为实证对象,构建自然社会经济全面耦合的农村贫困测度指标体系,分析区域农村贫困的影响机制,并运用GIS与BP神经网络模拟区域自然致贫指数、社会致贫指数和经济消贫指数的空间分布格局。在此基础上,提出了全面表征区域农村贫困程度的区域扶贫压力指数——一种新的区域农村贫困测度方法,为国家扶贫政策文件《财政扶贫资金管理办法》中关于财政扶贫资金基于区域农村贫困程度分配提供实践基础。     

A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor

The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56° N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n + 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models.