Impact of Brunswick river mouth training walls on adjacent beaches, Brunswick heads, New South Wales, Australia

River training walls have been built at scores of locations along the NSW coast and their impacts on shoreline change are still not fully understood. In this study, the Brunswick River entrance and adjacent beaches are selected for examination of the impact of the construction of major training walls. Thirteen sets of aerial photographs taken between 1947 and 1994 are used in a CIS approach to accurately determine tire shoreline Position, beach contours and sand volumes, and their changes in both time and space, and then to assess the contribution of both tire structures and natural hydrodynamic conditions to large scale (years-decades and kilometres) beach changes. The impact of the training walls can be divided into four stages: natural conditions prior to their construction (pre 1959), major downdrift erosion and updrift accretion during and. following the construction of the walls in 1959 similar to 1962 and 1966. diminishing impact of the walls between 1966 and 1987, and finally no apparent impact between 1987 similar to 1994. The impact extends horizontally about 8 km updrift and 17 km downdrift, and temporally up to 25 years..

Net primary productivity and spatial distribution of vegetation in an alpine wetland, Qinghai-Tibetan Plateau

To initially describe vegetation structure and spatial variation in plant biomass in a typical alpine wetland of the Qinghai-Tibetan Plateau, net primary productivity and vegetation in relationship to environmental factors were investigated. In 2002, the wetland remained flooded to an average water depth of 25 cm during the growing season, from July to mid-September. We mapped the floodline and vegetation distribution using GPS (global positioning system). Coverage of vegetation in the wetland was 100%, and the vegetation was zonally distributed along a water depth gradient, with three emergent plant zones (Hippuris vulgaris-dominated zone, Scirpus distigmaticus-dominated zone, and Carex allivescers-dominated zone) and one submerged plant zone (Potamogeton pectinatus-dominated zone). Both aboveground and belowground biomass varied temporally within and among the vegetation zones. Further, net primary productivity (NPP) as estimated by peak biomass also differed among the vegetation zones; aboveground NPP was highest in the Carex-dominated zone with shallowest water and lowest in the Potamogeton zone with deepest water. The area occupied by each zone was 73.5% for P. pectinatus, 2.6% for H. vulgaris, 20.5% for S. distigmaticus, and 3.4% for C. allivescers. Morphological features in relationship to gas-transport efficiency of the aerial part differed among the emergent plants. Of the three emergent plants, H. vulgaris, which dominated in the deeper water, showed greater morphological adaptability to deep water than the other two emergent plants.

Jiangella gansuensis gen. nov., sp nov., a novel actinomycete from a desert soil in north-west China

A novel actinomycete strain, designated YIM 002(T), was isolated from a desert soil sample in Gansu Province, north-west China. This actinomycete isolate formed well-differentiated aerial and substrate mycelia. In the early stages of growth, the substrate mycelia fragmented into short or elongated rods. Chemotaxonomically, it contained LL-2,6-diaminopimelic acid in the cell wall. The cell-wall sugars contained ribose and glucose. Phospholipids present were phosphatidylinositol mannosides, phosphatidylinositol and diphosphatidylglycerol. MK-9(H-4) was the predominant menaquinone. The major fatty acids were anteiso C-15:0 (35.92%), anteiso C-17:0 (15.84%), iso C-15:0 (10.40%), iso C-16:0 (7.07%) and C(17:10)w8c (9.37%). The G+C content of the DNA was 70 mol%. Phylogenetic analysis and signature nucleotide data based on 16S rRNA gene sequences showed that strain YIM 002(T) is distinct from all recognized genera of the family Nocardioidaceae in the suborder Propionibacterineae. On the basis of the phenotypic and genotypic characteristics, it is proposed that isolate YIM 002(T) be classified as a novel species in a new genus, Jiangella gansuensis gen. nov., sp. nov. The type strain is YIM 002(T) (= DSM 44835(T) = CCTCC AA 204001(T) = KCTC 19044(T)).

Isolation, characterization and crystal structure of natural eremophilenolide from Ligularia sagitta

A new eremophilenolide 1beta, 10beta-epoxy-6beta-acetoxy-3beta-angeloyloxy-8beta-hydrox y-eremophil-7(11)-en-8, 12alpha-olide (1), together with liguhodgsonal (2), esculetin (3) and beta-sitosterol (4), was isolated from the aerial parts of Ligularia sagitta. The structure of the new constituent (1) was elucidated by spectroscopic methods and confirmed by single-crystal X-ray diffraction.

三维环境中基于ESMF的多机器人协作观测方法

提出了一种基于扩展集员估计（ESMF）的多机器人协作观测方法，该方法将多机器人之间的观测数据融合过程嵌入到估计过程当中，从而减少了数据处理的过程，增强了算法的快速性。同时，这种方法在实现协作观测时只需要协作机器人传送观测信息而不是整个的估计信息，因此可以减轻多机器人系统的通信负担。除此之外，该方法在融合多机器人的观测数据过程中避免了多余的近似过程，增加了观测的准确性。最后，给出了三维环境下的仿真结果，验证了方法的可行性。

基于MIT规则的自适应Unscented卡尔曼滤波及其在旋翼飞行机器人容错控制的应用

提出一种新颖的基于MIT规则的自适应Unscented卡尔曼滤波(Unscented Kalman filter,UKF)算法,用来进行参数以及状态的联合估计。针对旋翼飞行机器人执行器提出一种执行器健康因子(Actuator health coefficients,AHCs)的故障模型结构,应用自适应UKF对AHCs参数进行在线估计,将联合估计的状态以及故障参数引入基于模型的反馈线性化控制结构,组成完整的容错控制系统。提出的自适应UKF算法以及容错控制结构经过中科院沈阳自动化研究所ServoHeli-20旋翼无人智能平台数学模型进行仿真试验验证,效果良好。

基于天际线识别的无人机实时姿态角估计

以无人机天际线识别为背景,提出了一种准确、实时的天际线识别算法,并由此估计姿态角。在结合实际情况的基础上,对天际线建立能量泛函模型,利用变分原理推出相应偏微分方程。在实际应用中出于对实时性的考虑,引入直线约束对该模型进行简化,然后利用由粗到精的思想识别天际线。首先,对图像预处理并垂直剖分,然后利用简化的水平直线模型对天际线进行粗识别,通过拟合获得天际线粗识别结果,最后在基于梯度和区域混合开曲线模型约束下精确识别天际线,并由此估计无人机滚动和俯仰姿态角。实验结果表明,该算法对天际线识别具有较好的鲁棒性、准确性和实时性。

基于多传感器融合的组合导航方法研究

本文提出一种基于多传感器融合的组合导航方法,能够在小型旋翼无人机上实现低成本、高精度导航定位.该方法通过建立导航系统的机械编排模型,设计了一个17状态的扩展卡尔曼滤波器(EKF).对加速计的零偏和陀螺仪的漂移进行在线估计,实时的补偿传感器的测量误差.从而对旋翼无人机的速度、位置、角速度和姿态等参数进行精确的估计.通过对实际飞行数据仿真实验,并对比参考的导航系统,证明该方法在飞机的全包线飞行下均能够解算出可靠的导航信息。

滑坡泥石流信息遥感获取与空间预测

On the issue of geological hazard evaluation（GHE）, taking remote sensing and GIS systems as experimental environment, assisting with some programming development, this thesis combines multi-knowledges of geo-hazard mechanism, statistic learning, remote sensing (RS), high-spectral recognition, spatial analysis, digital photogrammetry as well as mineralogy, and selects geo-hazard samples from Hong Kong and Three Parallel River region as experimental data, to study two kinds of core questions of GHE, geo-hazard information acquiring and evaluation model. In the aspect of landslide information acquiring by RS, three detailed topics are presented, image enhance for visual interpretation, automatic recognition of landslide as well as quantitative mineral mapping. As to the evaluation model, the latest and powerful data mining method, support vector machine (SVM), is introduced to GHE field, and a serious of comparing experiments are carried out to verify its feasibility and efficiency. Furthermore, this paper proposes a method to forecast the distribution of landslides if rainfall in future is known baseing on historical rainfall and corresponding landslide susceptibility map. The details are as following: (a) Remote sensing image enhancing methods for geo-hazard visual interpretation. The effect of visual interpretation is determined by RS data and image enhancing method, for which the most effective and regular technique is image merge between high-spatial image and multi-spectral image, but there are few researches concerning the merging methods of geo-hazard recognition. By the comparing experimental of six mainstream merging methods and combination of different remote sensing data source, this thesis presents merits of each method ,and qualitatively analyzes the effect of spatial resolution, spectral resolution and time phase on merging image. (b) Automatic recognition of shallow landslide by RS image. The inventory of landslide is the base of landslide forecast and landslide study. If persistent collecting of landslide events, updating the geo-hazard inventory in time, and promoting prediction model incessantly, the accuracy of forecast would be boosted step by step. RS technique is a feasible method to obtain landslide information, which is determined by the feature of geo-hazard distribution. An automatic hierarchical approach is proposed to identify shallow landslides in vegetable region by the combination of multi-spectral RS imagery and DEM derivatives, and the experiment is also drilled to inspect its efficiency. (c) Hazard-causing factors obtaining. Accurate environmental factors are the key to analyze and predict the risk of regional geological hazard. As to predict huge debris flow, the main challenge is still to determine the startup material and its volume in debris flow source region. Exerting the merits of various RS technique, this thesis presents the methods to obtain two important hazard-causing factors, DEM and alteration mineral, and through spatial analysis, finds the relationship between hydrothermal clay alteration minerals and geo-hazards in the arid-hot valleys of Three Parallel Rivers region. (d) Applying support vector machine (SVM) to landslide susceptibility mapping. Introduce the latest and powerful statistical learning theory, SVM, to RGHE. SVM that proved an efficient statistic learning method can deal with two-class and one-class samples, with feature avoiding produce ‘pseudo’ samples. 55 years historical samples in a natural terrain of Hong Kong are used to assess this method, whose susceptibility maps obtained by one-class SVM and two-class SVM are compared to that obtained by logistic regression method. It can conclude that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping. (e) Predicting the distribution of rainfall-induced landslides by time-series analysis. Rainfall is the most dominating factor to bring in landslides. More than 90% losing and casualty by landslides is introduced by rainfall, so predicting landslide sites under certain rainfall is an important geological evaluating issue. With full considering the contribution of stable factors (landslide susceptibility map) and dynamic factors (rainfall), the time-series linear regression analysis between rainfall and landslide risk mapis presented, and experiments based on true samples prove that this method is perfect in natural region of Hong Kong. The following 4 practicable or original findings are obtained: 1) The RS ways to enhance geo-hazards image, automatic recognize shallow landslides, obtain DEM and mineral are studied, and the detailed operating steps are given through examples. The conclusion is practical strongly. 2) The explorative researching about relationship between geo-hazards and alteration mineral in arid-hot valley of Jinshajiang river is presented. Based on standard USGS mineral spectrum, the distribution of hydrothermal alteration mineral is mapped by SAM method. Through statistic analysis between debris flows and hazard-causing factors, the strong correlation between debris flows and clay minerals is found and validated. 3) Applying SVM theory (especially one-class SVM theory) to the landslide susceptibility mapping and system evaluation for its performance is also carried out, which proves that advantages of SVM in this field. 4) Establishing time-serial prediction method for rainfall induced landslide distribution. In a natural study area, the distribution of landslides induced by a storm is predicted successfully under a real maximum 24h rainfall based on the regression between 4 historical storms and corresponding landslides.

地理信息系统支持下的滑坡灾害空间分析预测-以云南小江流域为例

As a typical geological and environmental hazard, landslide has been causing more and more property and life losses. However, to predict its accurate occurring time is very difficult or even impossible due to landslide's complex nature. It has been realized that it is not a good solution to spend a lot of money to treat with and prevent landslide. The research trend is to study landslide's spatial distribution and predict its potential hazard zone under certain region and certain conditions. GIS(Geographical Information System) is a power tools for data management, spatial analysis based on reasonable spatial models and visualization. It is new and potential study field to do landslide hazard analysis and prediction based on GIS. This paper systematically studies the theory and methods for GIS based landslide hazard analysis. On the basis of project "Mountainous hazard study-landslide and debris flows" supported by Chinese Academy of Sciences and the former study foundation, this paper carries out model research, application, verification and model result analysis. The occurrence of landslide has its triggering factors. Landslide has its special landform and topographical feature which can be identify from field work and remote sensing image (aerial photo). Historical record of landslide is the key to predict the future behaviors of landslide. These are bases for landslide spatial data base construction. Based on the plenty of literatures reviews, the concept framework of model integration and unit combinations is formed. Two types of model, CF multiple regression model and landslide stability and hydrological distribution coupled model are bought forward. CF multiple regression model comes form statistics and possibility theory based on data. Data itself contains the uncertainty and random nature of landslide hazard, so it can be seen as a good method to study and understand landslide's complex feature and mechanics. CF multiple regression model integrates CF (landslide Certainty Factor) and multiple regression prediction model. CF can easily treat with the problems of data quantifying and combination of heteroecious data types. The combination of CF can assist to determine key landslide triggering factors which are then inputted into multiple regression model. CF regression model can provide better prediction results than traditional model. The process of landslide can be described and modeled by suitable physical and mechanical model. Landslide stability and hydrological distribution coupled model is such a physical deterministic model that can be easily used for landslide hazard analysis and prediction. It couples the general limit equilibrium method and hydrological distribution model based on DEM, and can be used as a effective approach to predict the occurrence of landslide under different precipitation conditions as well as landslide mechanics research. It can not only explain pre-existed landslides, but also predict the potential hazard region with environmental conditions changes. Finally, this paper carries out landslide hazard analysis and prediction in Yunnan Xiaojiang watershed, including landslide hazard sensitivity analysis and regression prediction model based on selected key factors, determining the relationship between landslide occurrence possibility and triggering factors. The result of landslide hazard analysis and prediction by coupled model is discussed in details. On the basis of model verification and validation, the modeling results are showing high accuracy and good applying potential in landslide research.