SNAP-23蛋白在鲈鱼巨噬细胞白细胞介素1β分泌过程中的功能

SNARE蛋白家族是所有真核细胞胞吐及分泌作用中的关键因子，由其介导的运输囊泡膜与靶膜的锚靠、融合为胞内蛋白的运出提供了一条重要途径。体外试验表明，Syntaxin6-Syntaxin7-Vti1b，SNAP-23-Syntaxin4等SNARE核心蛋白之间精确的相互作用是哺乳动物巨噬细胞肿瘤坏死因子α (TNF-α)运输和分泌的必备条件，在机体非特异性免疫应答反应过程中起重要作用。 本研究受上述启示，旨在揭示SNARE蛋白在海洋鱼类免疫细胞内重要细胞因子白细胞介素1β (IL-1β)的分泌过程中的作用。参照Percoll密度梯度离心技术，从鲈鱼头肾组织分离纯化巨噬细胞进行稳定培养；利用RT-PCR方法克隆出鲈鱼t-SNARE蛋白SNAP-23和Syntaxin3的部分cDNA序列，再结合先前克隆的VAMP2和已知的鲈鱼IL-1β，TNF-α和IL-8的基因序列，设计特异性引物。利用Real-time PCR技术在mRNA水平上精确测定鲈鱼巨噬细胞中上述6种基因在革兰氏阴性菌脂多糖(LPS)分子刺激下的表达变化，发现SNAP-23基因与三种细胞因子基因的表达正相关；通过免疫印迹检测SNAP-23蛋白表达变化，利用酶联免疫吸附试验(ELISA)检测IL-1β的分泌水平，在蛋白水平上验证了SNAP-23表达与IL-1β分泌的正相关性；利用5`RACE和3`RACE技术克隆出鲈鱼SNAP-23全长基因，结合定点突变策略和靶向PCR克隆手段，构建鲈鱼SNAP-23野生型融合质粒pEGFP-SNAP-23wt，Cys缺失突变融合质粒pEGFP-SNAP-23ΔCys和模拟E型肉毒神经毒素(BoNT/E)切割突变融合质粒pEGFP-SNAP-23ΔBoNT/E，以及鲈鱼IL-1β野生型融合表达质粒IL-1β-pEGFP和IL-1β-pEYFP。所有融合蛋白均在鲈鱼巨噬细胞内成功表达，结合ELISA实验结果发现，SNAP-23野生型的表达对IL-1β的分泌有促进作用，而Cys缺失突变体的表达则抑制IL-1β向胞外分泌。首次证实了鱼类巨噬细胞内SNAP-23蛋白在IL-1β分泌过程中的重要作用。此外通过与GFP共表达，定位了IL-1β分子在巨噬细胞内的分布，发现新合成的IL-1β分子很可能像TNFα一样经“内质网－胞质－伪足－胞外” 的分泌路径运出胞外。

中国明对虾免疫系统中抗氧化相关基因的克隆与表达分析

对虾病害在世界范围内的广泛传播，给水产养殖和沿海农村经济造成了重大损失。深入开展对虾免疫机制研究并在此基础上寻找对虾疾病防治的有效方法已成为当务之急。研究表明，当对虾等甲壳动物受到外界病原刺激时，其体内的吞噬细胞在吞噬活动中会激活磷酸己糖支路的代谢，引起呼吸爆发，产生多种活性氧分子。另外，受到病原侵染的对虾还会产生其他多种免疫反应，这些免疫反应将消耗大量的能量（ATP），产能的呼吸链会加速运转，由此也会引发大量活性氧的产生。这些活性氧分子可以杀灭入侵的病原微生物，但同时由于活性氧分子反应的非特异性，它们也会对宿主的细胞、组织和器官造成严重伤害，进而导致对虾生理机能的损伤和免疫系统的破坏。所以，消除对虾体内因过度免疫反应产生的过量氧自由基将能够增强其抵御病原侵染的能力，提高免疫力。本论文从中国明对虾体内克隆了线粒体型超氧化物歧化酶（mMnSOD）、胞质型超氧化物歧化酶（cMnSOD）、过氧化氢酶（Catalase）和过氧化物还原酶（Peroxiredoxin）等四种与免疫系统相关的抗氧化酶基因，分析了它们的分子结构特征，组织分布及应答不同病原刺激的表达变化模式，并对其中的mMnSOD基因和Peroxiredoxin基因进行了体外重组表达、分离纯化和酶活性分析。 采用RACE技术从中国明对虾血细胞中克隆了两个超氧化物歧化酶（SOD）基因，通过序列比对分析发现，其中一个为mMnSOD基因，另一个为cMnSOD基因。mMnSOD基因的cDNA全长为1185个碱基，其中开放阅读框为660个碱基，编码220个氨基酸，其中推测的信号肽为20个氨基酸。多序列比对结果显示中国明对虾mMnSOD基因的推导氨基酸序列与罗氏沼虾、蓝蟹的推导氨基酸序列同源性分别为88%和82%。Northern blot结果表明，该基因在对虾的肝胰脏、血细胞、淋巴器官、肠、卵巢、肌肉和鳃等组织中均有表达。半定量RT-PCR结果显示，对虾感染病毒3 h时，该基因在血细胞和肝胰脏中的转录水平显著升高。此外，通过构建原核表达载体，本研究对该基因进行了体外重组表达，并对纯化的重组蛋白进行了质谱鉴定和酶活分析。cMnSOD基因的cDNA全长为1284个碱基，其中开放阅读框为861个碱基，编码287个氨基酸。多序列比对结果显示中国明对虾cMnSOD基因的推导氨基酸序列与斑节对虾和凡纳滨对虾的同源性高达98%和94%。组织半定量结果显示，cMnSOD基因在对虾被检测的各个组织中均有表达。 另外，半定量RT-PCR结果表明，对虾感染病毒23h时，该基因在肝胰脏中的转录上升到正常水平的3.5倍；而感染后59 h时，该基因在血细胞中的转录上升到正常水平的2.5倍。 利用根据其他生物过氧化氢酶保守氨基酸序列设计的简并引物，结合RACE技术，从中国明对虾肝胰脏中克隆到了过氧化氢酶基因的部分片段，片段长1725个碱基。多序列比对结果发现目前所得中国明对虾Catalase基因部分片段的推导氨基酸序列与罗氏沼虾和皱纹盘鲍Catalase氨基酸序列的同源性分别达到95%和73%。通过实时荧光定量PCR技术对中国明对虾Catalase基因在各个组织中的分布情况及病毒感染后该基因在血细胞和肝胰脏中的转录变化进行了研究。结果发现，该基因在肝胰脏、鳃、肠和血细胞中表达水平较高，在卵巢、淋巴器官和肌肉中的表达水平相对较弱；感染病毒23 h和37 h时，对虾血细胞和肝胰脏中该基因mRNA的表达量分别出现显著性上升。 依据中国明对虾头胸部cDNA文库提供的部分片段信息，结合SMART-RACE技术，从中国明对虾肝胰脏中克隆到了过氧化物还原酶基因（Peroxiredoxin）， 该基因的cDNA全长为942个碱基，其中开放阅读框为594个碱基，编码198个氨基酸。中国明对虾Peroxiredoxin基因的推断氨基酸序列与伊蚊、文昌鱼和果蝇等Peroxiredoxin基因的推断氨基酸序列同源性分别为77%、76%和73%。其蛋白理论分子量为22041.17 Da，pI为5.17。Northern blot结果表明，Peroxiredoxin基因在对虾的肝胰脏、血细胞、淋巴器官、肠、卵巢、肌肉和鳃等组织中均有表达。实时荧光定量PCR结果显示，弧菌感染后，该基因在对虾血细胞和肝胰脏中的转录水平都有明显变化并且表达模式不同。另外，对该基因进行了体外重组表达，并对纯化的重组蛋白进行了质谱鉴定和酶活性分析。酶活性分析表明，复性后的重组蛋白能在DTT存在的条件下还原H2O2。

改进的符号时间序列分析方法及其在电机故障诊断中的应用

提出了一种改进的基于符号时间序列分析的电机异常探测方法,该方法自适应地将符号序列中出现符号最多的符号区间重新划分为2个新的符号区间,使得数据密集区间可以分配到相对更多的符号,而数据稀疏区间则分配到较少的符号,提高了符号对于信号变化的灵敏度。电机转子断条故障的诊断实验结果表明:该方法较平均划分区间的方法对于电机异常诊断有着更高的灵敏度以及更好的鲁棒性和可靠性。

基于符号时间序列分析技术的电机故障诊断方法研究

随着经济建设的快速发展和电气化程度的不断提高，电机已被广泛应用于工业、农业、国防及人们日常生活的各个领域。从全球范围看，电机的用电量平均占世界用电总量的50％以上、占工业用电量的70％左右，然而在电机消耗的电能中有相当一部分被浪费掉了，其中电机带故障运行是造成电机运行效率偏低，能源浪费严重的主要原因之一。 电机在线监测及故障诊断系统对于减少由于电机故障引发的人员、财产的损失，减少由于故障引发的异常状态而导致的能源浪费有着重要的现实意义。在电机故障危害产生前发现故障并进行维护是电机故障诊断的核心思想，在保证电机故障诊断系统准确性的同时，系统的快速性与鲁棒性显得尤为重要。基于此，本论文从寻求系统的快速、稳定的性能入手，提出了基于符号时间序列分析的感应电机故障诊断框架，重点研究了计算代价小、噪声干扰不敏感的诊断方法，以期提高感应电机故障诊断系统的快速性与鲁棒性。论文的主要工作有： 1． 论文首先构建了一个基于符号时间序列分析的电机故障诊断框架，将电 机故障诊断分解为信号预处理、符号区间划分、符号统计分析三部分，有机地融合了统计分析、信号处理、信息论、模式识别等理论和方法，利用符号时间序列分析技术在强噪声中准确识别系统状态模式的良好性能，可以有效地解决电机故障诊断问题，并实现电机故障诊断量化分析，是对探索电机在线监测与诊断新方法的一次有益的尝试。 2．引入提升小波对信号进行前期处理，并针对常规提升小波固定预测滤波器的局限性，提出了基于梯度信息的自适应提升小波预测方法。该方法中预测滤波器并不是固定的，而是利用梯度的信息来确定预测算子。根据信号的陡峭程度选择预测算子可以更准确地预测信号，从而使原始信号中的平滑特征和陡峭特征可以在小波变换中完好地保留下来。仿真实验及实验室实验结果表明该方法可以有效地保留信号中蕴含的重要的特征信息，对于以提取、识别信号中特征信息为主的故障诊断技术来说具有非常重要的意义。 3．针对所采集现场信号的非均匀分布特点，论文提出了一种自适应符号化划分方法，既可以确保符号在数据密集区间和数据稀疏区间的合理分配，提高符号的利用率，又可以灵活地适应信号的特征，增强诊断系统对微弱故障信号的敏感度。故障诊断实验表明该方法简单有效，实现了故障初期的快速诊断，并且较平均区间划分方法有着更高的计算效率、更明显的诊断效果。 4．将相对熵的概念引入基于符号时间序列分析的电机故障诊断框架中，针对电机故障严重程度量化分析问题，提出了基于模糊相对熵及加权模糊相对熵的符号统计分析方法，并将该方法应用于感应电机的故障诊断与识别，建立了电机故障诊断模型。该方法可以更合理、充分地利用信息丰富的符号区间所蕴含的故障信息，实现了电机故障诊断与故障严重程度的识别。实验结果验证了该方法的合理性、有效性和可靠性。 5．将隐马尔可夫模型（HMM）引入到基于符号时间序列分析的电机故障诊断框架中，构造了基于HMM的电机故障诊断模型，并对HMM阶数选取问题给出了一个基于符号出现不确定信息熵的HMM阶数选取原则，使得模型在满足精度要求的同时，又尽可能地减少模型的计算代价，有效地提高了故障诊断的效率及可靠性。实验结果表明基于HMM的电机故障诊断方法有效地实现了电机转子断条故障、匝间短路故障的诊断与量化分析。

基于web的遥操作系统时延分析

采用预测控制算法给出了一种带有时延补偿器的新的控制结构,分别在前向通道和反馈通道设计补偿器对网络时延进行补偿.实验结果表明:带有预测器及补偿器的新的控制结构可以改善系统的动态性能,并且能够保证系统在具有时延和数据丢失的环境下的稳定性.

AUV定位信号检测延时的蒙特卡洛模拟分析

在简要介绍AUV声学定位声纳接收机原理基础上,分析了CW脉冲信号在极性相关检测电路中的传输过程,建立了极性相关积分检测延时仿真分析模型。提出采用蒙特卡洛模拟方法获取检测延时的分布特征和统计参数的观点。实验结果表明蒙特卡洛模拟实验与硬件电路实验结果一致,对于解决随机性检测延时问题具有很强的能力。获得的结果可为AUV定位声纳检测门限的设定、声学测距和定位精度分析以及水声通信延时分析提供参考。

载人潜器串口设备转接工业以太网的实时性建模与分析

根据具体的需求,为载人潜器设计了一种基于工业以太网的内部数据通讯和控制系统,其数据通讯的实时性是衡量控制系统的一个重要指标,因此,为了分析串行数据通信系统的实时性能,据其选用的传感器和网络架构的特点,建立了串口数据包传送时间延迟的数学模型;并在潜器平台上,以实测数据试验误差验证了该模型的准确性和普遍性,从而为开发人员对各种串口设备的参数设置提供理论指导;最后用该模型分析了载人潜器串行数据传送的实时性。

财政预测模型研究及应用

采用多种科学预测方法与财政经济实际相结合的方式建立了一个综合的财政收支系统动力学模型．这个模型集中了时间序列分析模型，灰色系统预测模型的参数综合性强和系统动力学模型结构分明，用动态反馈方式预测系统发展变化的特点，对东北两大城市预算内财政收支“八五”计划指标进行了全面定量的预测与分析，得到了很好的应用效果。

鄂尔多斯盆地上古生界低渗透砂岩有效储层识别与预测

Sulige Gasfield, with a basically proven reserve as high as one trillion cubic meters, is one giant gas field discovered in China. The major gas -bearing layers are Upper Paleozoic strata with fluvial-lacustrine sedimentary facies. Generally, gas reservoirs in this field are characteristic by "five low" properties, namely low porosity, low permeability, low formation pressure, low productivity and low gas abundance. Reservoirs in this field also feature in a large distribution area, thin single sandbody thickness, poor reservoir physical properties, thin effective reservoir thickness, sharp horizontal and/or vertical changes in reservoir properties as well as poor connectivity between different reservoirs. Although outstanding achievements have been acquired in this field, there are still several problems in the evaluation and development of the reservoirs, such as: the relation between seismic attributes and reservoir property parameters is not exclusive, which yields more than one solution in using seismic attributes to predict reservoir parameters; the wave impedance distribution ranges of sandstone and mudstone are overlapped, means it is impossible to distinguish them through the application of post-stack impedance inversion; studies on seismic petrophysics, reservoir geophysical properties, wave reflection models and AVO features have a poor foundation, makes it difficult to recognize the specific differences between tight sandstone and gas-bearing sandstone and their distribution laws. These are the main reasons causing the low well drilling success rate and poor economic returns, which usually result in ineffective development and utilization of the field. Therefore, it is of great importance to perform studies on identification and prediction of effective reservoirs in low permeable sandstone strata. Taking the 2D and 3D multiwave-multicomponent seismic exploration block in Su6-Su5 area of Sulige field as a study area and He 8 member as target bed, analysis of the target bed sedimentary characteristics and logging data properties are performed, while criteria to identify effective reservoirs are determined. Then, techniques and technologies such as pre-stack seismic information (AVO, elastic impedance, wave-let absorption attenuation) and Gamma inversion, reservoir litological and geophysical properties prediction are used to increase the precision in identifying and predicting effective reservoirs; while P-wave and S-wave impedance, ratio of P/S wave velocities, rock elastic parameters and elastic impedance are used to perform sandstone gas-bearing property identification and gas reservoir thickness prediction. Innovative achievements are summarized as follows: 1. The study of this thesis is the first time that multiwave-multicomponent seismic data are used to identify and predict non-marine classic reservoirs in China. Through the application of multiwave-multicomponents seismic data and integration of both pre-stack and post-stack seismic data, a set of workflows and methods to perform high-precision prediction of effective reservoirs in low permeable sandstone is established systematically. 2. Four key techniques to perform effective reservoir prediction including AVO analysis, pre-stack elastic wave impedance inversion, elastic parameters inversion, and absorption attenuation analysis are developed, utilizing pre-stack seismic data to the utmost and increasing the correct rate for effective reservoir prediction to 83% from the former 67% with routine methods. 3. This thesis summarizes techniques and technologies used in the identification reservoir gas-bearing properties using multiwave-multicomponent seismic data. And for the first time, quantitative analysis on reservoir fluids such as oil, gas, and/or water are carried out, and characteristic lithology prediction techniques through the integration of pre-stack and post-stack seismic prediction techniques, common seismic inversion and rock elastic parameters inversion, as well as P-wave inversion and converted wave inversion is put forward, further increasing the correct rate of effective reservoir prediction in this area to 90%. 4. Ten seismic attribute parameters are selected in the 3D multi-wave area to perform a comprehensive evaluation on effective reservoirs using weighted-factor method. The results show that the first class effective reservoir covers an area of 10.08% of the study area, while the second and the third class reservoirs take 43.8% and 46% respectively, sharply increasing the success rate for appraisal and development wells.

基于匹配追踪方法的地震信号时频分析研究

Seismic signal is a typical non-stationary signal, whose frequency is continuously changing with time and is determined by the bandwidth of seismic source and the absorption characteristic of the media underground. The most interesting target of seismic signal’s processing and explaining is to know about the local frequency’s abrupt changing with the time, since this kind of abrupt changing is indicating the changing of the physical attributes of the media underground. As to the seismic signal’s instantaneous attributes taken from time-frequency domain, the key target is to search a effective, non-negative and fast algorithm time-frequency distribution, and transform the seismic signal into this time-frequency domain to get its instantaneous power spectrum density, and then use the process of weighted adding and average etc. to get the instantaneous attributes of seismic signal. Time-frequency analysis as a powerful tool to deal with time variant non-stationary signal is becoming a hot researching spot of modern signal processing, and also is an important method to make seismic signal’s attributes analysis. This kind of method provides joint distribution message about time domain and frequency domain, and it clearly plots the correlation of signal’s frequency changing with the time. The spectrum decomposition technique makes seismic signal’s resolving rate reach its theoretical level, and by the method of all frequency scanning and imaging the three dimensional seismic data in frequency domain, it improves and promotes the resolving abilities of seismic signal vs. geological abnormal objects. Matching pursuits method is an important way to realize signal’s self-adaptive decomposition. Its main thought is that any signal can be expressed by a series of time-frequency atoms’ linear composition. By decomposition the signal within an over completed library, the time-frequency atoms which stand for the signal itself are selected neatly and self-adaptively according to the signal’s characteristics. This method has excellent sparse decomposition characteristics, and is widely used in signal de-noising, signal coding and pattern recognizing processing and is also adaptive to seismic signal’s decomposition and attributes analysis. This paper takes matching pursuits method as the key research object. As introducing the principle and implementation techniques of matching pursuits method systematically, it researches deeply the pivotal problems of atom type’s selection, the atom dictionary’s discrete, and the most matching atom’s searching algorithm, and at the same time, applying this matching pursuits method into seismic signal’s processing by picking-up correlative instantaneous messages from time-frequency analysis and spectrum decomposition to the seismic signal. Based on the research of the theory and its correlative model examination of the adaptively signal decomposition with matching pursuit method, this paper proposes a fast optimal matching time-frequency atom’s searching algorithm aimed at seismic signal’s decomposition by frequency-dominated pursuit method and this makes the MP method pertinence to seismic signal’s processing. Upon the research of optimal Gabor atom’s fast searching and matching algorithm, this paper proposes global optimal searching method using Simulated Annealing Algorithm, Genetic Algorithm and composed Simulated Annealing and Genetic Algorithm, so as to provide another way to implement fast matching pursuit method. At the same time, aimed at the characteristics of seismic signal, this paper proposes a fast matching atom’s searching algorithm by means of designating the max energy points of complex seismic signal, searching for the most optimal atom in the neighbor area of these points according to its instantaneous frequency and instantaneous phase, and this promotes the calculating efficiency of seismic signal’s matching pursuit algorithm. According to these methods proposed above, this paper implements them by programmed calculation, compares them with some open algorithm and proves this paper’s conclusions. It also testifies the active results of various methods by the processing of actual signals. The problems need to be solved further and the aftertime researching targets are as follows: continuously seeking for more efficient fast matching pursuit algorithm and expanding its application range, and also study the actual usage of matching pursuit method.

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.

小波域地震资料的处理方法（WSSP）研究

The modeling formula based on seismic wavelet can well simulate zero - phase wavelet and hybrid-phase wavelet, and approximate maximal - phase and minimal - phase wavelet in a certain sense. The modeling wavelet can be used as wavelet function after suitable modification item added to meet some conditions. On the basis of the modified Morlet wavelet, the derivative wavelet function has been derived. As a basic wavelet, it can be sued for high resolution frequency - division processing and instantaneous feature extraction, in acoordance with the signal expanding characters in time and scale domains by each wavelet structured. Finally, an application example proves the effectiveness and reasonability of the method. Based on the analysis of SVD (Singular Value Decomposition) filter, by taking wavelet as basic wavelet and combining SVD filter and wavelet transform, a new de - noising method, which is Based on multi - dimension and multi-space de - noising method, is proposed. The implementation of this method is discussed the detail. Theoretical analysis and modeling show that the method has strong capacity of de - noising and keeping attributes of effective wave. It is a good tool for de - noising when the S/N ratio is poor. To give prominence to high frequency information of reflection event of important layer and to take account of other frequency information under processing seismic data, it is difficult for deconvolution filter to realize this goal. A filter from Fourier Transform has some problems for realizing the goal. In this paper, a new method is put forward, that is a method of processing seismic data in frequency division from wavelet transform and reconstruction. In ordinary seismic processing methods for resolution improvement, deconvolution operator has poor part characteristics, thus influencing the operator frequency. In wavelet transform, wavelet function has very good part characteristics. Frequency - division data processing in wavelet transform also brings quite good high resolution data, but it needs more time than deconvolution method does. On the basis of frequency - division processing method in wavelet domain, a new technique is put forward, which involves 1) designing filter operators equivalent to deconvolution operator in time and frequency domains in wavelet transform, 2) obtaining derivative wavelet function that is suitable to high - resolution seismic data processing, and 3) processing high resolution seismic data by deconvolution method in time domain. In the method of producing some instantaneous characteristic signals by using Hilbert transform, Hilbert transform is very sensitive to high - frequency random noise. As a result, even though there exist weak high - frequency noises in seismic signals, the obtained instantaneous characteristics of seismic signals may be still submerged by the noises. One method for having instantaneous characteristics of seismic signals in wavelet domain is put forward, which obtains directly the instantaneous characteristics of seismic signals by taking the characteristics of both the real part (real signals, namely seismic signals) and the imaginary part (the Hilbert transfom of real signals) of wavelet transform. The method has the functions of frequency division and noise removal. What is more, the weak wave whose frequency is lower than that of high - frequency random noise is retained in the obtained instantaneous characteristics of seismic signals, and the weak wave may be seen in instantaneous characteristic sections (such as instantaneous frequency, instantaneous phase and instantaneous amplitude). Impedance inversion is one of tools in the description of oil reservoir. one of methods in impedance inversion is Generalized Linear Inversion. This method has higher precision of inversion. But, this method is sensitive to noise of seismic data, so that error results are got. The description of oil reservoir in researching important geological layer, in order to give prominence to geological characteristics of the important layer, not only high frequency impedance to research thin sand layer, but other frequency impedance are needed. It is difficult for some impedance inversion method to realize the goal. Wavelet transform is very good in denoising and processing in frequency division. Therefore, in the paper, a method of impedance inversion is put forward based on wavelet transform, that is impedance inversion in frequency division from wavelet transform and reconstruction. in this paper, based on wavelet transform, methods of time - frequency analysis is given. Fanally, methods above are in application on real oil field - Sansan oil field.

Qualitative evaluation of KA24 (Knowledge Access 24)

Durbin, J. & Urquhart, C. (2003). Qualitative evaluation of KA24 (Knowledge Access 24). Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: Knowledge Access 24 (NHS)

Monitoring the vegetation start of season (SOS) across the island of Ireland using the MERIS global vegetation index

The aim of this study was to develop a methodology, based on satellite remote sensing, to estimate the vegetation Start of Season (SOS) across the whole island of Ireland on an annual basis. This growing body of research is known as Land Surface Phenology (LSP) monitoring. The SOS was estimated for each year from a 7-year time series of 10-day composited, 1.2 km reduced resolution MERIS Global Vegetation Index (MGVI) data from 2003 to 2009, using the time series analysis software, TIMESAT. The selection of a 10-day composite period was guided by in-situ observations of leaf unfolding and cloud cover at representative point locations on the island. The MGVI time series was smoothed and the SOS metric extracted at a point corresponding to 20% of the seasonal MGVI amplitude. The SOS metric was extracted on a per pixel basis and gridded for national scale coverage. There were consistent spatial patterns in the SOS grids which were replicated on an annual basis and were qualitatively linked to variation in landcover. Analysis revealed that three statistically separable groups of CORINE Land Cover (CLC) classes could be derived from differences in the SOS, namely agricultural and forest land cover types, peat bogs, and natural and semi-natural vegetation types. These groups demonstrated that managed vegetation, e.g. pastures has a significantly earlier SOS than in unmanaged vegetation e.g. natural grasslands. There was also interannual spatio-temporal variability in the SOS. Such variability was highlighted in a series of anomaly grids showing variation from the 7-year mean SOS. An initial climate analysis indicated that an anomalously cold winter and spring in 2005/2006, linked to a negative North Atlantic Oscillation index value, delayed the 2006 SOS countrywide, while in other years the SOS anomalies showed more complex variation. A correlation study using air temperature as a climate variable revealed the spatial complexity of the air temperature-SOS relationship across the Republic of Ireland as the timing of maximum correlation varied from November to April depending on location. The SOS was found to occur earlier due to warmer winters in the Southeast while it was later with warmer winters in the Northwest. The inverse pattern emerged in the spatial patterns of the spring correlates. This contrasting pattern would appear to be linked to vegetation management as arable cropping is typically practiced in the southeast while there is mixed agriculture and mostly pastures to the west. Therefore, land use as well as air temperature appears to be an important determinant of national scale patterns in the SOS. The TIMESAT tool formed a crucial component of the estimation of SOS across the country in all seven years as it minimised the negative impact of noise and data dropouts in the MGVI time series by applying a smoothing algorithm. The extracted SOS metric was sensitive to temporal and spatial variation in land surface vegetation seasonality while the spatial patterns in the gridded SOS estimates aligned with those in landcover type. The methodology can be extended for a longer time series of FAPAR as MERIS will be replaced by the ESA Sentinel mission in 2013, while the availability of full resolution (300m) MERIS FAPAR and equivalent sensor products holds the possibility of monitoring finer scale seasonality variation. This study has shown the utility of the SOS metric as an indicator of spatiotemporal variability in vegetation phenology, as well as a correlate of other environmental variables such as air temperature. However, the satellite-based method is not seen as a replacement of ground-based observations, but rather as a complementary approach to studying vegetation phenology at the national scale. In future, the method can be extended to extract other metrics of the seasonal cycle in order to gain a more comprehensive view of seasonal vegetation development.

Separating the scattering and absorption coefficients using the real and imaginary parts of the refractive index with low-coherence interferometry.

We present an analytical method that yields the real and imaginary parts of the refractive index (RI) from low-coherence interferometry measurements, leading to the separation of the scattering and absorption coefficients of turbid samples. The imaginary RI is measured using time-frequency analysis, with the real part obtained by analyzing the nonlinear phase induced by a sample. A derivation relating the real part of the RI to the nonlinear phase term of the signal is presented, along with measurements from scattering and nonscattering samples that exhibit absorption due to hemoglobin.