朱庄水库流域径流量变化特征及影响因素

利用朱庄区域长系列水文资料,对径流量的年内、年际变化特征及变化趋势进行了分析研究,分析了人类活动影响引起下垫面变化导致径流变化的原因,估算了降水和下垫面变化对径流量的影响量。该区域径流量年内、年际变化较大;多年径流量系列呈减少趋势,尤其从20世纪70年代末以来径流量发生了显著变异;下垫面变化是径流量减少的主要原因。     

Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence

Chlorophyll fluorescence is related to photosynthesis and can serve as a remote sensing proxy for estimating photosynthetic energy conversion and carbon uptake. Recent advances in sensor technology allow remote measurements of the sun-induced chlorophyll fluorescence signal (Fs) at leaf and canopy scale. The commonly used Fraunhofer Line Depth (FLD) principle exploits spectrally narrow atmospheric oxygen absorption bands and relates Fs to the difference of the absorption feature depth of a fluorescensing and a non-fluorescensing surface. However, due to the nature of these narrow bands, Fs retrieval results depend not only on vegetation species type or environmental conditions, but also on instrument technology and processing algorithms. Thus, an evaluation of all influencing factors and their separate quantification is required to further improve Fs retrieval and to allow a reproducible interpretation of Fs signals.Here we present a modeling study that isolates and quantifies the impacts of sensor characteristics, such as spectral sampling interval (SSI), spectral resolution (SR), signal to noise ratio (SNR), and spectral shift (SS) on the accuracy of Fs measurements in the oxygen A band centered at 760 nm (O₂-A). Modeled high resolution radiance spectra associated with known Fs were spectrally resampled, taking into consideration the various sensor properties. Fs was retrieved using the three most common FLD retrieval methods, namely the original FLD method (sFLD), the modified FLD (3FLD) and the improved FLD (iFLD). The analysis investigates parameter ranges, which are representative for field and airborne instruments currently used in Fs research (e.g., ASD FieldSpec, OceanOptics HR, AirFLEX, AISA, APEX, CASI, and MERIS).Our results show that the most important parameter affecting the retrieval accuracy is SNR, SR accounts for ≤ 40% of the error, the SSI for ≤ 12%, and SS for ≤ 7% of the error. A trade-off study revealed that high SR can partly compensate for low SNR. There is a strong interrelation between all parameters and the impact of specific parameters can compensate or amplify the influence of others. Hence, the combination of all parameters must be considered by the evaluation of sensors and their potential for Fs retrieval. In general, the standard FLD method strongly overestimates Fs, while 3FLD and iFLD provide a more accurate estimation of Fs. We conclude that technical sensor specifications and the retrieval methods cause a significant variability in retrieved Fs signals. Results are intended to be one relevant component of the total uncertainty budget of Fs retrieval and have to be considered in the interpretation of retrieved Fs signals.     

Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations

Neural networks trained over radiative transfer simulations constitute the basis of several operational algorithms to estimate canopy biophysical variables from satellite reflectance measurements. However, only little attention was paid to the training process which has a major impact on retrieval performances. This study focused on the several modalities of the training process within neural network estimation of LAI, FCOVER and FAPAR biophysical variables. Performances were evaluated over both actual experimental observations and model simulations. The SAIL and PROSPECT radiative transfer models were used here to simulate the training and the synthetic test datasets. Measurements of LAI, FCOVER and FAPAR were achieved over the Barrax (Spain) agricultural site for a range of crop types concurrently to CHRIS/PROBA satellite image acquisition. Results showed that the spectral band selection was specific to LAI, FCOVER and FAPAR variables. The optimal band set provided significantly improved performances for LAI, while only small differences were observed for the other variables. Gaussian distributions of the radiative transfer model input variables performed better than uniform distributions for which no prior information was exploited. Including moderate uncertainties in the reflectance simulations used in the training process improved the flexibility of the neural network in cases where simulations departed slightly from observations. Simple neural network architecture with a single hidden layer of five tangent sigmoid transfer functions was performing as good as more complex architectures if the training dataset was larger than ten times the number of coefficients to tune. Small sensitivity of performances was observed depending on the way the solution was selected when several networks were trained in parallel. Finally, comparison with a NDVI based approach showed the generally better retrieval accuracy of neural networks.     

Adaptive picking of microseismic event arrival using a power spectrum envelope

An automatic, adaptive, spectrogram-based algorithm for picking the arrival time of microseismic data is proposed. The algorithm provides a significant improvement in the ratio of detected events to false triggers and in the resolution of the microseismic structure. It mainly addresses the problem of automatic picking when the signal-to-noise ratio (S/N) is low and so false and missed triggers frequently occur. It combines the short time average/long time average (STA/LTA) algorithm with an envelope algorithm. It also constructs an envelope from a time-frequency representation of the signal. The threshold is set dynamically, according to the existing noise level and the S/N ratio. The algorithm also uses the fixed LTA value to represent the noise level for a seismic record. It is applied to pick the arrival times of P-waves of local events recorded at eight stations.     

基于爬行器的大规模P2P IPTV测量

为了解大规模P2P IPTV系统中的用户行为特征和拓扑结构特征等内在信息,开发和部署了一个多协议P2P IPTV爬行器TVCrawer,对3个主流的P2P IPTV系统--PPLive,PPStream和UUSee进行了大量的主动测量,并对P2P IPTV系统中的用户行为和网络拓扑特征进行了分析和比较.主要发现包括:1...     

减少防撞雷达虚假目标的信号处理研究

从信号处理的角度分析了防撞雷达虚报警率、漏报警率偏高的原因,采用AR模型功率谱估计的Burg算法代替传统的FFT算法,并将粗神经网络应用于防撞雷达目标识别。仿真结果表明,此方法提高了雷达信号处理的准确度和目标识别率,能有效地降低漏报警、虚报警率。     

一种新型的认知无线电合作频谱感知算法*

为了提高认知无线电频谱感知性能,同时考虑到不同认知用户SU(Secondary User)具有不同的感知贡献和谈判力量,该文利用合作博弈理论提出了一种新的基于认知无线电的合作频谱感知非对称纳什谈判算法ANBS(Asymmetric Nash Bargaining Solution),该算法充分考虑到了每一个认知用户的感知可信度不尽相同的情况。仿真结果表明,与NBS(Nash Bargaining Solution)等算法相比,该算法不仅具有更强的合理性和可靠性,而且使系统整体感知性能得到了较大提高。     

煤制乙炔的热集成设计和动态性能分析

等离子裂解煤制乙炔工艺是一种处于试验阶段的新型煤化工路线。系统的节能、平稳运行对新工艺的经济性和可行性至关重要。本文基于夹点技术对煤制乙炔全流程进行了用能分析和诊断,得出了最优的能量利用目标;在考虑实际约束的情况下提出2套换热方案。进一步,利用路径法得出两套换热方案的传递函数,由此建立了动态仿真模型,进而得到2套换热方案对扰动的动态响应特性;在对传递函数进行分析的基础上,发现影响换热网络动态性能的公用工程位置和单个换热器换热充分度2个因素。结果表明,方案1和方案2均具有较好的动态性能,能够保障节能方案的平稳运行。     

气-固加压流化床压力脉动信号的分析

对气-固流化床特别是气-固加压流化床压力脉动信号进行深入分析有助于更好地了解该类反应器内的流动行为。为了研究压力脉动信号与加压气-固流化床内流动行为的关系,以FCC催化剂为实验物料,采用密相段内径300 mm、高度3800 mm、扩大段内径600mm、高度800mm的加压流化床,对其压力脉动信号进行快速傅里叶变换(FFT)和小波分析,获得了代表气泡行为的特征频率(D5-D7);分析此频段能量特征值随表观气速的变化规律,得出了流型转变速度u_c,并考察压力对u_c的影响。结果表明:压力波动能量集中在低频,随着气速的增加能量特征值先增加后减小,压力升高u_c减小。