基于SAPSO-LSSVM的蛋白质模型质量评估

针对传统蛋白质模型质量评估没有考虑同源信息的问题,提出了一种基于LS-SVM评估蛋白质模型质量的方法。首先,综合模拟退火(Simulated Annealing,SA)算法跳出局部最优解和粒子群(PSO)算法收敛速度快的特点,提出了模拟退火粒子群(SAPSO)算法;然后利用SAPSO算法来优化LS-SVM参数 和 ;最后得到最优模型来评估蛋白质模型质量。实验结果表明,经SAPSO优化LS-SVM参数所得到的评估预测误差较小,且预测值更稳定。     

长江口及邻近海域悬浮颗粒物对叶绿素a遥感反演算法的影响分析

利用水介质光辐射传输数值模型Hydrolight,结合前人对长江口及邻近海域水体的生物—光学模型研究,模拟不同光学水体的遥感反射率,并分析遥感反射率对悬浮颗粒物(SPM)的敏感性以及SPM对4种叶绿素a(Chla)反演算法(二波段、三波段、荧光基线高度(FLH)和综合叶绿素指数(SCI)算法)的影响。结果表明:由Hydrolight模拟得到的遥感反射率与现场同步实测的遥感反射率的均方根误差小于0.01sr-1,其中可实现遥感反射率在550~725nm波段较精确的模拟。遥感反射率对SPM的敏感性随着Chla浓度的升高而降低。二波段、三波段算法适合低SPM浓度水体的Chla浓度反演,FLH算法反演Chla浓度时易受SPM的影响,而SCI算法在中、高SPM浓度水体中消除SPM的影响进而反演Chla的潜力较好。     

基于日光诱导叶绿素荧光与反射率光谱的小麦条锈病探测研究

综合利用反射率光谱在作物生化参数探测的优势和叶绿素荧光在光合生理诊断的优势,开展了日光诱导叶绿素荧光(SIF)和反射率光谱指数协同的小麦条锈病光谱探测研究,以期提高小麦条锈病病情严重度的预测精度。利用O_2-A波段(760 nm)的SIF信号和对小麦条锈病病情严重度敏感的7种反射率光谱指数,基于支持向量机(SVM)、逐步回归(SR)以及神经网络(BP)算法,定量分析了反射率光谱指数和反射率光谱指数与SIF协同的小麦条锈病病情严重度(DI)光谱探测模型的预测精度。结果表明:①SIF与小麦条锈病病情严重度之间存在极显著的负相关关系,SIF与DI间的响应能有效地应用于小麦条锈病的遥感探测;②SIF结合反射率光谱指数的小麦条锈病病情严重度光谱模型探测精度均高于反射率光谱指数模型,SIF能够显著提高小麦条锈病病情严重度的光谱探测精度;③无论是利用反射率光谱指数还是SIF结合反射率光谱指数作为小麦条锈病病情严重度预测模型的输入参数,训练模型以BP模型的预测精度最高,但验证结果表明SVM与SR方法构建的病情严重度预测模型效果较优。     

基于HJ星高光谱数据红边参数的冬小麦叶面积指数反演

针对我国HJ-1A星搭载的高光谱成像仪(HSI)数据,探索基于HJ星高光谱影像的LAI反演研究,本文利用inverted Gaussian模型提取红谷位置、红边位置、红边振幅以及红边斜率4个红边参数,结合2009年4月、5月两期同步地面观测LAI数据,经过回归分析构建了反演叶面积指数的最优红边参数模型.结果表明红边位置、红边斜率和红边振幅与叶面积指数都达到了极显著相关,R2分别为0.5592,0.7796和0.8107说明HJ星高光谱影像数据在叶面积指数反演方面有很大的应用潜力.     

遗传算法和模拟退火算法求解TSP的性能分析

旅行商问题（Traveling Salesman Problem,TSP）是一个典型的组合优化问题,并且是一个NP难题,其可能的路径总数与城市数目是呈指数型增长的,所以一般很难精确地求出其最优解,因而寻找出有效的近似求解算法就具有重要的意义。目前求解TSP问题的主要方法有模拟退火算法（Simulated Annealing,SA）、遗传算法（Genetic Algorithm,GA）和神经网络算法等。GA是模拟生物在自然环境中的遗传和进化过程而形成的一种自适应的全局优化概率搜索算法。SA算法用于优化问题的出发点是基于物理中固体物质的退火过程与一般优化问题的相似性。文中将提出遗传算法和模拟退火算法求解TSP问题,通过试验比较两者求解TSP问题的性能,结果表明GA的性能要优于SA的性能。     

两种改进的模拟退火算法求解大值域约束满足问题

随机约束满足问题的相变现象及求解算法是NP-完全问题的研究热点。RB模型（Revised B）是一个非平凡的随机约束满足问题,它具有精确的可满足性相变现象和极易产生难解实例这两个重要特征。针对RB模型这一类具有大值域的随机约束满足问题,提出了两种基于模拟退火的改进算法即RSA（Revised Simulated Annealing Algorithm）和GSA（Genetic-simulated Annealing Algorithm）。将这两种算法用于求解RB模型的随机实例,数值实验结果表明：在进入相变区域时,RSA和GSA算法依然可以有效地找到随机实例的解,并且在求解效率上明显优于随机游走算法。在接近相变阈值点时,由这两种算法得到的最优解仅使得极少数的约束无法满足。     

一种改进遗传退火算法的图像分割方法

图像分割是图像处理和分析的基础,本文通过分析遗传算法（Genetic Algorithm, GA）在图像分割中的应用优劣,提出利用模拟退火思想的改进遗传退火（Genetic Simulated Annealing Algorithm, GASA）的图像阈值分割算法,算法整个运行过程由冷却温度进度表控制,使用改进的最大类间方差公式作为遗传算法的适应度函数,从而求得灰度图像的一个最佳阈值用于图像分割。实验结果表明,基于改进遗传退火算法的最大类间方差图像分割方法能较好提高算法的全局搜索能力,避免遗传算法陷入局部最优,并且能更快速、更稳定收敛到最佳的分割阈值,得到更好的图像分割效果。     

基于自适应模拟退火的NoC映射算法

在片上网络(Network on Chip,NoC)系统中,如何完成应用特征图到结构特征图的映射是影响系统实际性能的关键步骤之一。针对NoC系统越发庞大,映射算法耗时也随之增加的问题,提出了自适应模拟退火(Self-Adaptive Simulated Annealing,SASA)的NoC映射算法。采用相对平滑方式实现温度下降过程,针对模拟退火算法易陷于局部最优的缺点,采用自适应方法改变新解生成方式,提高了算法收敛于全局最优的概率。实验结果表明,该算法与常见NoC映射算法(如基于遗传的映射算法)相比,平均性能提升了5.3%,耗时缩短了11.1%。     

基于BP神经网络的地下水离子化合物含量高光谱反演方法研究

地下水水质污染日益加重,监测地下水离子化合物含量有利于地下水的动态管理与精准防治。离子化合物光谱响应信号微弱且反演机理尚不明晰,现有研究多数对离子化合物进行简单的定性分析,较少采用数理统计方法综合估算其含量。基于离子化合物光谱机理和高光谱数据冗余的特性,通过测量实验室配比的不同浓度钠、钾、钙3种离子化合物标准液的可见—近红外反射光谱（400～1 000 nm）,探究水体3种离子化合物的光谱响应机理、最佳预处理方式及特征波段优选算法,并基于最优特征波段构建BP神经网络模型以定量反演离子化合物含量。研究发现：(1)3种离子化合物整体反射率在波长400～1 000 nm处与含量成反比,与离子的电荷数和半径成正比;(2)基于主成分分析较连续投影法提取的特征谱段,构建的多元线性回归模型能够较好地反演水体离子化合物含量;(3)KCl最优反演模型的预处理方式为SG滤波,CaCl2和NaCl最优反演模型的预处理为SG滤波后进行反射率归一化;(4)相较于传统线性反演模型,PCA-BPNN非线性模型取得了最优的反演结果,其中钾离子化合物含量反演结果最优,其训练集R2和RMSE分别达到0.996 4和248...     

模拟退火遗传算法在多用户检测技术中的应用

将遗传算法GA(Genetic Algorithm)与模拟退火算法SA(Simulated Annealing)相结合,提出模拟退火遗传算法(SAGA),并将其应用于MC-CDMA无线通信系统的多用户检测技术中,以求降低多用户检测算法在实际应用中的复杂度并同时提高多用户检测器的性能。分析了遗传算法和模拟退火算法的性能,从理论上阐述了模拟退火遗传算法应用于多用户检测技术中的方法和可行性。理论分析表明,基于模拟退火遗传算法的多用户检测器的算法复杂度比传统多用户检测器低;数值仿真结果也表明前者在抗干扰能力上优于后者。     

Inversion of vegetation canopy reflectance models for estimating agronomic variables. V. Estimation of leaf area index and average leaf angle using measured canopy reflectances

The technique described earlier (Goel and Thompson, 1984b) for estimating agronomic parameters from bidirectional crop reflectance data is applied to a fully covered soybean canopy, using data measured in the field. This technique employs the inversion of a canopy reflectance model. It is shown that using the SAIL model one can estimate leaf area index (LAI) as well as average leaf angle (ALA) quite well, provided that the other canopy parameters (leaf reflectance and transmittance, soil reflectance, and fraction of diffused skylight) are known. Some suggestions are made for improving the SAIL model. This should improve the accuracy of estimation of not only LAI and ALA but should also allow the estimation of the complete leaf angle distribution.     

Inversion of vegetation canopy reflectance models for estimating agronomic variables. IV. Total inversion of the SAIL model

The estimability of all the canopy parameters for a vegetation canopy using only canopy reflectance (CR) data and the SAIL model is investigated, using techniques described earlier (Goel and Thompson, 1984a). It is shown that in principle such an estimation is possible, i.e., the SAIL model is mathematically totally invertible. An analysis of the sensitivity of the calculated values to changes in the CR data is presented. This analysis suggests that, given the expected accuracy of CR measurements and the accuracy of the SAIL model in representing CR in the infrared region, the agronomic parameters, leaf area index, and leaf angle distribution, can be estimated fairly accurately using ancillary data on spectral parameters.     

Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance

PROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (C_w), and leaf dry matter content (C_m). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.     

A direct search variant of the simulated annealing algorithm for optimization involving continuous variables

A memory-based simulated annealing algorithm is proposed which fundamentally differs from the previously developed simulated annealing algorithms for continuous variables by the fact that a set of points rather than a single working point is used. The implementation of the new method does not need differentiability properties of the function being optimized. The method is well tested on a range of problems classified as easy, moderately difficult and difficult. The new algorithm is compared with other simulated annealing methods on both test problems and practical problems. Results showing an improved performance in finding the global minimum are given.Scope and purposeThe inherent difficulty of global optimization problems lies in finding the very best optimum (maximum or minimum) from a multitude of local optima. Many practical global optimization problems of continuous variables are non-differentiable and noisy and even the function evaluation may involve simulation of some process. For such optimization problems direct search approaches are the methods of choice. Simulated annealing is a stochastic global optimization algorithm, initially designed for combinatorial (discrete) optimization problems. The algorithm that we propose here is a simulated annealing algorithm for optimization problems involving continuous variables. It is a direct search method. The strengths of the new algorithm are: it does not require differentiability or any other properties of the function being optimized and it is memory-based. Therefore, the algorithm can be applied to noisy and/or not exactly known functions. Although the algorithm is stochastic in nature, it can memorise the best solution. The new simulated annealing algorithm has been shown to be reliable, fast, general purpose and efficient for solving some difficult global optimization problems.     

Comparative study of suits and sail canopy reflectance models

In order to fully exploit the potential of remote sensing from aircraft and spacecraft altitude to map vegetation and estimate key agronomic parameters such as leaf area index (LAI) and biomass, the relationships between the canopy reflectance and properties of canopy elements must be well understood. A number of canopy reflectance models exist in the literature. Much of the model(s) verifications have been done primarily with observations from nadir direction. This was partly due to near nadir view observations of the Landsat series of satellites and partly due to the convenience of such measurements. The most significant development in these models, their ability to predict angular reflectance properties could not, however, be evaluated. The Laboratory for Applications of Remote Sensing, Purdue University, with strong support from modeling community, collected two excellent data sets, one on corn and one on soybeans, that contain the full hemisphere of off-nadir reflectance measurements. These data sets have been used to verify the multilayer one-component (leaves) Suits and SAIL canopy reflectance models. Software to evaluate multilayer multicomponent canopies was developed, but could not be evaluated because of the lack of information on stems. This evaluation suggests that these and similar other models have generic deficiency. The SAIL model, because of a more realistic inclusion of the leaf angle distribution, is in better agreement with observations than the Suits model. Further improvement and additional evaluation of these models is needed.     

Water content estimation in vegetation with MODIS reflectance data and model inversion methods

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content (r²=0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.     

Estimating live fuel moisture content from remotely sensed reflectance

Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content which express the amount of water in a leaf relative to its area. FMC is related to both leaf water content and leaf dry matter content, and the relationships between FMC and remotely sensed reflectance will therefore be affected by variation in both leaf biophysical properties. This paper uses spectral reflectance data from the Leaf Optical Properties EXperiment (LOPEX) and modelled data from the Prospect leaf reflectance model to examine the relationships between FMC, leaf equivalent water thickness (EWT) and a range of spectral vegetation indices (VI) designed to estimate leaf and canopy water content. Significant correlations were found between FMC and all of the selected vegetation indices for both modelled and measured data, but statistically stronger relationships were found with leaf EWT; overall, the water index (WI) was found to be most strongly correlated with FMC. The accuracy of FMC estimation was very low when the global range of FMC was examined, but for a restricted range of 0-100%, FMC was estimated with a root-mean-square error (RMSE) of 15% in the model simulations and 51% with the measured data. The paper shows that the estimation of live FMC from remotely sensed vegetation indices is likely to be problematic when there is variability in both leaf water content and leaf dry matter content in the target leaves. Estimating FMC from remotely sensed data at the canopy level is likely to be further complicated by spatial and temporal variations in leaf area index (LAI). Further research is required to assess the potential of canopy reflectance model inversion to estimate live fuel moisture content where a priori information on vegetation properties may be used to constrain the inversion process.     

求解SAT问题的拟人退火算法

该文利用一个简单的变换，将可满足性（SAT）问题转换为一个求相应目标函数最小值的优化问题，提出了一种用于跳出局部陷阱的拟人策略，基于模拟退火算法和拟人策略，为SAT问题的高效近注解得出了拟人退火算法（PA），该方法不仅具有模拟退火算法的全局收敛性质，而且具有一定的并行性，继承性。数值实验表明，对于本文随机产生的测试问题例，采用拟人策略的模拟退火算法的结果优于局部搜索算法，模拟退火算法以及近来国际上流行的WALKSAT算法，因此拟人退火算法是可行的和有效的。     

Modeling canopy reflectance and microwave backscattering coefficient

Various approaches to model canopy reflectance (CR) in the visible/infrared region and backscattering coefficient (BSC) in the microwave region are compared and contrasted. It is noted that BSC can be related to CR in the source direction (the “hot spot” direction). By assuming a frequency dependent leaf reflectance and transmittance it is shown that the observed dependence of BSC on leaf area index, leaf angle distribution, angle of incidence, soil moisture content, and frequency can be simulated by a CR model. Thus both BSC and CR can, in principle, be calculated using a single model which has essentially the same parameters as many CR models do.     

双层植被结构冠层光谱特性的理论模拟

利用双层冠层反射率模型ACRM，模拟不同叶面积指数LAI、含水量Cw和结构参数N下，波长是820nm和1600nm冠层反射率的角度分布。结果说明，该模型能准确模拟出“热点”效应 |冠层反射率角度分布对LAI的敏感性小于LAI和Cw以及LAI和N的共同作用。其中LAI和Cw共同作用对波长1 600 nm反射率角度分布的影响非常显著，而LAI和N的共同作用在820 nm略微大于1 600 nm。另外，提取冠层含水量的土壤可调节水分指数SAWI受冠层结构的影响也较大。今后在模型选取中应该更好的考虑冠层结构影响。