Relationship between the attenuation of downwelling irradiance at 490 nm with the attenuation of PAR (400 nm-700 nm) in the Baltic Sea

The vertical attenuation coefficient of diffuse downwelling irradiance at 490 nm (K_d 490) is a parameter that we routinely derive from SeaWiFS images of the Baltic Sea. Here, through model simulations, we examine the relationship between K_d(490), and the vertical attenuation coefficient of PAR (K_d PAR), as this later coefficient determines the light available for aquatic photosynthesis. A simple semi-analytical model is used to predict K_d(490) and K_d(PAR), as a function of the concentrations of chlorophyll, colored dissolved organic material (CDOM), suspended inorganic, and suspended organic particulate material. A series of model simulations based on variations in these optically significant constituents over a range realistic for the Baltic Sea, are used to define the relationship between the two attenuation coefficients.

Kd(PAR)=0.6677Kd^0.6763(490).     

灰色GM(1,1,t α)模型与自忆性原理的耦合及应用

针对实际工程应用中传统GM(1,1)模型预测的局限性,以含时间幂次项的灰色GM(1,1,tα)模型为基础,构建了灰色GM(1,1,tα)与自忆性原理的耦合预测模型;用动力系统自忆性原理来克服传统灰色模型对初值比较敏感的弱点;将灰色GM(1,1,t2)自忆性模型应用于某沿海高速软土地基沉降的模拟和预测,获得了满意的模拟和预测精度.实验算例表明,所提出的新模型显著地改善了传统灰色预测模型的模拟预测精度.     

Lattice structure of e-implications on L

In this paper, we consider the set of all e-implications on L^∗ and define two operations on the set of all representable e-implications on L^∗, thus endowing it with monoid structure which is also a distributive complete lattice.     

On the H fixed-lag smoothing: how to exploit the information preview

The problem of the continuous-time H^∞ fixed-lag smoothing over the infinite horizon is studied. The first solution to the problem is derived in terms of one algebraic Riccati equation of the same dimension as in the filtering case and the mechanism by which the performance improvements with respect to the H^∞ filtering occur is clarified. It is shown that the H^∞ smoother exploits the information preview in an “H² manner”.     

Application of rhodamine B thiolactone to fluorescence imaging of Hg in Arabidopsis thaliana

Exposure to mercury causes severe damage to plants, animals and even humans. Concern over mercury toxicity has encouraged the development of efficient, sensitive, and selective methods for the in vivo detection of mercury. Although a variety of studies have been published describing fluorescence imaging of mercury in animal cells and tissues, no in vivo monitoring has been developed for plant systems until now. In this paper, we report the semi-quantitative fluorescence imaging of Hg²⁺ ions in a common model plant Arabidopsis thaliana (A. thaliana), with rhodamine B thiolactone (RBS) as a novel Hg²⁺ probe. The experimental results show that RBS is plant cell wall and cell membrane permeable, and the probe responds selectively to Hg²⁺ ions instead of the other species in plant systems. Real-time monitoring of Hg²⁺ absorption in roots of A. thaliana by RBS shows that saturation of Hg²⁺ uptake could occur in a short period of 3 days at most. The transportation and accumulation of Hg²⁺ ions in roots of A. thaliana have also been studied, revealing that most of Hg²⁺ ions reside in root cap and meristematic zone, and only a small amount of Hg²⁺ ions can reach the maturation zone. This indicates that the interaction of Hg²⁺ ions with any Hg²⁺-philic species including proteins in these regions may be responsible for plant poisoning and even death.     

Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain)

In typical Case 2 waters, accurate remote sensing retrieval of chlorophyll a (chla) is still a challenging task. In this study, focusing on the Galician rias (ΝW Spain), algorithms based on neural network (NN) techniques were developed for the retrieval of chla concentration in optically complex waters, using Medium Resolution Imaging Spectrometer (MERIS) data. There is considerable interest in the accurate estimation of chla for the Galician rias, because of the economic and social importance of the extensive culture of mussels, and the high frequency of harmful algal events. Fifteen MERIS full resolution (FR) cloud-free images paired with in situ chla data (for 2002-2004 and 2006-2008) were used for the development and validation of the NN. The scope of NN was established from the clusters obtained using fuzzy c-mean (FCM) clustering techniques applied to the satellite-derived data. Three different NNs were developed: one including the whole data set, and two others using only points belonging to one of the clusters. The input data for these latter two NNs was chosen depending on the quality level, defined on the basis of quality flags given to each data set. The fitting results were fairly good and proved the capability of the tool to predict chla concentrations in the study area. The best prediction was given for the NN trained with high-quality data using the most abundant cluster data set. The performance parameters in the validation set of this NN were R² = 0.86, mean percentage error (MPE) = − 0.14, root mean square error (RMSE) = 0.75 mg m^− 3, and relative RMSE = 66%. The NN developed in this study detected accurately the peaks of chla, in both training and validation sets. The performance of the Case-2-Regional (C2R) algorithm, routinely used for MERIS data, was also tested and compared with our best performing NN and the sea-truthing data. Results showed that this NN outperformed the C2R, giving much higher R² and lower RMSE values.This study showed that the combination of in situ data and NN technology improved the retrieval of chla in Case 2 waters, and could be used to obtain more accurate chla maps. A local-based algorithm for the chla retrieval from an ocean colour sensor with the characteristics of MERIS would be a great support in the quantitative monitoring and study of harmful algal events in the coastal waters of the Rias Baixas. The limitations and possible improvements of the developed chla algorithms are also discussed.     

Interpolating G Bézier surfaces over irregular curve networks for ship hull design

We propose a local method of constructing piecewise G¹ Bézier patches to span an irregular curve network, without modifying the given curves at odd- and 4-valent node points. Topologically irregular regions of the network are approximated by implicit surfaces, which are used to generate split curves, which subdivide the regions into triangular and/or rectangular sub-regions. The subdivided regions are then interpolated with Bézier patches. We analyze various singular cases of the G¹ condition that is to be met by the interpolation and propose a new G¹ continuity condition using linear and quartic scalar weight functions. Using this condition, a curve network can be interpolated without modification at 4-valent nodes with two collinear tangent vectors, even in the presence of singularities. We demonstrate our approach in a ship hull.     

The build of n-Bits Binary Coding ICBP Ensemble System

To optimize the construct of an ensemble of many relatively small and simple classifiers may be more realistic than to optimize the design of an individual large and complex classifier. Problems of local minima and slow convergence may be mitigated within an ensemble system where the decisions obtained from locally optimal components classifiers are integrated together. However, it is very difficult to design the structure of individual neural networks (NNs) in an ensemble and the architecture of a whole ensemble. In n-Bits Binary Coding ICBP Ensemble System (nBBC-ICBP-ES), the crucial parameter that is required to be set a priori is an appropriate number of hidden nodes of the corresponding improved circular back-propagation (ICBP) root model. Thereby, both the number of individual ICBPs and the architecture of each ICBP component in an nBBC-ICBP-ES can be decided straightly. nBBC-ICBP-ES is computationally more efficient, with relatively fewer user specified parameters, and does not need any manual division to the training data set for the purpose of its construction. It is rather easy to be understood and implemented, while inheriting the benefits of ICBP root model in a natural manner. Simulation and t-test results on four large-scale benchmark classification data sets demonstrated that, in most cases, nBBC-ICBP-ES significantly improved the classification and generalization performances of the two typically large single ICBPs, Same-2^N_h-ICBP-ES (i.e., an ensemble system with the same 2^N_h ICBP components), conventional Bagging and AdaBoost ensemble. We conclude that, for NN applications in pattern recognition, assembling many small NNs might be better than just utilizing an individual large one, and further, assembling many heterogeneous small NNs might be better than assembling many homogeneous ones. The proposed nBBC-ICBP-ES is simple but efficient and effective, and also potentially significant for the applications of NN ensemble.     

H performance of preview control systems

Performance of the preview control is investigated in terms of H^∞-criterion. In the infinite-horizon time setting, the limitation of H^∞ performance in preview control action is characterized with the Hamiltonian matrix. By employing the property of Hamiltonian matrix, the solvability and analytic solution for the preview full-information control problem are clarified based on an auxiliary introduced matrix Riccati equation. The property of resulting control law is illustrated with a numerical example.     

A practical construction of G smooth biquintic B-spline surfaces over arbitrary topology

The motivation of this paper is to develop a local scheme of constructing G¹ smooth B-spline surfaces with single interior knots over arbitrary topology. In this paper, we obtain the conditions of G¹ continuity between two adjacent biquintic B-spline surfaces with interior single knots. These conditions are directly represented by the relevant control points of the two B-spline surfaces. By utilizing these G¹ conditions, we develop the first local scheme of constructing G¹ smooth biquintic B-spline surfaces with interior single knots for arbitrary topological type. The high complexity of deriving the local G¹ scheme is well overwhelmed. The biquintic is the lowest degree for which there exists a local scheme of constructing G¹ smooth B-spline surfaces with interior single knots over arbitrary topology.     

Strongly normal sets of contractible tiles in N dimensions

The second and third authors and others have studied collections of (usually) convex “tiles”—a generalization of pixels or voxels—in R² and R³ that have a property called strong normality (SN): for any tile P, only finitely many tiles intersect P, and any nonempty intersection of those tiles also intersects P. This paper extends basic results about strong normality to collections of contractible polyhedra in Rⁿ whose nonempty intersections are contractible. We also give sufficient (and trivially necessary) conditions on a locally finite collection of contractible polyhedra in R² or R³ for their nonempty intersections to be contractible.     

Infinity problems and countability problems for ω-automata

The infinity problem for ω-automata is to decide if the ω-language recognized by a given automaton is infinite; the countability problem is to decide if a given automaton recognizes a countable ω-language. We prove that these problems are NLogspace-complete for (nondeterministic) Büchi, generalized Büchi, Muller, Rabin and parity automata.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.     

Another look at finite horizon H control problems for systems with input delays

We discuss a finite horizon H^∞ control problem for time-varying systems with input delays. Clarifying a relationship between two H^∞ control problems in input delay case and in measurement delay case, we derive a solution in input delay case based on the known result for the H^∞ control problem in measurement delay case, and show that the solution has the same predictor-observer structure as the solution in measurement delay case has. Using this structural information on the solution, we also present an elementary proof of the solution to the finite horizon H^∞ control problem for systems with input delays, which is based only on completion of squares.     

Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: Principles and validation

A neural network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), the fraction of vegetation cover (fCover), and the canopy chlorophyll content (LAI×C_ab). The neural network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the network are first presented. The estimated variables are then compared to other existing products, LAI- and fAPAR-MODIS and MGVI-MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).     

受复杂多通道通信约束的网络化系统H2/H∞滤波

多通道网络化系统中每个通道存在不尽相同的网络不确定性因素, 使得H₂/H_∞ 滤波更加困难. 对此, 提出一种受多通道通信约束的网络化系统滤波方法. 首先, 基于最大数据包错序思想解决了传感器到滤波器之间的复杂多通道通信约束的问题; 然后, 建立了更加普适的融合多通道通信约束的滤波误差动态系统模型, 证明了在已知最长网络延时和最大连续丢包数情况下, 所设计的滤波器可使系统随机稳定且满足??2/??∞ 性能指标. 仿真结果表明该方法可行且有效.

     

Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma

Many fundamental ecosystem properties and dynamics are determined by plant water stress, particularly in dryland ecosystems where water is usually limiting. Indeed, under severe drought, plant water stress and associated insect infestations can produce landscape-scale mortality. Despite the fundamental importance of plant water stress in determining properties and dynamics at ecosystem and landscape scales, approaches for remotely sensing plant water stress are largely lacking, particularly for conifers. We evaluated the remotely sensed detection of foliar drought stress in two conifer species, Pinus edulis and Juniperus monosperma, which are co-dominants of extensive-juniper woodlands in North America, the first of which experienced extensive mortality in association with a recent drought. Needle spectra were made on these species in the field using an integrating sphere and portable spectrometer. Two indices of foliar water condition, plant water content (% of dry mass) and plant water potential, were compared to five spectral analyses: continuum removal of the 970 and 1200 nm water absorption features, the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), and the red edge wavelength position. For P. edulis, plant water content was significantly correlated with four of the five indices: NDVI (R²=0.71) and NDWI (R²=0.68) which exhibited stronger relationships than 970 nm continuum removal (R²=0.57) or red edge position (R²=0.45). All five indices were significantly correlated with P. edulis water content when trees undergoing mortality were included in analyses (R²=0.60-0.93). Although the correlations were weaker than for plant water content, plant water potential was significantly correlated with NDWI (R²=0.49), 970 nm (R²=0.44), NDVI (R²=0.35), and red edge (R²=0.34); again all five indices had significant relationships when trees undergoing mortality were included (R²=0.51-0.86). The relationships were weaker for J. monosperma: water content was significantly related to 970 nm (R²=0.50) and 1200 nm (R²=0.37) continuums and NDVI (R²=0.33), while water potential was related only to 1200 nm (R²=0.40). Our results demonstrate a critical link between plant physiological characteristics tied to water stress and associated spectral signatures for two extensive co-occurring conifer species.     

Structure and humidity sensing properties of La1−xKxCo0.3Fe0.7O3−δ perovskite

The effects of K-substitution at La-site of La_1−xK_xCo_0.3Fe_0.7O_3-δ perovskite on its structure and humidity sensing properties were studied in detail. The XRD, SEM-EDS, N₂ adsorption-desorption measurements (BET), ICP-AES and XPS were used to characterize the microstructure of La_1−xK_xCo_0.3Fe_0.7O_3−δ perovskite. The results show that the partial substitution of K at La-site has no obvious effect on the crystal phase, morphology and surface area of samples, but leads to the increased oxygen vacancies and surface enriched K⁺. The sensitivity of humidity sensor based on all samples was evaluated by measuring the impedance response to the humidity changes. The partial substitution of K at La-site significantly enhanced the humidity sensitivity of La_1−xK_xCo_0.3Fe_0.7O_3−δ perovskite at low relative humidity (RH). By correlating the structure of material with its sensing properties, the probable reasons that lead to the remarkable sensitivity enhancement of the K-substitution samples compared with the unsubstituted sample (LaCo_0.3Fe_0.7O_3−δ) were given; moreover, the sensing mechanism was also discussed by the complex impedance spectra in detail.     

A power-set construction for reducing Büchi automata to non-determinism degree two

Büchi automata are finite automata that accept languages of infinitely long strings, so-called ω-languages. It is well known that, unlike in the finite-string case, deterministic and non-deterministic Büchi automata accept different ω-language classes, i.e., that determination of a non-deterministic Büchi automaton using the classical power-set construction will yield in general a deterministic Büchi automaton which accepts a superset of the ω-language accepted by the given non-deterministic automaton.In this paper, a power-set construction to a given Büchi automaton is presented, which reduces the degree of non-determinism of the automaton to at most two, meaning that to each state and input symbol, there exist at most two distinct successor states. The constructed Büchi automaton of non-determinism degree two and the given Büchi automaton of arbitrary non-determinism degree will accept the same ω-language.     

Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle

This study takes advantage of a regionally specific algorithm and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to deliver more accurate, detailed chlorophyll a (chla) maps of optically complex coastal waters during an upwelling cycle. MERIS full resolution chla concentrations and in situ data were obtained on the Galician (NW Spain) shelf and in three adjacent rias (embayments), sites of extensive mussel culture that experience frequent harmful algal events. Regionally focused algorithms (Regional neural network for rias Baixas or NNRB) for the retrieval of chla in the Galician rias optically complex waters were tested in comparison to sea-truth data. The one that showed the best performance was applied to a series of six MERIS (FR) images during a summer upwelling cycle to test its performance. The best performance parameters were given for the NN trained with high-quality data using the most abundant cluster found in the rias after the application of fuzzy c-mean clustering techniques (FCM). July 2008 was characterized by three periods of different meteorological and oceanographic states. The main changes in chla concentration and distribution were clearly captured in the images. After a period of strong upwelling favorable winds a high biomass algal event was recorded in the study area. However, MERIS missed the high chlorophyll upwelled water that was detected below surface in the ria de Vigo by the chla profiles, proving the necessity of in situ observations. Relatively high biomass “patches” were mapped in detail inside the rias. There was a significant variation in the timing and the extent of the maximum chla areas. The maps confirmed that the complex spatial structure of the phytoplankton distribution in the rias Baixas is affected by the surface currents and winds on the adjacent continental shelf. This study showed that a regionally specific algorithm for an ocean color sensor with the characteristics of MERIS in combination with in situ data can be of great help in chla monitoring, detection and study of high biomass algal events in an area affected by coastal upwelling such as the rias Baixas.