932 resultados para spectroscopic parameter


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FTIR and Raman spectra of FeClMoO4 single crystal and polycrystalline Na2MoO4, Na2MoO4·2H2O and Na2MoO4·2D2O are recorded and analysed. The band positions for different modes suggest that MoO4 tetrahedron is more distorted in FeClMoO4. The larger splitting observed for the bending modes and partial retention of degeneracy of the asymmetric stretching mode indicate that angular distortion is greater than liner distortion in MoO4 2 ion in FeClMoO4 confirming x-ray data. The non-appearance of the n1 and n2 modes in the IR and partial retention of the degeneracies of various modes show that MoO4 2 ion retains Td symmetry in Na2MoO4. Wavenumber values of the n1 mode indicate that the distortion of MoO4 tetrahedra in the four crystals are in the order FeClMoO4\ Na2MoO4·2H2O\Na2MoO4·2D2O\Na2MoO4. The water bands suggest the presence of two crystallographically distinct water molecules in Na2MoO4·2H2O. They form strong hydrogen bonds

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Mn(II) complexes derived from a set of acylhydrazones were synthesised and characterized by elemental analyzes, IR, UV–vis and X-band EPR spectral studies as well as conductivity and magnetic susceptibility measurements. In the reported complexes, the hydrazones exist either in the keto or enolate form, as evidenced by IR spectral data. Crystal structures of two complexes are well established using single crystal X-ray diffraction studies. In both of these complexes two equivalent monoanionic ligands are coordinated in a meridional fashion using cis pyridyl, trans azomethine nitrogen and cis enolate oxygen atoms positioned very nearly perpendicular to each other. EPR spectra in DMF solutions at 77 K show hyperfine sextets and in some of the complexes the low intensity forbidden lines lying between each of the two hyperfine lines are also observed

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A real-time analysis of renewable energy sources, such as arable crops, is of great importance with regard to an optimised process management, since aspects of ecology and biodiversity are considered in crop production in order to provide a sustainable energy supply by biomass. This study was undertaken to explore the potential of spectroscopic measurement procedures for the prediction of potassium (K), chloride (Cl), and phosphate (P), of dry matter (DM) yield, metabolisable energy (ME), ash and crude fibre contents (ash, CF), crude lipid (EE), nitrate free extracts (NfE) as well as of crude protein (CP) and nitrogen (N), respectively in pretreated samples and undisturbed crops. Three experiments were conducted, one in a laboratory using near infrared reflectance spectroscopy (NIRS) and two field spectroscopic experiments. Laboratory NIRS measurements were conducted to evaluate to what extent a prediction of quality parameters is possible examining press cakes characterised by a wide heterogeneity of their parent material. 210 samples were analysed subsequent to a mechanical dehydration using a screw press. Press cakes serve as solid fuel for thermal conversion. Field spectroscopic measurements were carried out with regard to further technical development using different field grown crops. A one year lasting experiment over a binary mixture of grass and red clover examined the impact of different degrees of sky cover on prediction accuracies of distinct plant parameters. Furthermore, an artificial light source was used in order to evaluate to what extent such a light source is able to minimise cloud effects on prediction accuracies. A three years lasting experiment with maize was conducted in order to evaluate the potential of off-nadir measurements inside a canopy to predict different quality parameters in total biomass and DM yield using one sensor for a potential on-the-go application. This approach implements a measurement of the plants in 50 cm segments, since a sensor adjusted sideways is not able to record the entire plant height. Calibration results obtained by nadir top-of-canopy reflectance measurements were compared to calibration results obtained by off-nadir measurements. Results of all experiments approve the applicability of spectroscopic measurements for the prediction of distinct biophysical and biochemical parameters in the laboratory and under field conditions, respectively. The estimation of parameters could be conducted to a great extent with high accuracy. An enhanced basis of calibration for the laboratory study and the first field experiment (grass/clover-mixture) yields in improved robustness of calibration models and allows for an extended application of spectroscopic measurement techniques, even under varying conditions. Furthermore, off-nadir measurements inside a canopy yield in higher prediction accuracies, particularly for crops characterised by distinct height increment as observed for maize.

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Auf dem Gebiet der Strukturdynamik sind computergestützte Modellvalidierungstechniken inzwischen weit verbreitet. Dabei werden experimentelle Modaldaten, um ein numerisches Modell für weitere Analysen zu korrigieren. Gleichwohl repräsentiert das validierte Modell nur das dynamische Verhalten der getesteten Struktur. In der Realität gibt es wiederum viele Faktoren, die zwangsläufig zu variierenden Ergebnissen von Modaltests führen werden: Sich verändernde Umgebungsbedingungen während eines Tests, leicht unterschiedliche Testaufbauten, ein Test an einer nominell gleichen aber anderen Struktur (z.B. aus der Serienfertigung), etc. Damit eine stochastische Simulation durchgeführt werden kann, muss eine Reihe von Annahmen für die verwendeten Zufallsvariablengetroffen werden. Folglich bedarf es einer inversen Methode, die es ermöglicht ein stochastisches Modell aus experimentellen Modaldaten zu identifizieren. Die Arbeit beschreibt die Entwicklung eines parameter-basierten Ansatzes, um stochastische Simulationsmodelle auf dem Gebiet der Strukturdynamik zu identifizieren. Die entwickelte Methode beruht auf Sensitivitäten erster Ordnung, mit denen Parametermittelwerte und Kovarianzen des numerischen Modells aus stochastischen experimentellen Modaldaten bestimmt werden können.

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Aus den im Rahmen dieser Forschungsarbeit empirisch gewonnenen Erkenntnissen werden Gestaltungsempfehlungen für das Public Debt Management abgeleitet. Diese zeigen, dass ein wirtschaftliches Public Debt Management nicht ein ausschließlich kostenminimierendes (sparsames), sondern ein kosten-risiko-optimales Public Debt Management mit effektiven internen und externen Überwachungsinstrumenten und wirksamer externer Finanzkontrolle sein muss.

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Das Ziel der vorliegenden Arbeit war die Herstellung und Charakterisierung mikromechanisch durchstimmbarer, dielektrischer Fabry-Pérot-Filter im nahen Infrarot-Bereich bei einer Zentralwellenlänge von λc = 950 nm. Diese Bauelemente wurden auf Basis kostengünstiger Technologien realisiert, dank deren Entwicklung extreme Miniaturisierung und gleichzeitig hohe spektrale Anforderungen möglich sind. Der Vorteil solcher Filter liegt darin, dass sie direkt in einen Photodetektor integriert werden können und mit ganz wenigen Komponenten zu einem kompakten Spektrometermodul zusammengesetzt werden können. Die Baugröße ist nur durch die Größe des Photodetektors limitiert und die gesamte Intensität des einfallenden Lichts kann vorteilhaft auf eine einzelne Filtermembran des Fabry-Pérot-Filters fokussiert werden. Für den Filteraufbau werden zwei hochreflektierende, dielektrische DBR-Spiegel, ein organisches Opferschichtmaterial, welches zur Erzeugung einer Luftkavität im Filter dient, und zwei unterschiedliche Elektroden aus ITO und Aluminium verwendet. Die mikromechanische Auslenkung der freigelegten Filtermembran geschieht mittels elektrostatischer Aktuation, wobei auf diese Weise die Kavitätshöhe des Fabry-Pérot-Filters geändert wird und somit dieser im erforderlichen Spektralbereich optisch durchgestimmt wird. Das in dieser Arbeit gewählte Filterkonzept stellt eine Weiterentwicklung eines bereits bestehenden Filterkonzepts für den sichtbaren Spektralbereich dar. Zum Einen wurden in dieser Arbeit das vertikale und das laterale Design der Filterstrukturen geändert. Eine entscheidende Änderung lag im mikromechanisch beweglichen Teil des Fabry-Pérot-Filters. Dieser schließt den oberen DBR-Spiegel und ein aus dielektrischen Schichten und der oberen Aluminium-Elektrode bestehendes Membranhaltesystem ein, welches später durch Entfernung der Opferschicht freigelegt wird. Die Fläche des DBR-Spiegels wurde auf die Fläche der Filtermembran reduziert und auf dem Membranhaltesystem positioniert. Zum Anderen wurde im Rahmen dieser Arbeit der vertikale Schichtaufbau des Membranhaltesystems variiert und der Einfluss der gewählten Materialien auf die Krümmung der freistehenden Filterstrukturen, auf das Aktuationsverhalten und auf die spektralen Eigenschaften des gesamten Filters untersucht. Der Einfluss der mechanischen Eigenschaften dieser Materialien spielt nämlich eine bedeutende Rolle bei der Erhaltung der erforderlichen optischen Eigenschaften des gesamten Filters. Bevor Fabry-Pérot-Filter ausgeführt wurden, wurde die mechanische Spannung in den einzelnen Materialien des Membranhaltesystems bestimmt. Für die Messung wurde Substratkrümmungsmethode angewendet. Es wurde gezeigt, dass die Plasmaanregungsfrequenzen der plasmaunterstützten chemischen Gasphasenabscheidung bei einer Prozesstemperatur von 120 °C die mechanische Spannung von Si3N4 enorm beeinflussen. Diese Ergebnisse wurden im Membranhaltesystem umgesetzt, wobei verschiedene Filter mit unterschiedlichen mechanischen Eigenschaften des Membranhaltesystems gezeigt wurden. Darüber hinaus wurden optische Eigenschaften der Filter unter dem Einfluss des lateralen Designs der Filterstrukturen untersucht. Bei den realisierten Filtern wurden ein optischer Durchstimmbereich von ca. 70 nm und eine spektrale Auflösung von 5 nm erreicht. Die erreichte Intensität der Transmissionslinie liegt bei 45-60%. Diese Parameter haben für den späteren spektroskopischen Einsatz der realisierten Fabry-Pérot-Filter eine hohe Bedeutung. Die Anwendung soll erstmalig in einem „Proof of Concept“ stattfinden, wobei damit die Oberflächentemperatur eines GaAs-Wafers über die Messung der spektralen Lage seiner Bandlücke bestimmt werden kann.

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This report examines how to estimate the parameters of a chaotic system given noisy observations of the state behavior of the system. Investigating parameter estimation for chaotic systems is interesting because of possible applications for high-precision measurement and for use in other signal processing, communication, and control applications involving chaotic systems. In this report, we examine theoretical issues regarding parameter estimation in chaotic systems and develop an efficient algorithm to perform parameter estimation. We discover two properties that are helpful for performing parameter estimation on non-structurally stable systems. First, it turns out that most data in a time series of state observations contribute very little information about the underlying parameters of a system, while a few sections of data may be extraordinarily sensitive to parameter changes. Second, for one-parameter families of systems, we demonstrate that there is often a preferred direction in parameter space governing how easily trajectories of one system can "shadow'" trajectories of nearby systems. This asymmetry of shadowing behavior in parameter space is proved for certain families of maps of the interval. Numerical evidence indicates that similar results may be true for a wide variety of other systems. Using the two properties cited above, we devise an algorithm for performing parameter estimation. Standard parameter estimation techniques such as the extended Kalman filter perform poorly on chaotic systems because of divergence problems. The proposed algorithm achieves accuracies several orders of magnitude better than the Kalman filter and has good convergence properties for large data sets.

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We present a technique for the rapid and reliable evaluation of linear-functional output of elliptic partial differential equations with affine parameter dependence. The essential components are (i) rapidly uniformly convergent reduced-basis approximations — Galerkin projection onto a space WN spanned by solutions of the governing partial differential equation at N (optimally) selected points in parameter space; (ii) a posteriori error estimation — relaxations of the residual equation that provide inexpensive yet sharp and rigorous bounds for the error in the outputs; and (iii) offline/online computational procedures — stratagems that exploit affine parameter dependence to de-couple the generation and projection stages of the approximation process. The operation count for the online stage — in which, given a new parameter value, we calculate the output and associated error bound — depends only on N (typically small) and the parametric complexity of the problem. The method is thus ideally suited to the many-query and real-time contexts. In this paper, based on the technique we develop a robust inverse computational method for very fast solution of inverse problems characterized by parametrized partial differential equations. The essential ideas are in three-fold: first, we apply the technique to the forward problem for the rapid certified evaluation of PDE input-output relations and associated rigorous error bounds; second, we incorporate the reduced-basis approximation and error bounds into the inverse problem formulation; and third, rather than regularize the goodness-of-fit objective, we may instead identify all (or almost all, in the probabilistic sense) system configurations consistent with the available experimental data — well-posedness is reflected in a bounded "possibility region" that furthermore shrinks as the experimental error is decreased.

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The literature related to skew–normal distributions has grown rapidly in recent years but at the moment few applications concern the description of natural phenomena with this type of probability models, as well as the interpretation of their parameters. The skew–normal distributions family represents an extension of the normal family to which a parameter (λ) has been added to regulate the skewness. The development of this theoretical field has followed the general tendency in Statistics towards more flexible methods to represent features of the data, as adequately as possible, and to reduce unrealistic assumptions as the normality that underlies most methods of univariate and multivariate analysis. In this paper an investigation on the shape of the frequency distribution of the logratio ln(Cl−/Na+) whose components are related to waters composition for 26 wells, has been performed. Samples have been collected around the active center of Vulcano island (Aeolian archipelago, southern Italy) from 1977 up to now at time intervals of about six months. Data of the logratio have been tentatively modeled by evaluating the performance of the skew–normal model for each well. Values of the λ parameter have been compared by considering temperature and spatial position of the sampling points. Preliminary results indicate that changes in λ values can be related to the nature of environmental processes affecting the data

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This paper deals with fault detection and isolation problems for nonlinear dynamic systems. Both problems are stated as constraint satisfaction problems (CSP) and solved using consistency techniques. The main contribution is the isolation method based on consistency techniques and uncertainty space refining of interval parameters. The major advantage of this method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements, and model errors. Interval calculations bring independence from the assumption of monotony considered by several approaches for fault isolation which are based on observers. An application to a well known alcoholic fermentation process model is presented

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Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models.