913 resultados para Filter-rectify-filter-model


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We consider the speech production mechanism and the asso- ciated linear source-filter model. For voiced speech sounds in particular, the source/glottal excitation is modeled as a stream of impulses and the filter as a cascade of second-order resonators. We show that the process of sampling speech signals can be modeled as filtering a stream of Dirac impulses (a model for the excitation) with a kernel function (the vocal tract response),and then sampling uniformly. We show that the problem of esti- mating the excitation is equivalent to the problem of recovering a stream of Dirac impulses from samples of a filtered version. We present associated algorithms based on the annihilating filter and also make a comparison with the classical linear prediction technique, which is well known in speech analysis. Results on synthesized as well as natural speech data are presented.

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A model of the auditory periphery assembled from analog network submodels of all the relevant anatomical structures is described. There is bidirectional coupling between networks representing the outer ear, middle ear and cochlea. A simple voltage source representation of the outer hair cells provides level-dependent basilar membrane curves. The networks are translated into efficient computational modules by means of wave digital filtering. A feedback unit regulates the average firing rate at the output of an inner hair cell module via a simplified modelling of the dynamics of the descending paths to the peripheral ear. This leads to a digital model of the entire auditory periphery with applications to both speech and hearing research.

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Back-pressure on a diesel engine equipped with an aftertreatment system is a function of the pressure drop across the individual components of the aftertreatment system, typically, a diesel oxidation catalyst (DOC), catalyzed particulate filter (CPF) and selective catalytic reduction (SCR) catalyst. Pressure drop across the CPF is a function of the mass flow rate and the temperature of the exhaust flowing through it as well as the mass of particulate matter (PM) retained in the substrate wall and the cake layer that forms on the substrate wall. Therefore, in order to control the back-pressure on the engine at low levels and to minimize the fuel consumption, it is important to control the PM mass retained in the CPF. Chemical reactions involving the oxidation of PM under passive oxidation and active regeneration conditions can be utilized with computer numerical models in the engine control unit (ECU) to control the pressure drop across the CPF. Hence, understanding and predicting the filtration and oxidation of PM in the CPF and the effect of these processes on the pressure drop across the CPF are necessary for developing control strategies for the aftertreatment system to reduce back-pressure on the engine and in turn fuel consumption particularly from active regeneration. Numerical modeling of CPF's has been proven to reduce development time and the cost of aftertreatment systems used in production as well as to facilitate understanding of the internal processes occurring during different operating conditions that the particulate filter is subjected to. A numerical model of the CPF was developed in this research work which was calibrated to data from passive oxidation and active regeneration experiments in order to determine the kinetic parameters for oxidation of PM and nitrogen oxides along with the model filtration parameters. The research results include the comparison between the model and the experimental data for pressure drop, PM mass retained, filtration efficiencies, CPF outlet gas temperatures and species (NO2) concentrations out of the CPF. Comparisons of PM oxidation reaction rates obtained from the model calibration to the data from the experiments for ULSD, 10 and 20% biodiesel-blended fuels are presented.

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Short-term traffic flow data is characterized by rapid and dramatic fluctuations. It reflects the nature of the frequent congestion in the lane, which shows a strong nonlinear feature. Traffic state estimation based on the data gained by electronic sensors is critical for much intelligent traffic management and the traffic control. In this paper, a solution to freeway traffic estimation in Beijing is proposed using a particle filter, based on macroscopic traffic flow model, which estimates both traffic density and speed.Particle filter is a nonlinear prediction method, which has obvious advantages for traffic flows prediction. However, with the increase of sampling period, the volatility of the traffic state curve will be much dramatic. Therefore, the prediction accuracy will be affected and difficulty of forecasting is raised. In this paper, particle filter model is applied to estimate the short-term traffic flow. Numerical study is conducted based on the Beijing freeway data with the sampling period of 2 min. The relatively high accuracy of the results indicates the superiority of the proposed model.

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The application of inverse filtering techniques for high-quality singing voice analysis/synthesis is discussed. In the context of source-filter models, inverse filtering provides a noninvasive method to extract the voice source, and thus to study voice quality. Although this approach is widely used in speech synthesis, this is not the case in singing voice. Several studies have proved that inverse filtering techniques fail in the case of singing voice, the reasons being unclear. In order to shed light on this problem, we will consider here an additional feature of singing voice, not present in speech: the vibrato. Vibrato has been traditionally studied by sinusoidal modeling. As an alternative, we will introduce here a novel noninteractive source filter model that incorporates the mechanisms of vibrato generation. This model will also allow the comparison of the results produced by inverse filtering techniques and by sinusoidal modeling, as they apply to singing voice and not to speech. In this way, the limitations of these conventional techniques, described in previous literature, will be explained. Both synthetic signals and singer recordings are used to validate and compare the techniques presented in the paper.

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This paper establishes practical stability results for an important range of approximate discrete-time filtering problems involving mismatch between the true system and the approximating filter model. Using local consistency assumption, the practical stability established is in the sense of an asymptotic bound on the amount of bias introduced by the model approximation. Significantly, these practical stability results do not require the approximating model to be of the same model type as the true system. Our analysis applies to a wide range of estimation problems and justifies the common practice of approximating intractable infinite dimensional nonlinear filters by simpler computationally tractable filters.

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This paper establishes practical stability results for an important range of approximate discrete-time filtering problems involving mismatch between the true system and the approximating filter model. Practical stability is established in the sense of an asymptotic bound on the amount of bias introduced by the model approximation. Our analysis applies to a wide range of estimation problems and justifies the common practice of approximating intractable infinite dimensional nonlinear filters by simpler computationally tractable filters.

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Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

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World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.

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详细分析了蓝绿激光穿过大气海洋信道的信道效应和物理特性,利用理论分析和蒙特卡罗模拟方法完成对光信道仿真。介绍了大气海洋激光通信蒙特卡罗模拟方法及计算步骤。研究了不同厚度云层对光束投影面积的展宽,和光脉冲穿过不同深度海水后的空间分布和时域信号波形,讨论了其不同的展宽机理,并对信道的噪声分布进行了分析。发现云层对光脉冲展宽作用在云层厚度500 m时达到饱和,信号能量的随机起伏随海水深度的增加而增大,但能量分布的半峰全宽并不增加,大气海洋的综合信道效应可以用时延滤波器进行建模。

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Manufacturas Hortúa Leal (MHL) es una PYME familiar dedicada a la producción de calzado y artículos de cuero. Debido a la sobre oferta en el mercado colombiano, la empresa ha perdido clientes importantes y como consecuencia sus ganancias han disminuido considerablemente durante los últimos años. Para garantizar la perdurabilidad de la organización, la gerencia ha propuesto como alternativa la búsqueda de mercados extranjeros para exportar el calzado. El problema radica en la inexperiencia de la empresa en materia de exportación y en la incertidumbre del mercado al que se debe exportar. Este trabajo se enfoca en medir el potencial exportador de la empresa para determinar si está en condiciones de desarrollar un proceso de expansión y examinar a través de un filtro los países candidatos para exportar el calzado.