Blind signal separation techniques using array antennas are expected to bring many advantages in the radio monitoring system. In several techniques, Independent Component Analysis (ICA) attracts much attention because...
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ISBN:
(纸本)9784885523021
Blind signal separation techniques using array antennas are expected to bring many advantages in the radio monitoring system. In several techniques, Independent Component Analysis (ICA) attracts much attention because of its convenience and effectiveness. In this paper, we focus on sobi algorithm in ICA and examine the performance under the effect of mutual coupling of array elements.
Complexity pursuit (CP) has recently been proposed as an elegant and simple solution to blindly (i.e. without measuring the inputs) separate the modal contributions in the vibration responses of a structure. This pote...
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Complexity pursuit (CP) has recently been proposed as an elegant and simple solution to blindly (i.e. without measuring the inputs) separate the modal contributions in the vibration responses of a structure. This potentially finds considerable interest in operational modal analysis and related applications. This paper analyses the theoretical ins and outs of the method. It also revises its physical interpretation in the modal analysis context. CP is found to separate components which are the least dispersive (i.e. invariant under linear filtering), a property that well characterizes the modal responses of lightly damped systems. However, it is also found to suffer from the same limitations as other blind source separation methods used in the state-ofthe-art, namely the difficulty to separate strongly coupled modes and to identify complex mode shapes. Finally a generalization of CP is proposed which intends to widen its applicability. Interestingly, the generalized CreP happens to include the well-known sobi algorithm as a particular case.
Usually, dam monitoring systems are based on both boundary conditions (temperature, rainfall, water level, etc.) and structural responses (displacements, rotations, pore pressures, etc.). Statistical analysis tools ar...
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Usually, dam monitoring systems are based on both boundary conditions (temperature, rainfall, water level, etc.) and structural responses (displacements, rotations, pore pressures, etc.). Statistical analysis tools are widely used to compare the current response of the dam with a whole set of recorded data, in order to determine eventual unwanted behaviors. The main drawback of this approach is that the structural response quantities are related to the external loads using analytical functions, whose parameters do not have physical meaning. In this paper, a new approach, based on Blind Source Separation (BSS) to find out the contributions of the external loads: air temperature and hydrostatic pressure, structure deformation and identify the irreversible component in structural response, is presented. Finally, it presented a case study whose purpose is to assess the separation of the contributions due to the external loads mentioned above without a priori knowledge of the generator phenomena or of the propagation environment, and use only the crest displacements of a concrete dam.
The development of satellites with the strong temporal repetitiveness and development of remote sensing techniques resulted in the advancement of change detection techniques from geospatial imagery. The natural events...
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ISBN:
(纸本)9781728140643
The development of satellites with the strong temporal repetitiveness and development of remote sensing techniques resulted in the advancement of change detection techniques from geospatial imagery. The natural events cause many modifications in the control process of the ecosystems. There is a necessity of using a method capable to map, categorize and monitor areas affected by natural events along time. In this article, a novel methodology of change detection is proposed in order to improve the change detection from multi-date satellite image using the source separation. The results obtained by our methodology are efficient and effective.
The paper presents a new method for multivariate time series forecasting using Blind Source Separation(BSS),as a preprocessing *** idea of this approach is to do the forecasting in the space of independent components(...
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The paper presents a new method for multivariate time series forecasting using Blind Source Separation(BSS),as a preprocessing *** idea of this approach is to do the forecasting in the space of independent components(sources),and then to transform back the results to the original time series *** forecasting can be done separately and with a different method for each component,depending on its time *** paper gives also the conceptual description of sobi algorithm for blind source separation in the case of instantaneous mixture models,using second order *** method has been applied in simulation to an artificial multivariate time series with five components,generated from three sources and a mixing matrix,randomly generated.
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