We tackle the challenge of non parametric detection of breakpoints in spatiotemporal series by introducing a method to identify variations in process parameters, such as the mean and covariance matrix, without prior d...
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We tackle the challenge of non parametric detection of breakpoints in spatiotemporal series by introducing a method to identify variations in process parameters, such as the mean and covariance matrix, without prior determination of the number of breakpoints. This method relies on an adaptive algorithm that assesses the stability of parameters over selected time intervals, allowing effective management of structural breaks in complex spatio-temporal series. To illustrate our results, we present a simulation study as well as a practical application to the distribution of elephants in various parks in Gabon.
Appropriate selection of search operators plays a critical role in meta-heuristic algorithm design. adaptive selection of suitable operators to the characteristics of different optimization stages is an important task...
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This paper focuses on the new model for the classification of railhead defects, through images acquired by a rail inspection vehicle. In this regard, we discuss the use of set-membership concept, derived from the adap...
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This paper focuses on the new model for the classification of railhead defects, through images acquired by a rail inspection vehicle. In this regard, we discuss the use of set-membership concept, derived from the adaptive filter theory, into the training procedure of an upper and lower singleton type-2 fuzzy logic system, aiming to reduce computational complexity and to increase the convergence speed. The performance is based on the data set composed of images provided by a Brazilian railway company, which covers the four possible railhead defects (cracking, flaking, head-check and spalling) and the normal condition of the railhead. Additionally, we apply different levels of additive white Gaussian noise in the images in order to challenge the proposed model. Finally, we discuss performance analysis in terms of convergence speed, computational complexity reduction, and classification ratio. The reported results show that the proposal achieved improved convergence speed, slightly higher classification ratio and remarkable computation complexity reduction when we limit the number of epochs for training, which may be required under real-time constraint or low computational resource availability.
This article presents a comprehensive overview of the applications of bio-inspired algorithms in planar circuit design. Drawing inspiration from the adaptive and self-organising behaviours observed in biological syste...
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In this paper the algorithm for adaptive testing of students’ knowledge in distance learning and an assessment of its effectiveness in the educational process has been proposed. The results of the study are based on ...
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The BeiDou Navigation Satellite System (BDS) is widely used in various fields such as military and civilian. However, the low transmit power and high spatial attenuation make the satellite navigation signal with very ...
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A robust adaptive fault tolerant control rate is designed for a class of nonlinear dynamic system with actuator failures and external disturbances. While the states were not measurable and external disturbance and act...
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It is known that adaptive filtering algorithms may tackle relevant communication tasks. In order to reduce the adaptation rate, the least mean squares algorithm and its normalized version may be implemented in a block...
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It is known that adaptive filtering algorithms may tackle relevant communication tasks. In order to reduce the adaptation rate, the least mean squares algorithm and its normalized version may be implemented in a block manner, so that the filter coefficients are adjusted once per each output block. This letter advances a stochastic model that is able to predict the learning capabilities of time-domain block extensions of these algorithms, and demonstrates that their behaviour is not governed by trivial generalizations of the rules presented by standard implementations. The devised model decouples the radial and angular distribution of input data for the sake of emphasizing the factors that drive the algorithms learning behaviour. Both algorithms are demonstrated to solve a local and deterministic optimization problem. This novel point of view is employed to derive new versions of these algorithms that are able to enhance asymptotic performance by the usage of coefficient reusing techniques. Theoretical results reveal good adherence to simulated learning curves and the proposed algorithms outperform the standard ones in steady-state.
When the classical gray stretching algorithm is applied in digital low light level devices, it cannot meet the requirements of illumination environment change in large dynamic range, and the image processed under very...
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In order to meet the increasing demand for electric vehicles, automotive suppliers such as ZF Friedrichshafen AG are trying to develop modular electric motor platforms. In order to find the optimal platform and to be ...
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