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The estimation of battery state-of-charge (SOC) is crucial for the safety and reliability of electric vehicles. This paper develops an auxiliary particle filter based on a Markov-chain Monte-Carlo (MCMC) method. Compared with the standard particle filter, it improves the estimation accuracy by incorporating auxiliary sampling and enhances its robustness by using MCMC resampling. Simulation results...
Sectorized antennas are a promising class of antennas for enabling direction-of-arrival (DoA) estimation and successive transmitter localization. In contrast to antenna arrays, sectorized antennas do not require multiple transceiver branches and can be implemented using a single RF front-end only, thus reducing the overall size and cost of the devices. However, for good localization performance the...
In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data. In this paper, we propose a novel approach to explicitly modelling the uncertainty of average F1 scores...
Generalized linear models (GLM) are discussed in this paper, which are used widely in the field of robust parameter design involving non-normal response variables. As for the estimation problems such as data over-dispersion which exist generally in robust parameter design, the Markov chain Monte Carlo (MCMC) approach based on adaptive rejection metropolis sampling algorithm is brought forward to simulate...
It is well known that the classical regression analysis, especially parametric regression analysis, is one of the important methods of extracting information from data sets. A family of regression functions, called fuzzy c-regression models (FCRM), has been presented which can be used to characterize the linear relationship to certain types of mixed data. More generally, an effective and robust method,...
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