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The purpose of this paper is to present explicitly the solution curve for commutative affine control systems on Lie groups under the assumption that the automorphisms associated with the linear vector fields commutes. If we assume that the derivations associated with the linear vector fields of the system are inner, we obtain a simpler solution and we show some results of controllability. To finish, we work with conjugation by homomorphism of Lie groups between affine systems.
相似文献Existing acoustic scene classification (ASC) systems often fail to generalize across different recording devices. In this work, we present an unsupervised domain adaptation method for ASC based on data standardization and feature projection. First, log-amplitude spectro-temporal features are standardized in a band-wise fashion over samples and time. Then, both source- and target-domain samples are projected onto the span of the principal eigenvectors of the covariance matrix of source-domain training data. The proposed method, being devised as a preprocessing procedure, is independent of the choice of the classification algorithm and can be readily applied to any ASC model at a minimal cost. Using the TUT Urban Acoustic Scenes 2018 Mobile Development dataset, we show that the proposed method can provide an absolute increment of over 10% compared to state-of-the-art unsupervised adaptation methods. Furthermore, the proposed method consistently outperforms a recent ASC model that ranked first in Task 1-A of the 2021 DCASE Challenge when evaluated on various unseen devices from the TAU Urban Acoustic Scenes 2020 Mobile Development dataset. In addition, our method appears robust even when provided with a small amount of target-domain data, proving effective using as few as 90 seconds of test audio recordings. Finally, we show that the proposed adaptation method can also be employed as a feature extraction stage for shallower neural networks, thus significantly reducing model complexity.
相似文献Optical networks are currently the only technology capable of providing extremely high data transmission rates. Because of this, systems must be increasingly efficient and immune to failures. One way to improve network efficiency is to use dynamic approaches like Adaptive Control of Operating Point, which consists of autonomously choosing the best operating point for optical amplifiers on the link, thus providing the best configuration concerning Quality of transmission. Unlike the previous works that focused on optimizing Optical Signal-To-Noise Ratio, our proposal and analysis are focused on maximizing the transmission rate. In this paper, we compare the results obtained by five different and widely used evolutionary and swarm-based algorithms in the search for maximizing the transmission rate in optical links. We have observed that the differential evolution provided the best results in the analyzed scenarios.
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