Modular multilevel converters (MMCs) have the potential to improve the performance of high- and medium-power applications, such as renewable energy generation and fast charging stations. The functioning of MMCs relies...
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In this paper we compare different operation strategies for alkaline electrolyzis (AEL) systems using numeric simulation. We derived general models for mass and energy balances from literature and composed them into a...
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The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth,safe,and green ***,effective traffic monitoring is an essential topic alongside the planning ...
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The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth,safe,and green ***,effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management *** contrast to classical traffic detection solutions,this study investigates the correlation between travelers'social activities and road *** s's primary goal is to investigate the presence of the relationship between social activity and road traffic,which might allow an infrastructure-independent traffic monitoring technique as ***'s general activities at Point of Interest(POI)locations(measured as occupancy parameter)are correlated with traffic data so that,finally,proper proxys can be defined for link-level average traffic speed *** method is tested and evaluated using real-world traffic and POI occupancy data from Budapest(District XI.).The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic,which holds promise for future practical applicability.
Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical P...
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Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar state-transition functions, i.e. only slight differences in parameters, can have significantly different time-series outputs. Furthermore, the classical PGNN cost function regularizes the model estimate over the entire state space with a constant trade-of hyperparameter. In this paper, we introduce a novel model augmentation strategy for nonlinear state-space model identification based on PGNN, using a weighted function regularization (W-PGNN). The proposed approach can efficiently augment the prior physics-based state-space models based on measurement data. A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model. This ensures the estimated model follows the baseline physics model functions in regions where the data has low information content, while placing greater trust in the data when a high informativity is present. The effectiveness of the proposed strategy over the current PGNN method is demonstrated on a benchmark example.
Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation...
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In a real Hilbert space setting, we investigate the asymptotic behavior of the solutions of the nonautonomous Arrow-Hurwicz differential system. We show that its solutions weakly converge in average towards a saddle p...
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Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and often a conside...
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Recent advances in deep-learning-based identification of dynamic systems have resulted in a new generation of approaches utilizing state-space neural models with innovation noise structure, improved reformulation of m...
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Recent advances in deep-learning-based identification of dynamic systems have resulted in a new generation of approaches utilizing state-space neural models with innovation noise structure, improved reformulation of multiple shooting, batch optimization, and a subspace identification-inspired form of encoders. The latter is used to learn a reconstructability map to estimate model states from past inputs and outputs. By using the SUBNET approach, which belongs to the state-of-the-art of these methods, we show how to effectively use these approaches to identify reliable vehicle models from data both in continuous and discrete time, respectively. We showcase the approach on the identification of the dynamics of a Crazyflie 2.1 nano-quadcopter and an F1tenth electric car both in a high-fidelity simulation environment, and in case of the electric car, on real measured data. The results indicate that new-generation of deep-learning methods offer Efficient system identification of vehicle dynamics in practice.
This paper presents the design of a model predictive scheduling strategy to address the inland waterborne transport (IWT) problem considering bridges that must open to enable vessel passage. The main contribution is t...
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