This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange ...
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Nowadays production companies are in a difficult situation since batch sizes are decreasing, the number of product variants is growing, and the demand is difficult to forecast. New technologies enable to design more c...
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This article presents a study on the use of photovoltaic panels for storing electrical energy intended to power an HVAC unit placed in a residential room. To highlight this aspect, two specific scenarios will be consi...
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ISBN:
(数字)9798331539511
ISBN:
(纸本)9798331539528
This article presents a study on the use of photovoltaic panels for storing electrical energy intended to power an HVAC unit placed in a residential room. To highlight this aspect, two specific scenarios will be considered. Thus, we aim to analyze whether the generated and stored energy is sufficient to fulfil the thermal comfort requirements of the user, while also considering the primary objective of minimizing energy consumption. To achieve this goal, the HVAC unit will be controlled by implementing a fuzzy system that determines its optimal activation based on the comfort level obtained from the user. The fuzzy system presents as inputs both environmental parameters and specific personal perception parameters of the user. The research results provide an insight into the efficiency of implementing photovoltaic energy storage systems for residential applications and propose solutions for optimizing energy consumption in the context of ensuring adequate thermal comfort. Thus, the study contributes to the development of sustainable and personalized energy solutions for smart homes.
We present Self-Tuning Tube-based Model Predictive control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tub...
We present Self-Tuning Tube-based Model Predictive control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes. Our algorithm leverages concentration results to bound the system uncertainty set with prescribed confidence, and guarantees robust constraint satisfaction for this set, along with recursive feasibility and input-to-state stability. Persistence of excitation is ensured without compromising the algorithm’s asymptotic performance or increasing its computational complexity. We demonstrate the performance of our algorithm using numerical experiments.
Syncmers represent a novel class of methods for selecting k-mers that exhibit robustness against mutations in flanking sequences and demonstrate superior conservation in mutated sequences. Nevertheless, syncmers may g...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Syncmers represent a novel class of methods for selecting k-mers that exhibit robustness against mutations in flanking sequences and demonstrate superior conservation in mutated sequences. Nevertheless, syncmers may generate a higher frequency of repetitive seed matches compared to alternative techniques, such as minimizers, which can result in increased computational time. In this article, we introduce weighted minimizer sampling, which integrates weighted minimizer sampling and syncmer sampling to enhance the sensitivity and accuracy of long-read mapping. We modified two state-of-the-art long-read mappers, Minimap2 and Winnowmap, by substituting the sketching sampling methods with weighted minimizer sampling. We assessed their sensitivity and accuracy using simulated and real datasets. The experimental results indicate that weighted minimizer sampling significantly improves the sensitivity and accuracy of long-read mapping. The source code is available at GitHub: https://***/hexinkaoan/weighted-syncmer.
This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is u...
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ISBN:
(数字)9798350340266
ISBN:
(纸本)9798350340273
This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals; so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme.
In this paper, we propose an algorithm for detecting artifacts in long-term video-EEG monitoring data in the problem of diagnosing cerebral ischemia after subarachnoid hemorrhage. The algorithm is based on a threshold...
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Dynamic pricing has become a prevalent strategy for balancing supply and demand in urban sharing economy platforms such as Uber and Airbnb. The dynamic pricing algorithms, however, are black-boxes and have encountered...
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ISBN:
(数字)9798350384031
ISBN:
(纸本)9798350384048
Dynamic pricing has become a prevalent strategy for balancing supply and demand in urban sharing economy platforms such as Uber and Airbnb. The dynamic pricing algorithms, however, are black-boxes and have encountered issues of discrimination. While existing studies have focused on group fairness within these algorithms, limited attention has been paid to individual fairness. The key challenge is in quantifying individual similarity within temporal-spatial dimensions and the inaccessibility of the algorithms. We propose a novel framework to assess and explain individual fairness of dynamic pricing algorithms. We define individual fairness by measuring individual similarity on latent temporal-spatial representation learned from relevant downstream tasks. We also introduce a triplet loss as a fairness constraint for fair representation. As the dynamic pricing algorithms are inaccessible, we proposed a sampling based Cohort Shapley explanation method to explain the discriminatory instances. We conduct experiments on datasets from both Uber ride-sharing and Airbnb pricing platforms. Our experimental results demonstrate that our proposed triplet loss approach strikes a balance between fairness and downstream task performance. Our case study illustrates that the proposed explanation method provides reasonable and clear explanations for instances of individual unfairness.
This paper introduces a novel control framework to address the satisfaction of multiple time-varying output constraints in uncertain high-order MIMO nonlinear control systems. Unlike existing methods, which often assu...
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Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are ...
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