Nowadays, the optimally developed system design has been important with the improvement of the technology. The mathematical model approach with the proper controller design provides a more efficient system with its ou...
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Due to the excellent interpretability and classification performance, the Takagi-Sugeno-Kang fuzzy classifier (TSK-FC) has drawn great attention. However, different patterns own their respective homogeneities, and the...
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Currently, microgrid clusters (MGC) are being increasingly used owing to their benefits and human needs. MGs can be composed of power generation, consumption, and storage elements. They permit the combination of DC an...
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This work presents a cost-efficient hardware architecture design of 2-D sliding discrete Fourier transform (SDFT). The proposed design requires the lowest eight real adders and six real multipliers in hardware resourc...
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Nowadays, gimbal systems have been a very critical role to obtain some important information related to live images photos and target detection in unmanned aerial vehicles. The most basic performance criterion of gimb...
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The efficient operation of HVAC&R systems are based on keeping indoor temperature and air quality at an optimum level without disturbing comfort. Starting from this point, in this experimental research, the factor...
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In the noisy intermediate-scale quantum (NISQ) era, the capabilities of variational quantum algorithms are greatly constrained due to a limited number of qubits and the shallow depth of quantum circuits. We may view t...
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In the noisy intermediate-scale quantum (NISQ) era, the capabilities of variational quantum algorithms are greatly constrained due to a limited number of qubits and the shallow depth of quantum circuits. We may view these variational quantum algorithms as weak learners in supervised learning. Ensemble methods are general approaches to combining weak learners to construct a strong one in machine learning. In this paper, by focusing on classification, we theoretically establish and numerically verify a learning guarantee for quantum adaptive boosting (AdaBoost). The supervised-learning risk bound describes how the prediction error of quantum AdaBoost on binary classification decreases as the number of boosting rounds and sample size increase. We further empirically demonstrate the advantages of quantum AdaBoost by focusing on a 4-class classification. The quantum AdaBoost not only outperforms several other ensemble methods, but in the presence of noise it can also surpass the ideally noiseless but unboosted primitive classifier after only a few boosting rounds. Our work indicates that in the current NISQ era, introducing appropriate ensemble methods is particularly valuable in improving the performance of quantum machine learning algorithms.
The article presents the modeling of an absorption machine and a low level control system necessary to ultimately control the cooling power demanded by air conditioning systems. The modeling of the components has been...
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The article presents the modeling of an absorption machine and a low level control system necessary to ultimately control the cooling power demanded by air conditioning systems. The modeling of the components has been implemented in Simulink in order to obtain a simulator of an absorption-machine-based cooling plant for controller design purposes. In the simulator, the proposed refrigeration system presents control loops to maintain the output variables of the flow rates and temperatures in established references. Finally, the results of the simulation are presented, which show the behavior of the absorption machine system and support the good performance of the proposed low level control system.
Recent advances in semiconductors industry and microelectronics have created new opportunities for integrating various technologies in energy harvesting projects. These advancements have simplified the process of capt...
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In this paper, we extend a constraint-based coverage control for robotic sensor networks based on control barrier functions (CBFs) for environments with known obstacles and different types of unmanned vehicles (UV). T...
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In this paper, we extend a constraint-based coverage control for robotic sensor networks based on control barrier functions (CBFs) for environments with known obstacles and different types of unmanned vehicles (UV). To this end, we use a sensing function that considers the vertices of the obstacles to compute the route and the distance regarding the UVs. This way, obstacle avoidance becomes intrinsic to the CBF ensuring the coverage performance. Moreover, we consider different obstacles and speeds for each type of UV. Finally, the proposed algorithm is illustrated with a heterogeneous fleet of UVs and obstacles in a simulated thermosolar power plant.
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