In order to solve the problem that the traditional scheduling algorithm of sensor network node is constrained by the energy of the node itself, this paper proposes a new scheduling algorithm of sensor network node bas...
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In order to solve the problem that the traditional scheduling algorithm of sensor network node is constrained by the energy of the node itself, this paper proposes a new scheduling algorithm of sensor network node based on artificial neural network (ANN). Aiming at the sensor network of ANN, a multi-objective task scheduling model is established. The optimal solution of task scheduling is obtained by particle swarm optimisation algorithm. The energy balance degree is set as the final decision-making index, and the energy consumption of the optimal solution centralised node is chosen as the final task scheduling strategy to complete the scheduling of sensor network nodes. The experimental results show that the proposed algorithm has higher coverage and lower energy consumption in the scheduling process, which has certain advantages.
The automatic seizure detection system is designed to aid the physician's decision-making process with recognizing the sought EEG segments. Increasing the system sensitivity is the goal of several studies. In fact...
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ISBN:
(纸本)9781538652398
The automatic seizure detection system is designed to aid the physician's decision-making process with recognizing the sought EEG segments. Increasing the system sensitivity is the goal of several studies. In fact, ameliorating this criterion allows to find the same interpretations as found with a visual scanning A patient-specific system is able to set its optimal parameters according to the patient which makes it more accurate than non patient-specific system. This paper introduces a new patient specific system with genetic and practical swarm optimisation algorithms. The results show that the proposed system is able to reach acceptable performances. Moreover, the use of the genetic algorithm improves the system sensitivity (95%) more than the practical swarm optimization (91%) which makes it a better method for the system parameter optimisation.
A new swarm optimisation algorithm, that is, fruit fly optimisationalgorithm is proposed to tuned several classical controllers for multi-area multi-source interconnected power system under deregulated environment. I...
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A new swarm optimisation algorithm, that is, fruit fly optimisationalgorithm is proposed to tuned several classical controllers for multi-area multi-source interconnected power system under deregulated environment. In deregulated environment, for multi-source combination of reheat thermal, hydro and nuclear generating units in each control area with appropriate generation rate constraint and AC/DC link. The performance of several controller such as integral (I), proportional-integral, proportional-integral-derivative, integral-double derivative (IDD) and proportional-integral-double derivative (PIDD) are compared under different power system scenario and it is found that PIDD controller performs better than other controllers. Then sensitivity analysis is performed with system parameter variation from their nominal values. Sensitivity analysis reveals that optimum gains of PIDD controller need not be reset for wide variation in system parameters.
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