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Building Bluetooth scatternets is the focus of a lot of research activity where scatternet formation algorithms are developed to optimize various performance criteria.
In order to improve the resource utilisation and reduce the power consumption, two new algorithms, namely Optimum Estimation Algorithm (OEA) and Power-Oriented Adaptation (POA) algorithm, are developed to optimize the proposed 2-level channel multiplexing scheme.
The paper presents two new concepts: First, a set of clustering algorithms that model the supply-demand characterization of four different substations clusters, and second, a semi-supervised machine learning and clustering technique are developed to optimize the losses and automate the process of identifying loss factors contributing to the total loss.
Many of the works are developed to optimize communication-related metrics, such as transmission delay or throughput.
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Furthermore, a suitable gradient descent optimization technique was developed to optimize landmark locations by operating only on the surface structures.
This optimization technique is developed to optimize the usage of power.
An optimization process was developed to optimize the parameters of the classifier in order to increase the detection rate accuracy.
In this paper, a network-based optimization model was developed to optimize the pin-type flow field configuration.
In order to maximize the improvement, a genetic algorithm (GA -based optimization procedure is developed to optimize the distribution of CFRP and steel.
An optimization approach is developed to optimize the "area to point" heat conduction problem based on the concept of flow pattern construction.
The algorithm of particle swarm optimization (PSO) with mutation is developed to optimize the parameters of constructed wavelet LS-SVM regression (W-LSSVR).
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