Simulation results show that the HCR model achieves significantly better performance in prediction accuracy than the SCR model.
The model achieved significantly improved predictions than the conventional models and was in satisfactory agreement with the field-test data of an in-operation GSHP system, demonstrating the importance of systematic consideration of the dynamic system operational characteristics and 3D heat transfer processes in the GHEs.
The developed neuro fuzzy model achieved significantly higher prediction abilities for different formulations.
By training prior knowledge networks (PKNs) against experimental data, this method produces models shown to achieve significantly better predictive power than untrained models.
It has been applied to ASR combined with hidden Markov models (HMM) and reported to achieve significantly higher accuracy than the conventional GMM (Gaussian Mixture Model -HMM scheModel -HMMouscheme domains [14, 18–20].
In these models, the impaired cluster was found to achieve significantly lower scores than both other clusters in almost all traits (Table 4).
Trained on word and syntactic features, the model can filter out erroneous species entity relations and achieve significantly better micro-averages than the rule- and parser-based systems, and better macro-averages than the supervised classification approach.
The results suggest that both the modeling of tag distributions and the profile-clustering step for data preprocessing are necessary for SeqSpider to achieve significantly better performance than conventional algorithms.
Nanoparticle-embedded magnetic medium is designed, modeled, prepared and characterized, which achieves significantly increased operation frequency to multiple-GHz for RF inductor applications.
The simulation scheme introduced here achieves significantly improved efficiencies.
