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The purpose of this paper is to investigate a novel axial flux-switching motor with sandwiched permanent magnet for direct drive electric vehicles (EVs), in which the…
The purpose of this paper is to investigate a novel axial flux-switching motor with sandwiched permanent magnet for direct drive electric vehicles (EVs), in which the torque density is increased and the cogging torque is decreased. For reducing the back-electromotive force (EMF) harmonics and cogging torque, a twisted structure is employed. To improve the dynamic performance of the axial field flux-switching sandwiched permanent magnet (AFFSSPM) motor a space vector modulation-direct torque and flux control scheme is proposed.
A multi-objective optimization is performed by means of artificial neural network and non-sorting genetic algorithm II to minimize the cogging torque while preserving the average torque.
A comparative study between two proposed machines and the conventional flux-switching permanent magnet (FSPM) machine is accomplished and the static electromagnetic characteristics are analyzed. It is demonstrated that the proposed model with twisted structure has significantly improved performance over the conventional FSPM machine in back-EMF and efficiency. The proposed controller has a speed loop only and contains neither the current loop nor hysteresis control. The AFFSSPM motor exhibits excellent dynamic performance with this scheme.
The axial flux-switching permanent-magnet machine is one of the most efficient machines but the AFFSSPM with sandwiched permanent magnet has not been specially reported to date. Thus in this paper, the authors report on optimal design of an axial flux-switching sandwiched permanent magnet machine for electric vehicles and investigate its dynamic performance.
This paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach…
This paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.
Taking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.
The results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.
Note that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.
The paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.
As the storage and processing requirement of digital information is increasing on the cloud, it is very difficult for the single cloud provider (CP) to meet the resource…
As the storage and processing requirement of digital information is increasing on the cloud, it is very difficult for the single cloud provider (CP) to meet the resource requirement. Multiple providers form a federation for the execution of users’ requests. For the federated cloud, this paper aims to address the issue distribution of users’ request for resources and revenue among the providers by offering fair and stable distribution models for the federated cloud.
This paper uses cooperative game (CG)-theoretical models, i.e. Shapley–Shubik power index (SSPI) and Banzhaf power index (BPI) for distribution. Performance is analysed using variance and monotonicity using a case study.
Numerical analysis is done using two scenarios. Monotonicity is evaluated. Results show that SSPI performs better as compared to BPI in terms of fairness accuracy and the framework provide the fair distribution of revenue among providers in the federated cloud.
The proposed framework works efficiently under the specific defined conditions.
Paper provides the fair distribution. It assist the centralised cloud exchange in managing the users’ request in such a way every CPs, in the federated cloud will get an equal chance of serving the users’ request. The framework also provides the stable federation. Proposed work provides less rejection rate of users’ request. Finally, it assists the providers in increasing their profits in the federation.
This paper presents a CG theoretic-based framework for the distribution of resources required and revenue. The framework analysed the performance of distribution models by considering the variance and monotonicity for multiple users’ requests.