The endpoint carbon content of steelmaking is an important criterion for steel quality. Aiming at increasing the accuracy of endpoint carbon content prediction in basic oxygen furnace (BOF) steelmaking, this paper use...
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ISBN:
(纸本)9781538637821
The endpoint carbon content of steelmaking is an important criterion for steel quality. Aiming at increasing the accuracy of endpoint carbon content prediction in basic oxygen furnace (BOF) steelmaking, this paper uses case-based reasoning (CBR) method to predict the endpoint carbon content of BOF steelmaking. In CBR, case retrieval makes a significant impact on reasoning result. Therefore, we apply affinity propagation (ap) clustering algorithm and waterfilling algorithm to enhance the case retrieval so as to improve the accuracy and stability of endpoint carbon content prediction. Through the simulation experiment, this paper compares the new model we proposed with the widely used method at present. The results show that the improved CBR can obviously improve the accuracy of endpoint carbon content prediction.
The normalized LMS (NLMS) algorithm has been successfully used in many system identification problems. However, the NLMS algorithm is known to exhibit low convergence speed especially when the input data covariance ma...
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ISBN:
(纸本)0780374029
The normalized LMS (NLMS) algorithm has been successfully used in many system identification problems. However, the NLMS algorithm is known to exhibit low convergence speed especially when the input data covariance matrix is ill-conditioned. In this paper, we consider a sub-optimal implementation of the affine projection (ap) algorithm based on a prewithening mechanism, which renders the convergence characteristics less sensitive to the coloring of the input signal spectrum than is the case for the NLMS algorithm. Comparisons with the ap algorithm are given to validate our approach. Implementation details are discussed in the context of hands-free telephony where echo cancelling and speech coding algorithms are integrated on the same DSP board.
Recently, restingstate functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain ...
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Recently, restingstate functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain voxels are not independent units and adjacent voxels are always highly correlated, so functional connectivity maps contain redundant information, which not only impairs the computational efficiency during clustering, but also reduces the accuracy of clustering results. The aim of this study was to propose featurereduction approaches to reduce the redundancy and to develop semisimulated data with defined ground truth to evaluate these approaches. We proposed a featurereduction approach based on the Affinity Propagation algorithm （apA） and compared it with the classic feature reduction approach based on Principal Component Analysis （PCA）. We tested the two approaches to the parcellation of both semisimulated and real seed regions using the Kmeans algorithm and designed two experiments to evaluate their noise resistance. We found that all functional connectivitymaps （with/without feature reduction） provided correct information for the parcellation of the semi simulated seed region and the computational efficiency was greatly improved by both feature reduction approaches. Meanwhile, the apAbased featurereduction approach outperformed the PCA based approach in noiseresistance. The results suggested that functional connectivity maps can provide correct information for cortical parcellation, and featurereduction does not significantly change the information. Considering the improvement in computational efficiency and the noiseresistance, featurereduction of functional connectivity maps before cortical parcellation is both feasible and necessary.
As clean energy technologies proliferate globally, demand for critical minerals has surged, sparking widespread concern about the security of the critical minerals supply chain. However, given the traditional scientom...
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As clean energy technologies proliferate globally, demand for critical minerals has surged, sparking widespread concern about the security of the critical minerals supply chain. However, given the traditional scientometric methods lack of consideration of the importance of keywords and thematic analysis means cannot adaptively extract the core theme, this paper extracts the literature related to critical mineral supply chain security (CMSCS) from the Web of Science literature database system during 1995-2022. We first use scientometrics to analyze the publication volume, author collaboration, institutional collaboration, and national collaboration of CMSCS;secondly, we use cosine distance and nearest neighbor classification algorithms to analyze the dynamic development status of CMSCS, and finally, we utilize the TF-IDF algorithm and the Latent Dirichlet Allocation (LDA) text mining method to model the topics in this field. The results show that (1) global research on CMSCS is rapidly developing. China currently has the most literature in the field of CMSCS. The US, China, and the United Kingdom have the most influential research. (2) The authors collaborated to form nine core teams. The core institutions are the Chinese Academy of Sciences (CAS), Massachusetts Institute of Technology (MIT), and Politecnico di Milano. Most collaborative teams or institutions have strong internal collaborative links. (3) The research hotspots of CMSCS are influenced by national policies, technological development, and social consumption demand, "circular economy", "lithium battery", "recycling potential", and "recycling technology" are the directions that need to be focused on in the future. (4) Supply chain risk assessment, resilience enhancement, and recycling are important topics for future research in this field. In addition, future research on CMSCS should focus on assessing the vulnerability, resilience, and robustness of the critical mineral supply chain from the perspective o
Clustering movement trajectories to get the motion feature of object is one of the goals of the trajectory *** at the large scale trajectory data,to address the low efficiency of clustering,this paper proposes a hiera...
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Clustering movement trajectories to get the motion feature of object is one of the goals of the trajectory *** at the large scale trajectory data,to address the low efficiency of clustering,this paper proposes a hierarchical trajectory clustering algorithm based on time series(HTCTS).The algorithm first divides trajectory data by time interval,and then respectively cluster the sub ***,for all cluster subset,HTCTS executes cluster algorithm again to produce the final clustering *** experimental results show that HTCTS algorithm in clustering efficiency and quality is superior than the trajectory clustering algorithms which cluster the whole dataset directly.
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