Introduced the working principles of screen bowl centrifuge and analyzed the influence of screen bowl centrifuge's structural parameters and operational parameters on dewatering. In order to improve coal slime dew...
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Introduced the working principles of screen bowl centrifuge and analyzed the influence of screen bowl centrifuge's structural parameters and operational parameters on dewatering. In order to improve coal slime dewatering results and operational reliability, according to the comparison of screen bowl centrifuge's structural parameters, operational parameters and dewatering results used at home and broad, proposed that, proper design parameters should be determined by the property of coal slime;the separation factor and manufacture level of homemade screen bowl centrifuge should be improved;the research on sieve and wear parts should be strengthened to improve the service life of wear parts;planetary gear differential case should be used to improve the work reliability of whole machine and the automatization level of centrifuge control should be enhanced.
The mineral grinding process is an important procedure in the mineral processing. Its technical performance index is the particle size, which is closely related to the overall performance of the mineral processing. In...
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The mineral grinding process is an important procedure in the mineral processing. Its technical performance index is the particle size, which is closely related to the overall performance of the mineral processing. In this paper, we present the construction of a software platform of the supervisory control of the grinding process to control its particle size into the target range. The platform provides the researcher and the engineer with an interactive tool to investigate the supervisory control algorithms and their parameters selection. The supervisory control strategy, the structure and the function of the software platform are given in the paper, where two experiments are given to evaluate the software platform and the results show its validity and efficiency.
Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences f...
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Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN.
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