Dependence of heat transport on the strength and shear rate of prescribed circulating flows

We study numerically the dependence of heat transport on the maximum velocity and shear rate of physical circulating flows, which are prescribed to have the key characteristics of the large-scale mean flow observed in turbulent convection. When the side-boundary thermal layer is thinner than the viscous boundary layer, the Nusselt number (Nu), which measures the heat transport, scales with the normalized shear rate to an exponent 1/3. On the other hand, when the side-boundary thermal layer is thicker, the dependence of Nu on the Peclet number, which measures the maximum velocity, or the normalized shear rate when the viscous boundary layer thickness is fixed, is generally not a power law. Scaling behavior is obtained only in an asymptotic regime. The relevance of our results to the problem of heat transport in turbulent convection is also discussed.Comment: 7 pages, 7 figures, submitted to European Physical Journal

Generalized camera calibration model for trapezoidal patterns on the road

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Real-Time Estimation of Lane-to-Lane Turning Flows at Isolated Signalized Junctions

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CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning

© 1989-2012 IEEE. The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into two models: coupled data embedding (CDE) for clustering and coupled outlier scoring of high-dimensional data (COSH) for outlier detection. These show that CURE is flexible for value clustering and coupling learning between value clusters for different learning tasks. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data w.r.t. an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised encoding methods and three state-of-the-art similarity measures, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters

Enhanced exponential rule scheduling algorithm for real-time traffic in LTE network

Nowadays, mobile communication is growing rapidly and become an everyday commodity. The vast deployment of real-time services in Long Term Evolution (LTE) network demands for the scheduling techniques that support the Quality of Service (QoS) requirements. LTE is designed and implemented to fulfill the users’ QoS. However, 3GPP does not define the specific scheduling technique for resource distribution which leads to vast research and development of the scheduling techniques. In this context, a review of the recent scheduling algorithm is reported in the literature. These schedulers in the literature cause high Packet Loss Rate (PLR), low fairness, and high delay. To cope with these disadvantages, we propose an enhanced EXPRULE (eEXPRULE) scheduler to improve the radio resource utilization in the LTE network. Extensive simulation works are carried out and the proposed scheduler provides a significant performance improvement for video application without sacrificing the VoIP performance. The eEXPRULE scheduler increases video throughput, spectrum efficiency, and fairness by 50%, 13%, and 11%, respectively, and reduces the video PLR by 11%