Showing 1,501 - 1,520 results of 8,258 for search '(((spine OR shine) OR (spike OR (line OR find))) OR (((pina OR ming) OR peng) OR link))', query time: 0.20s Refine Results
  1. 1501

    Rapid process modeling of the aerosol jet printing based on gaussian process regression with latin hypercube sampling by Zhang, Haining, Moon, Seung Ki, Ngo, T. H., Tou, J., Bin Mohamed Yusoff, M. A.

    Published 2021
    “…Hence, it is necessary to develop a small data set based machine learning approach to model relationship between the process parameters and the line morphology. In this paper, we propose a rapid process modeling method for AJP process and consider sheath gas flow rate, carrier gas flow rate, stage speed as AJP process parameters, and line width and line roughness as the line morphology. …”
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    Journal Article
  2. 1502

    Integrated vision functions for the guidance of smart vehicle by Wong, Swee Meng.

    Published 2008
    “…Three basic vision functions for road navigation - lane finding, obstacle detection and depth assessment of obstacles- are implemented in the real-time vision system.…”
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    Thesis
  3. 1503

    Self-healing algorithm for real-time multicast tree by Giam, Pin Leong.

    Published 2008
    “…However, these algorithms are not resilient to link failures and would require intensive computation and expensive restructuring of the routing tree. …”
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    Thesis
  4. 1504

    A back-to-back converter system for aerospace applications by Hao Zhe, Chua

    Published 2018
    “…The ability to control the power flow allows the DC-link voltage to be regulated, which results in size reduction of the DC-link capacitor without affecting its performance. …”
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    Final Year Project (FYP)
  5. 1505

    Falling for Rachel / by Roberts, Nora, 1950- author 465662

    Published 2014
    “…Public defender Rachel Stanislaski is not what he has in mind—until he discovers there’s a lot more to the beautiful, coolheaded attorney than meets the eye…and finds himself falling for her, hook, line and sinker.…”
    text
  6. 1506
  7. 1507

    Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community by Zhang, Chengyu, Sánchez, Benjamín J., Li, Feiran, Cheng, Eiden Wei Quan, Scott, William T., Liebal, Ulf W., Blank, Lars M., Mengers, Hendrik G., Anton, Mihail, Rangel, Albert Tafur, Mendoza, Sebastián N., Zhang, Lixin, Nielsen, Jens, Lu, Hongzhong, Kerkhoven, Eduard J.

    Published 2024
    “…Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.…”
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    Journal Article
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