Showing 1,341 - 1,360 results of 1,657 for search '((sainae OR (sspine OR fine)) OR ((((pina OR bina) OR sping) OR peng) OR pin))', query time: 0.20s Refine Results
  1. 1341

    Value investment with machine learning by Wang, Jiwei

    Published 2024
    “…Further optimization led to the creation of the Improved Fundamental and Technical Factors Model, which achieved an impressive annualized return of 50\% and a Sharpe ratio of 1.85. After fine-tuning key parameters, the final optimized model demonstrated exceptional performance, with an annualized return of 59.82\%, a Sharpe ratio of 2.13, and a win rate of 75\%. …”
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    Final Year Project (FYP)
  2. 1342

    Ig.n.is. by Ong, Jian'an.

    Published 2011
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    Final Year Project (FYP)
  3. 1343

    Microstructural evolution during mechanical milling of Nd-Fe-B nanocomposites by Toh, Hon Kun.

    Published 2011
    “…The rate of chemical disorder as a function of milling intensity was studied by Extended X-ray Absorption Fine Structure (EXAFS) technique. XRD results revealed that higher milling intensity increased the rate of phase transformation and the rate of change in crystal sizes and strain but did not affect the steady state phase composition. …”
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    Final Year Project (FYP)
  4. 1344

    Singapore premiere. by Ong, Shannon.

    Published 2013
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    Final Year Project (FYP)
  5. 1345

    Plateau by Png, Hui Min, Goh Timothy Wei Wen

    Published 2016
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    Final Year Project (FYP)
  6. 1346

    Parametric study of gravure printing process for R2R printed electronics by Pua, Suan Tai

    Published 2017
    “…In this study, however, various printing parameters were investigated to establish their effects on printed line width and film thickness, in an attempt to achieve fine line printing resolution over a large printing area. …”
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    Final Year Project (FYP)
  7. 1347

    A supervised two-channel learning method for hidden Markov model and application on lip reading by Foo, Say Wei, Dong, Liang

    Published 2009
    “…This method is specially designed to train HMMs for fine recognition from similar observations. The prominent features of this method are 1.) the criterion function is based on the difference between training sequences, and 2.) a twochannel structure is adopted to maintain the validity of the HMM. …”
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    Conference Paper
  8. 1348

    Elastic modulus, hardness and creep performance of SnBi alloys using nanoindentation by Shen, Lu, Septiwerdani, Pradita, Chen, Zhong

    Published 2013
    “…At the intermediate stress region (200–370 MPa), dislocation climb is the dominant creep mechanism with stress exponents around 5–8. When fine lamellar structure is the dominant constituent of the microstructure, phase boundary sliding is identified as the rate-controlling mechanism in the low stress region (<200 MPa).…”
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    Journal Article
  9. 1349

    DECA : recovering fields of physical quantities from incomplete sensory data by Vasilakos, Athanasios V., Xiang, Liu, Luo, Jun, Deng, Chenwei, Lin, Weisi

    Published 2013
    “…Exploiting both the low-rank nature of real-world events and the redundancy in sensory data, DECA combines matrix completion with a fine-tuned compressed sensing technique to conduct a dual-level reconstruction process. …”
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    Conference Paper
  10. 1350

    Knowledge-based reactive planning and re-planning – a case-study approach by Djemai, Ramzi, Vassilev, Vassil, Ouazzane, Karim, Dey, Maitreyee

    Published 2024
    “…Planning for uncertainties arising from indoor evacuations can be complex as there’s a fine balance to strike between a too-detailed plan and one that’s too vague. …”
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    Conference or Workshop Item
  11. 1351

    Vision-language modelling for radiological imaging and reports in the low data regime by Windsor, R, Jamaludin, A, Kadir, T, Zisserman, A

    Published 2024
    “…Combined, they significantly improve retrieval compared to fine-tuning CLIP, roughly equivalent to training with 10x the data. …”
    Conference item
  12. 1352

    A comparative analysis of missing data imputation techniques on sedimentation data by Loh, Wing Son, Ling, Lloyd, Chin, Ren Jie, Lai, Sai Hin, Loo, Kar Kuan, Seah, Choon Sen

    Published 2024
    “…A comparative analysis on the missing fine sediment data imputation performance was made based on four different techniques, namely the k-Nearest Neighbourhood (k-NN), Support Vector Regression (SVR), Multiple Regression (MR), and Artificial Neural Network (ANN), under the single imputation (SI) and multiple imputation (MI) regimes. …”
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    Article
  13. 1353
  14. 1354
  15. 1355
  16. 1356
  17. 1357
  18. 1358
  19. 1359

    Towards semantic, debiased and moment video retrieval by Satar, Burak

    Published 2025
    “…Our approach tackles this challenge for the first time on long, fine-grained, and untrimmed egocentric videos, while existing methodologies target short, coarse-grained, and trimmed third-person videos. …”
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    Thesis-Doctor of Philosophy
  20. 1360

    Towards optimal scheduling of deep learning training jobs in GPU clusters by Gao, Wei

    Published 2025
    “…Second, it reuses the fine-tuning runtime of FMF workloads to reduce the significant context switch overhead. …”
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    Thesis-Doctor of Philosophy