Registered Data

[CT170]

  • Session Time & Room
    • CT170 (1/1) : 4E @D408 [Chair: Eduard Sebastian Scheiterer]
  • Classification
    • CT170 (1/1) : Mathematical programming (90C) / Mathematical biology in general (92B)

[02415] Propagation of epistemic uncertainty though a multi-layerd geometrically exact beam

  • Session Time & Room : 4E (Aug.24, 17:40-19:20) @D408
  • Type : Contributed Talk
  • Abstract : Uncertainty is ever-present in engineering. In this work, we demonstrate the effect of parameter uncertainty on a carbon spring prosthetic foot. The prosthesis is built with multiple layers of carbon fibre laminate. This layered structure is accounted for via homogenisation of the material parameters in the geometrically exact beam model of the prosthesis. Homogenising the material parameters introduces additional uncertainty. The resulting uncertain deformation envelopes and stored energy envelopes are examined.
  • Classification : 90C70, 70E55, 74-XX
  • Format : Talk at Waseda University
  • Author(s) :
    • Eduard Sebastian Scheiterer (Institute of Applied Dynamics - Friedrich-Alexander Universität Erlangen-Nürnberg)
    • Sigrid Leyendecker (Institute of Applied Dynamics - Friedrich-Alexander Universität Erlangen-Nürnberg)

[02367] Applying the 2 Steps SLP method to the UC-ACOPF problem

  • Session Time & Room : 4E (Aug.24, 17:40-19:20) @D408
  • Type : Contributed Talk
  • Abstract : The Unit Commitment (UC) problem is a widely used tool for the daily management of power transmission networks in modern economies. While the classical UC is a mixed-integer linear problem, when the AC Power Flow (ACPF) equations are included as constraints it becomes a mixed-integer nonlinear problem (MINLP). The 2-Step SLP method has been successfully applied to solving MINLP problems for gas networks, and here we will analyze its performance for power networks.
  • Classification : 90Cxx
  • Format : Talk at Waseda University
  • Author(s) :
    • Dolores Gómez (Universidade de Santiago de Compostela)
    • Alfredo Ríos-Albores (Universidade de Santiago de Compostela)
    • Pilar Salgado (Universidade de Santiago de Compostela)

[02584] Malmquist Productivity Index under Fuzzy Environment

  • Session Time & Room : 4E (Aug.24, 17:40-19:20) @D408
  • Type : Contributed Talk
  • Abstract : Malmquist productivity index (MPI) is widely used to estimate the productivity change by calculating the relative performance of homogeneous organizations for different time periods using data envelopment analysis (DEA). Although in real-world applications, traditional MPI method is tedious due to ambiguous or imprecise data. Thus, traditional DEA is integrated with fuzzy. In this study, novel integrated MPI method is proposed under fuzzy environment. To show the applicability and effectiveness of the proposed model, numerical example is also discussed.
  • Classification : 90C70, 90C05, 90B50
  • Author(s) :
    • Shivi Agarwal (Birla Institute of Technology and Science, Pilani)
    • Trilok Mathur (Birla Institute of Technology and Science, Pilani)
    • Swati Goyal (Birla Institute of Technology and Science, Pilani)

[00972] Reducing Communication in Federated Learning with Variance Reduction Methods

  • Session Time & Room : 4E (Aug.24, 17:40-19:20) @D408
  • Type : Contributed Talk
  • Abstract : In Federated Learning $\text{(}$FL$\text{)}$, inter-client heterogeneity and partial participation of clients at each communication cause client sampling error. We control this client sampling error by developing a novel single-loop variance reduction algorithm. While sampling a small number of clients, the proposed FL algorithms require provably fewer or at least equivalent communication rounds compared to any existing method, for finding first and even second-order stationarypoints in the general nonconvex setting, and under the PL condition.
  • Classification : 90Cxx, 68Wxx, 68Txx
  • Format : Talk at Waseda University
  • Author(s) :
    • Kazusato Oko (The University of Tokyo, AIP RIKEN)
    • Shunta Akiyama (The University of Tokyo)
    • Tomoya Murata ( The University of Tokyo, NTT DATA Mathematical Systems Inc.)
    • Taiji Suzuki (The University of Tokyo, AIP RIKEN)

[02039] Intelligent Computing Models for Super-large Protein Complex Prediction

  • Session Time & Room : 4E (Aug.24, 17:40-19:20) @D408
  • Type : Contributed Talk
  • Abstract : Improved from our Fast Fourier Transform based prediction methods, recently we have designed new artificial intelligence enhanced computing models to predict the super-large protein complex structures, which can give out results from monomer sequences and show good results and promise advances.
  • Classification : 92B20, 68T07
  • Format : Talk at Waseda University
  • Author(s) :
    • Xinqi Gong (Renmin University of China)