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Sökning: WFRF:(Dong Sidi)

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1.
  • Keasar, Chen, et al. (författare)
  • An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12
  • 2018
  • Ingår i: Scientific Reports. - : NATURE PUBLISHING GROUP. - 2045-2322. ; 8
  • Tidskriftsartikel (refereegranskat)abstract
    • Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.
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2.
  • Xiong, Binyu, et al. (författare)
  • Peak Power Estimation of Vanadium Redox Flow Batteries Based on Receding Horizon Control
  • 2023
  • Ingår i: IEEE Journal of Emerging and Selected Topics in Power Electronics. - 2168-6777 .- 2168-6785. ; 11:1, s. 154-165
  • Tidskriftsartikel (refereegranskat)abstract
    • The peak power of a vanadium redox flow battery (VRB) reflects its capability to continuously absorb or release energy. Accurate estimation of peak power is essential for the safe, reliable, and efficient operation of VRB systems, but also challenging as it is limited by various factors, such as currents, flow rates, temperature, and state of charge. This article proposes a new online model-based peak power estimation scheme for VRBs. First, the model parameters and system states are accurately estimated using the recursive least squares with forgetting and the unscented Kalman filter, respectively. Next, based on a linear time-varying VRB model and the estimated states, the peak power estimation is formulated into an optimal control problem, and the problem is solved using the receding horizon control (RHC). The influence of the predictive horizon on the estimated peak power is discussed. Finally, the effectiveness of the proposed RHC-based peak power estimation scheme is experimentally verified on a 5-kW/3-kWh VRB platform.
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