Kunming Wang - IEEE Xplore Author Profile

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Author details

Kunming Wang

Publications

1

Citations

8

Publications by Year

20222022

Co-Authors:

Xu HanNingfei JiaoWeiguo LiuShuai MaoKe Shen

Show All Co-Authors (7)

Kunming Wang

Affiliation

AVIC Shaanxi Aero Electric Company

Ltd.

Xi’an, China

Publication Topics

Conductance States,
Diode Rectifier,
Fault Diagnosis,
Fault Diagnosis Method,
Faulty Conditions,
Flux Linkage,
General Mode,
Harmonic Components,
Healthy Conditions,
Maximum Voltage,
Open Circuit,
Open-circuit Conditions

Biography

Kunming Wang received the M.S. degree from Shenyang University of Technology, Shenyang, China, in 2020.,He is currently an Engineer with AVIC Shaanxi Aero Electric Company, Ltd., Xi’an, China. His research interests include motor control strategy and power electronic conversion technology in aerospace power system.(Based on document published on 5 October 2021).

Publications

1

Citations

8

Publications by Year

20222022

Co-Authors:

Xu Han
Ningfei Jiao
Weiguo Liu
Shuai Mao
Ke Shen

Show All Co-Authors (7)

Author's Published Works

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Rüdiger Westermann(3)
Alexander Kumpf(3)
Michael Riemer(2)
Marc Rautenhaus(2)
Josef Stumpfegger(1)
Computer Graphics & Visualization Group, Technische Universität München, Garching, Germany(1)
Institute of Atmospheric Physics, Johannes Gutenberg Universität Mainz, Mainz, Germany(1)
Technische Universitat Munchen, Munchen, Bayern, DE(1)
Universitat Hamburg, Hamburg, Hamburg, DE(1)
Computer Graphics & Visualization Group, Technical University of Munich (TUM), München, Germany(1)
IEEE Transactions on Visualization and Computer Graphics(3)
IEEE(3)
Media(3)
Ensemble Forecasts(2)
Ensemble Members(2)
Hurricane(2)
North Atlantic(2)
Parallel Coordinates(2)

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Visual Analysis of Multi-Parameter Distributions Across Ensembles of 3D Fields

Alexander Kumpf;Josef Stumpfegger;Patrick Fabian Härtl;Rüdiger Westermann

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 10 | Journal Article |

Cited by: Papers (5)

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For an ensemble of 3D multi-parameter fields, we present a visual analytics workflow to analyse whether and which parts of a selected multi-parameter distribution is present in all ensemble members. Supported by a parallel coordinate plot, a multi-parameter brush is applied to all ensemble members to select data points with similar multi-parameter distribution. By a combination of spatial sub-divi...Show More

Visual Analysis of Multi-Parameter Distributions Across Ensembles of 3D Fields

Alexander Kumpf;Josef Stumpfegger;Patrick Fabian Härtl;Rüdiger Westermann

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 10 | Journal Article |

Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities

Alexander Kumpf;Marc Rautenhaus;Michael Riemer;Rüdiger Westermann

IEEE Transactions on Visualization and Computer Graphics

Year: 2019 | Volume: 25, Issue: 1 | Journal Article |

Cited by: Papers (19)

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Ensemble sensitivity analysis (ESA) has been established in the atmospheric sciences as a correlation-based approach to determine the sensitivity of a scalar forecast quantity computed by a numerical weather prediction model to changes in another model variable at a different model state. Its applications include determining the origin of forecast errors and placing targeted observations to improv...Show More

Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities

Alexander Kumpf;Marc Rautenhaus;Michael Riemer;Rüdiger Westermann

IEEE Transactions on Visualization and Computer Graphics

Year: 2019 | Volume: 25, Issue: 1 | Journal Article |

Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses

Alexander Kumpf;Bianca Tost;Marlene Baumgart;Michael Riemer;Rüdiger Westermann;Marc Rautenhaus

IEEE Transactions on Visualization and Computer Graphics

Year: 2018 | Volume: 24, Issue: 1 | Journal Article |

Cited by: Papers (38)

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In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be ...Show More

Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses

Alexander Kumpf;Bianca Tost;Marlene Baumgart;Michael Riemer;Rüdiger Westermann;Marc Rautenhaus

IEEE Transactions on Visualization and Computer Graphics

Year: 2018 | Volume: 24, Issue: 1 | Journal Article |

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