Vinh Q. Dang - IEEE Xplore Author Profile

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Author details

Vinh Q. Dang

Also published under: Vinh Dang

Publications

7

Citations

185

Publications by Year

20202024

Co-Authors:

Daniel ArndtSalvatore CampioneJan CieskoNathan EllingwoodRahulkumar Gayatri

Show All Co-Authors (37)

Vinh Q. Dang

Also published under: Vinh Dang

Affiliation

Sandia National Laboratories

Albuquerque

NM, USA

Publication Topics

Block Diagonal,
Iterative Solver,
Krylov Subspace,
Singular Value,
Singular Value Decomposition,
Singular Vectors,
Actual Error,
Angular Response,
Angular Sweep,
Benchmark,
Binary Tree,
Cavity Region

Biography

Vinh Dang received the PhD degree in electrical engineering from the Catholic University of America in 2015. He is currently a senior member of Technical Staff with Sandia National Laboratories. His research interests include high-performance computing and parallel dense linear algebra or solvers.(Based on document published on 14 July 2021).

Publications

7

Citations

185

Publications by Year

20202024

Co-Authors:

Daniel Arndt
Salvatore Campione
Jan Ciesko
Nathan Ellingwood
Rahulkumar Gayatri

Show All Co-Authors (37)

Author's Published Works

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Showing 1-5 of 5 results

Conferences (4)

Journals (1)

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Koyo Sato(5)
Toshiaki Tsuji(3)
Sho Sakaino(3)
Shunsuke Ono(2)
Masahide Oikawa(1)
Tokyo Institute of Technology(2)
Department of Electrical, and Electronic Systems, Saitama University, Saitama, Japan(1)
Graduate School of System and Information Engineering, University of Tsukuba, JST PRESTO, Ibaraki, Japan(1)
Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan(1)
JST PRESTO(1)
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(1)
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society(1)
IEEE Robotics and Automation Letters(1)
IEEE(5)
Lisbon, Portugal(1)
Prague, Czech Republic(1)
Rhodes Island, Greece(1)
Seoul, Korea, Republic of(1)
Anomaly Detection(2)
Background Pixels(2)
Contact Conditions(2)
Convolutional Layers(2)
Detection Function(2)

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Results

Enhancing Hyperspectral Anomaly Detection by Difference-of-Convex Sparse Anomaly Modeling

Koyo Sato;Kazuki Naganuma;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Cited by: Papers (3)

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We propose a hyperspectral (HS) anomaly detection method using a novel characterization of anomalies. Among HS anomaly detection approaches, decomposition-based methods, which simultaneously estimate a background part and an anomaly part from an HS image, have attracted much attention. In these methods, various approaches have been proposed for mathematical modeling of the background part, but the...Show More

Enhancing Hyperspectral Anomaly Detection by Difference-of-Convex Sparse Anomaly Modeling

Koyo Sato;Kazuki Naganuma;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Robust Hyperspectral Anomaly Detection with Simultaneous Mixed Noise Removal via Constrained Convex Optimization

Koyo Sato;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Hyperspectral (HS) anomaly detection is the task of identifying pixels with spectral signatures that differ significantly from surrounding pixels. Most existing anomaly detection methods do not take into account the effect of noise in HS images, or if they do, it is only Gaussian noise. In practice, however, it is inevitable that non-Gaussian noise is superimposed on HS images due to sensor failur...Show More

Robust Hyperspectral Anomaly Detection with Simultaneous Mixed Noise Removal via Constrained Convex Optimization

Koyo Sato;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

A Force Recognition System for Distinguishing Click Responses of Various Objects

Koyo Sato;Sho Sakaino;Toshiaki Tsuji

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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This study proposes a technique for determining completion of robotic tasks considering a characteristics that some features on force responses are common among various objects. In particular, this paper focuses on the click response because its pattern is common to many objects, although the magnitude of the response differs depending on the object. A discriminator for detecting the click respons...Show More

A Force Recognition System for Distinguishing Click Responses of Various Objects

Koyo Sato;Sho Sakaino;Toshiaki Tsuji

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2021 | Conference Paper |

Contact Feature Recognition Based on MFCC of Force Signals

Toshiaki Tsuji;Koyo Sato;Sho Sakaino

IEEE Robotics and Automation Letters

Year: 2021 | Volume: 6, Issue: 3 | Journal Article |

Cited by: Papers (20)

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Contact-based tasks such as assembly and grinding often require the information on contact states. This letter therefore proposes a recognition method based on the Mel Frequency Cepstrum Coefficient (MFCC) of force signals. It demonstrates that the combination of MFCCs and time delayed neural networks is effective for learning features of contact events. This method is able to recognize instantane...Show More

Contact Feature Recognition Based on MFCC of Force Signals

Toshiaki Tsuji;Koyo Sato;Sho Sakaino

IEEE Robotics and Automation Letters

Year: 2021 | Volume: 6, Issue: 3 | Journal Article |

Success/Failure Identification of Skill Movement by Neural Network Using Force Information

Koyo Sato;Masahide Oikawa;Kyo Kutsuzawa;Sho Sakaino;Toshiaki Tsuji

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

Year: 2019 | Conference Paper |

Cited by: Papers (1)

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Currently, in the field of FA(Factory Automation), automation has been accomplished for only tasks with high reproducibility. Meanwhile, there remain a range of areas where tasks cannot reliably be performed due to their complexity or larger environmental variation. In these cases, the reliability of the task can be improved by recognizing the task failure through success/failure identifications a...Show More

Success/Failure Identification of Skill Movement by Neural Network Using Force Information

Koyo Sato;Masahide Oikawa;Kyo Kutsuzawa;Sho Sakaino;Toshiaki Tsuji

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

Year: 2019 | Volume: 1 | Conference Paper |

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