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Ju-Ik Oh

Also published under: Ju-ik Oh

Publications

Citations

160

Publications by Year

20182024

Co-Authors:

ByungKuon AhnSoo-Chang ChaeHyunyoung ChoIl-Nam ChoGyeong Won Choi

Show All Co-Authors (37)

Ju-Ik Oh

Also published under: Ju-ik Oh

Affiliation

Korea Research Institute of Standards and Science (KRISS), Daejeon, Korea

Publication Topics

Antenna Array,
Patch Antenna,
Radiation Pattern,
Peak Gain,
Beampattern,
Phased Array Antenna,
Beam Steering,
Phased Array,
Antenna Characteristics,
Antenna Design,
Array Factor,
Azimuth Angle

Biography

Ju-Ik Oh received the B.S. degree in electronic engineering from Inha University, Incheon, Republic of Korea, in 2017, and the M.S. degree in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, in 2019, where he is currently pursuing the Ph.D. degree in electrical engineering.,His current research interests include millimeter-wave antennas, RF front-end design, and antenna array systems.(Based on document published on 28 May 2021).

Publications

Citations

160

Publications by Year

20182024

Co-Authors:

ByungKuon Ahn
Soo-Chang Chae
Hyunyoung Cho
Il-Nam Cho
Gyeong Won Choi

Show All Co-Authors (37)

Author's Published Works

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Lei Cheng(2)
Yik-Chung Wu(2)
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National and Kapodistrian University of Athens, Athens, Greece(1)
Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking (IPCAN), Hangzhou, China(1)
Shenzhen Research Institute of Big Data, Shenzhen, China(1)
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China(1)
Department of Electronic Systems, Aalborg University, Aalborg, Denmark(1)
2023 31st European Signal Processing Conference (EUSIPCO)(1)
IEEE Transactions on Signal Processing(1)
IEEE(2)
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Helsinki, Finland(1)
Bayesian Inference(2)
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Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data

Zhongtao Chen;Lei Cheng;Yik-Chung Wu

2023 31st European Signal Processing Conference (EUSIPCO)

Year: 2023 | Conference Paper |

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To analyze irregular multi-dimensional data with unaligned dimensions, which frequently appear in real-world signal processing and machine learning tasks, parallel factor analysis 2 (PARAFAC2) has become the state-of-the-art (SOTA) tensor model that yields interpretable learning results. Like other tensor decomposition models, tensor rank learning in PARAFAC2 is vital to overcome overfitting/under...Show More

Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data

Zhongtao Chen;Lei Cheng;Yik-Chung Wu

2023 31st European Signal Processing Conference (EUSIPCO)

Year: 2023 | Conference Paper |

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior

Lei Cheng;Zhongtao Chen;Qingjiang Shi;Yik-Chung Wu;Sergios Theodoridis

IEEE Transactions on Signal Processing

Year: 2022 | Volume: 70 | Journal Article |

Cited by: Papers (27)

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Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential yet challenging problem. In particular, since thetensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a t...Show More

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior

Lei Cheng;Zhongtao Chen;Qingjiang Shi;Yik-Chung Wu;Sergios Theodoridis

IEEE Transactions on Signal Processing

Year: 2022 | Volume: 70 | Journal Article |

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