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Kyong-Jin Hwang - IEEE Xplore Author Profile

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Author details

Kyong-Jin Hwang

Publications

1

Citations

8

Publications by Year

20082008

Co-Authors:

Won-Ho ChoiIn-Shik HanHee-Hwan JiDae-Byung KimHeui-Seung Lee

Show All Co-Authors (9)

Kyong-Jin Hwang

Affiliation

Department of Electronics Engineering

Chungnam National University

Daejeon, South Korea

Publication Topics

Carrier Mobility,
Charge Density,
Compressive Strain,
Compressive Stress,
Device Degradation,
Device Performance,
Device Reliability,
Effective Mobility,
Effects Of Stress,
Film Stress,
Fourier Transform Infrared,
Gate Oxide

Biography

Kyong-Jin Hwang received the B.S. degree in electronic engineering from Kyonggi University, Suwon, Korea, in 2001, and the M.S. degree in electronic engineering from Chungnam National University, Daejeon, Korea, in 2006.,In 2000, he was with the Semiconductor System IC R&D Center, Hynix, Inc., Choongbuk, Korea. From 2001 to 2007, he worked on the process development in Hynix and the MagnaChip for logic devices and CMOS image sensor. He is currently with the MagnaChip Semiconductor Ltd., Chongju, Korea. His current research interests include electrostatic-discharge protection network and devices for display drive IC.(Based on document published on 20 May 2008).

Publications

1

Citations

8

Publications by Year

20082008

Co-Authors:

Won-Ho Choi
In-Shik Han
Hee-Hwan Ji
Dae-Byung Kim
Heui-Seung Lee

Show All Co-Authors (9)

Author's Published Works

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Showing 1-8 of 8 results

Conferences (4)

Journals (3)

Magazines (1)

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Ze Yang Ding(8)
Junn Yong Loo(8)
Chee Pin Tan(8)
Surya Girinatha Nurzaman(4)
Vishnu Monn Baskaran(4)
School of Information Technology, Monash University Malaysia(3)
School of Engineering, Monash University Malaysia(3)
School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia(2)
School of Information Technology, Monash University Malaysia, Selangor, Malaysia(2)
School of Engineering and the School of Information Technology, Monash University Malaysia, Bandar Sunway, Malaysia(1)
2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(2)
2023 IEEE International Conference on Robotics and Automation (ICRA)(1)
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
IEEE Instrumentation & Measurement Magazine(1)
IEEE Robotics and Automation Letters(1)
IEEE(8)
Media(3)
Kuching, Malaysia(2)
Hyderabad, India(1)
London, United Kingdom(1)
Neural Network(5)
Recurrent Neural Network(5)
Deep Learning(4)
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ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing

Hwa Hui Tew;Fan Ding;Gaoxuan Li;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges. In light of this, we propose a deep spatio-temporal hypergraph convolutional neural network for soft sensing (ST-HCSS). In particular, our proposed framework is able to construct and ...Show More

ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing

Hwa Hui Tew;Fan Ding;Gaoxuan Li;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Cross-Domain Transfer Learning Using Attention Latent Features for Multi-Agent Trajectory Prediction

Jia Quan Loh;Xuewen Luo;Fan Ding;Hwa Hui Tew;Junn Yong Loo;Ze Yang Ding;Susilawati Susilawati;Chee Pin Tan

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

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With the advancements of sensor hardware, traf-fic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligent transportation systems. However, existing solutions are often tailored to specific traffic networks at particular time periods. Consequently, deep learning models trained on one network may struggle to generalize effe...Show More

Cross-Domain Transfer Learning Using Attention Latent Features for Multi-Agent Trajectory Prediction

Jia Quan Loh;Xuewen Luo;Fan Ding;Hwa Hui Tew;Junn Yong Loo;Ze Yang Ding;Susilawati Susilawati;Chee Pin Tan

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes

Hwa Hui Tew;Gaoxuan Li;Fan Ding;Xuewen Luo;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

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Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process variables. To tackle these challenges, we present a framework known as a Knowledg...Show More

KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes

Hwa Hui Tew;Gaoxuan Li;Fan Ding;Xuewen Luo;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

Sigma-Point Kalman Filter With Nonlinear Unknown Input Estimation via Optimization and Data-Driven Approach for Dynamic Systems

Junn Yong Loo;Ze Yang Ding;Vishnu Monn Baskaran;Surya Girinatha Nurzaman;Chee Pin Tan

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 10 | Journal Article |

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Most works on joint state and unknown input (UI) estimation require the assumption that the UIs are linear; this is potentially restrictive as it does not hold in many intelligent autonomous systems. To overcome this restriction and circumvent the need to linearize the system, we propose a derivative-free UI sigma-point Kalman filter (SPKF-nUI), where the SPKF is interconnected with a general nonl...Show More

Sigma-Point Kalman Filter With Nonlinear Unknown Input Estimation via Optimization and Data-Driven Approach for Dynamic Systems

Junn Yong Loo;Ze Yang Ding;Vishnu Monn Baskaran;Surya Girinatha Nurzaman;Chee Pin Tan

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 10 | Journal Article |

Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework

Shageenderan Sapai;Junn Yong Loo;Ze Yang Ding;Chee Pin Tan;Raphaël C.-W. Phan;Vishnu Monn Baskaran;Surya Girinatha Nurzaman

2023 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are necessary for deep models to perform effectively, which requires exhaustive and quality data collection, particularly of state labels. Consequently, obtaining labelled state data for soft robotic systems is challenged f...Show More

Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework

Shageenderan Sapai;Junn Yong Loo;Ze Yang Ding;Chee Pin Tan;Raphaël C.-W. Phan;Vishnu Monn Baskaran;Surya Girinatha Nurzaman

2023 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2023 | Conference Paper |

A Zero-Shot Soft Sensor Modeling Approach Using Adversarial Learning for Robustness Against Sensor Fault

Ze Yang Ding;Junn Yong Loo;Surya Girinatha Nurzaman;Chee Pin Tan;Vishnu Monn Baskaran

IEEE Transactions on Industrial Informatics

Year: 2023 | Volume: 19, Issue: 4 | Journal Article |

Cited by: Papers (17)

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Soft sensors are widely used in many industrial systems to monitor key variables that are difficult to measure, using measurements from other available physical sensors. Because physical sensors are susceptible to faults, it is crucial for soft sensor models to be robust against them. Recently, deep learning has shown promising results in developing data-driven soft sensors for various application...Show More

A Zero-Shot Soft Sensor Modeling Approach Using Adversarial Learning for Robustness Against Sensor Fault

Ze Yang Ding;Junn Yong Loo;Surya Girinatha Nurzaman;Chee Pin Tan;Vishnu Monn Baskaran

IEEE Transactions on Industrial Informatics

Year: 2023 | Volume: 19, Issue: 4 | Journal Article |

Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing

Ze Yang Ding;Junn Yong Loo;Vishnu Monn Baskaran;Surya Girinatha Nurzaman;Chee Pin Tan

IEEE Robotics and Automation Letters

Year: 2021 | Volume: 6, Issue: 2 | Journal Article |

Cited by: Papers (15)

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The mechanical compliance of soft robots comes at a cost of higher uncertainty in their sensing and perception, which deteriorates the accuracy of predictive models. Predictive uncertainty, which expresses the confidence behind model predictions, is necessary to compensate for the loss of accuracy in soft robot perceptive models. Nevertheless, developing a general framework to capture uncertaintie...Show More

Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing

Ze Yang Ding;Junn Yong Loo;Vishnu Monn Baskaran;Surya Girinatha Nurzaman;Chee Pin Tan

IEEE Robotics and Automation Letters

Year: 2021 | Volume: 6, Issue: 2 | Journal Article |

Future trends in I&M: Indirect sensing in soft robots using observers/filters

Chee Pin Tan;Surya Nurzaman;Junn Yong Loo;Ze Yang Ding

IEEE Instrumentation & Measurement Magazine

Year: 2020 | Volume: 23, Issue: 1 | Magazine Article |

Cited by: Papers (4)

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A note from Melanie Ooi: Soft robotics is a very new and interesting research area with many applications, and sensing in soft robots remains a big challenge. Our guest columnists from Monash University Malaysia, Dr. Chee Pin Tan and Dr. Surya Nurzaman, have expertise in state estimation and soft robotics, respectively. Together with their graduate research students, they share with us how state e...Show More

Future trends in I&M: Indirect sensing in soft robots using observers/filters

Chee Pin Tan;Surya Nurzaman;Junn Yong Loo;Ze Yang Ding

IEEE Instrumentation & Measurement Magazine

Year: 2020 | Volume: 23, Issue: 1 | Magazine Article |

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