Academic Profile

Yuting Bai received the Ph.D. degree in control science and engineering from Beijing Institute of Technology, the M.S. degree in management science and engineering from Beijing Technology and Business University, and the B.S. degree in automation from Beijing Technology and Business University. He is now an associate professor in Beijing Technology and Business University. His research mainly covers information fusion, machine learning and decision-making method.

Editorial Roles

No Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

ICCK Publications

Total Publications: 3
Free Access | Research Article | 08 January 2026
Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles
ICCK Transactions on Machine Intelligence | Volume 2, Issue 1: 38-52, 2026 | DOI: 10.62762/TMI.2025.444910
Abstract
To address the challenge of effectively leveraging multi-source data for automated operational assessment of Unmanned Surface Vehicles (USVs) and utilizing digital technologies for monitoring and control, this paper proposes a data-driven state assessment method for surface unmanned systems and develops a digital twin system tailored for USVs. First, a dual-channel feature modeling mechanism is constructed by integrating physically interpretable statistical features with temporal convolutional features. Second, a complementary modeling strategy is adopted using CatBoost for static classification and GRU for dynamic modeling, while a Covariance Intersection (CI) fusion strategy is introduced... More >

Graphical Abstract
Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles
Open Access | Research Article | 22 March 2025 | Cited: 1 , Scopus 1
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
Chinese Journal of Information Fusion | Volume 2, Issue 1: 38-58, 2025 | DOI: 10.62762/CJIF.2025.919344
Abstract
With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >

Graphical Abstract
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
Open Access | Research Article | 08 June 2024 | Cited: 4 , Scopus 4
GPS Tracking Based on Stacked-Serial LSTM Network
Chinese Journal of Information Fusion | Volume 1, Issue 1: 50-62, 2024 | DOI: 10.62762/CJIF.2024.361889
Abstract
Maneuvering target tracking, as a core task in multi-sensor information fusion, is widely used in unmanned vehicles, missile navigation, and underwater ship localization, where real-time and robust state estimation is critical. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a Bayesian-inspired stacked LSTM fusion network (SLSTM) for uncertain motion characteristics. The network consists of two LSTM fusion networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is us... More >

Graphical Abstract
GPS Tracking Based on Stacked-Serial LSTM Network