Tingli Su received her B.E. degree in Mechatronic Engineering and the Ph.D. degree in the direction of Control Science and Engineering from Beijing Institute of Technology, Beijing, China, in 2007 and 2013. During the period of 2009.10-2012.9, she had a total of 2 years and a half working as an academic collaborator in University of Bristol, U.K. and finished most of her Ph.D. research there. Since 2013 she has been with School of Computer and Information Engineering, Beijing Technology and Business University as a Lecturer, and was promoted to be the Associate Professor in October, 2018. Her research interests include multi-sensor fusion, statistical signal processing, robust filtering, Bayesian theory, target tracking and dynamic analysis. In particular, her present major interest is multi-sensor fusion, Bayesian estimation and big data tendency analysis.
This paper focuses on the needs of automation professional talent cultivation in the context of the construction of new engineering disciplines, and takes the course “Freshman Engineering Experience” as the research object, and carries out a systematic exploration of teaching reform in response to the problems of insufficient professional cognition and disconnection between theory and practice that exist in the current engineering education. By restructuring the curriculum system, innovating teaching methods and optimizing the practice platform, a progressive cultivation mode of “Cognition-Practice-Innovation” has been constructed. In curriculum design, the combination of professiona... More >
The Tuned Liquid Damper (TLD) method offers a practical and cost-effective solution for seismic design. Accurate modeling of the TLD system’s dynamic behavior is crucial for optimizing its performance. In this study, the nonlinear dynamics of the TLD system are characterized using the Housner model, with parameters estimated via a nonlinear state estimation approach. To address challenges associated with model discretization and unknown noise processes, we introduce a Robust Extended Kalman Filter (REKF) that incrementally incorporates uncertainties to more accurately capture system dynamics. The proposed method is evaluated through real-time hybrid simulation, employing seismic input sign... More >
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
Open Access
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Research Article
| 08 June 2024
| Cited:
4
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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
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