ASME

Journal of Computing and Information Science in Engineering

May 2023 Spotlight: Data-Driven Sensor Selection for Signal Estimation of Vertical Wheel Forces in Vehicles

Share This Post

On May 19th, 10:30 AM (Eastern, US and Canada), Professor Mian Li (Shanghai Jiao Tong University) presented the May 2023 JCISE Spotlight Talk on his paper, Data-Driven Sensor Selection for Signal Estimation of Vertical Wheel Forces in Vehicles, ASME J. Comput. Inf. Sci. Eng. June 2023, 23(3): 031010, co-authored with Xueke Zheng, Ying Wang, Le Wang, Runze Cai, and Yu Qiu. 

Abstract

Sensor selection is one of the key factors that dictate the performance of estimating vertical wheel forces in vehicle durability design. To select K most relevant sensors among S candidate ones that best fit the response of one vertical wheel force, it has (S K)  possible choices to evaluate, which is not practical unless K or S is small. In order to tackle this issue, this paper proposes a data-driven method based on maximizing the marginal likelihood of the data of the vertical wheel force without knowing the dynamics of vehicle systems. Although the resulting optimization problem is a mixed-integer programming problem, it is relaxed to a convex problem with continuous variables and linear constraints. The proposed sensor selection method is flexible and easy to implement, and the hyper-parameters do not need to be tuned using additional validation data sets. The feasibility and effectiveness of the proposed method are verified using numerical examples and experimental data. In the results of different data sizes and model orders, the proposed method has better fitting performance than that of the group lasso method in the sense of the 2-norm based metric. Also, the computational time of the proposed method is much less than that of the enumeration-based method.

GO TO ASME DIGITAL COLLECTION

Visit the ASME Digital Collection archives for JCISE

More To Explore

Announcements

Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

Geometric information, such as three-dimensional (3D) shapes and network topologies, has been increasingly explored in manufacturing research. For example, characterizing geometric information in 3D-printed parts, in-situ or ex-situ, opens opportunities for defect detection, quality improvement, and product customization. However, geometric data mining remains critically challenging. Geometric information is embedded in complex data structures, such as 3D point clouds, graphs, meshes, voxels, high-dimensional images, and tensors, which possess challenges for analysis due to their high-dimensionality, high-volume, unstructured, multimodality characteristics. Additional challenges stem from compromised data quality (e.g., noisy and incomplete data), the need for registration, etc.

Announcements

2023 Reviewer’s Recognition

The Editor and Editorial Board of the Journal of Computing and Information Science in Engineering would like to thank all of the reviewers for volunteering their expertise and time reviewing manuscripts in 2023. Serving as reviewers for the journal is a critical service necessary to maintain the quality of our publication and to provide the authors with a valuable peer review of their work.

JCISE VIDEOS