Multi-task Learning for Dependability Assessment of Industrial Wireless Communication Systems⁎This work was supported by a Grant from the Key Research and Development Program of Zhejiang Province(No.2021C04018). Issue 4 (2021)

• Record Type: Journal Article
• Authors: Sun, Danfeng; Rauchhaupt, Lutz; Jumar, Ulrich
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 4(2021)
• Page Start: 165
• Page End: 170
• Publication Date: 2021
• Subjects: Machine leaning -- multi-task learning -- sequence-to-sequence learning -- dependability assessment -- wireless communication system; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.10.028
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Wireless communication systems for the industrial domain will rapidly grow in the forthcoming years, which is bringing a strong need for quantitative dependability assessment. Empirical formulas and artificial intelligence have been applied in dependability assessment for years. However, these methods always focused on a single task, while multiple tasks are common and have inner relationships, such as dependability scoring and prediction. These relationships are useful and can improve every task performance. Hence, we propose the multi-task learning model to solve three tasks: dependability scoring, prediction, and scoring prediction, where we design a denoising sequence to sequence neural network with a decay attention. To validate the novelty, we measured and collected a dataset from a realistic system, and then we compared the multi-task learning results of three tasks with benchmarks, which indicates superiority.