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Toward Solving Domain Adaptation with Limited Source Labeled Data
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-6420-8316
Halmstad University, School of Information Technology.ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology.ORCID iD: 0000-0003-3272-4145
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2023 (English)In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW) / [ed] Jihe Wang; Yi He, Thang N. Dinh; Christan Grant; Meikang Qiu; Witold Pedrycz, Piscataway, NJ: IEEE Computer Society, 2023, p. 1240-1246Conference paper, Published paper (Refereed)
Abstract [en]

The success of domain adaptation relies on high-quality labeled data from the source domain, which is a luxury setup for applied machine learning problems. This article investigates a particular challenge: the source labeled data are neither plentiful nor sufficiently representative. We studied the challenge of limited data with an industrial application, i.e., forklift truck activity recognition. The task is to develop data-driven methods to recognize forklift usage performed in different warehouses with a large scale of signals collected from the onboard sensors. The preliminary results show that using pseudo-labeled data from the source domain can significantly improve classification performance on the target domain in some tasks. As the real-world problems are much more complex than typical research settings, it is not clearly understood in what circumstance the improvement may occur. Therefore, we provided discussions regarding this phenomenon and shared several inspirations on the difficulty of understanding and debugging domain adaptation problems in practice. © 2023 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2023. p. 1240-1246
Series
Proceedings ... ICDM workshops, E-ISSN 2375-9259
Keywords [en]
Activity Recognition, DANN, Domain Adaptation, Limited Data, Pseudo-label, Time-Series
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-52854DOI: 10.1109/ICDMW60847.2023.00161ISI: 001164077500152Scopus ID: 2-s2.0-85186143362ISBN: 9798350381641 (electronic)OAI: oai:DiVA.org:hh-52854DiVA, id: diva2:1843333
Conference
23rd IEEE International Conference on Data Mining Workshops (ICDMW 2023), Shanghai, China, 1-4 December, 2023
Funder
Knowledge Foundation
Note

Funding: The Knowledge Foundation, Halmstad University, and Toyota Material Handling Europe

Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-07-01Bibliographically approved
In thesis
1. Learning Representations for Forklift Activity Recognition
Open this publication in new window or tab >>Learning Representations for Forklift Activity Recognition
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Machine Activity Recognition (MAR) is a research topic that focuses on the development of data-driven methods to improve productivity monitoring. The application and the perspective of MAR research jointly influence the diffi- culty of a MAR problem. Unlike previous MAR works, which have studied construction machinery from the viewpoint of the user, this project focuses on logistics equipment from the viewpoint of the original equipment manufac- turer. In terms of the application, forklift trucks have flexible functions and complex usage. The former is an intrinsic characteristic, as forklifts are me- chanically agile, and the latter is an extrinsic factor, as forklift usage can vary greatly with different drivers, loads, work shifts, and warehouse environments. As for the standpoint, manufacturers have customers who use their products all over the world. Studying a single machine or machines in a single site, i.e. the conventional MAR setting, cannot provide a general understanding of the equipment usage. Therefore, existing MAR methods with external sensory data and only supervised learning techniques are impractical in this case.

This thesis investigates learning representation-based methods for recog- nizing forklift routine activities using on-board sensory signals. Three methods are developed to capture important data features to overcome the challenges of forklift MAR. First, by pre-training autoencoders with unlabeled data and then fine-tuning them with pseudo-labeled data, discriminative features can be ex- tracted. Classifiers built on these features can outperform conventional MAR solutions that use only the labeled data. Second, training gated recurrent unit networks to recognize activities in different contexts can help to learn a repre- sentation that captures activities and their transitions, which further improves the MAR result. Third, implementing domain adversarial-training neural net- works with pseudo-labeled data can essentially compensate for the limited la- beled data from source domains, leading to representations that are informative for more than one domain. In addition, testing the full method on a real truck has demonstrated the applicability of the proposed method and the feasibility of an online MAR solution.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2024. p. 33
Series
Halmstad University Dissertations ; 119
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-54111 (URN)978-91-89587-55-7 (ISBN)978-91-89587-54-0 (ISBN)
Public defence
2024-08-30, S3030, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2024-07-12 Created: 2024-06-26 Last updated: 2024-07-31Bibliographically approved

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Chen, KunruRögnvaldsson, ThorsteinnNowaczyk, SławomirPashami, Sepideh

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