https://www.mdu.se/

mdu.sePublications
Change search
Link to record
Permanent link

Direct link
Alternative names
Publications (10 of 105) Show all publications
Andersson, T., Bohlin, M., Ahlskog, M. & Olsson, T. (2024). Interpretable ML model for quality control of locks using counterfactual explanations. In: : . Paper presented at 2024 8th International Conference on Artificial Intelli-gence, Automation and Control Technologies (AIACT 2024). , Article ID 7.
Open this publication in new window or tab >>Interpretable ML model for quality control of locks using counterfactual explanations
2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an interpretable machinelearning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate anapproach that can improve interpretability of anomalousclassifications obtained from an anomaly detection model. Weevaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as ourbaseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations andreduces computational cost. The explanations are presentedtogether with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approachesemploying optimization techniques yielded explanations withgreater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.

Keywords
—Explainable artificial intelligence, Counterfactual explanation, Anomaly detection, Principal component analysis
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-66503 (URN)
Conference
2024 8th International Conference on Artificial Intelli-gence, Automation and Control Technologies (AIACT 2024)
Funder
Knowledge Foundation, No 20200132 01 H
Note

In press

Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2024-04-26Bibliographically approved
Andersson, T., Bohlin, M., Ahlskog, M. & Olsson, T. (2024). Interpretable ML model for quality control of locks using counterfactual explanations. In: : . Paper presented at 2024 8th International Conference on Artificial Intelli-gence, Automation and Control Technologies (AIACT 2024). , Article ID 7.
Open this publication in new window or tab >>Interpretable ML model for quality control of locks using counterfactual explanations
2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an interpretable machinelearning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate anapproach that can improve interpretability of anomalousclassifications obtained from an anomaly detection model. Weevaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as ourbaseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations andreduces computational cost. The explanations are presentedtogether with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approachesemploying optimization techniques yielded explanations withgreater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.

Keywords
—Explainable artificial intelligence, Counterfactual explanation, Anomaly detection, Principal component analysis
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-66504 (URN)
Conference
2024 8th International Conference on Artificial Intelli-gence, Automation and Control Technologies (AIACT 2024)
Funder
Knowledge Foundation, No 20200132 01 H
Note

In press

Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2024-04-26Bibliographically approved
Wickberg, P., Fattouh, A., Afshar, S. Z. & Bohlin, M. (2023). Adopting a Digital Twin Framework for Autonomous Machine Operation at Construction Sites. In: Proc. CAA Int. Conf. Veh. Control Intell., CVCI: . Paper presented at Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Adopting a Digital Twin Framework for Autonomous Machine Operation at Construction Sites
2023 (English)In: Proc. CAA Int. Conf. Veh. Control Intell., CVCI, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous machines are expected to be vastly used at construction sites as they can efficiently perform repetitive and dangerous tasks. However, ensuring the operational safety of such autonomous machines in a highly dynamic environment is challenging. Although autonomous machines usually are equipped with a perception system that permits them to navigate locally, there is a need to share a global view of the construction site to reduce the risk of accidents or errors. A digital twin of the construction site map has the potential of fusing the real-time perception from different sources at the site, such as different autonomous machines working at the construction site, analysing them and sharing the needed information to operate safely and effectively at the site. This paper proposes the adoption of the recently published standard, ISO 23247 digital twin framework for manufacturing, to implement and maintain a dynamic map of construction sites. The proposed framework will enable safe and efficient operation of autonomous machines on construction sites.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Construction site, Digital twin, Maps, Operational Design Domain, Safety, Traversability
National Category
Civil Engineering
Identifiers
urn:nbn:se:mdh:diva-66153 (URN)10.1109/CVCI59596.2023.10397254 (DOI)2-s2.0-85185388766 (Scopus ID)9798350340488 (ISBN)
Conference
Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023
Available from: 2024-02-28 Created: 2024-02-28 Last updated: 2024-02-28Bibliographically approved
Helali Moghadam, M., Borg, M., Saadatmand, M., Mousavirad, S. J., Bohlin, M. & Lisper, B. (2023). Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing. Journal of Software: Evolution and Process
Open this publication in new window or tab >>Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing
Show others...
2023 (English)In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481Article in journal (Refereed) Published
Abstract [en]

This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.

Place, publisher, year, edition, pages
John Wiley and Sons Ltd, 2023
Keywords
advanced driver assistance systems, deep learning, evolutionary computation, lane-keeping system, machine learning testing, search-based testing, Automobile drivers, Biomimetics, Budget control, Deep neural networks, Embedded systems, Genetic algorithms, Learning systems, Particle swarm optimization (PSO), Software testing, Case-studies, Lane keeping, Machine-learning, Software Evolution, Software process, Test scenario
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-63851 (URN)10.1002/smr.2591 (DOI)001021376500001 ()2-s2.0-85163167144 (Scopus ID)
Available from: 2023-07-12 Created: 2023-07-12 Last updated: 2023-07-19Bibliographically approved
Bashir, S., Abbas, M., Saadatmand, M., Enoiu, E. P., Bohlin, M. & Lindberg, P. (2023). Requirement or Not, That is the Question: A Case from the Railway Industry. In: Lecture Notes In Computer Science: . Paper presented at 29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023, Barcelona, Spain, 17-20 April, 2023 (pp. 105-121). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Requirement or Not, That is the Question: A Case from the Railway Industry
Show others...
2023 (English)In: Lecture Notes In Computer Science, Springer Science and Business Media Deutschland GmbH , 2023, p. 105-121Conference paper, Published paper (Refereed)
Abstract [en]

[Context and Motivation] Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. [Question/problem] Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. [Principal ideas/results] We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. [Contribution] There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 13975 LNCS
Keywords
NLP, Requirements classification, Requirements identification, tender documents, Deep learning, Information retrieval systems, Natural language processing systems, Requirements engineering, Risk perception, Text processing, Bidding process, F1 scores, Human errors, Manual identification, Public dataset, Railway industry, Requirement identification, Requirements classifications, State-of-the-art approach, Classification (of information)
National Category
Software Engineering
Identifiers
urn:nbn:se:mdh:diva-62331 (URN)10.1007/978-3-031-29786-1_8 (DOI)001210623500008 ()2-s2.0-85152587069 (Scopus ID)9783031297854 (ISBN)
Conference
29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023, Barcelona, Spain, 17-20 April, 2023
Available from: 2023-04-26 Created: 2023-04-26 Last updated: 2024-06-05Bibliographically approved
Andersson, T., Ahlskog, M., Olsson, T. & Bohlin, M. (2023). Sample size prediction for anomaly detection in locks. In: Procedia CIRP: . Paper presented at Procedia CIRP (pp. 870-874). Elsevier B.V.
Open this publication in new window or tab >>Sample size prediction for anomaly detection in locks
2023 (English)In: Procedia CIRP, Elsevier B.V. , 2023, p. 870-874Conference paper, Published paper (Refereed)
Abstract [en]

Artificial intelligence in manufacturing systems is currently most used for quality control and predictive maintenance. In the lock industry, quality control of final assembled cylinder lock is still done by hand, wearing out the operators' wrists and introducing subjectivity which negatively affects reliability. Studies have shown that quality control can be automated using machine-learning to analyse torque measurements from the locks. The resulting performance of the approach depends on the dimensionality and size of the training dataset but unfortunately, the process of gathering data can be expensive so the amount collected data should therefore be minimized with respect to an acceptable performance measure. The dimensionality can be reduced with a method called Principal Component Analysis and the training dataset size can be estimated by repeated testing of the algorithms with smaller datasets of different sizes, which then can be used to extrapolate the expected performance for larger datasets. The purpose of this study is to evaluate the state-of-the-art methods to predict and minimize the needed sample size for commonly used machine-learning algorithms to reach an acceptable anomaly detection accuracy using torque measurements from locks. The results show that the learning curve with the best fit to the training data does not always give the best predictions. Instead, performance depends on the amount of data used to create the curve and the particular machine-learning algorithm used. Overall, the exponential and power-law functions gave the most reliable predictions and the use of principal component analysis greatly reduced the learning effort for the machine-learning algorithms. With torque measurements from 50-150 locks, we predicted a detection accuracy of over 95% while the current method of using the human tactile sense gives only 16% accuracy.

Place, publisher, year, edition, pages
Elsevier B.V., 2023
Keywords
Anomaly detection, Learning curves, Machine learning, Quality control, Sample size prediction
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-66095 (URN)10.1016/j.procir.2023.09.090 (DOI)2-s2.0-85184582755 (Scopus ID)
Conference
Procedia CIRP
Available from: 2024-02-20 Created: 2024-02-20 Last updated: 2024-04-26Bibliographically approved
Hogdahl, J. & Bohlin, M. (2022). A Combined Simulation-Optimization Approach for Robust Timetabling on Main Railway Lines. Transportation Science
Open this publication in new window or tab >>A Combined Simulation-Optimization Approach for Robust Timetabling on Main Railway Lines
2022 (English)In: Transportation Science, ISSN 0041-1655, E-ISSN 1526-5447Article in journal (Refereed) Epub ahead of print
Abstract [en]

Performance aspects such as travel time, punctuality, and robustness are conflicting goals of utmost importance for railway transports. To successfully plan railway traffic, it is therefore important to strike a balance between planned travel times and expected delays. In railway operations research, a lot of attention has been given to construct models and methods to generate robust timetables-that is, timetables with the potential to withstand design errors, incorrect data, and minor everyday disturbances. Despite this, the current state of practice in railway planning is to construct timetables manually, possibly with support of microsimulation for robustness evaluation. This paper aims to narrow the gap between the state-of-the-art optimization-based research approaches and the current state of practice to construct timetables by combining simulation and optimization. The paper proposes a combined simulation-optimization approach for double-track lines, which generalizes previous work to allow full flexibility in the order of trains by including a new and more generic model to predict delays. By utilizing delay data from simulation, the approach can make socioeconomically optimal modifications of a given timetable by minimizing predicted disutility-the weighted sum of scheduled travel time and total predicted delay. In a large simulation experiment on the heavily congested Swedish Western Main Line, it is demonstrated that compared with a real-life, manually constructed timetable, large reductions of delays as well as improvements in punctuality could be obtained for a small cost of marginally longer travel times. The cost of scheduled in-vehicle travel time and mean delay was reduced by 5% on average, representing a large improvement for a highly utilized railway line. Furthermore, a separate scaling experiment indicates that the approach can also be suitable for larger problems.

Place, publisher, year, edition, pages
INFORMS, 2022
Keywords
timetabling, train scheduling, delay prediction, punctuality, railroad
National Category
Computer Engineering
Identifiers
urn:nbn:se:mdh:diva-60036 (URN)10.1287/trsc.2022.1158 (DOI)000854172900001 ()2-s2.0-85150301044 (Scopus ID)
Available from: 2022-10-21 Created: 2022-10-21 Last updated: 2023-04-12Bibliographically approved
Helali Moghadam, M., Saadatmand, M., Borg, M., Bohlin, M. & Lisper, B. (2022). An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning. Software quality journal, 127-159
Open this publication in new window or tab >>An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
Show others...
2022 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, p. 127-159Article in journal (Refereed) Published
Abstract [en]

Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learnt by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learnt policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments on a simulated environment, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process, and performs adaptively without access to source code and performance models.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Performance testing, Stress testing, Test case generation, Reinforcement learning, Autonomous testing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-47471 (URN)10.1007/s11219-020-09532-z (DOI)000627215600001 ()2-s2.0-85102446552 (Scopus ID)
Available from: 2020-04-06 Created: 2020-04-06 Last updated: 2023-09-13Bibliographically approved
Andersson, T., Bohlin, M., Olsson, T. & Ahlskog, M. (2022). Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks. In: IFIP Advances in Information and Communication Technology: WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022. Paper presented at WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022, Gyenongju, South Korea, 25-29 September, 2022 (pp. 27-34). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
2022 (English)In: IFIP Advances in Information and Communication Technology: WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022, Springer Science and Business Media Deutschland GmbH , 2022, p. 27-34Conference paper, Published paper (Refereed)
Abstract [en]

Historically, cylinder locks’ quality has been tested manually by human operators after full assembly. The frequency and the characteristics of the testing procedure for these locks wear the operators’ wrists and lead to varying results of the quality control. The consistency in the quality control is an important factor for the expected lifetime of the locks which is why the industry seeks an automated solution. This study evaluates how consistently the operators can classify a collection of locks, using their tactile sense, compared to a more objective approach, using torque measurements and Machine Learning (ML). These locks were deliberately chosen because they are prone to get inconsistent classifications, which means that there is no ground truth of how to classify them. The ML algorithms were therefore evaluated with two different labeling approaches, one based on the results from the operators, using their tactile sense to classify into ‘working’ or ‘faulty’ locks, and a second approach by letting an unsupervised learner create two clusters of the data which were then labeled by an expert using visual inspection of the torque diagrams. The results show that an ML-solution, trained with the second approach, can classify mechanical anomalies, based on torque data, more consistently compared to operators, using their tactile sense. These findings are a crucial milestone for the further development of a fully automated test procedure that has the potential to increase the reliability of the quality control and remove an injury-prone task from the operators.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2022
Keywords
Binary classification, Cylinder lock, Machine learning, Multiple experts, Torque data, Cylinders (shapes), Learning algorithms, Quality assurance, Quality control, Torque, Expected lifetime, Human abilities, Human operator, Machine-learning, Multiple expert, Tactile sense, Testing procedure, Locks (fasteners)
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-60550 (URN)10.1007/978-3-031-16407-1_4 (DOI)000869718800004 ()2-s2.0-85140472723 (Scopus ID)9783031164064 (ISBN)
Conference
WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022, Gyenongju, South Korea, 25-29 September, 2022
Available from: 2022-11-03 Created: 2022-11-03 Last updated: 2024-04-26Bibliographically approved
Wickberg, P., Fattouh, A., Afshar, S., Sjöberg, J. & Bohlin, M. (2022). Dynamic Maps Requirements for Autonomous Navigation in Construction Sites. In: The 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA22): . Paper presented at The 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA22), 27-29 December, 2022.
Open this publication in new window or tab >>Dynamic Maps Requirements for Autonomous Navigation in Construction Sites
Show others...
2022 (English)In: The 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA22), 2022Conference paper, Published paper (Refereed)
Abstract [en]

Construction sites are a special kind of off-road environment that needs dedicated dynamic maps to enable autonomous navigation in such terrains. In this paper, challenges for autonomous navigation on construction sites are first identified. Later, requirements for dynamic maps for autonomous navigation on construction sites are proposed based on the identified challenges.

Series
International Conference on Communications Signal Processing and their Applications ICCSPA, ISSN 2377-682X
Keywords
Construction sites, autonomous navigation, autonomous machines, off-road, map creation, map update
National Category
Engineering and Technology Control Engineering
Research subject
Computer Science; Innovation and Design
Identifiers
urn:nbn:se:mdh:diva-61191 (URN)10.1109/ICCSPA55860.2022.10019082 (DOI)000972628300028 ()2-s2.0-85147551963 (Scopus ID)
Conference
The 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA22), 27-29 December, 2022
Projects
TRUST-SOSIndTech
Available from: 2022-12-11 Created: 2022-12-11 Last updated: 2023-05-31Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1597-6738

Search in DiVA

Show all publications