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Publications (10 of 92) Show all publications
Barua, S., Ahmed, M. U., Ahlström, C., Begum, S. & Funk, P. (2017). Automated EEG Artifact Handling with Application in Driver Monitoring. IEEE journal of biomedical and health informatics, 22(5), 1350-1361
Open this publication in new window or tab >>Automated EEG Artifact Handling with Application in Driver Monitoring
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2017 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 5, p. 1350-1361Article in journal (Refereed) Published
Abstract [en]

Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Artifacts, Clustering, Electroencephalogram, Independent Component Analysis, Wavelet decomposition
National Category
Signal Processing
Identifiers
urn:nbn:se:mdh:diva-37347 (URN)10.1109/JBHI.2017.2773999 (DOI)000441795800003 ()2-s2.0-85035807991 (Scopus ID)
Projects
VDM - Vehicle Driver MonitoringSafeDriver: A Real Time Driver's State Monitoring and Prediction System
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2019-01-10Bibliographically approved
Tomasic, I., Andersson, A. & Funk, P. (2017). Mixed-Effect Models for the Analysis and Optimization of Sheet-Metal Assembly Processes. IEEE Transactions on Industrial Informatics, 13(5), 2194-2202, Article ID 7857795.
Open this publication in new window or tab >>Mixed-Effect Models for the Analysis and Optimization of Sheet-Metal Assembly Processes
2017 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 13, no 5, p. 2194-2202, article id 7857795Article in journal (Refereed) Published
Abstract [en]

Assembly processes can be affected by various parameters, which is revealed by the measured geometrical characteristics (GCs) of the assembled parts deviating from the nominal values. Here, we propose a mixed-effect model (MEM) application for the purposes of analyzing variations in assembly cells, as well as for screening the input variables and characterization. MEMs make it possible to take into account statistical dependencies that originate from repeated measurements on the same assembly. The desirability functions approach was used to describe how to find corrective or control actions based on the fitted MEM. Objectives: To examine the usefulness of the MEM between the positions of the in-going parts as the input controllable variables and the measured GCs as the outputs. Methods: The data from 34 car frontal cross members (each measured three times) were experimentally collected in a laboratory environment by intentionally changing the positions of the in-going parts, assembling the parts, and subsequently measuring their GCs. A single MEM that completely describes the assembly process was fitted between the GCs and the positions of the in-going parts. Results: We present a modeling technique that can be used to establish which measured GCs are influenced by which controllable variables, and how this occurs. The fitted MEM shows evidence that the variability of some GCs changes over time. The natural variation in the system (i.e., unmodeled variations) is about two times larger than the variation between the assembled cross members. We also present two cases that demonstrate how to use the fitted MEM desirability functions to find corrective or control actions. Conclusion: MEMs are very useful tools for analyzing the assembly processes for car-body parts, which are nonlinear processes with multiple inputs and multiple correlated outputs. MEMs can potentially be applied in numerous industrial processes, since modern manufacturing plants measure all important process variables, which is the sole prerequisite for MEMs applications. 

National Category
Control Engineering
Identifiers
urn:nbn:se:mdh:diva-37188 (URN)10.1109/TII.2017.2670062 (DOI)000412361900008 ()2-s2.0-85031672248 (Scopus ID)
Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2018-07-25Bibliographically approved
Tomasic, I., Erdem, I., Rahman, H., Andersson, A. & Funk, P. (2016). Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Process. In: Swedish Production Symposium 2016 SPS 2016: . Paper presented at Swedish Production Symposium 2016 SPS 2016, 25 Oct 2016, Lund, Sweden.
Open this publication in new window or tab >>Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Process
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2016 (English)In: Swedish Production Symposium 2016 SPS 2016, 2016Conference paper, Published paper (Refereed)
Abstract [en]

The assembly quality is affected by various factors within which fixture variations are the most important. For that reason significant research on fixture variations has already been done. In this work we propose a linear mixed models (LMMs) application for the purpose of analyzing sources of variation in the fixture Objective: To estimate the strength of influences of different sources of variation on the control and assembly fixtures. The variables considered are: time, operator, default pin positions, shifts from the default pin positions . Methods: The data was collected through assembly and measurement for repeatability and experimental corrective actions. We use LMMs to model the relation between features measured on the assembled parts and the input variables of interest. The LMMs allow taking into account the correlation of observations contained in the dataset. We also use graphical data presentation methods to explore the data. Results: The expected results are the strengths of influences of the individual variables considered, and the pairwise interactions of between the variables, on the assembled parts variations.

Keywords
Quality Control, Assembly Processes
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-37073 (URN)
Conference
Swedish Production Symposium 2016 SPS 2016, 25 Oct 2016, Lund, Sweden
Projects
AproC, Automated Process Control
Available from: 2017-10-26 Created: 2017-10-26 Last updated: 2017-10-26Bibliographically approved
Bergman, J. E. S., Bruhn, F., Funk, P., Isham, B., Rincon-Charris, A., Capo-Lugo, P. & Åhlen, L. (2015). Exploiting Artificial Intelligence for Analysis and Data Selection on-board the Puerto Rico CubeSat. In: : . Paper presented at European Planetary Science Congress 2015, held 27 September - 2 October, 2015 in Nantes, France.
Open this publication in new window or tab >>Exploiting Artificial Intelligence for Analysis and Data Selection on-board the Puerto Rico CubeSat
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2015 (English)Conference paper, Published paper (Refereed)
Keywords
Artificial Intelligence, Cube Satellite, Heterogoeneous Computing
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-35422 (URN)
Conference
European Planetary Science Congress 2015, held 27 September - 2 October, 2015 in Nantes, France
Projects
GIMME-SPACE
Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2017-06-12Bibliographically approved
Olsson, T., Xiong, N., Källström, E., Holst, A. & Funk, P. (2015). Fault Diagnosis via Fusion of Information from a Case Stream. In: Case-Based Reasoning Research and Development. Proceeding of the the 23th International Conference on Case-Based Reasoning (ICCBR-2015): . Paper presented at 23th International Conference on Case-Based Reasoning (ICCBR-2015), 28th September-30th September 2015, Frankfurt am Main, Germany (pp. 275-289).
Open this publication in new window or tab >>Fault Diagnosis via Fusion of Information from a Case Stream
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2015 (English)In: Case-Based Reasoning Research and Development. Proceeding of the the 23th International Conference on Case-Based Reasoning (ICCBR-2015), 2015, p. 275-289Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel approach to fault diagnosis applied to a stream of cases. The approach uses a combination of case-based reasoning and information fusion to do classification. The approach consists of two steps. First, we perform local anomaly detection on-board a machine to identify anomalous individual cases. Then, we monitor the stream of anomalous cases using a stream anomaly detector based on a sliding window approach. When the stream anomaly detector identifies an anomalous window, the anomalous cases in the window are classified using a CBR classifier. Thereafter, the individual classifications are aggregated into a composite case with a single prediction using a information fusion method. We compare three information fusion approaches: simple majority vote, weighted majority vote and Dempster-Shafer fusion. As baseline for comparison, we use the classification of the last identified anomalous case in the window as the aggregated prediction.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9343
Series
Lecture Notes in Artificial Intelligence
Keywords
Case-based Reasoning, Information Fusion, Anomaly Detection, Fault Diagnosis
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29085 (URN)10.1007/978-3-319-24586-7_19 (DOI)000367594000019 ()2-s2.0-84952023366 (Scopus ID)978-3-319-24585-0 (ISBN)
Conference
23th International Conference on Case-Based Reasoning (ICCBR-2015), 28th September-30th September 2015, Frankfurt am Main, Germany
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2018-01-11Bibliographically approved
Funk, P. (2015). Why hybrid case-based reasoning will change the future of health science and healthcare. In: CEUR Workshop Proceedings: . Paper presented at 23rd International Conference on Case-Based Reasoning, ICCBR 2015, 28 September 2015 through 30 September 2015 (pp. 199-204).
Open this publication in new window or tab >>Why hybrid case-based reasoning will change the future of health science and healthcare
2015 (English)In: CEUR Workshop Proceedings, 2015, p. 199-204Conference paper, Published paper (Refereed)
Abstract [en]

The rapid development of the medical field makes it impossible even for experts in the field to keep up with new treatments and experience. Already in 2010 all medical knowledge doubled in 3,5 years, to keep up to date with all development even in a narrow field is today far beyond human capacity. The need for decision support is increasingly important to ensure optimal treatment of patients, especially if patients are not "standard patients" matching a gold standard treatment. By ensuring confidentiality and collecting structured cases on a large scale will enable clinical decision support far beyond what is possible today and will be a major leap in healthcare. 

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-31467 (URN)2-s2.0-84962616124 (Scopus ID)
Conference
23rd International Conference on Case-Based Reasoning, ICCBR 2015, 28 September 2015 through 30 September 2015
Available from: 2016-04-22 Created: 2016-04-22 Last updated: 2016-04-22Bibliographically approved
Begum, S., Barua, S., Ahmed, M. U. & Funk, P. (2014). A Fusion Based System for Physiological Sensor Signal Classification. In: Medicinteknikdagarna 2014 MTD10: . Paper presented at Medicinteknikdagarna 2014 MTD10, 14-16 Oct 2014, Göteborg, Sweden.
Open this publication in new window or tab >>A Fusion Based System for Physiological Sensor Signal Classification
2014 (English)In: Medicinteknikdagarna 2014 MTD10, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

Keywords
Sensor FusionPhysiological DataMultivariate Multiscale Entropy AnalysisCase-Based Reasoning
National Category
Medical Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-28144 (URN)
Conference
Medicinteknikdagarna 2014 MTD10, 14-16 Oct 2014, Göteborg, Sweden
Projects
VDM - Vehicle Driver Monitoring
Available from: 2015-06-08 Created: 2015-06-08 Last updated: 2017-01-25Bibliographically approved
Olsson, T., Gillblad, D., Funk, P. & Xiong, N. (2014). Case-Based Reasoning for Explaining Probabilistic Machine Learning. International Journal of Computer Science & Information Technology (IJCSIT), 6(2), 87-101
Open this publication in new window or tab >>Case-Based Reasoning for Explaining Probabilistic Machine Learning
2014 (English)In: International Journal of Computer Science & Information Technology (IJCSIT), ISSN 0975-4660, E-ISSN 0975-3826, Vol. 6, no 2, p. 87-101Article in journal (Refereed) Published
Abstract [en]

This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

Keywords
Case-based Reasoning, Case-based Explanation, Artificial Intelligence, Decision Support, Machine Learning
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-26442 (URN)10.5121/ijcsit (DOI)
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsCREATE ITEA2
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved
Olsson, T., Gillblad, D., Funk, P. & Xiong, N. (2014). Explaining probabilistic fault diagnosis and classification using case-based reasoning. In: Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings. Paper presented at 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. (pp. 360-374).
Open this publication in new window or tab >>Explaining probabilistic fault diagnosis and classification using case-based reasoning
2014 (English)In: Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings, 2014, p. 360-374Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8765
Keywords
Case-based Explanation, Classification, Machine Learning, Artificial intelligence, Classification (of information), Computer aided diagnosis, Fault detection, Learning systems, Probability, Probability distributions, Case based, Case-based approach, Logistic regressions, Probabilistic classification, Probabilistic classifiers, Probabilistic fault diagnosis, Probability modeling, Similarity metrics, Case based reasoning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-27478 (URN)2-s2.0-84921634116 (Scopus ID)978-3-319-11208-4 (ISBN)978-3-319-11209-1 (ISBN)
Conference
22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014.
Available from: 2015-02-06 Created: 2015-02-06 Last updated: 2015-09-22Bibliographically approved
Olsson, T., Källström, E., Gillblad, D., Funk, P., Lindström, J., Håkansson, L., . . . Larsson, J. (2014). Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches. In: Proceedings of the ICCBR 2014 Workshops: . Paper presented at Workshop on Synergies between CBR and Data Mining at the 22nd International Conference on Case-Based Reasoning (CBRDM’14).
Open this publication in new window or tab >>Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches
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2014 (English)In: Proceedings of the ICCBR 2014 Workshops, 2014Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision mak- ing. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely in- tegrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions. 

Keywords
Case-based Reasoning, Machine Learning, Signal Processing, Fault Diagnosis
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29088 (URN)
Conference
Workshop on Synergies between CBR and Data Mining at the 22nd International Conference on Case-Based Reasoning (CBRDM’14)
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5562-1424

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