| 1. |
- Nedic, Mitja, et al.
(author)
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Herglotz functions and applications in electromagnetics
- 2020
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In: Advances in Mathematical Methods for Electromagnetics. - : Institution of Engineering and Technology. - 9781785613845 - 9781785613852 ; , s. 491-514
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Book chapter (other academic/artistic)abstract
- Herglotz functions inevitably appear in pure mathematics, mathematical physics, and engineering with a wide range of applications. In particular, they are the pertinent functions to model passive systems, and thus appear in modeling of electromagnetic phenomena in circuits, antennas, materials, and scattering. In this chapter, we review the basic theory of Herglotz functions and its applications to determine sum rules and physical bounds for passive systems.
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| 2. |
- Jiang, Yuning, 1993-, et al.
(author)
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Multi-Level Vulnerability Modeling of Cyber-Physical Systems
- 2018
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Conference paper (peer-reviewed)abstract
- Vulnerability is defined as ”weakness of an asset or control that can be exploited by a threat” according to ISO/IEC 27000:2009, and it is a vital cyber-security issue to protect cyber-physical systems (CPSs) employed in a range of critical infrastructures (CIs). However, how to quantify both individual and system vulnerability are still not clear. In our proposed poster, we suggest a new procedure to evaluate CPS vulnerability. We reveal a vulnerability-tree model to support the evaluation of CPS-wide vulnerability index, driven by a hierarchy of vulnerability-scenarios resulting synchronously or propagated by tandem vulnerabilities throughout CPS architecture, and that could be exploited by threat agents. Multiple vulnerabilities are linked by boolean operations at each level of the tree. Lower-level vulnerabilities in the tree structure can be exploited by threat agents in order to reach parent vulnerabilities with increasing CPS criticality impacts. At the asset-level, we suggest a novel fuzzy-logic based valuation of vulnerability along standard metrics. Both the procedure and fuzzy-based approach are discussed and illustrated through SCADA-based smart power-grid system as a case study in the poster, with our goal to streamline the process of vulnerability computation at both asset and CPS levels.
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| 3. |
- Bjorklund, Svante, et al.
(author)
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Features for micro-Doppler based activity classification
- 2015
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In: IET radar, sonar & navigation. - : Institution of Engineering and Technology (IET). - 1751-8784 .- 1751-8792. ; 9:9, s. 1181-1187
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Journal article (peer-reviewed)abstract
- Safety and security applications benefit from better situational awareness. Radar micro-Doppler signatures from an observed target carry information about the target's activity, and have potential to improve situational awareness. This article describes, compares, and discusses two methods to classify human activity based on radar micro-Doppler data. The first method extracts physically interpretable features from the time-velocity domain such as the main cycle time and properties of the envelope of the micro-Doppler spectra and use these in the classification. The second method derives its features based on the components with the most energy in the cadence-velocity domain (obtained as the Fourier transform of the time-velocity domain). Measurements from a field trial show that the two methods have similar activity classification performance. It is suggested that target base velocity and main limb cadence frequency are indirect features of both methods, and that they do often alone suffice to discriminate between the studied activities. This is corroborated by experiments with a reduced feature set. This opens up for designing new more compact feature sets. Moreover, weaknesses of the methods and the impact of non-radial motion are discussed.
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| 4. |
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| 5. |
- Björklund, Svante, et al.
(author)
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On distinguishing between human individuals in micro-Doppler signatures
- 2013
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In: 14th International Radar Symposium (IRS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467348218 ; , s. 865-870
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Conference paper (peer-reviewed)abstract
- Radar micro-Doppler signatures (MDS) of humans are created by movements of body parts, such as legs and arms. MDSs can be used in security applications to detect humans and classify their type and activity. Target association and tracking, which can facilitate the classification, become easier if it is possible to distinguish between human individuals by their MDSs. By this we mean to recognize the same individual in a short time frame but not to establish the identity of the individual. In this paper we perform a statistical experiment in which six test persons are able to distinguish between walking human individuals from their MDSs. From this we conclude that there is information in the MDSs of the humans to distinguish between different individuals, which also can be used by a machine. Based on the results of the best test persons we also discuss features in the MDSs that could be utilized to make this processing possible.
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| 6. |
- Heskebeck, Frida, et al.
(author)
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Multi-Armed Bandits in Brain-Computer Interfaces
- 2022
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In: Frontiers in Human Neuroscience. - : Frontiers Media S.A.. - 1662-5161. ; 16
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Research review (peer-reviewed)abstract
- The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in many procedures of Brain-Computer Interfaces (BCIs) and MAB has previously been used to investigate, e.g., what mental commands to use to optimize BCI performance. However, MAB optimization in the context of BCI is still relatively unexplored, even though it has the potential to improve BCI performance during both calibration and real-time implementation. Therefore, this review aims to further describe the fruitful area of MABs to the BCI community. The review includes a background on MAB problems and standard solution methods, and interpretations related to BCI systems. Moreover, it includes state-of-the-art concepts of MAB in BCI and suggestions for future research.
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| 7. |
- Hultgren, Anders, et al.
(author)
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Limit Cycles in an Industrially Applied Hybrid System
- 2010
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In: 2010 11th International Workshop on 11th IEEE Workshop on Variable Structure Systems. - Mexico city : IEEE. - 9781424458295 ; , s. 277-282
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Conference paper (peer-reviewed)abstract
- An industrially applied LCC power converter is modelled as a hybrid system. It is found that the hybrid system with three continuous states and one logic state given a parameter set up and different initial conditions has among other solutions one central closed trajectory and two types of limit cycles, on the left and the right side. The other solutions are indicated.
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| 8. |
- Källström, Elisabeth, et al.
(author)
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On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
- 2018
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In: International Journal of Prognostics and Health Management. - : PHM Society. - 2153-2648. ; 9:1
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Journal article (peer-reviewed)abstract
- In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machinein combination with feature extraction and classification methods may be utilized.This paper, based on a study at Volvo Construction Equipment,presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in a heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components,the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomalyis detected, the Case-Based diagnosis module is activated for fault severity estimation.
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| 9. |
- Wilroth, Johanna, et al.
(author)
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Improving EEG-based decoding of the locus of auditory attention through domain adaptation
- 2023
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In: Journal of Neural Engineering. - : Institute of Physics (IOP). - 1741-2560 .- 1741-2552. ; 20:6
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Journal article (peer-reviewed)abstract
- Objective. This paper presents a novel domain adaptation (DA) framework to enhance the accuracy of electroencephalography (EEG)-based auditory attention classification, specifically for classifying the direction (left or right) of attended speech. The framework aims to improve the performances for subjects with initially low classification accuracy, overcoming challenges posed by instrumental and human factors. Limited dataset size, variations in EEG data quality due to factors such as noise, electrode misplacement or subjects, and the need for generalization across different trials, conditions and subjects necessitate the use of DA methods. By leveraging DA methods, the framework can learn from one EEG dataset and adapt to another, potentially resulting in more reliable and robust classification models. Approach. This paper focuses on investigating a DA method, based on parallel transport, for addressing the auditory attention classification problem. The EEG data utilized in this study originates from an experiment where subjects were instructed to selectively attend to one of the two spatially separated voices presented simultaneously. Main results. Significant improvement in classification accuracy was observed when poor data from one subject was transported to the domain of good data from different subjects, as compared to the baseline. The mean classification accuracy for subjects with poor data increased from 45.84% to 67.92%. Specifically, the highest achieved classification accuracy from one subject reached 83.33%, a substantial increase from the baseline accuracy of 43.33%. Significance. The findings of our study demonstrate the improved classification performances achieved through the implementation of DA methods. This brings us a step closer to leveraging EEG in neuro-steered hearing devices. © 2023 The Author(s). Published by IOP Publishing Ltd.
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| 10. |
- Xu, Cheng, et al.
(author)
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Scalable Validation of Industrial Equipment using a Functional DSMS
- 2017
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In: Journal of Intelligent Information Systems. - : Springer. - 0925-9902 .- 1573-7675. ; 48:3, s. 553-577
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Journal article (peer-reviewed)abstract
- A stream validation system called SVALI is developed in order to continuouslyvalidate data streams from industrial equipment. The functional data model of SVALI allows the user to dene meta-data and queries about the equipment in terms of types and functions. The two system functions model-andvalidate and learn-and-validate provide such validation functionality. The experiments show that parallel stream processing enables SVALI to scale very well with respect to response time and system throughput. The paper is based on a real world application for wheel loader slippage detection at Volvo Construction Equipment.
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