| 1. |
- Eleftheroglou, Nick, et al.
(författare)
-
Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification
- 2019
-
Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 254
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.
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| 2. |
- Eleftheroglou, Nick, et al.
(författare)
-
Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries
- 2019
-
Ingår i: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019. - : Prognostics and Health Management Society.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.
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| 3. |
- Fairley, Jacqueline A., et al.
(författare)
-
Wavelet analysis for detection of phasic electromyographic activity in sleep : of mother wavelet and dimensionality reduction
- 2014
-
Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 48:1, s. 77-84
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Tidskriftsartikel (refereegranskat)abstract
- Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500. ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1. s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets
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| 4. |
- Georgoulas, George, et al.
(författare)
-
An exploratory approach to fetal heart rate–pH-based systems
- 2021
-
Ingår i: Signal, Image and Video Processing. - : Springer. - 1863-1703 .- 1863-1711. ; 15:1, s. 43-51
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents an exploratory approach of the fetal heart rate (FHR) analysis, aiming to highlight potential limitations of the current predictive modeling attempts. To do so, a set of features that are usually encountered in FHR analysis as well as features extracted using a variant of symbolic aggregate approximation were projected onto a lower-dimensional space where patterns can easily be discerned. The results show, both in a qualitative and a quantitative manner, that there is high overlap between the classes that are formed using solely the umbilical cord pH information, irrespective of the selected dimensionality reduction method. These findings suggest that there is probably a limit to the performance expectation of the current pH-based systems and that alternative approaches should be also pursued to enhance the utility of computer-based decision support technologies.
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| 5. |
- Georgoulas, Georgios, et al.
(författare)
-
An ordinal classification approach for CTG categorization
- 2017
-
Ingår i: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). - Piscataway, NJ : IEEE. - 9781509028092 ; , s. 2642-2645
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Konferensbidrag (refereegranskat)abstract
- Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.
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| 6. |
- Georgoulas, Georgios, et al.
(författare)
-
Automatizing the broken bar detection process via Short Time Fourier Transform and two-dimensional Piecewise Aggregate Approximation representation
- 2014
-
Konferensbidrag (refereegranskat)abstract
- This work presents an automated approach for detecting broken rotor bars in induction machines using the stator current during startup operation. The currents are analyzed using the well-known Short Time Fourier Transform (STFT) producing a two-dimensional time-frequency representation. This representation contains information regarding the presence of a characteristic transient component but requires further processing before it can be fed into a standard classification algorithm. In this work, this part is performed using the two dimensional extension of Piecewise Aggregate Approximation (PAA) that can deal with the two dimensional representation of STFT. The results (with both simulated and experimental data) suggest that the method can be used for the automatic detection of broken bars and even for determining the fault severity. Moreover, its low computational burden makes it ideal for its future use in online, unsupervised systems, as well as in portable condition monitoring devices.
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| 7. |
- Georgoulas, George, et al.
(författare)
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Automatizing the detection of rotor failures in induction motors operated via soft-starters
- 2016
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Ingår i: Annual Conference of the IEEE Industrial Electronics Society, IECON 2015. - Piscataway, NJ : IEEE Communications Society. - 9781479917624 ; , s. 3743-3748
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Konferensbidrag (refereegranskat)abstract
- Implementation of unsupervised induction motor condition monitoring systems has drawn an increasing attention recently among motor drives manufacturers. In the case of soft- starters the possibility of incorporating fault detection features to their conventional functions provides an added value to those elements. Design and development of advanced algorithms that are able to automatically detect and alert about possible failures without requiring continuous human inspection is an especially challenging research goal. In this paper, an algorithm for the automatic detection of rotor damages in induction motors in the case of soft starting is proposed. The twofold approach relies, first, on the application of a time-frequency transform to the starting current signal and, second, on a pattern recognition stage based on the treatment of the time-frequency representation as a symbolic sequence. The innovation of this work is the implementation of the proposed approach for the automatic detection of rotor cage faults in soft-started motors. The experimental results prove the usefulness of the approach for the automatic detection of such faults and its potential for possible future implementation in soft-started machines.
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| 8. |
- Georgoulas, George, 1976-, et al.
(författare)
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Exploring the Detectability of Short-Circuit Faults in Inverter-Fed Induction Motors
- 2018
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Ingår i: Proceedings IECON 2018. - : IEEE. ; , s. 5930-5935
-
Konferensbidrag (refereegranskat)abstract
- This paper explores the possibility of creating an automatic method for assessing the condition of induction motor circuits fed by inverters. The stator current and magnetic flux are processed in the frequency domain and a feature selection stage is employed to pinpoint the most informative components to further be fed to a classifier that performs the assessment of the motor circuit. The results are promising, indicating that short circuit detection as well as quantification is feasible using noninvasive techniques.
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| 9. |
- Georgoulas, George G., 1976-, et al.
(författare)
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Harmony search augmented with optimal computing budget allocation capabilities for noisy optimization
- 2013
-
Ingår i: IAENG International Journal of Computer Science. - 1819-656X .- 1819-9224. ; 40:4, s. 285-290
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Tidskriftsartikel (refereegranskat)abstract
- In this work we introduce the combinatory use of Harmony Search (HS) with Optimal Computing Budget Allocation (OCBA) as a means to tackle noisy optimization situations as those that occur during the execution of Discrete Event Systems (DES) for modeling complex systems. The OCBA procedure is employed for the exclusion of the worst harmony during the memory updating process in order to minimize the computational cost and at the same time retain a pool of promising solutions. The proposed hybrid approach is tested on real valued test functions as a proof of concept and the results are promising in case of small computational budgets.
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| 10. |
- Georgoulas, Georgios, et al.
(författare)
-
Investigating pH based evaluation of fetal heart rate (FHR) recordings
- 2017
-
Ingår i: Health and Technology. - : Springer. - 2190-7188 .- 2190-7196. ; 7:2/3, s. 241-254
-
Tidskriftsartikel (refereegranskat)abstract
- Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.
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| 11. |
- Georgoulas, Georgios, et al.
(författare)
-
Rolling element bearings diagnostics using the Symbolic Aggregate approXimation
- 2015
-
Ingår i: Mechanical systems and signal processing. - : Elsevier. - 0888-3270 .- 1096-1216. ; 60, s. 229-242
-
Tidskriftsartikel (refereegranskat)abstract
- Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults
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| 12. |
- Kanellakis, Christoforos, et al.
(författare)
-
Image Enhancing in Poorly Illuminated Subterranean Environments for MAV Applications : A Comparison Study
- 2019
-
Ingår i: Computer Vision Systems. - Cham : Springer. ; , s. 511-520
-
Konferensbidrag (refereegranskat)abstract
- This work focuses on a comprehensive study and evaluation of existing low-level vision techniques for low light image enhancement, targeting applications in subterranean environments. More specifically, an emerging effort is currently pursuing the deployment of Micro Aerial Vehicles in subterranean environments for search and rescue missions, infrastructure inspection and other tasks. A major part of the autonomy of these vehicles, as well as the feedback to the operator, has been based on the processing of the information provided from onboard visual sensors. Nevertheless, subterranean environments are characterized by a low natural illumination that directly affects the performance of the utilized visual algorithms. In this article, an novel extensive comparison study is presented among five State-of the-Art low light image enhancement algorithms for evaluating their performance and identifying further developments needed. The evaluation has been performed from datasets collected in real underground tunnel environments with challenging conditions from the onboard sensor of a MAV.
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| 13. |
- Kanellakis, Christoforos, et al.
(författare)
-
On Image based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments
- 2019
-
Ingår i: Advances in Computer Vision. - Cham : Springer. ; , s. 265-279
-
Konferensbidrag (refereegranskat)abstract
- Micro Aerial Vehicles (MAV)s have been distinguished, in the last decade, for their potential to inspect infrastructures in an active manner and provide critical information to the asset owners. Inspired by this trend, the mining industry is lately focusing to incorporate MAVs in their production cycles. Towards this direction, this article proposes a novel method to enhance 3D reconstruction of low-light environments, like underground tunnels, by using image processing. More specifically, the main idea is to enhance the low light resolution of the collected images, captured onboard an aerial platform, before inserting them to the reconstruction pipeline. The proposed method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm that limits the noise, while amplifies the contrast of the image. The overall efficiency and improvement achieved of the novel architecture has been extensively and successfully evaluated by utilizing data sets captured from real scale underground tunnels using a quadrotor.
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| 14. |
- Kanellakis, Christoforos, et al.
(författare)
-
Open Space Attraction Based Navigation in Dark Tunnels for MAVs
- 2019
-
Ingår i: Computer Vision Systems. - Cham : Springer. ; , s. 110-119
-
Konferensbidrag (refereegranskat)abstract
- This work establishes a novel framework for characterizing the open space of featureless dark tunnel environments for Micro Aerial Vehicles (MAVs) navigation tasks. The proposed method leverages the processing of a single camera to identify the deepest area in the scene in order to provide a collision free heading command for the MAV. In the sequel and inspired by haze removal approaches, the proposed novel idea is structured around a single image depth map estimation scheme, without metric depth measurements. The core contribution of the developed framework stems from the extraction of a 2D centroid in the image plane that characterizes the center of the tunnel’s darkest area, which is assumed to represent the open space, while the robustness of the proposed scheme is being examined under varying light/dusty conditions. Simulation and experimental results demonstrate the effectiveness of the proposed method in challenging underground tunnel environments.
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| 15. |
- Kanellakis, Christoforos, et al.
(författare)
-
Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation
- 2021
-
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 66477-66485
-
Tidskriftsartikel (refereegranskat)abstract
- This work establishes a robocentric framework around a non-linear Model Predictive Control (NMPC) for autonomous navigation of quadrotors in tunnel-like environments. The proposed framework enables obstacle free navigation capabilities for resource constraint platforms in areas with critical challenges including darkness, textureless surfaces as well as areas with self-similar geometries, without any prior knowledge. The core contribution of the proposed framework stems from the merging of perception dynamics in a model-based optimization approach, aligning the vehicles heading to the tunnels’ open space expressed in the x axis coordinate in the image frame of the most distant area. Moreover, the aerial vehicle is considered as a free-flying object that plans its actions using egocentric onboard sensors. The proposed method can be deployed in both fully illuminated indoor corridors or featureless dark tunnels, leveraging visual processing from either RGB-D or monocular sensors for generating direction commands to keep flying in the proper direction. Multiple experimental field trials demonstrate the effectiveness of the proposed method in challenging environments.
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| 16. |
- Kanellakis, Christoforos, et al.
(författare)
-
Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform's altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.
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| 17. |
- Kanellakis, Christoforos, et al.
(författare)
-
Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments
- 2020
-
Ingår i: 21th IFAC World Congress. - : Elsevier. ; , s. 9288-9294
-
Konferensbidrag (refereegranskat)abstract
- This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform’s altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.
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| 18. |
- Kanellakis, Christoforos, et al.
(författare)
-
Where to look : a collection of methods for MAV heading correction in underground tunnels
- 2020
-
Ingår i: IET Image Processing. - : The Institution of Engineering and Technology. - 1751-9659 .- 1751-9667. ; 14:10, s. 2020-2027
-
Tidskriftsartikel (refereegranskat)abstract
- Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified, methods tackling the problem of MAVs heading regulation while navigating in dark and textureless tunnel areas. More specifically, four different methods are presented in this work with the common goal to identify open space in the tunnel and align the MAV heading using either visual sensor in methods a) single image depth estimation, b) darkness contour detection, c) Convolutional Neural Network (CNN) regression and 2D Lidar sensor in method d) range geometry. For the works a)-c) the dark scene in the middle of the tunnel is considered as open space and is processed and converted to yaw rate command, while d) examines the geometry of the range measurements to calculate the yaw rate command. Experimental results from real underground tunnel demonstrate the performance of the methods in the field, while setting the ground for further developments in the aerial robotics community.
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| 19. |
- Karvelis, Petros, et al.
(författare)
-
A Laser Dot Tracking Method for the Assessment of Sensorimotor Function of the Hand
- 2017
-
Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017. - Piscataway. NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509045334 ; , s. 217-222
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Konferensbidrag (refereegranskat)abstract
- Assessment of sensorimotor function is crucial during the rehabilitation process of various physical disorders, including impairments of the hand. While moment performance can be accurately assessed in movement science laboratories involving highly specialized personnel and facilities there is a lack of feasible objective methods for the general clinic. This paper describes a novel approach to sensorimotor assessment using an intuitive test and a specifically tailored image processing pipeline for the quantification of the test. More specifically the test relies on the patient being instructed on following a zig-zag pattern using a handled laser pointer. The movement of the pointer is tracked using image processing algorithm capable of automating the whole procedure. The method has potential for feasible objective clinical assessment of the hand and other body parts
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| 20. |
- Karvelis, Petros, et al.
(författare)
-
A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors
- 2015
-
Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 11:5, s. 1028-1037
-
Tidskriftsartikel (refereegranskat)abstract
- One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.
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| 21. |
- Karvelis, Petros, et al.
(författare)
-
An automated thermographic image segmentation method for induction motor fault diagnosis
- 2015
-
Ingår i: IECON 2014. - Piscataway, NJ : IEEE Communications Society. - 9781479940332 ; , s. 3396-3402
-
Konferensbidrag (refereegranskat)abstract
- Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.
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| 22. |
- Karvelis, Petros, et al.
(författare)
-
An intelligent icons approach for rotor bar fault detection
- 2013
-
Ingår i: IECON Proceedings (Industrial Electronics Conference). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781479902248 ; , s. 5526-5531
-
Konferensbidrag (refereegranskat)abstract
- In this paper we propose the use of Intelligent Icons for both automatic assessment and representation of asynchronous machines' condition. The method focuses on the analysis of the start-up current for the isolation of a component that is able to pinpoint faulty signatures. The analysis is based on the application of Empirical Mode Decomposition (EMD) which acts as an adaptive filter during the start up and subsequently on the application of Symbolic Aggregate approXimation (SAX) for the transformation of the extracted component into a symbolic representation. Using this symbolic representation, an automated detection procedure can be developed that discriminates between faulty and normal conditions using an intelligent Icons approach while at the same time the information can be presented to the user in a more intuitive way
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| 23. |
- Karvelis, Petros, et al.
(författare)
-
Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization
- 2015
-
Ingår i: Physiological Measurement. - : Institute of Physics (IOP). - 0967-3334 .- 1361-6579. ; 36:5, s. 1001-1024
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Tidskriftsartikel (refereegranskat)abstract
- The most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions - the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natural ordering, and representation of increased severity, for obtaining the final results. The results are promising suggesting that more effort should be put into this proposed approach.
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| 24. |
- Karvelis, Petros, et al.
(författare)
-
Ensemble learning for forecasting main meteorological parameters
- 2018
-
Ingår i: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). - Piscataway, N.J. : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3711-3714
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Konferensbidrag (refereegranskat)abstract
- The significant role of predicting weather conditions in daily life, the new era of innovative machine learning approaches along with the availability of high volumes of data and high computer performance capabilities, creates increasing perspectives for novel improved short-range forecasting of main meteorological parameters. Among the various algorithms for forecasting parameters, ensemble learning approaches are able to generate simple models which provide accurate predictions for regression problems. The advantage of ensembles with respect to single models is that they perform remarkably well for a variety of problems. The main aim of this ongoing research is to provide some preliminary assessment of the applicability of ensemble learning for wind speed forecasting. In this work, forecasting results of a single and two ensemble models are presented and compared.
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| 25. |
- Karvelis, Petros, et al.
(författare)
-
Semi-automated annotation of phasic electromyographic activity
- 2014
-
Konferensbidrag (refereegranskat)abstract
- Recent research on manual/visual identification of phasic muscle activity utilizing the phasic electromyographic metric (PEM) in human polysomnograms (PSGs) cites evidence that PEM is a potentially reliable quantitative metric to assist in distinguishing between neurodegenerative disorder populations and age-matched controls. However, visual scoring of PEM activity is time consuming-preventing feasible implementation within a clinical setting. Therefore, here we propose an assistive/semi-supervised software platform designed and tested to automatically identify and characterize PEM events in a clinical setting that will be extremely useful for sleep physicians and technicians. The proposed semi-automated approach consists of four levels: A) Signal Parsing, B) Calculation of quantitative features on candidate PEM events, C) Classification of PEM and non-PEM events using a linear classifier, and D) Post-processing/Expert feedback to correct/remove automated misclassifications of PEM and Non-PEM events. Performance evaluation of the designed software compared to manual labeling is provided for electromyographic (EMG) activity from the PSG of a control subject. Results indicate that the semi-automated approach provides an excellent benchmark that could be embedded into a clinical decision support system to detect PEM events that would be used in neurological disorder identification and treatment.
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| 26. |
- Karvelis, Petros, et al.
(författare)
-
Short Time Wind Forecasting with Uncertainty
- 2019
-
Ingår i: The 10th International Conference on Information, Intelligence, Systems and Applications, 15-17 July 2019, Patras, Greece. - : IEEE. ; , s. 511-518
-
Konferensbidrag (refereegranskat)abstract
- Forecasting the weather and especially the wind is important for a number of applications like wind farms or for maritime operations. Nowadays machine learning techniques are becoming more reliable and robust for forecasting due to the fact that a plethora of available datasets exist. However, forecasts for shorter time horizon less than two hour is not reliable due to the frequent wind fluctuations. Nevertheless, the need for algorithms that can have a small memory and cpu footprint is needed for hardware e.g. microcontrollers that are on board of vessels. In this manuscript a method for short time wind forecasting is proposed and scaled for a microcontroller. The method also computes prediction intervals with a certain probability. Our method was tested using real data recorded from a weather station on board of a ship conducting trips across the Aegean Sea (Greece).
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| 27. |
- Karvelis, Petros, et al.
(författare)
-
Symbolic time series analysis of the soft starting transient in induction machines
- 2015
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Ingår i: 2015 IEEE International Conference on Industrial Technology (ICIT 2015) to be held in Seville, Spain, March 17-19, 2015.. - Piscataway, NJ : IEEE Communications Society. - 9781479978007 ; , s. 3243-3248
-
Konferensbidrag (refereegranskat)abstract
- Induction motors are in the heart of almost every production line especially due to their robustness under harsh environments. Nevertheless, even induction machines are prone to faults. Among them, the faults related to the breakage of rotor bars have received special attention by the research community with a number of methods proposed both for the case of steady state as well as for transient operation. For the latter, methods relying on the analysis of the start-up transient have proven to be able to effectively isolate the faulty component that is created by the asymmetry caused by the bar breakage. However, very little work has been done concerning the soft starting of induction machines. In this work, preliminary results of the application of a symbolic time series technique for the analysis of the transient, when the motor is controlled by a soft starter, will be presented and experimentally evaluated.
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| 28. |
- Karvelis, Petros, et al.
(författare)
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Topic recommendation using Doc2Vec
- 2018
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Konferensbidrag (refereegranskat)abstract
- The ever-increasing number of electronic content stored in digital libraries requires a significant amount of effort in cataloguing and has led to self-deposit solutions where the authors submit and publish their own digital records. Even in self-deposit, going through the abstract and assigning subject terms or keywords is a time consuming and expensive process, yet crucial for the metadata quality of the record that affects retrieval. Therefore, an automatic, or even a semi-automatic process that can recommend topics for a new entry is of huge practical value. A system that can address that has to rely basically on two components, one component for efficiently representing the relevant information of the new document and one component for recommending an appropriate set of topics based on the representation of the previous stage. In this work, different candidate solutions for both components are investigated and compared. For the first stage both distributed Document to Vector (doc2vec) and conventional Bag of Words (BoW) components are employed, while for the latter two different transformation approaches from the field of multi-label classification are compared. For the comparison, a collection of Ph.D. abstracts (~19000 documents) from the MIT Libraries Dspace repository is used suggesting that different combinations can provide high quality solutions.
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| 29. |
- Mansouri, Sina Sharif, et al.
(författare)
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MAV Navigation in Unknown Dark Underground Mines Using Deep Learning
- 2020
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Ingår i: European Control Conference 2020. - : IEEE. ; , s. 1943-1948
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Konferensbidrag (refereegranskat)abstract
- This article proposes a Deep Learning (DL) method to enable fully autonomous flights for low-cost Micro Aerial Vehicles (MAVs) in unknown dark underground mine tunnels. This kind of environments pose multiple challenges including lack of illumination, narrow passages, wind gusts and dust. The proposed method does not require accurate pose estimation and considers the flying platform as a floating object. The Convolutional Neural Network (CNN) supervised image classifier method corrects the heading of the MAV towards the center of the mine tunnel by processing the image frames from a single on-board camera, while the platform navigates at constant altitude and desired velocity references. Moreover, the output of the CNN module can be used from the operator as means of collision prediction information. The efficiency of the proposed method has been successfully experimentally evaluated in multiple field trials in an underground mine in Sweden, demonstrating the capability of the proposed method in different areas and illumination levels.
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| 30. |
- Mansouri, Sina Sharif, et al.
(författare)
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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning
- 2017
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 4727-4732
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Tidskriftsartikel (refereegranskat)abstract
- Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions.
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| 31. |
- Mansouri, Sina Sharif, et al.
(författare)
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Vision-based MAV Navigation in Underground Mine Using Convolutional Neural Network
- 2019
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Ingår i: IECON 2019. - : IEEE. ; , s. 750-755
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Konferensbidrag (refereegranskat)abstract
- This article presents a Convolutional Neural Network (CNN) method to enable autonomous navigation of low-cost Micro Aerial Vehicle (MAV) platforms along dark underground mine environments. The proposed CNN component provides on-line heading rate commands for the MAV by utilising the image stream from the on-board camera, thus allowing the platform to follow a collision-free path along the tunnel axis. A novel part of the developed method consists of the generation of the data-set used for training the CNN. More specifically, inspired from single image haze removal algorithms, various image data-sets collected from real tunnel environments have been processed offline to provide an estimation of the depth information of the scene, where ground truth is not available. The calculated depth map is used to extract the open space in the tunnel, expressed through the area centroid and is finally provided in the training of the CNN. The method considers the MAV as a floating object, thus accurate pose estimation is not required. Finally, the capability of the proposed method has been successfully experimentally evaluated in field trials in an underground mine in Sweden.
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| 32. |
- Mansouri, Sina Sharif, et al.
(författare)
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Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks
- 2019
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Ingår i: IECON 2019. - : IEEE. ; , s. 192-197
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This article proposes a novel visual framework for detecting tunnel crossings/junctions in underground mine areas towards the autonomous navigation of Micro Aeril Vehicles (MAVs). Usually mine environments have complex geometries, including multiple crossings with different tunnels that challenge the autonomous planning of aerial robots. Towards the envisioned scenario of autonomous or semi-autonomous deployment of MAVs with limited Line-of-Sight in subterranean environments, the proposed module acknowledges the existence of junctions by providing crucial information to the autonomy and planning layers of the aerial vehicle. The capability for a junction detection is necessary in the majority of mission scenarios, including unknown area exploration, known area inspection and robot homing missions. The proposed novel method has the ability to feed the image stream from the vehicles’ on-board forward facing camera in a Convolutional Neural Network (CNN) classification architecture, expressed in four categories: 1) left junction, 2) right junction, 3) left & right junction, and 4) no junction in the local vicinity of the vehicle. The core contribution stems for the incorporation of AlexNet in a transfer learning scheme for detecting multiple branches in a subterranean environment. The validity of the proposed method has been validated through multiple data-sets collected from real underground environments, demonstrating the performance and merits of the proposed module.
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| 33. |
- Röijezon, Ulrik, et al.
(författare)
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Proprioceptive Disturbance in Chronic Neck Pain: Discriminate Validity and Reliability of Performance of the Clinical Cervical Movement Sense Test
- 2022
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Ingår i: Frontiers in Pain Research. - : Frontiers Media S.A.. - 2673-561X. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- Chronic neck pain is associated with sensorimotor dysfunctions, which may develop symptoms, affect daily activities, and prevent recovery. Feasible, reliable, and valid objective methods for the assessment of sensorimotor functions are important to identify movement impairments and guide interventions. The aim of this study was to investigate the discriminative validity of a clinical cervical movement sense test, using a laser pointer and an automatic video-based scoring system. Individuals with chronic neck pain of idiopathic onset (INP), traumatic onset (TNP), and healthy controls (CON) were tested. Associations between movement sense and neck disability were examined and the repeatability of the test was investigated. A total of 106 participants (26 INP, 28 TNP, and 52 CON) were included in a cross-sectional study. Acuity, Speed, Time, and NormAcuity (i.e., normalized acuity by dividing acuity with movement time) were used as outcome measures. ANOVAs were used for group comparisons and Pearson correlations for associations between movement sense variables and neck disability index (NDI). Notably, 60 of the participants (30 CON, 17 INP, and 13 TNP) performed the test on a second occasion to explore test-retest reliability. Results revealed a reduced NormAcuity for both INP and TNP compared with CON (p < 0.05). The neck pain groups had similar Acuity but longer Time compared with CON. Among TNP, there was a fair positive correlation between Acuity and NDI, while there was a negative correlation between Acuity and NDI among INP. Reliability measures showed good to excellent ICC values between tests, but standard error of measurements (SEM) and minimal detectable change (MDC) scores were high. The results showed that NormAcuity is a valuable measure to identify disturbed cervical movement sense among INP and TNP. While Acuity was similar between the groups, different strategies, such as longer Time, to perform the task among neck patient groups were used. Few differences were identified between the neck pain groups, but altered strategies may exist. Reliability was acceptable, and the test is feasible to perform in the clinic. However, the technical complexity of the automated image analysis is a concern. Future developments will provide more feasible solutions.
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| 34. |
- Spilka, Jiří, et al.
(författare)
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Discriminating normal from "abnormal" pregnancy cases using an automated FHR evaluation method
- 2014
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Konferensbidrag (refereegranskat)abstract
- Electronic fetal monitoring has become the gold standard for fetal assessment both during pregnancy as well as during delivery. Even though electronic fetal monitoring has been introduced to clinical practice more than forty years ago, there is still controversy in its usefulness especially due to the high inter- and intra-observer variability. Therefore the need for a more reliable and consistent interpretation has prompted the research community to investigate and propose various automated methodologies. In this work we propose the use of an automated method for the evaluation of fetal heart rate, the main monitored signal, which is based on a data set, whose labels/annotations are determined using a mixture model of clinical annotations. The successful results of the method suggest that it could be integrated into an assistive technology during delivery.
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| 35. |
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