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  • 1.
    Avdelidis, Nicolas P.
    et al.
    University of Thessaly, Center of Research and Evaluation of Human Performance, Department of Physical Education and Sports Science, Karyes, Trikala.
    Kappatos, Vassilios
    Department of Technology and Innovation (ITI), University of Southern Denmark (SDU).
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kravelis, Petros S.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta.
    Deli, Chariklia K.
    University of Thessaly, Center of Research and Evaluation of Human Performance, Department of Physical Education and Sports Science, Karyes, Trikala.
    Theodorakeas, Panagiotis
    National Technical University of Athens, NDT Lab, Materials Science and Engineering Department, School of Chemical Engineering, Zografou Campus, Athens.
    Giakas, Giannis
    University of Thessaly, Center of Research and Evaluation of Human Performance, Department of Physical Education and Sports Science, Karyes, Trikala.
    Tsiokanos, Athanasios L.
    University of Thessaly, Center of Research and Evaluation of Human Performance, Department of Physical Education and Sports Science, Karyes, Trikala.
    Koui, Maria
    National Technical University of Athens, NDT Lab, Materials Science and Engineering Department, School of Chemical Engineering, Zografou Campus, Athens.
    Jamurtas, Athanasios
    University of Thessaly, Center of Research and Evaluation of Human Performance, Department of Physical Education and Sports Science, Karyes, Trikala.
    Detection and characterization of exercise induced muscle damage (EIMD) via thermography and image processing2017In: Smart Materials and Nondestructive Evaluation for Energy Systems 2017: Portland, United States,  27-28 March 2017 / [ed] Norbert G. Meyendorf, SPIE - International Society for Optical Engineering, 2017, 101710RConference paper (Refereed)
    Abstract [en]

    Exercise induced muscle damage (EIMD), is usually experienced in i) humans who have been physically inactive for prolonged periods of time and then begin with sudden training trials and ii) athletes who train over their normal limits. EIMD is not so easy to be detected and quantified, by means of commonly measurement tools and methods. Thermography has been used successfully as a research detection tool in medicine for the last 6 decades but very limited work has been reported on EIMD area. The main purpose of this research is to assess and characterize EIMD, using thermography and image processing techniques. The first step towards that goal is to develop a reliable segmentation technique to isolate the region of interest (ROI). A semi-automatic image processing software was designed and regions of the left and right leg based on superpixels were segmented. The image is segmented into a number of regions and the user is able to intervene providing the regions which belong to each of the two legs. In order to validate the image processing software, an extensive experimental investigation was carried out, acquiring thermographic images of the rectus femoris muscle before, immediately post and 24, 48 and 72 hours after an acute bout of eccentric exercise (5 sets of 15 maximum repetitions), on males and females (20-30 year-old). Results indicate that the semi-automated approach provides an excellent bench-mark that can be used as a clinical reliable tool

  • 2.
    Fanti, Maria Pia
    et al.
    Polytechnic of Bari.
    Iacobellis, Giorgio
    Polytechnic of Bari.
    Ukovich, Walter
    University of Trieste.
    Boschian, Valentina
    University of Trieste.
    Georgoulas, Georgios
    Technological Educational Institute of Epirus, Arta.
    Stylios, Chrysostomos D.
    Technological Educational Institute of Epirus, Arta.
    A simulation based Decision Support System for logistics management2015In: Journal of Computational Science, ISSN 1877-7503, E-ISSN 1877-7511, Vol. 10, 86-96 p.Article in journal (Refereed)
    Abstract [en]

    This paper deals with designing and developing a Decision Support System (DSS) that will be able to manage the flow of goods and the business transactions between a port and a dry port. An integrated DSS architecture is proposed and specified and the main components are designed on the basis of simulation and optimization modules. In order to show the use and implementation of the DSS, this work tests and analyzes the case of the area of the Trieste port and manages the export flows of freights between a dry port and a seaport. An integrated approach is designed mainly at tactical and operational decision level exploiting simulation and optimization approaches and especially metaheuristic approaches

  • 3.
    Gavrilis, Dimitris
    et al.
    Department of Electrical Engineering and Computer Technology, University of Patras.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vasiloglou, Nikolaos
    Ismion Inc.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Inelligent Assistant for Physicians2016In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL, 17-20 August 2016,, 2016Conference paper (Refereed)
    Abstract [en]

    This paper presents a software tool developed for assisting physicians during an examination process. The tool consists of a number of modules with the aim to make the examination process not only quicker but also fault proof moving from a simple electronic medical records management system towards an intelligent assistant for the physician. The intelligent component exploits users inputs as well as well established standards to line up possible suggestions for filling in the examination report. As the physician continues using it, the tool keeps extracting new knowledge. The architecture of the tool is presented in brief while the intelligent component which builds upon the notion of multilabel learning is presented in more detail. Our preliminary results from a real test case indicate that the performance of the intelligent module can reach quite high performance without a large amount of data.

  • 4.
    Gavrilis, Dimitris
    et al.
    Department of Electrical Engineering and Computer Technology, University of Patras.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vasiloglou, Nikolaos
    Ismion Inc.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Intelligent Assistant for Physicians2016In: IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC): Orlando, FL, USA, 16-20 Aug. 2016, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016, 2586-2589 p., 7591259Conference paper (Refereed)
    Abstract [en]

    This paper presents a software tool developed for assisting physicians during an examination process. The tool consists of a number of modules with the aim to make the examination process not only quicker but also fault proof moving from a simple electronic medical records management system towards an intelligent assistant for the physician. The intelligent component exploits users inputs as well as well established standards to line up possible suggestions for filling in the examination report. As the physician continues using it, the tool keeps extracting new knowledge. The architecture of the tool is presented in brief while the intelligent component which builds upon the notion of multilabel learning is presented in more detail. Our preliminary results from a real test case indicate that the performance of the intelligent module can reach quite high performance without a large amount of data.

  • 5.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Alarcon, Vicente Climente
    Department of Electrical Engineering and Automation, Aalto University, Espoo.
    Dritsas, Leonidas
    University of Patras, Department of Electrical Engineering.
    Antonino-Daviu, Jose Alfonso
    Institute of Energy Engineering, University of Valencia, Spain.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Start-up analysis methods for the diagnosis of rotor asymmetries in induction motors-seeing is believing2016In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, 372-377 p., 7536045Conference paper (Refereed)
    Abstract [en]

    This article presents a qualitative analysis of different methods proposed for the diagnosis of broken rotor bars using the stator current during start-up operation. The slip dependent components, caused by the asymmetry, which is created by the breakage of rotor bar(s) and especially the left sideband harmonic (LSH) component, can create a distinctive pattern in a time- frequency plane. Short Time Fourier Transform, Wavelet analysis, and Winger-Ville Distribution are evaluated by using signals coming from motors operating in real industrial settings. The corresponding analysis presents the pros and the cons of these approaches for their potential application under realistic industrial conditions using the larger number of real life cases encountered in the literature.

  • 6.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Climente-Alarcón, Vicente
    Department of Electrical Engineering and Automation, Aalto University.
    Antonino-Daviu, José Alfonso
    Instituto Tecnologico de la Energia, Universitat Politècnica de València.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Arkkio, Antero
    Department of Electrical Engineering and Automation, Aalto University.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Multi-label Classification Approach for the Detection of Broken Bars and Mixed Eccentricity Faults Using the Start-up Transient2017In: IEEE International Conference on Industrial Informatics (INDIN), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, 430-433 p., 7819198Conference paper (Refereed)
    Abstract [en]

    In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.

  • 7.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Climente-Alarcón, Vicente
    Department of Electrical Engineering and Automation, Aalto University.
    Antonino-Daviu, José Alfonso
    Instituto Tecnologico de la Energia, Universitat Politècnica de València.
    Tsoumas, Ioannis P.
    ABB Corporate Research, Baden-Dättwil.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Arkkio, Antero
    Department of Electrical Engineering and Automation, Aalto University.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    The use of a multilabel classification framework for the detection of broken bars and mixed eccentricity faults based on the start-up transient2017In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 13, no 2, 625-634 p., 7778161Article in journal (Refereed)
    Abstract [en]

    In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three 'problem transformation' methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation

  • 8.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kappatos, V.
    Department of Technology and Innovation (ITI), University of Southern Denmark (SDU).
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Acoustic emission localization on ship hull structures using a deep learning approach2016In: Vibroengineering Procedia, ISSN 2345-0533, Vol. 9, 56-61 p.Article in journal (Refereed)
    Abstract [en]

    this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor

  • 9.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Gavrilis, Dimitris
    Department of Electrical Engineering and Computer Technology, University of Patras.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An ordinal classification approach for CTG categorization2017In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Piscataway, NJ: IEEE, 2017, 2642-2645 p.Conference paper (Refereed)
    Abstract [en]

    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.

  • 10.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta.
    Spilka, Jiří
    CIIRC, Czech Technical, University in Prague.
    Chudáček, Václav
    CIIRC, Czech Technical, University in Prague.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Lhotská, Lenka
    CIIRC, Czech Technical, University in Prague.
    Investigating pH based evaluation of fetal heart rate (FHR) recordings2017In: Health and Technology, ISSN 2190-7188, E-ISSN 2190-7196Article in journal (Refereed)
    Abstract [en]

    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

  • 11.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Bearing fault detection and diagnosis by fusing vibration data2016In: IECON Proceedings (Industrial Electronics Conference), Piscataway, NJ: IEEE Computer Society, 2016, 6955-6960 p., 7794118Conference paper (Refereed)
    Abstract [en]

    This article presents a simple method for the detection and diagnosis of bearing faults, by fusing the information coming from two accelerometers. The method relies on three simple and intuitive features, extracted from the data coming from accelerometers placed at two different sites of the system under investigation. Our preliminary results indicate that by using simple statistical measures, such as the elements of the covariance matrix of the two sensors, faults at an early stage can be detected. In our the proposed scheme, the extracted features are fed to a k-nearest neighbour classifier for diagnosis purposes or to an ensemble of one-class detectors, if only the information from normal situation is available. As it is proved, based on experimental results, in both scenarios a remarkably high detection/diagnostic performance is achieved.

  • 12.
    Goldin, E.
    et al.
    GSTAT, Israel.
    Feldman, D.
    GSTAT, Israel.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Castaño Arranz, Miguel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cloud computing for big data analytics in the Process Control Industry2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, 1373-1378 p., 7984310Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0

  • 13.
    Herceg, Domagoj
    et al.
    IMT School for Advanced Studies Lucca.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sopasakis, Pantelis
    KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics.
    Castaño Arranz, Miguel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Patrinos, Panagiotis K.
    KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics.
    Bemporad, Alberto
    IMT School for Advanced Studies Lucca.
    Niemi, Jan
    Swerea MEFOS, Box 812, Lulea.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, 1361-1366 p., 7984308Conference paper (Refereed)
    Abstract [en]

    The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions

  • 14.
    Kappatos, V.
    et al.
    Department of Technology and Innovation (ITI), University of Southern Denmark (SDU).
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Avdelidis, Nicolas P.
    Department of Electrical and Computer Engineering, Université Laval, Quebec City.
    Salonitis, Konstantinos
    Manufacturing Department, Cranfield University.
    Tidal stream generators, current state and potential opportunities for condition monitoring2016In: Vibroengineering Procedia, ISSN 2345-0533, Vol. 8, 285-293 p.Article in journal (Refereed)
    Abstract [en]

    Tidal power industry has made significant progress towards commercialization over the past decade. Significant investments from sector leaders, strong technical progress and positive media coverage have established the credibility of this specific renewable energy source. However, its progress is being retarded by operation and maintenance problems, which results in very low operational availability times, as low as 25 %. This paper presents a literature review of the current state of tidal device operators as well as some commercial tidal turbine condition monitoring solutions. Furthermore, an overview is given of the global tidal activity status (tidal energy market size and geography), the key industry activity and the regulations-standards related with tidal energy industry. Therefore, the main goal of this paper is to provide a bird's view of the current status of the tidal power industry to serve as a roadmap for the academia regarding the real needs of the tidal power industry

  • 15.
    Karvelis, Petros
    et al.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Georgoulas, Georgios
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Artas.
    Tsoumas, Ioannis P.
    ABB Corporate Research, Baden-Dättwil.
    Antonino-Daviu, José Alfonso
    Instituto de Ingeniería Energética, Universitat Politècnica de València.
    Climente-Alarcón, Vicente
    Department of Electrical Engineering and Automation, Aalto University.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors2015In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 11, no 5, 1028-1037 p., 7175053Article in journal (Refereed)
    Abstract [en]

    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.

  • 16.
    Karvelis, Petros
    et al.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Faleij, Ragnar
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Georgoulas, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Laser Dot Tracking Method for the Assessment of Sensorimotor Function of the Hand2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway. NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, 217-222 p., 7984121Conference paper (Refereed)
    Abstract [en]

    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

  • 17.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the covering of a polygonal region with fixed size rectangles with an application towards aerial inspection2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, 1316-1320 p., 7984300Conference paper (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles (UAVs) equipped with remote visual sensing can be used in wide range of applications. However, guaranteeing the full coverage of the area and translating this coverage in a path planning problem, it is a quite challenging task. Thus, in this article a well-known and well-investigated family of hard optimization problems, covering a polygonal region (target area) with fixed size rectangles (camera frustrum), is studied. The problem is formulated mathematically and solved using metaheuristic optimization algorithms. The proposed novel algorithmic scheme requires an a priori 2D model of the target area, while it tries to maximize the coverage with a minimum number of fixed size rectangles. Finally, multiple simulation results are presented that prove the efficacy of the proposed scheme

  • 18.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Remaining Useful Battery Life Prediction for UAVs based on Machine Learning*2017In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, 4727-4732 p.Article in journal (Refereed)
    Abstract [en]

    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.

  • 19.
    Röijezon, Ulrik
    et al.
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Faleij, Ragnar
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Kravelis, Petros S.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A new clinical test for sensorimotor function of the hand: development and preliminary validation2017In: BMC Musculoskeletal Disorders, ISSN 1471-2474, E-ISSN 1471-2474, Vol. 18, no 1, 407Article in journal (Refereed)
    Abstract [en]

    Background

    Sensorimotor disturbances of the hand such as altered neuromuscular control and reduced proprioception have been reported for various musculoskeletal disorders. This can have major impact on daily activities such as dressing, cooking and manual work, especially when involving high demands on precision and therefore needs to be considered in the assessment and rehabilitation of hand disorders. There is however a lack of feasible and accurate objective methods for the assessment of movement behavior, including proprioception tests, of the hand in the clinic today. The objective of this observational cross- sectional study was to develop and conduct preliminary validation testing of a new method for clinical assessment of movement sense of the wrist using a laser pointer and an automatic scoring system of test results.

    Methods

    Fifty physiotherapists performed a tracking task with a hand-held laser pointer by following a zig-zag pattern as accurately as possible. The task was performed with left and right hand in both left and right directions, with three trials for each hand movement. Each trial was video recorded and analysed with a specifically tailored image processing pipeline for automatic quantification of the test. The main outcome variable was Acuity, calculated as the percent of the time the laser dot was on the target line during the trial.

    Results

    The results showed a significantly better Acuity for the dominant compared to non-dominant hand. Participants with right hand pain within the last 12 months had a significantly reduced acuity (p < 0.05), and although not significant there was also a similar trend for reduced Acuity also for participants with left hand pain. Furthermore, there was a clear negative correlation between Acuity and Speed indicating a speed-accuracy trade off commonly found in manual tasks. The repeatability of the test showed acceptable intra class correlation (ICC2.1) values (0.68-0.81) and standard error of measurement values ranging between 5.0–6.3 for Acuity.

    Conclusions

    The initial results suggest that the test may be a valid and feasible test for assessment of the movement sense of the hand. Future research should include assessments on different patient groups and reliability evaluations over time and between testers.

  • 20.
    Seghiour, Abdellatif
    et al.
    Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et Diélectriques, Université Amar Telidji de Laghouat, Laghouat.
    Seghier, Tahar
    Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et Diélectriques, Université Amar Telidji de Laghouat, Laghouat.
    Zegnini, Boubakeur
    Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et Diélectriques, Université Amar Telidji de Laghouat, Laghouat.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Diagnosis of the combined rotor faults using air gap magnetic flux density spectrum for an induction machine2017In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348Article in journal (Refereed)
    Abstract [en]

    This paper presents a method for the diagnosis of induction machines faults. The proposed method is capable to detect the presence of both dynamic eccentricity and broken rotor bar faults. Several studies have attempted to model an induction machine with isolated faults and provide methods for detecting them. However, the challenge begins, with the occurrence of combined defects which produce fault signatures that are difficult to separate. The novel proposed method is based on the measured air-gap magnetic flux density spectrum, which allows for the detection of combined faults. A finite element method is used for modelling the induction machine under faulty conditions, where the faults of rotor bars are created by a deleting operation of the boundary condition which is added to the air-gap part. Then, the dynamic eccentricity is formed by the movements of the rotating rotor’s centre with different ratings. From a modelling perspective, the contribution of the current work is the establishment of the relation of the air-gap of the rotor for modelling this kind of eccentricity fault. In addition, the proposed model of the air-gap includes two parts; one related to the stator and another one to the rotor, called statoric air-gap and rotoric air-gap respectively. The rotoric air-gap is employed for the dynamic eccentricity modelling. Computer simulations are presented using the air-gap magnetic vector and the magnetic field in X and Y components, to confirm the robustness of the proposed technique. Finally, the air-gap magnetic flux density spectrum is used for the analysis of combined rotor faults.

  • 21.
    Seghiour, Abdellatif
    et al.
    Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et Diélectriques, Université Amar Telidji de Laghouat, Laghouat.
    Seghier, Tahar
    Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et Diélectriques, Université Amar Telidji de Laghouat, Laghouat.
    Zegnini, Boubakeur
    Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et Diélectriques, Université Amar Telidji de Laghouat, Laghouat.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Multi-Class Classification Approach for the Diagnosis of Broken Rotor Bars based on Air-Gap Magnetic Flux Density2017In: Electrotehnica, Electronica, Automatica (EEA), ISSN 1582-5175, Vol. 65, no 2, 31-39 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, condition monitoring of induction machines using air-gap magnetic flux density spectrum via artificial neural networks is presented. The proposed scheme is chosen due to its effectiveness, simplicity, and low cost that used for the detection of broken rotor bar faults. The spectrum of the air-gap magnetic flux density is estimated using the Fast Fourier Transform, which can capture the fault related to harmonic components. The extracted information is then utilized by a machine-learning paradigm in a Multi-class classification approach for the detection of broken rotor bars, for both, adjacent and non-adjacent using artificial neural networks as a classification method. The obtained simulation results of the healthy and faulty conditions using finite elements prove the applicability of the proposed method.

  • 22.
    Stylios, Chrysostomos D.
    et al.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Georgoulas, Georgios
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Spilka, Jiří
    CIIRC, Czech Technical, University in Prague.
    Chudáček, Václav
    CIIRC, Czech Technical, University in Prague.
    Lhotská, Lenka
    CIIRC, Czech Technical, University in Prague.
    Least squares support vector machines for FHR classification and assessing the pH based categorization2016Conference paper (Refereed)
    Abstract [en]

    Cardiotocography (CTG) is the major monitoring tool for fetal well-being surveillance during labor. It consists of two distinctive signals: the Fetal Heart Rate (FHR) and the Uterine Contractions signal. The CTG interpretation is classically performed by obstetricians with visual inspection for reassuring or ominous patterns, which are associated with fetus’ condition. Deviations of the CTG and especially of the (FHR) from normality can be an indication of oxygen deprivation during the stressful labor process, which can lead to major neurological damage to the fetus or even death. This compromise is usually reflected at the pH level of newborn’s blood. Therefore pH levels are usually used for the discrimination between healthy and compromised fetuses. In this work we present our preliminary results of the application of a machine learning approach, using least squares support vector machines, to FHR classification using the largest CTG openaccess database so far

  • 23.
    Tsoumas, Ioannis P.
    et al.
    ABB Corporate Research, Baden-Dättwil.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Antonino-Daviu, José Alfonso
    Instituto Tecnológico de la Energía, Universitat Politécnica de Valencia.
    Analytical investigation of the startup transient in asynchronous motors with rotor asymmetry2016In: Proceedings: 2016 22nd International Conference on Electrical Machines, ICEM 2016, Piscataway, NU: Institute of Electrical and Electronics Engineers (IEEE), 2016, 2861-2866 p., 7732929Conference paper (Refereed)
    Abstract [en]

    An analytical investigation of the current transient after switch-on at standstill of an induction motor with rotor asymmetry is carried out in the present work. The purpose of this investigation is the calculation of the different current components and their evolution, especially of those that can be used for the detection of the asymmetry. Since fault detection methods based on transient analysis use this component to detect the rotor asymmetry, the exact knowledge of its evolution as well as of the evolution of other transient components which may interfere or hinder the detection of the fault is of great importance.

  • 24.
    Vachtsevanos, George
    et al.
    School of Electrical and Computer, Engineering, Georgia Institute of Technology, Atlanta.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fault Diagnosis, Failure Prognosis and Fault Tolerant Control of Aerospace/Unmanned Aerial Systems2016In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, 366-371 p., 7536041Conference paper (Refereed)
    Abstract [en]

    Fault-tolerant control and operation of complex unmanned and aircraft systems is an emerging technology intended to provide the designer and operator with flexibility, interoperability, sustainment and reliability under changing operational requirements or mission profiles. Moreover, it is intended to reconfigure online hardware and software to maintain the operational integrity of the system in the event of contingencies (fault/failure modes). This paper presents an hierarchical architecture that uses available sensor information, fault isolation, failure prognosis, system restructuring and controller reconfiguration. The fault tolerant control framework relies on prognostic information to reconfigure system components and preserve the operational integrity of the aircraft. The hierarchical structure starts at the lowest component level and migrates to the middle system/subsystem level ending with the final mission level. We illustrate the methodology using an electro-mechanical actuator (EMA).

1 - 24 of 24
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