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  • Peng, Cong
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Goswami, Prashant
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Bai, Guohua
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    An Ontological Approach to Integrate Health Resources from Different Categories of Services2018In: HEALTHINFO 2018, The Third International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing, International Academy, Research and Industry Association (IARIA), 2018, p. 48-54Conference paper (Refereed)
    Abstract [en]

    Effective and convenient self-management of health requires collaborative utilization of health data from different services provided by healthcare providers, consumer-facing products and even open data on the Web. Although health data interoperability standards include Fast Healthcare Interoperability Resources (FHIR) have been developed and promoted, it is impossible for all the different categories of services to adopt in the near future. The objective of this study aims to apply Semantic Web technologies to integrate the health data from heterogeneously built services. We present an Web Ontology Language (OWL)-based ontology that models together health data from FHIR standard implemented services, normal Web services and Linked Data. It works on the resource integration layer of the presented layered integration architecture. An example use case that demonstrates how this method integrates the health data into a linked semantic health resource graph with the proposed ontology is presented.

  • Javadi, Mohammad Saleh
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Computer Vision Algorithms for Intelligent Transportation Systems Applications2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In recent years, Intelligent Transportation Systems (ITS) have emerged as an efficient way of enhancing traffic flow, safety and management. These goals are realized by combining various technologies and analyzing the acquired data from vehicles and roadways. Among all ITS technologies, computer vision solutions have the advantages of high flexibility, easy maintenance and high price-performance ratio that make them very popular for transportation surveillance systems. However, computer vision solutions are demanding and challenging due to computational complexity, reliability, efficiency and accuracy among other aspects.

    In this thesis, three transportation surveillance systems based on computer vision are presented. These systems are able to interpret the image data and extract the information about the presence, speed and class of vehicles, respectively. The image data in these proposed systems are acquired using Unmanned Aerial Vehicle (UAV) as a non-stationary source and roadside camera as a stationary one. The goal of these works is to enhance the general performance in accuracy and robustness of the systems with variant illumination and traffic conditions.

    This is a compilation thesis in systems engineering consists of three parts. The red thread through each part is a transportation surveillance system. The first part presents a change detection system using aerial images of a cargo port. The extracted information shows how the space is utilized at various times for further management and development of the port. The proposed solution can be used at different viewpoints and illumination levels e.g. sunset. The method is able to transform the images taken from different viewpoints and match them together and then using a proposed adaptive local threshold to detect discrepancies between them. In the second part, a vision-based vehicle's speed estimation system is presented. The measured speeds are essential information for law enforcement as well as estimation of traffic flow at certain points on the road. The system employs several intrusion lines to extract the movement pattern of each vehicle (non-equidistant sampling) as an input feature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are also taken into account to address the uncertainty in the measurements and to obtain the probability density function of the vehicle's speed. In the third part, a vehicle classification system is provided to categorize vehicles into “private cars", “light trailers", “lorry or bus" and “heavy trailer". This information can be used by authorities for surveillance and development of the roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. The system has been constructed using prior knowledge of traffic regulations regarding each class of vehicle in order to enhance the classification performance.

  • Abghari, Shahrooz
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Data Modeling for Outlier Detection2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

    Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

    We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

  • Javadi, Mohammad Saleh
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Rameez, Muhammad
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Dahl, Mattias
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features2018In: Procedia Computer Science, Elsevier, 2018, Vol. 126, p. 7p. 1344-1350Conference paper (Refereed)
    Abstract [en]

    Vehicle classification has a significant use in traffic surveillance and management. There are many methods proposed to accomplish this task using variety of sensorS. In this paper, a method based on fuzzy c-means (FCM) clustering is introduced that uses dimensions and speed features of each vehicle. This method exploits the distinction in dimensions features and traffic regulations for each class of vehicles by using multiple FCM clusterings and initializing the partition matrices of the respective classifierS. The experimental results demonstrate that the proposed approach is successful in clustering vehicles from different classes with similar appearanceS. In addition, it is fast and efficient for big data analysiS.

  • Sigvant, Mats
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    Pilthammar, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    Hol, J.
    TriboForm Engineering, NLD.
    Wiebenga, J. H.
    TriboForm Engineering, NLD.
    Chezan, T.
    Tata Steel, NLD.
    Carleer, B.
    AutoForm Engineering, DEU.
    Van Den Boogaard, A. H.
    University of Twente, NLD.
    Friction in Sheet Metal Forming Simulations: Modelling of New Sheet Metal Coatings and Lubricants2018In: IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing , 2018, Vol. 418, no 1, article id 012093Conference paper (Refereed)
    Abstract [en]

    The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of a door-inner part in Volvo Cars production have been presented. This paper focuses on the tribology conditions during early tryout of dies for new car models with an emphasis on the effect of the usage of new steel material coatings and lubricants on forming results. The motivation for the study is that the majority of the forming simulations at Volvo Cars are performed to secure the die tryout, i.e. solve as many problems as possible in forming simulations before the final design of the die and milling of the casting. In the current study, three closure parts for the new Volvo V60 model have been analysed with both Coulomb and TriboForm friction models. The simulation results from the different friction models are compared using thickness measurements of real parts, and 3D geometry scanning data of the parts. Results show the improved prediction accuracy of forming simulations when using the TriboForm friction model, demonstrating the ability to account for the effect of new sheet metal coatings and lubricants in sheet metal forming simulations. © Published under licence by IOP Publishing Ltd.

  • Sievert, Thomas
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Rasch, Joel
    Molflow, SWE.
    Carlström, Anders
    RUAG Space AB, SWE.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Comparing reflection signatures in radio occultation measurements using the full spectrum inversion and phase matching methods2018In: PROCEEDINGS VOLUME 10786; Remote Sensing of Clouds and the Atmosphere XXIII, SPIE - International Society for Optical Engineering, 2018Conference paper (Refereed)
    Abstract [en]

    Global Navigation Satellite System Radio Occultation (GNSS-RO) is an important technique used to sound the Earth's atmosphere and provide data products to numerical weather prediction (NWP) systems as well as toclimate research. It provides a high vertical resolution and SI-traceability that are both valuable complements toother Earth observation systems. In addition to direct components refracted in the atmosphere, many received RO signals contain reflected components thanks to the specular and relatively smooth characteristics of the ocean. These reflected components can interfere the retrieval of the direct part of the signal, and can also contain meteorological information of their own, e.g., information about the refractivity at the Earth's surface. While the conventional method to detect such reflections is by using radio-holographic methods, it has been shown that it is possible to see reflections using wave optics inversion, specically while inspecting the amplitude of the output of phase matching (PM). The primary objective of this paper is to analyze the appearance of these reflections in the amplitude output from another wave optics algorithm, namely the much faster full spectrum inversion (FSI). PM and FSI are closely related algorithms - they both use the method of stationary phase to derive the bending angle from a measured signal. We apply our own implementation of FSI to the same GNSS-RO measurements that PM was previously applied to and show that the amplitudes of the outputs again indicate reflection in the surface of the ocean. Our results show that the amplitudes output from the FSI and PM algorithms are practically identical and that the reflection signatures thus appear equally well.

  • Kuzminykh, Ievgeniia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Carlsson, Anders
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Franksson, Robin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Liljegren, Alexander
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Measuring a LoRa Network: Performance, Possibilities and Limitations2018In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Springer, 2018, Vol. 11118, p. 116-128Conference paper (Refereed)
    Abstract [en]

    Low power wide area (LPWA) technologies becomes popular for IoT use cases because LPWA is enable the broad range communications and allows to transmit small amounts of information in a long distance. Among LPWA technologies there are LTE-M, SigFox, LoRa, Symphony Link, Ingenu RPMA, Weightless, and NB-IoT. Currently all these technologies suffer from lack of documentation about deployment recommendation, have non-investigated limitations that can affect implementations and products using such technologies. This paper is focused on the testing of LPWAN LoRa technology to learn how a LoRa network gets affected by different environmental attributes such as distance, height and surrounding area by measuring the signal strength, signal to noise ratio and any resulting packet loss. The series of experiments for various use cases are conducted using a fully deployed LoRa network made up of a gateway and sensor available through the public network. The results will show the LoRa network limitation for such use cases as forest, city, open space. These results allow to give the recommendation for companies during early analysis and design stages of network life circle, and help to choose properly technology for deployment an IoT application.

  • Kuzminykh, Ievgeniia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Carlsson, Anders
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Analysis of Assets for Threat Risk Model in Avatar-Oriented IoT Architecture2018In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2018, ruSMART 2018. Lecture Notes in Computer Science, vol 11118 / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y. (eds), Springer, 2018, Vol. 11118, p. 52-63Conference paper (Refereed)
    Abstract [en]

    This paper represents new functional architecture for the Internet of Things systems that use an avatar concept in displaying interaction between components of the architecture. Object-oriented representation of “thing” in the avatar concept allows simplify building and deployment of IoT systems over the web network and bind “things” to such application protocols as HTTP, CoAP, and WebSockets mechanism. The assets and stakeholders for ensuring security in IoT were specified. These assets are needed to isolate the risks associated with each of assets of IoT system. Example of Thing Instance’s description and its functionality using JSON format is shown also in the paper.