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  • 1.
    Al-Chalabi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Al-Douri, Yamur
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Time Series Forecasting using ARIMA Model: A Case Study of Mining Face Drilling Rig2018Conference paper (Refereed)
    Abstract [en]

    This study implements an AutoregressiveIntegrated Moving Average (ARIMA) model to forecast totalcost of a face drilling rig used in the Swedish mining industry.The ARIMA model shows different forecasting abilities usingdifferent values of ARIMA parameters (p, d, q). However,better estimation for the ARIMA parameters is required foraccurate forecasting. Artificial intelligence, such as multiobjective genetic algorithm based on the ARIMA model, couldprovide other possibilities for estimating the parameters. Timeseries forecasting is widely used for production control,production planning, optimizing industrial processes andeconomic planning. Therefore, the forecasted total cost data ofthe face drilling rig can be used for life cycle cost analysis toestimate the optimal replacement time of this rig.

  • 2.
    Bilal, Mohd
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    A Heuristic Search Algorithm for Asteroid Tour Missions2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Since the discovery of Ceres, asteroids have been of immense scientific interest and intrigue. They hold answers to many of the fundamental questionsabout the formation and evolution of the Solar System. Therefore, a missionsurveying the asteroid belt with close encounter of carefully chosen asteroidswould be of immense scientific benefit. The trajectory of such an asteroidtour mission needs to be designed such that asteroids of a wide range ofcompositions and sizes are encountered; all with an extremely limited ∆Vbudget.This thesis presents a novel heuristic algorithm to optimize trajectoriesfor an asteroid tour mission with close range flybys (≤ 1000 km). The coresearch algorithm efficiently decouples combinatorial (i.e. choosing the asteroids to flyby)and continuous optimization (i.e. optimizing critical maneuversand events) of what is essentially a mixed integer programming problem.Additionally, different methods to generate a healthy initial population forthe combinatorial optimization are presented.The algorithm is used to generate a set of 1800 feasible trajectories withina 2029+ launch frame. A statistical analysis of these set of trajectories isperformed and important metrics for the search are set based on the statistics.Trajectories allowing flybys to prominent families of asteroids like Flora andNysa with ∆V as low as 4.99 km/s are obtained.Two modified implementations of the algorithm are presented. In a firstiteration, a large sample of trajectories is generated with a limited numberof encounters to the most scientifically interesting targets. While, a posteriori, trajectories are filled in with as many small targets as possible. Thisis achieved in two different ways, namely single step extension and multiplestep extension. The former fills in the trajectories with small targets in onestep, while the latter optimizes the trajectory by filling in with one asteroid per step. The thesis also presents detection of asteroids for successfullyperforming flybys. A photometric filter is developed which prunes out badlyilluminated asteroids. The best trajectory is found to perform well againstthis filter such that nine out of the ten planned flybys are feasible.

  • 3.
    D'Orazio, Christian Javier
    et al.
    School of Information Technology and Mathematical Sciences, University of South Australia, Australia.
    Rongxing, Lu
    Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada.
    Choo, Kim Kwang Raymond
    School of Information Technology and Mathematical Sciences, University of South Australia, Australia.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Markov adversary model to detect vulnerable iOS devices and vulnerabilities in iOS apps2017In: Applied Mathematics and Computation, ISSN 0096-3003, E-ISSN 1873-5649, Vol. 293, p. 523-544Article in journal (Refereed)
    Abstract [en]

    With the increased convergence of technologies whereby a user can access, store and transmit data across different devices in real-time, risks will arise from factors such as lack of appropriate security measures in place and users not having requisite levels of security awareness and not fully understanding how security measures can be used to their advantage. In this paper, we adapt our previously published adversary model for digital rights management (DRM) apps and demonstrate how it can be used to detect vulnerable iOS devices and to analyse (non-DRM) apps for vulnerabilities that can potentially be exploited. Using our adversary model, we investigate several (jailbroken and non-jailbroken) iOS devices, Australian Government Medicare Expert Plus (MEP) app, Commonwealth Bank of Australia app, Western Union app, PayPal app, PocketCloud Remote Desktop app and Simple Transfer Pro app, and reveal previously unknown vulnerabilities. We then demonstrate how the identified vulnerabilities can be exploited to expose the user's sensitive data and personally identifiable information stored on or transmitted from the device. We conclude with several recommendations to enhance the security and privacy of user data stored on or transmitted from these devices.

  • 4.
    Eliasson, Anton
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Anomaly Detection in Industrial Networks using a Resource-Constrained Edge Device2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The detection of false data-injection attacks in industrial networks is a growing challenge in the industry because it requires knowledge of application and protocol specific behaviors. Profinet is a common communication standard currently used in the industry, which has the potential to encounter this type of attack. This motivates an examination on whether a solution based on machine learning with a focus on anomaly detection can be implemented and used to detect abnormal data in Profinet packets. Previous work has investigated this topic; however, a solution is not available in the market yet. Any solution that aims to be adopted by the industry requires the detection of abnormal data at the application level and to run the analytics on a resource-constrained device. This thesis presents an implementation, which aims to detect abnormal data in Profinet packets represented as online data streams generated in real-time. The implemented unsupervised learning approach is validated on data from a simulated industrial use-case scenario. The results indicate that the method manages to detect all abnormal behaviors in an industrial network. 

  • 5.
    Enström, Olof
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Authentication Using Deep Learning on User Generated Mouse Movement Images2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Continuous authentication using behavioral biometrics can provide an additional layer of protection against online account hijacking and fraud. Mouse dynamics classification is the concept of determining the authenticity of a user through the use of machine learning algorithms on mouse movement data. This thesis investigates the viability of state of the art deep learning technologies in mouse dynamics classification by designing convolutional neural network classifiers taking mouse movement images as input. For purposes of comparison, classifiers using the random forest algorithm and engineered features inspired by related works are implemented and tested on the same data set as the neural network classifier.

    A technique for lowering bias toward the on-screen location of mouse movement images is introduced, although its effectiveness is questionable and requires further research to thoroughly investigate. This technique was named 'centering', and is used for the deep learning-based classification methods alongside images not using the technique. The neural network classifiers yielded single action classification accuracies of 66% for centering, and 78% for non-centering. The random forest classifiers achieved the average accuracy of 79% for single action classification, which is very close to the results of other studies using similar methods. In addition to single action classification, a set based classification is made. This is the method most suitable for implementation in an actual authentication system as the accuracy is much higher.

    The neural network and random forest classifiers have different strengths. The neural network is proficient at classifying mouse actions that are of similar appearance in terms of length, location, and curvature. The random forest classifiers seem to be more consistent in these regards, although the accuracy deteriorates for especially long actions. As the different classification methods in this study have different strengths and weaknesses, a composite classification experiment was made where the output was determined by the least ambiguous output of the two models. This composite classification had an accuracy of 83%, meaning it outperformed both the individual models.

  • 6.
    Hansson, Markus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Tracking and Serving Geolocated Ads, Load Balancing, and Scaling of Server Resources2018Independent thesis Advanced level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This thesis explores the creation of a scaling, containerized, advertisement server that will be used by Gold Town Games AB to better integrate ads into their application(s). The server is built as a Docker image that will be used to create server instances on AWS Elastic Container Service for automatic scaling and server resource configuration.

    The server was created with the intention that GTG will have full control over what advertisements are shown in their application(s) and to seamlessly integrate sponsored logos onto jerseys or sports fields. This will not only serve as a source of income with advertisers paying for ad space, but it will also make the game elements more realistic as we have come to expect teams and stadiums to be sponsored and plastered with company logos. Another important part when displaying advertisement is to track statistics for the ads, since without a way to show advertisers that their ads are shown and that they are generating engagement it is very hard to sell the ad space.

  • 7.
    Hedlund, Nicklas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    TicTacTraining: Coordination of multiple clients in a web based exergame2019Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The traditional way to coordinate multiple clients in a multiplayer based game on multiple platforms is to create an implementation on a per-platform basis - resulting in often four different implementations, one for each major platform - ie. iOS, Android, Windows, and Linux based operating systems. This report examines the possibility of replacing multiple so called “native apps” with a single web based implementation - granting users access on all devices that supports modern browsers, and discusses what tools were used in the development of the application and why. 

  • 8.
    Johansson, Ted
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Tactical Simulation in Air-To-Air Combat: Evolutionary Algorithms and Behavior Tree Framework2018Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
  • 9.
    Nordin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Terrain sensor for semi active suspension in CV902017Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The combat vehicle, CV90 has a semi-active hydraulic suspension system which uses inertial measurements for regulation to improve accessibility. To improve performance further measurements of future terrain can be used to, for example, prepare for impacts. This master's thesis investigates the ability to use existing sensors and new sensors to facilitate these measurements.

    Two test runs were performed, with very different conditions and outcomes. The results seem to suggest that a sweeping LIDAR was the most accurate and robust solution. However, using a very recent visual odometry algorithm, promising results were achieved using an Infra-red heat camera. Especially given that no efforts were put into adjusting parameters for that particular algorithm.

  • 10.
    Pham, Thi An
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. PERCCOM.
    Improving the Effectiveness of Building Automation by adaption to the Users Context2019Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
    Abstract [en]

    The operations of either residential housing or commercial buildings are energy intensive, estimated to occupy around 40% of all energy consumed worldwide by the year 2030 (by GeSI, SMARTer2030). ICT-enabled smart home or building solutions are expected to contribute to sustainability gain in term of improving energy and resource efficiency. These technologies not only enable buildings to be automated and centrally controlled but also help to provide a healthier and more comfortable living or working environment. While studies in smart home system show good results in reducing the energy consumption of a building by automating tasks to tear down unused appliances, most of the applications are limited implemented based on fixed schedule reassembling user behavior or routines, which is one of the major obstacles for home automation systems (HAS) to be widely acquired. As a solution for this matter, this study aims at exploring actual contexts of user for HAS to adapt in a more meaningful way so that not only the goal of reduced energy consumption is improved, but the user comfort is also taken care of in the best way. Using available studies on the expected reaction in HAS (in this work we focus on German Use case), a rule-based dictionary will be defined as a set of meaningful adaptions which can later be implemented on top of a home automation platform. Then, the study will present the assessment of this model in comparison with available studies to prove an improvement for energy efficiency.

  • 11.
    Sandström, David
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Dynamic Occlusion of Virtual Objects in an 'Augmented Reality' Environment2018Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis explores a way of increasing the perception of reality within an ''Augmented Reality'' application by making real objects able to obstruct the view of virtual objects. This mimics how real opaque objects occlude each other and thus making virtual objects behave the same way will improve the user experience of Augmented Reality users. The solution uses Unity as the engine with plugins for ARKit and OpenCV. ARKit provides the Augmented Reality experience and can detect real world flat surfaces on which virtual objects can be placed. OpenCV is used for image processing to detect real world objects which can then be translated into virtual silhouettes within Unity that can interact with, and occlude, the virtual objects. The end result is a system that can handle the occlusion in real time, while allowing both the real and virtual objects to translate and rotate within the scene while still maintaining the occlusion. The big drawback of the solution is that it requires a well defined environment without visual clutter and with even lighting to work as intended. This makes it unsuitable for outdoor usage.

  • 12.
    Sjölund, Johannes
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Real-time Thermal Flow Predictions for Data Centers: Using the Lattice Boltzmann Method on Graphics Processing Units for Predicting Thermal Flow in Data Centers2018Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of this master thesis is to investigate the usage of the Lattice Boltzmann Method (LBM) of Computational Fluid Dynamics (CFD) for real-time prediction of indoor air flows inside a data center module. Thermal prediction is useful in data centers for evaluating the placement of heat-generating equipment and air conditioning.

    To perform the simulation a program called RAFSINE was used, written by Nicholas Delbosc at the University of Leeds, which implemented LBM on Graphics Processing Units (GPUs) using NVIDIA CUDA. The program used the LBM model called Bhatnagar-Gross-Krook (BGK) on a 3D lattice and had the capability of executing thermal simulations in real-time or faster than real-time. This fast rate of execution means a future application for this simulation could be as a predictive input for automated air conditioning control systems, or for fast generation of training data sets for automatic fault detection systems using machine learning.

    In order to use the LBM CFD program even from hardware not equipped with NVIDIA GPUs it was deployed on a remote networked server accessed through Virtual Network Computing (VNC). Since RAFSINE featured interactive OpenGL based 3D visualization of thermal evolution, accessing it through VNC required use of the VirtualGL toolkit which allowed fast streaming of visualization data over the network.

    A simulation model was developed describing the geometry, temperatures and air flows of an experimental data center module at RISE SICS North in Luleå, Sweden, based on measurements and equipment specifications. It was then validated by comparing it with temperatures recorded from sensors mounted in the data center.

    The thermal prediction was found to be accurate on a room-level within ±1° C when measured as the average temperature of the air returning to the cooling units, with a maximum error of ±2° C on an individual basis. Accuracy at the front of the server racks varied depending on the height above the floor, with the lowest points having an average accuracy of ±1° C, while the middle and topmost points had an accuracy of ±2° C and ±4° C respectively.

    While the model had a higher error rate than the ±0.5° C accuracy of the experimental measurements, further improvements could allow it to be used as a testing ground for air conditioning control or automatic fault detection systems.

  • 13.
    Waranoi, Elias
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Advertisement Tool: Web client, server and Cassandra DB implementation2019Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This report is on the steps involved in creating a single page application web page, with an accompanying server done with ASP.NET Core, made to communicate with a Cassandra database. All this is made for an advertisement tool where users can add and edit advertisements in a Cassandra database. The database model takes into account both the users of the advertisement tool and the users downloading and viewing the advertisements. The server relies heavily on model binding through ASP.NET Core, to catch and manipulate information from the client tool given through HTTP requests. Usage of the model-view-controller service within ASP.NET Core helps decouple the server logic while partial views helps to decouple view logic. A discussion on whether Cassandra is the correct choice of database is made. The database stores the statistics and information related to advertisements and the access URLs to the advertisements inside of an Amazon S3 bucket.

  • 14.
    Widforss, Aron
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Avalanche Visualisation Using Satellite Radar2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Avalanche forecasters need precise knowledge about avalanche activity in large remote areas. Manual methods for gathering this data have scalability issues. Synthetic aperture radar satellites may provide much needed complementary data. This report describes Avanor, a system presenting change detection images of such satellite data in a web map client. Field validation suggests that the data in Avanor show at least 75 percent of the largest avalanches in Scandinavia with some small avalanches visible as well.

1 - 14 of 14
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