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  • 1.
    Abdalmoaty, Mohamed
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Coimbatore Anand, Sribalaji
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för elektroteknik, Signaler och system.
    Teixeira, André
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för elektroteknik, Signaler och system. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Privacy and Security in Network Controlled Systems via Dynamic Masking2023Inngår i: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 56, nr 2, s. 991-996Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, we propose a new architecture to enhance the privacy and security of networked control systems against malicious adversaries. We consider an adversary which first learns the system using system identification techniques (privacy), and then performs a data injection attack (security). In particular, we consider an adversary conducting zero-dynamics attacks (ZDA) which maximizes the performance cost of the system whilst staying undetected. Using the proposed architecture, we show that it is possible to (i) introduce significant bias in the system estimates obtained by the adversary: thus providing privacy, and (ii) efficiently detect attacks when the adversary performs a ZDA using the identified system: thus providing security. Through numerical simulations, we illustrate the efficacy of the proposed architecture

  • 2.
    Abdalmoaty, Mohamed
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Medvedev, Alexander
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Noise reduction in Laguerre-domain discrete delay estimation2022Inngår i: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, s. 6254-6259Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper introduces a stochastic framework for a recently proposed discrete-time delay estimation method in Laguerre-domain, i.e. with the delay block input and output signals being represented by the corresponding Laguerre series. A novel Laguerre-domain disturbance model allowing the involved signals to be square-summable sequences is devised. The relation to two commonly used time-domain disturbance models is clarified. Furthermore, by forming the input signal in a certain way, the signal shape of an additive output disturbance can be estimated and utilized for noise reduction. It is demonstrated that a significant improvement in the delay estimation error is achieved when the noise sequence is correlated. The noise reduction approach is applicable to other Laguerre-domain problems than pure delay estimation.

  • 3.
    Abd-Elrady, Emad
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Harmonic signal modeling based on the Wiener model structure2002Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The estimation of frequencies and corresponding harmonic overtones is a problem of great importance in many situations. Applications can, for example, be found in supervision of electrical power transmission lines, in seismology and in acoustics. Generally, a periodic function with an unknown fundamental frequency in cascade with a parameterized and unknown nonlinear function can be used as a signal model for an arbitrary periodic signal. The main objective of the proposed modeling technique is to estimate the fundamental frequency of the periodic function in addition to the parameters of the nonlinear function.

    The thesis is divided into four parts. In the first part, a general introduction to the harmonic signal modeling problem and different approaches to solve the problem are given. Also, an outline of the thesis and future research topics are introduced.

    In the second part, a previously suggested recursive prediction error method (RPEM) for harmonic signal modeling is studied by numerical examples to explore the ability of the algorithm to converge to the true parameter vector. Also, the algorithm is modified to increase its ability to track the fundamental frequency variations.

    A modified algorithm is introduced in the third part to give the algorithm of the second part a more stable performance. The modifications in the RPEM are obtained by introducing an interval in the nonlinear block with fixed static gain. The modifications that result in the convergence analysis are, however, substantial and allows a complete treatment of the local convergence properties of the algorithm. Moreover, the Cramér–Rao bound (CRB) is derived for the modified algorithm and numerical simulations indicate that the method gives good results especially for moderate signal to noise ratios (SNR).

    In the fourth part, the idea is to give the algorithm of the third part the ability to estimate the driving frequency and the parameters of the nonlinear output function parameterized also in a number of adaptively estimated grid points. Allowing the algorithm to automatically adapt the grid points as well as the parameters of the nonlinear block, reduces the modeling errors and gives the algorithm more freedom to choose the suitable grid points. Numerical simulations indicate that the algorithm converges to the true parameter vector and gives better performance than the fixed grid point technique. Also, the CRB is derived for the adaptive grid point technique.

    Fulltekst (ps)
    fulltext
  • 4.
    Abd-Elrady, Emad
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Nonlinear Approaches to Periodic Signal Modeling2005Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    Periodic signal modeling plays an important role in different fields. The unifying theme of this thesis is using nonlinear techniques to model periodic signals. The suggested techniques utilize the user pre-knowledge about the signal waveform. This gives these techniques an advantage as compared to others that do not consider such priors.

    The technique of Part I relies on the fact that a sine wave that is passed through a static nonlinear function produces a harmonic spectrum of overtones. Consequently, the estimated signal model can be parameterized as a known periodic function (with unknown frequency) in cascade with an unknown static nonlinearity. The unknown frequency and the parameters of the static nonlinearity are estimated simultaneously using the recursive prediction error method (RPEM). A treatment of the local convergence properties of the RPEM is provided. Also, an adaptive grid point algorithm is introduced to estimate the unknown frequency and the parameters of the static nonlinearity in a number of adaptively estimated grid points. This gives the RPEM more freedom to select the grid points and hence reduces modeling errors.

    Limit cycle oscillations problem are encountered in many applications. Therefore, mathematical modeling of limit cycles becomes an essential topic that helps to better understand and/or to avoid limit cycle oscillations in different fields. In Part II, a second-order nonlinear ODE is used to model the periodic signal as a limit cycle oscillation. The right hand side of the ODE model is parameterized using a polynomial function in the states, and then discretized to allow for the implementation of different identification algorithms. Hence, it is possible to obtain highly accurate models by only estimating a few parameters.

    In Part III, different user aspects for the two nonlinear approaches of the thesis are discussed. Finally, topics for future research are presented.

    Fulltekst (pdf)
    FULLTEXT01
  • 5.
    Abd-Elrady, Emad
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Söderström, Torsten
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Wigren, Torbjörn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Periodic signal analysis using orbits of nonlinear ODEs based on the Markov estimate2004Konferansepaper (Fagfellevurdert)
  • 6.
    Abd-Elrady, Emad
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Söderström, Torsten
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Wigren, Torbjörn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Periodic signal modeling based on Liénard's equation2004Inngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 49, nr 10, s. 1773-1778Artikkel i tidsskrift (Fagfellevurdert)
  • 7.
    Abd-Elrady, Emad
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Söderström, Torsten
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Wigren, Torbjörn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Periodic signal modeling based on Liénard's equation2003Rapport (Annet vitenskapelig)
    Fulltekst (pdf)
    fulltext
  • 8.
    Abrahamsson, Anna
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Variance Adaptive Quantization and Adaptive Offset Selection in High Efficiency Video Coding2016Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Video compression uses encoding to reduce the number of bits that are used forrepresenting a video file in order to store and transmit it at a smaller size. Adecoder reconstructs the received data into a representation of the original video.Video coding standards determines how the video compression should beconducted and one of the latest standards is High Efficiency Video Coding (HEVC).One technique that can be used in the encoder is variance adaptive quantizationwhich improves the subjective quality in videos. The technique assigns lowerquantization parameter values to parts of the frame with low variance to increasequality, and vice versa. Another part of the encoder is the sample adaptive offsetfilter, which reduces pixel errors caused by the compression. In this project, thevariance adaptive quantization technique is implemented in the Ericsson researchHEVC encoder c65. Its functionality is verified by subjective evaluation. It isinvestigated if the sample adaptive offset can exploit the adjusted quantizationparameters values when reducing pixel errors to improve compression efficiency. Amodel for this purpose is developed and implemented in c65. Data indicates thatthe model can increase the error reduction in the sample adaptive offset. However,the difference in performance of the model compared to a reference encoder is notsignificant.

    Fulltekst (pdf)
    Variance Adaptive Quantization and Adaptive Offset Selection
  • 9.
    Abrahamsson, Richard
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Estimation Problems in Array Signal Processing, System Identification, and Radar Imagery2006Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    This thesis is concerned with parameter estimation, signal processing, and applications.

    In the first part, imaging using radar is considered. More specifically, two methods are presented for estimation and removal of ground-surface reflections in ground penetrating radar which otherwise hinder reliable detection of shallowly buried landmines. Further, a study of two autofocus methods for synthetic aperture radar is presented. In particular, we study their behavior in scenarios where the phase errors leading to cross-range defocusing are of a spatially variant kind.

    In the subsequent part, array signal processing and optimal beamforming is regarded. In particular, the phenomenon of signal cancellation in adaptive beamformers due to array perturbations, signal correlated interferences and limited data for covariance matrix estimation is considered. For the general signal cancellation problem, a class of improved adaptive beamformers is suggested based on ridge-regression. Another set of methods is suggested to mitigate signal cancellation due to correlated signal and interferences based on a novel way of finding a characterization of the interference subspace from observed array data. Further, a new minimum variance beamformer is presented for high resolution non-parametric spatial spectrum estimation in cases where the impinging signals are correlated. Lastly, a multitude of enhanced covariance matrix estimators from the statistical literature are studied as an alternative to other robust adaptive beamforming methods. The methods are also applied to space-time adaptive processing where limited data for covariance matrix estimation is a common problem.

    In the third and final part the estimation of the parameters of a general bilinear problem is considered. The bilinear model is motivated by the application of identifying submarines from their electromagnetic signature and by the identification of a Hamerstein-Wiener model of a non-linear dynamic system. An efficient approximate maximum-likelihood method with closed form solution is suggested for estimating the bilinear model parameters.

  • 10. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    A robust sliding mode controller with internal model for closed-loop artificial pancreas2010Inngår i: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 48, nr 12, s. 1191-1201Artikkel i tidsskrift (Fagfellevurdert)
  • 11. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Internal model sliding mode control approach for glucose regulation in type 1 diabetes2010Inngår i: Biomedical Signal Processing and Control, ISSN 1746-8094, Vol. 5, nr 2, s. 94-102Artikkel i tidsskrift (Fagfellevurdert)
  • 12.
    Aghanavesi, Somayeh
    et al.
    Dalarna Univ, Dept Comp Engn, Falun, Sweden..
    Westin, Jerker
    Dalarna Univ, Dept Comp Engn, Falun, Sweden..
    Bergquist, Filip
    Univ Gothenburg, Dept Pharmacol, Inst Neurosci & Physiol, Gothenburg, Sweden..
    Nyholm, Dag
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Landtblom: Neurologi.
    Askmark, Håkan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Landtblom: Neurologi.
    Aquilonius, Sten-Magnus
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Landtblom: Neurologi.
    Constantinescu, Radu
    Univ Gothenburg, Dept Clin Neurosci, Gothenburg, Sweden..
    Medvedev, Alexander
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Spira, Jack
    Sensidose AB, Sollentuna, Sweden..
    Ohlsson, Fredrik
    Chalmers Univ, Gothenburg, Sweden..
    Thomas, Ilias
    Dalarna Univ, Dept Stat, Falun, Sweden..
    Ericsson, Anders
    Irisity AB, Gothenburg, Sweden..
    Buvarp, Dongni Johansson
    Univ Gothenburg, Dept Clin Neurosci & Rehabil, Gothenburg, Sweden..
    Memedi, Mevludin
    Orebro Univ, Informat, Orebro, Sweden..
    A multiple motion sensors index for motor state quantification in Parkinson's disease2020Inngår i: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 189, artikkel-id 105309Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

    Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

    Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

    Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

  • 13. Agüero, Juan C.
    et al.
    Godoy, Boris I.
    Goodwin, Graham C.
    Wigren, Torbjörn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Scenario-based EM identification for FIR systems having quantized output data2009Inngår i: Proc. 15th IFAC Symposium on System Identification, International Federation of Automatic Control , 2009, s. 66-71Konferansepaper (Fagfellevurdert)
  • 14. Agüero, Juan C.
    et al.
    Goodwin, Graham C.
    Lau, Katrina
    Wang, Meng
    Silva, Eduardo I.
    Wigren, Torbjörn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Three-degree of freedom adaptive power control for CDMA cellular systems2009Inngår i: Proc. 28th Global Telecommunications Conference, IEEE Communications Society, 2009, s. 2793-2798Konferansepaper (Fagfellevurdert)
  • 15.
    Ahl, Philip
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Optimal Cooperative Platooning Using Micro-Transactions2020Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    The urge to consume does not seem to stop, thus, the need for transportation of goods will most likely not decrease. At the same time jurisdictions and regulations around greenhouse gas emissions are sharpening and pushing the industry towards a more environmentally friendly state. The freight and transportation industry is facing a huge challenge in the upcoming years and solutions are needed to feed the demand of society. Two, of many, proposals of solving, at least, parts of the above mentioned problem is platooning and the look-ahead controller. Platooning denotes the concept of slipstream where maximum utilization of aerodynamic drag reduction is endeavoured. The lookahead controller exploits the surrounding topographical information in order to yield an optimal driving strategy, often resulting in that the vehicle initiates the phenomenon of pulse and glide, which denotes alternating between high load operation points and freewheeling, i.e. engaging neutral gear. This work has sought to investigate these concepts to determine whether or not additional fuel-efficiency can be added by manipulating and re-designing the control unit of the system. The proposed addition is built upon the look-ahead controller and supplements it by enabling communication between vehicles such that micro-transactions may occur in order to aid decision making regarding the choice of driving strategies. A vehicle model, a platoon model and the novel optimization based look-ahead-controller was synthesized and developed, where dynamic programming was used as the optimization solver of the controller. The look-ahead controller was verified such that one can conclude that it behaves according to the assumptions of such a system. The proposed micro-transaction system was also verified to conclude that it behaves as assumed, yielding a reduction in fuel consumption. For a platoon of two members, a 1.2% and 1.7% reduction in fuel consumption for the leading and following vehicle respectively was obtained, compared to an identical platooning setup, using a lookahead controller, but where no negotiations using micro-transactions are allowed between the vehicles.

    Fulltekst (pdf)
    fulltext
  • 16. Ahmed-Ali, Tarek
    et al.
    Tiels, Koen
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Schoukens, Maarten
    Giri, Fouad
    Sampled-data adaptive observer for state-affine systems with uncertain output equation2019Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 103, s. 96-105Artikkel i tidsskrift (Fagfellevurdert)
  • 17. Ahmed-Ali, Tarek
    et al.
    Tiels, Koen
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Schoukens, Maarten
    Giri, Fouad
    Sampled-Data Based State and Parameter Estimation for State-Affine Systems with Uncertain Output Equation2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation (PE) conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates.

  • 18.
    Albaba, Adnan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi.
    Medvedev, Alexander
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Online Model-Based Beat-by-beat Heart Rate Estimation2020Inngår i: 2020 American Control Conference (ACC), 2020, s. 539-544Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A method for estimating the instantaneous heart rate (HR) using the morphological features of one electrocardiogram (ECG) cycle (beat) at a time is proposed. This work is not aimed at introducing an alternative way for HR estimation, but rather illustrates the utility of model-based ECG analysis in online individualized monitoring of the heart function. The HR estimation problem is reduced to fitting one parameter, whose value is related to the nine parameters of a realistic nonlinear model of the ECG and estimated from data by nonlinear least-squares optimization. The method feasibility is evaluated on synthetic ECG signals as well as signals acquired from MIT-BIH databases at Physionet website. Moreover, the performance of the method was tested under realistic free-moving conditions using a wearable ECG and HR monitor with encouraging results.

  • 19.
    Albaba, Adnan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi.
    Medvedev, Alexander
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Patient-Specific Electrocardiogram Monitoring by Model-Based Stochastic Anomaly Detection2020Inngår i: 2020 European Control Conference (ECC), 2020, s. 735-740Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A novel model-based method for patient-specific detection of deformed electrocardiogram (ECG) beats is proposed and tested. Five parameters of a patient-specific nonlinear ECG model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal ECG beats. We demonstrate the utility of the proposed approach by validating it on annotated arrhythmia data recorded under clinical conditions.

  • 20.
    Alemayehu, Brook
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Johnsons, Fredrik
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Maskininlärning inom kommersiella fastigheter: Prediktion av framtida hyresvakanser2018Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    The purpose of this thesis is to investigate the possibilities of predicting vacancies in the real estate market by using machine learning models in terms of classification. These models were mainly based on data from contracts between a Swedish real estate company and their tenants. Attributes such as annual renting cost and rental area for each contract were supplemented with additional data regarding financial and geographical information about the tenants. The data was stored in three different formats with the first having binary classes which aim is to predict if the tenant is moving out within a year or more. The format of the second and third version were both multi classification problems that aims to classify if the tenants might terminate their contract within a specific interval with the length of three and six months.

    Based on the results from Microsoft Azure Machine Learning Studio, it is discovered that the multi classification problems perform rather poorly due to the classes being unbalanced. Regarding the  performance of the binary model, a more satisfying result was obtained but not to the extend to say that the model can be used to determine a vacancy with high accuracy. It should rather be used as a risk analysis tool to detect if a tenant is showing tendencies that could result in a future vacancy. A major pitfall of this thesis was the lack of data and the financial information not being specific enough. The performance of the models will likely increase with a larger dataset and more accurate financial information. 

    Fulltekst (pdf)
    fulltext
  • 21.
    Alenlöv, Johan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Olsson, Jimmy
    Particle-based adaptive-lag online marginal smoothing in general state-space models2019Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, nr 21, s. 5571-5582Artikkel i tidsskrift (Fagfellevurdert)
  • 22. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Mendonça, Teresa
    Rocha, Paula
    A compartmental model-based control strategy for NeuroMuscular Blockade level2011Inngår i: Proc. 18th IFAC World Congress, International Federation of Automatic Control , 2011, s. 599-604Konferansepaper (Fagfellevurdert)
  • 23. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Wigren, Torbjörn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Mendonça, Teresa
    Contributions to the initialization of online identification algorithms for anæsthesia: the NeuroMuscular Blockade case study2010Inngår i: Proc. 18th Mediterranean Conference on Control and Automation, Piscataway, NJ: IEEE , 2010, s. 1341-1346Konferansepaper (Fagfellevurdert)
  • 24. Alonso, Hugo
    et al.
    Mendonça, Teresa
    Lemos, João M.
    Wigren, Torbjörn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    A simple model for the identification of drug effects2009Inngår i: Proc. 6th International Symposium on Intelligent Signal Processing, Piscataway, NJ: IEEE , 2009, s. 269-273Konferansepaper (Fagfellevurdert)
  • 25.
    Alverbäck, Adam
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    LQG-control of a Vertical Axis Wind Turbine with Focus on Torsional Vibrations2012Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    In this thesis it has been investigated if LQG control could be used to mitigate torsional oscillations in a variable speed, fixed pitch wind turbine. The wind turbine is a vertical axis wind turbine with a 40 m tall axis that is connected to a generator. The power extracted by the turbine is delivered to the grid via a passive rectifier and an inverter. By controlling the grid side inverter the current is controlled and hence the rotational speed can be controlled. A state space model was developed for the LQG controller. The model includes both the dynamics of the electrical system as swell as the two mass system, consisting of the turbine and the generator connected with a flexible shaft. The controller was designed to minimize a quadratic criterion that punishes both torsional oscillations, command following and input signal magnitude. Integral action was added to the controller to handle the nonlinear aerodynamic torque.

    The controller was compared to the existing control system that uses a PI controller to control the speed, and tested usingMATLAB Simulink. Simulations show that the LQG controller is just as good as the PI controller in controlling the speed of the turbine, and has the advantage that it can be tuned such that the occurrence of torsional oscillations is mitigated. The study also concluded that some external method of dampening torsional oscillations should be implemented to mitigate torsional oscillations in case of a grid fault or loss of PWM signal.

    Fulltekst (pdf)
    fulltext
  • 26. Ancuti, Codruta O.
    et al.
    Luo, Ziwei
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Gustafsson, Fredrik K.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Zhao, Zheng
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Sjölund, Jens
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Schön, Thomas B.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Busch, Christoph
    NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report2023Inngår i: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancover: Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams that competed in the final testing phase, and the proposed solutions demonstrated the cutting-edge in image dehazing technology.

  • 27.
    Andersson, Carl
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Deep learning applied to system identification: A probabilistic approach2019Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification.

    Our goal for sequential modeling within the scope of this thesis is to obtain a model with good predictive and/or generative capabilities. The motivation behind this is that such a model can then be used in other areas, such as control or reinforcement learning. The model could also be used as a stepping stone for machine learning problems or for pure recreational purposes.

    Paper I and Paper II focus on how to apply deep learning to common system identification problems. Paper I introduces a novel way of regularizing the impulse response estimator for a system. In contrast to previous methods using Gaussian processes for this regularization we propose to parameterize the regularization with a neural network and train this using a large dataset. Paper II introduces deep learning and many of its core concepts for a system identification audience. In the paper we also evaluate several contemporary deep learning models on standard system identification benchmarks. Paper III is the odd fish in the collection in that it focuses on the mathematical formulation and evaluation of calibration in classification especially for deep neural network. The paper proposes a new formalized notation for calibration and some novel ideas for evaluation of calibration. It also provides some experimental results on calibration evaluation.

    Delarbeid
    1. Data-driven impulse response regularization via deep learning
    Åpne denne publikasjonen i ny fane eller vindu >>Data-driven impulse response regularization via deep learning
    2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
    Serie
    IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-366186 (URN)10.1016/j.ifacol.2018.09.081 (DOI)000446599200002 ()
    Konferanse
    SYSID 2018, July 9–11, Stockholm, Sweden
    Tilgjengelig fra: 2018-10-08 Laget: 2018-11-22 Sist oppdatert: 2022-04-04bibliografisk kontrollert
    2. Deep convolutional networks in system identification
    Åpne denne publikasjonen i ny fane eller vindu >>Deep convolutional networks in system identification
    Vise andre…
    2019 (engelsk)Inngår i: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, s. 3670-3676Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

    sted, utgiver, år, opplag, sider
    IEEE, 2019
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-397528 (URN)10.1109/CDC40024.2019.9030219 (DOI)000560779003058 ()978-1-7281-1398-2 (ISBN)
    Konferanse
    CDC 2019, December 11–13, Nice, France
    Forskningsfinansiär
    Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2016-06079
    Tilgjengelig fra: 2020-03-12 Laget: 2019-11-21 Sist oppdatert: 2022-04-04bibliografisk kontrollert
    3. Evaluating model calibration in classification
    Åpne denne publikasjonen i ny fane eller vindu >>Evaluating model calibration in classification
    Vise andre…
    2019 (engelsk)Inngår i: 22nd International Conference on Artificial Intelligence and Statistics, 2019, s. 3459-3467Konferansepaper, Publicerat paper (Fagfellevurdert)
    Serie
    Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-397519 (URN)000509687903053 ()
    Konferanse
    AISTATS 2019, April 16–18, Naha, Japan
    Tilgjengelig fra: 2019-04-25 Laget: 2019-11-21 Sist oppdatert: 2023-04-26bibliografisk kontrollert
    Fulltekst (pdf)
    fulltext
  • 28.
    Andersson, Carl
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Deep probabilistic models for sequential and hierarchical data2022Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Consider the problem where we want a computer program capable of recognizing a pedestrian on the road. This could be employed in a car to automatically apply the brakes to avoid an accident. Writing such a program is immensely difficult but what if we could instead use examples and let the program learn what characterizes a pedestrian from the examples. Machine learning can be described as the process of teaching a model (computer program) to predict something (the presence of a pedestrian) with help of data (examples) instead of through explicit programming.

    This thesis focuses on a specific method in machine learning, called deep learning. This method can arguably be seen as sole responsible for the recent upswing of machine learning in academia as well as in society at large. However, deep learning requires, in human standards, a huge amount of data to perform well which can be a limiting factor.  In this thesis we describe different approaches to reduce the amount of data that is needed by encoding some of our prior knowledge about the problem into the model. To this end we focus on sequential and hierarchical data, such as speech and written language.

    Representing sequential output is in general difficult due to the complexity of the output space. Here, we make use of a probabilistic approach focusing on sequential models in combination with a deep learning structure called the variational autoencoder. This is applied to a range of different problem settings, from system identification to speech modeling.

    The results come in three parts. The first contribution focus on applications of deep learning to typical system identification problems, the intersection between the two areas and how they can benefit from each other. The second contribution is on hierarchical data where we promote a multiscale variational autoencoder inspired by image modeling. The final contribution is on verification of probabilistic models, in particular how to evaluate the validity of a probabilistic output, also known as calibration.

    Delarbeid
    1. Data-driven impulse response regularization via deep learning
    Åpne denne publikasjonen i ny fane eller vindu >>Data-driven impulse response regularization via deep learning
    2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
    Serie
    IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-366186 (URN)10.1016/j.ifacol.2018.09.081 (DOI)000446599200002 ()
    Konferanse
    SYSID 2018, July 9–11, Stockholm, Sweden
    Tilgjengelig fra: 2018-10-08 Laget: 2018-11-22 Sist oppdatert: 2022-04-04bibliografisk kontrollert
    2. Deep convolutional networks in system identification
    Åpne denne publikasjonen i ny fane eller vindu >>Deep convolutional networks in system identification
    Vise andre…
    2019 (engelsk)Inngår i: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, s. 3670-3676Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

    sted, utgiver, år, opplag, sider
    IEEE, 2019
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-397528 (URN)10.1109/CDC40024.2019.9030219 (DOI)000560779003058 ()978-1-7281-1398-2 (ISBN)
    Konferanse
    CDC 2019, December 11–13, Nice, France
    Forskningsfinansiär
    Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2016-06079
    Tilgjengelig fra: 2020-03-12 Laget: 2019-11-21 Sist oppdatert: 2022-04-04bibliografisk kontrollert
    3. Learning deep autoregressive models for hierarchical data
    Åpne denne publikasjonen i ny fane eller vindu >>Learning deep autoregressive models for hierarchical data
    2021 (engelsk)Inngår i: IFAC PapersOnLine, Elsevier BV Elsevier, 2021, Vol. 54, nr 7, s. 529-534Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hierarchical variational autoencoder and downsampling to achieve superior computational complexity. We evaluate the proposed model on two different types of sequential data: speech and handwritten text. The results are promising with the proposed model achieving state-of-the-art performance.

    sted, utgiver, år, opplag, sider
    ElsevierElsevier BV, 2021
    Emneord
    Deep learning, variational autoencoders, nonlinear systems
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-457738 (URN)10.1016/j.ifacol.2021.08.414 (DOI)000696396200091 ()
    Konferanse
    19th IFAC Symposium on System Identification (SYSID), JUL 13-16, 2021, Padova, ITALY
    Forskningsfinansiär
    Swedish Research CouncilKjell and Marta Beijer Foundation
    Tilgjengelig fra: 2021-11-12 Laget: 2021-11-12 Sist oppdatert: 2024-01-15bibliografisk kontrollert
    4. Evaluating model calibration in classification
    Åpne denne publikasjonen i ny fane eller vindu >>Evaluating model calibration in classification
    Vise andre…
    2019 (engelsk)Inngår i: 22nd International Conference on Artificial Intelligence and Statistics, 2019, s. 3459-3467Konferansepaper, Publicerat paper (Fagfellevurdert)
    Serie
    Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-397519 (URN)000509687903053 ()
    Konferanse
    AISTATS 2019, April 16–18, Naha, Japan
    Tilgjengelig fra: 2019-04-25 Laget: 2019-11-21 Sist oppdatert: 2023-04-26bibliografisk kontrollert
    Fulltekst (pdf)
    UUThesis_Andersson,C_2022
    Fulltekst (pdf)
    UUThesis_Andersson,C_2022
    Download (jpg)
    presentationsbild
  • 29.
    Andersson, Carl
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Horta Ribeiro, Antônio
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Univ Fed Minas Gerais, Grad Program Elect Engn, Ave Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil.
    Tiels, Koen
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Wahlström, Niklas
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Schön, Thomas B.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Deep convolutional networks in system identification2019Inngår i: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, s. 3670-3676Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

  • 30.
    Andersson, Carl R.
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Wahlström, Niklas
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Schön, Thomas B.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Artificiell intelligens.
    Learning deep autoregressive models for hierarchical data2021Inngår i: IFAC PapersOnLine, Elsevier BV Elsevier, 2021, Vol. 54, nr 7, s. 529-534Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hierarchical variational autoencoder and downsampling to achieve superior computational complexity. We evaluate the proposed model on two different types of sequential data: speech and handwritten text. The results are promising with the proposed model achieving state-of-the-art performance.

    Fulltekst (pdf)
    fulltext
  • 31.
    Andersson, Carl
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Wahlström, Niklas
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Schön, Thomas B.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Data-driven impulse response regularization via deep learning2018Konferansepaper (Fagfellevurdert)
  • 32.
    Andersson, Helena
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Individualized mathematical modeling of neural activation in electric field2017Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Deep Brain Stimulation (DBS) is a treatment of movement disorders such as Parkinson's disease and essential tremor. Today it has been used in more than 80.000 patients. Electrical stimulation is administered by an implanted pulse generator through an electrode surgically placed in a target brain area specific to the treated disease. Opposed to alternative purely surgical treatment procedures, DBS is reversible and can be turned off.

    In this project, the aim is to individualise an already existing computational model of DBS, but also to look at optimisation of the treatment by developing a neuron model. It has been executed the following way. To localise the target area for the electrode, Magnetic Resonance Imaging (MRI) is used. An MRI image consists of volume elements called voxels. By analysing these voxels, it is possible to set up a coordinate system for the position of different parts of the brain. To build up an individualised model of the DBS, an MRI image is segmented into tissues of different conductivity thus resulting in a more accurate description of the electrical field around the electrode. To visualize the stimuli coverage for the medical staff, the MRI image of the target area, the electrode, and the electrical field produced by the stimuli are depicted in the same figure. From the results, we can draw the conclusion that this method works well for individualising the computational model of DBS, but it has only been used on one MRI scan so far so it needs further testing to obtain more data to compare with.

    The neuron model is a temporospatial mathematical model of a single neuron for the prediction of activation by a given electrically applied field generated by a DBS lead. The activation model is intended to be part of a patient-specific model of an already existing computational model of DBS. The model originate from a neuron model developed by Hodgkin and Huxley (HH). The original HH model only takes into account one compartment and, to make the neuron model more accurate, it is combined with a cable model. The simulation results obtained with the model have been validated against an established and widely accepted neuron model. The results correlated highly to each other with only minor differences. To see how position and orientation impact on activation, the developed HH model was tested for different pulse widths, distances from the lead, and rotations of the neuron relative to the lead. A larger pulse width makes activation more likely and so does a larger amplitude. Thicker neurons are more likely to get activated, neurons closer to the lead and also neurons perpendicular to the lead. From the results we can draw the conclusion that this method is a good way to stimulate neural activation of a single neuron. In future research, it might be possible to compare results from the neuron model with patient's response to treatment.

    Fulltekst (pdf)
    fulltext
  • 33.
    Andersson, Helena
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Medvedev, Alexander
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Cubo, Rubén
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    The impact of deep brain stimulation on a simulated neuron: Inhibition, excitation, and partial recovery2018Inngår i: Proc. 16th European Control Conference, IEEE, 2018, s. 2034-2039Konferansepaper (Fagfellevurdert)
  • 34.
    Andersson, Jennifer R.
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Moya, Jose Alonso
    Univ Carlos III Madrid, Dept Comp Sci & Engn, Leganes, Spain..
    Schwickerath, Ulrich
    CERN, IT Dept, Geneva, Switzerland..
    Anomaly Detection for the Centralised Elasticsearch Service at CERN2021Inngår i: FRONTIERS IN BIG DATA, ISSN 2624-909X, Vol. 4, artikkel-id 718879Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    For several years CERN has been offering a centralised service for Elasticsearch, a popular distributed system for search and analytics of user provided data. The service offered by CERN IT is better described as a service of services, delivering centrally managed and maintained Elasticsearch instances to CERN users who have a justified need for it. This dynamic infrastructure currently consists of about 30 distinct and independent Elasticsearch installations, in the following referred to as Elasticsearch clusters, some of which are shared between different user communities. The service is used by several hundred users mainly for logs and service analytics. Due to its size and complexity, the installation produces a huge amount of internal monitoring data which can be difficult to process in real time with limited available person power. Early on, an idea was therefore born to process this data automatically, aiming to extract anomalies and possible issues building up in real time, allowing the experts to address them before they start to cause an issue for the users of the service. Both deep learning and traditional methods have been applied to analyse the data in order to achieve this goal. This resulted in the current deployment of an anomaly detection system based on a one layer multi dimensional LSTM neural network, coupled with applying a simple moving average to the data to validate the results. This paper will describe which methods were investigated and give an overview of the current system, including data retrieval, data pre-processing and analysis. In addition, reports on experiences gained when applying the system to actual data will be provided. Finally, weaknesses of the current system will be briefly discussed, and ideas for future system improvements will be sketched out.

    Fulltekst (pdf)
    FULLTEXT01
  • 35.
    Andersson, Melanie
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Multi-Class Imbalanced Learning for Time Series Problem: An Industrial Case Study2020Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Classification problems with multiple classes and imbalanced sample sizes present a new challenge than the binary classification problems. Methods have been proposed to handle imbalanced learning, however most of them are specifically designed for binary classification problems. Multi-class imbalance imposes additional challenges when applied to time series classification problems, such as weather classification. In this thesis, we introduce, apply and evaluate a new algorithm for handling multi-class imbalanced problems involving time series data. Our proposed algorithm is designed to handle both multi-class imbalance and time series classification problems and is inspired by the Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification algorithm. The feasibility of our proposed algorithm is studied through an empirical evaluation performed on a telecom use-case at Ericsson, Sweden where data from commercial microwave links is used for weather classification. Our proposed algorithm is compared to the currently used model at Ericsson which is a one-dimensional convolutional neural network, as well as three other deep learning models. The empirical evaluation indicates that the performance of our proposed algorithm for weather classification is comparable to that of the current solution. Our proposed algorithm and the current solution are the two best performing models of the study.

    Fulltekst (pdf)
    fulltext
  • 36.
    Andrée, Anton
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Catch the fraudster: The development of a machine learning based fraud filter2020Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    E-commerce has seen a rapid growth the last two decades, making it easy for customers to shop wherever they are. The growth has also led to new kinds of fraudulent activities affecting the customers. To make customers feel safe while shopping online, companies like Resurs Bank are implementing different kinds of fraud filters to freeze transactions that are thought to be fraudulent. The latest type of fraud filter is based on machine learning. While this seems to be a promising technology, data and algorithms need to be tuned properly to the task at hand.

    This thesis project gives a proof of concept of realizing a machine learning based fraud filter for Resurs Bank. Based on a literature study, available data and explainability requirements, this work opts for a supervised learning approach based on Random Forests with a sliding window to overcome concept drift. The inherent class imbalance of the setting makes the area-under-the-receiver operating-curve a suitable metric. This approach provided promising results that a machine learning based fraud filter can add value to companies like Resurs Bank.

    An alternative approach on how to incorporate non-numerical features by using recurrent neural networks (RNN) was implemented and compared. The non-numerical feature was transformed by a pre-trained RNN-model to a numerical representation that reflects the features suspiciousness. This new numerical feature was then included in the Random Forest model and the result demonstrated that this approach can add valuable insight to the fraud detection field.

    Fulltekst (pdf)
    fulltext
  • 37.
    Andrén, Jakob
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Online Minimum Jerk Velocity Trajectory Generation: for Underwater Drones2023Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    This thesis studies real-time reference ramping of human input for remotely operated vehicles and its effect on system control, power usage, and user experience. The implementation, testing, and evaluation were done on the remotely operated Blueye Pioneer underwater drone.

    The developed method uses minimum jerk trajectories for transitioning between varying target velocities with a constant end jerk target. It has a low computational cost and runs in real-time on the Blueye Pioneer underwater drone. The presented method produces a well-defined reference with continuous position, velocity, and acceleration states that can be used in the feedback loop.

    Experiments and simulations show that the method produces a smoother and more predictable motion path for the user. The motions are better suited for video recordings and remote navigation, compared to the direct usage of human input velocity. The smoother reference reduces the controller tracking error, the peak control input, and the energy usage. The introduced acceleration reference state is used for feedforward control on the system. It improves the feeling of controlling the drone by reducing the system lag, the position tracking error, and the rise time for velocity changes.

    Fulltekst (pdf)
    fulltext
  • 38.
    Anubhab, Ghosh
    et al.
    KTH Royal Institute of Technology.
    Abdalmoaty, Mohamed
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Chatterjee, Saikat
    KTH Royal Institute of Technology.
    Hjalmarsson, Håkan
    KTH Royal Institute of Technology.
    DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical modelsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks in learning an estimator. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks -- long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator. 

  • 39.
    Anubhab, Ghosh
    et al.
    KTH Royal Institute of Technology.
    Fontcuberta, Aleix Espuña
    Abdalmoaty, Mohamed
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Chatterjee, Saikat
    KTH Royal Institute of Technology.
    Time-Varying Normalizing Flows for Dynamical Signals2022Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We develop a time-varying normalizing flow (TVNF) for explicit generative modeling of dynamical signals. Being explicit, it can generate samples of dynamical signals, and compute the likelihood of a (given) dynamical signal sample. In the proposed model, signal flow in the layers of the normalizing flow is a function of time, which is realized using an encoded representation that is the output of a recurrent neural network (RNN). Given a set of dynamical signals, the parameters of TVNF are learned according to a maximum-likelihood approach in conjunction with gradient descent (backpropagation). Use of the proposed model is illustrated for a toy application scenario-maximum-likelihood based speech-phone classification task.

  • 40.
    Arabaci, Okan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Blockchain consensus mechanisms: the case of natural disasters2018Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Blockchain is described as a trustworthy distributed service for parties that do not fully trust each other. It enables business transactions to be handled without a third party or central governance. For this distributed and concurrent communication to work, a consensus mechanism needs to be implemented into the blockchain protocol. This mechanism will dictate how and when new blocks can be added and in some cases, by whom.

    The medical industry suffers from many informational inefficiencies. Data is scattered across many different databases and the lack of coordination often results in mishandling of the data. This is especially clear when a natural disaster hits and time is of the essence.

    The purpose of this thesis is to assess how much a blockchain solution and its consensus mechanism can resist unusual behavior before they behave erratically. This involves analyzing design parameters and translating parameters from a disaster into a simulation to run tests. Overall, this thesis will explore if blockchain is a compatible solution to the difficulties in natural disaster response. This was obtained by conducting a qualitative study and developing a prototype and simulating disaster parameters in the prototype blockchain network. A set of test cases was created.

    The results show that the resilience differs significantly depending on consensus mechanism. Key parameters include consensus finality, scalability, byzantine tolerance, performance and blockchain type. Blockchain is well suited to handle typical challenges in natural disaster response: it results in faster allocation of medical care and more accurate information collection, as well as in a system which allows seamlessly for the integration of external organizations in the blockchain network. 

    Fulltekst (pdf)
    fulltext
  • 41.
    Areskog, Oskar
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Labelling Customer Actions in an Autonomous Store Using Human Action Recognition2022Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Automation is fundamentally changing many industries and retail is no exception. Moonshopis a South African venture trying to solve the problem of autonomous grocery storesusing cameras and computer vision. This project is the continuation of a hackathon heldto explore different methods for Human Action Recognition in Moonshop’s stores.Throughout the project a pipeline for data processing has been developed and two typesof Graph-Convolutional Networks, CTR-GCN and ST-GCN, have been implementedand evaluated on the data produced by this pipeline. The resulting scores aren’t goodenough to call it a success. However, this is not necessarily a fault of the models. Rather,there wasn’t enough data to train on and the existing data was of varying to low quality.This makes it complicated to justly judge the models’ performances. In the future, moreresources should be spent on generating more and better data in order to really evaluatethe feasibility of using Human Action Recognition and Graph-Convolutional Networksat Moonshop.

    Fulltekst (pdf)
    Labelling-Customer-Actions-Using-HAR
  • 42.
    Aronsson, Erik
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Crondahl, Olle
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Maskininlärning applicerat på data över biståndsinsatser: En studie i hur prediktiva modeller kan tillämpas för analys på Sida2017Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    The purpose of this master's thesis was to study if machine learning can be used asdecision support at the Swedish International Development Agency (Sida) in their work to provide financial aid. The aim was to examine the recurringphenomenon of increased number of aid disbursements towards the end of the year. A study and presentation of the data has been done to show the disbursementdistribution of Sida's operating departments. Moreover, qualitative interviews with different roles at Sida have been done to highlight the complexity of the agency and toexplain why and how different disbursement patterns occur. The approach has been to use classification models as well as regression models applied to data ofaid contributions from Sida's database. The classification models used were Decision Tree, k-Nearest Neighbour and Gradient Boosted Tree and thepurpose with the models was to illustrate which features of a contribution that are likely to be of importance for whether a disbursement occurs in December or earlier.The regression models used were linear models with the aim to predict if disbursements are likely to be delayed relative to the prognosis. The classificationmodel succeeded to point out three attributes that had influence on the classification result. The general conclusions of the report are that data ofcontributions generated in different IT-systems and various work routines at Sida's departments affect the quality of the data and the models’ accuracies negatively.Furthermore, insufficient amounts of data due to changes in Sida's information management has created difficulties when using data driven models to predict latedisbursements.

    Fulltekst (pdf)
    fulltext
  • 43.
    Aronsson, Oscar
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Development and Implementation of an Advanced Storage Model2017Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    The European process industry is in need of modernization if it shall retain its competitiveness on the growing global market and at the same time reduce the environmental impact that the industrial activities have. The concept behind industrial automation has been very successful in increasing the efficiency for the material handling process, but some industries still have a lack in the field of automation. One of these industries is the mining industry.

    ABB is currently working within the EU-funded project DISIRE in order to increase the amount of traceability and therefore also the potential of automation in the mining industry by introducing a flow simulation over the mine infrastructure. But one of the largest inherited problems that this industry has over other process industries is that the flow partly consists of a batch structure where the continuous flows of the product only takes place between bunkers and buffer zones.

    ABB has developed a Matlab simulation where these bunkers are modelled by a simple queue algorithm which does not take the blending or time delays of the ore into account. The main task of this master thesis was to investigate which different modelling approaches that could increase the accuracy of the simulation. The Cellular Automata (CA) were found to be most suitable modelling approach due to its simplicity and a Matlab toolbox were developed and implemented based on the theories behind CA. The results were partly evaluated with the results of an ongoing experiment at Luleå university and by comparison to theories of granular media movement. The CPU-time for the silo flow with 10.000 particles in a flat silo using a MacBook Pro 2.26GHz was about 8 seconds.

  • 44.
    Arvidsson, Edvin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Improving on Inventory Management Using Time Series Forecasting2021Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    In this master thesis project, four well known time series forecasting models areconstructed and tuned with the purpose of predicting the future consumption of glueon one of AkzoNobels customers production lines. The goal was to examine thepossibility of utilizing their vastly collected data with these models to improve on theinventory management for both AkzoNobel and their customers. The predictedproduct usage rate would aid in the customers' decision making about when neworders of product should be placed, based on when the current storage tanks areforecasted to be emptied. This information could also be useful for AkzoNobelthemselves. The data that is handled in this project is a time series with timestampsfor every glue consumption process on the customers production line since 2017. Asubgoal was to determine what data resolution would be the most effective formodelling, so each model has two versions, one using higher and one using lowerresolution data. The models that are examined are a seasonal naive model,along-short term memory model, a Facebook Prophet model as well as two separateAutoregressive Integrated Moving Average models, specifically one automaticallyandone manually constructed. Beyond these models, a combined model using trueaveraging of the two automatic ARIMA models was examined as well.

     

    Ultimately it was found that, for most models, forecasting ahead with a one day resolution was the most accurate using the models trained on one-day-separated-data, compared to three-hour-separated-data. Further it is presented that the best models are the two naive models, closely followed by the one-day-case automatic ARIMA and Prophet models. These models also performed similarly on simple tests for predicting a date when a tank will be empty. Mostly differing around four days on average from the true date for an empty tank on those tests, with a max forecast range of forty days. It is concluded that it is possible to sufficiently model the data to a point where the best models in this project could be an effective tool for both the AkzoNobel and its customers.  

    Fulltekst (pdf)
    Improving_on_Inventory_Management_Using_Time_Series_Forecasting
  • 45. Arvidsson, Åke
    et al.
    Rydén, Tobias
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Load transients in pooled cellular core network nodes2015Inngår i: Performance evaluation (Print), ISSN 0166-5316, E-ISSN 1872-745X, Vol. 90, s. 18-35Artikkel i tidsskrift (Fagfellevurdert)
  • 46.
    Arvola, Maja
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Deep Learning for Dose Prediction in Radiation Therapy: A comparison study of state-of-the-art U-net based architectures2021Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Machine learning has shown great potential as a step in automating radiotherapy treatment planning. It can be used for dose prediction and a popular deep learning architecture for this purpose is the U-net. Since it was proposed in 2015, several modifications and extensions have been proposed in the literature. In this study, three promising modifications are reviewed and implemented for dose prediction on a prostate cancer data set and compared with a 3D U-net as a baseline. The tested modifications are residual blocks, densely connected layers and attention gates. The different models are compared in terms of voxel error, conformity, homogeneity, dose spillage and clinical goals. The results show that the performance was similar in many aspects for the models. The residual blocks model performed similar or better than the baseline in almost all evaluations. The attention gates model performed very similar to the baseline and the densely connected layers were uneven in the results, often with low dose values in comparison to the baseline. The study also shows the importance of consistent ground truth data and how inconsistencies affect metrics such as isodose Dice score and Hausdorff distance.

    Fulltekst (pdf)
    fulltext
  • 47.
    Aubry, Augusto
    et al.
    CNR, IREA.
    De Maio, Antonio
    Universit`a degli Studi di Napoli “Federico II”.
    Piezzo, Marco
    Universit`a degli Studi di Napoli “Federico II”.
    Naghsh, Mohammad Mahdi
    Isfahan University of Technology.
    Soltanalian, Mojtaba
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Stoica, Peter
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Cognitive Radar Waveform Design for Spectral Coexistence in Signal-Dependent Interference2014Konferansepaper (Fagfellevurdert)
  • 48.
    Axelsson, Adam
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Machine Learning assisted gNodeB Data Link Layer Capacity Management2023Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    In the uplink direction of 5G New Radio, signals are sent between Ra-dio Units and Digital Units. The production of these signals is non-deterministic, leading to signals often being produced in bursts. Thesesignal bursts can lead to exceeding the Data Link Layer capacity, whichcauses packet losses. It is possible to control the burstiness by delay-ing signals over time. However, excessive delays should be avoidedsince the processing of signals must be completed within strict time con-straints. In this paper, two machine-learning-based algorithms with theobjective of avoiding packet losses by introducing delays to signals wereproposed. One algorithm was based on the symbol number of the sig-nals, and the other one used a queue-based approach. Only the symbol-based algorithm was thoroughly evaluated. Visualizations of test data,as well as lab tests, showed that the symbol-based algorithm was ableto efficiently delay signals in order to reduce the maximum load on theData Link Layer.

    Fulltekst (pdf)
    fulltext
  • 49.
    Axelsson, Nils
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Integration of prescribed-performance and boundary-layer control for systems with uncertain dynamics2024Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Controlling systems with uncertain dynamics is crucial in systems theory, especially for unmanned vehicles operating in challenging and unknown environments. One key application involves developing control methods to ensure collision-free trajectory tracking for unmanned surface vehicles (USVs) at sea.

    Modern control methods for such systems often encounter unwanted high-frequency oscillations, known as chattering, in the control signals. To address this, continuous approximations of discontinuous functions in the control law have proven effective in reducing chattering. This approach is integrated into a prescribed-performance control scheme, which has previously achieved asymptotic tracking for systems with uncertain dynamics.

    We employ Lyapunov stability analysis to determine if theoretical bounds for error performance can be smaller than the prescribed funnel functions when incorporating continuous approximations in a boundary-layer. For both first- and second-order systems, we show that system trajectories reach an arbitrarily small boundary-layer set in finite time. This allows us to derive a priori known error bounds that are smaller than the prescribed funnels.

    Simulations support the theoretical results, demonstrating a significant reduction in chattering while achieving asymptotic tracking errors two orders of magnitude smaller than the funnel functions. 

    Fulltekst (pdf)
    fulltext
  • 50.
    Ayotte, John
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.
    Dynamic positioning of a semi-submersible, multi-turbine wind power platform2015Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    As a growing market for offshore wind power has created a niche for deep-water installations, offshore floating wind solutions have become more and more viable as a renewable energy source. This technology is currently in development and as with many new technologies, many traditional design methods are found lacking. In the multi-turbine platform design investigated, turbine units are placed closely together to conserve material use and reduce cost, however with such tightly spaced turbines; wake interaction poses a threat to the productivity and the lifespan of the installation. In order to fully capitalize on the substantial increase in available wind energy far at sea, it is important that these floating parks operate in an optimal way. The platform investigated in this report sports 3, 6MW turbines which must be positioned such that wake interference is minimized; the platform must always bear a windward heading. 

    Maneuvering ocean going vessels has been practiced using automated dynamic positioning systems in the gas and oil industry for over 50 years, often employing submerged thrusters as a source of propulsion. These systems are mostly diesel powered and require extra operational maintenance, which would otherwise increase the cost and complexity of a floating wind farm. In this paper, it is suggested that the wind turbines themselves may be used to provide the thrust needed to correct the platform heading, thus eliminating the practical need for submerged thrusters. By controlling the blade pitch of the wind turbines, a turning moment (torque) can be exerted on the platform to correct heading (yaw) relative wind direction. Using the Hexicon H3-18MW platform as a starting point; hydrodynamic, aerodynamic and electromechanical properties of the system are explored, modeled and attempts at model predictive control are made. Preliminary results show that it is possible to control the H3’s position (in yaw) relative the wind using this novel method.

    Fulltekst (pdf)
    Dynamic positioning of a semi-submersible, multi-turbine wind power platform
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