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See detailProbabilistic Framework for the Characterization of Surfaces and Edges in Range Images, with Application to Edge Detection
Lejeune, Antoine ULg; Verly, Jacques ULg; Van Droogenbroeck, Marc ULg

in IEEE Transactions on Pattern Analysis & Machine Intelligence (in press)

We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images, which is useful in many applications of computer vision, such as filtering, edge ... [more ▼]

We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images, which is useful in many applications of computer vision, such as filtering, edge detection, feature extraction, and classification. We use the geometrical nature of the data to derive an analytic expression for the joint probability density function (pdf) for the random variables used to model the ranges of a set of pixels in a local neighborhood of an image. We decompose this joint pdf by considering independently the cases where two real world points corresponding to two neighboring pixels are locally on the same real world surface or not. In particular, we show that this joint pdf is linked to the Voigt pdf and not to the Gaussian pdf as it is assumed in some applications. We apply our framework to edge detection and develop a locally adaptive algorithm that is based on a probabilistic decision rule. We show in an objective evaluation that this new edge detector performs better than prior art edge detectors. This proves the benefits of the probabilistic characterization of the local neighborhood as a tool to improve applications that involve range images. [less ▲]

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See detailOperation of distribution systems within secure limits using real-time Model Predictive Control
Soleimani Bidgoli, Hamid ULg; Valverde, Gustavo; Aristidou, Petros et al

in Rueda Torres, Jose (Ed.) Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems (in press)

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See detailAutomated multimodal volume registration based on supervised 3D anatomical landmark detection
Vandaele, Rémy ULg; LALLEMAND, François ULg; MARTINIVE, Philippe ULg et al

in SCITEPRESS Digital Library (in press)

We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely ... [more ▼]

We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely Randomized Trees and multi-resolution voxel windows. A least-squares fitting algorithm is then used for rigid registration based on the landmark positions as predicted by these detectors in the two imaging modalities. Experiments are carried out with this method on a dataset of pelvis CT and CBCT scans related to 45 patients. On this dataset, our fully automatic approach yields results very competitive with respect to a manually assisted state-of-the-art rigid registration algorithm. [less ▲]

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See detailEnhancement of Transmission System Voltage Stability through Local Control of Distribution Networks
Valverde, Gustavo; Aristidou, Petros; Van Cutsem, Thierry ULg

in Rueda Torres, Jose (Ed.) Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems (in press)

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See detailProbabilistic Reliability Management Approach and Criteria for Power System Short-term Operational Planning
Karangelos, Efthymios ULg; Wehenkel, Louis ULg

in Probabilistic Reliability Management Approach and Criteria for Power System Short-term Operational Planning (in press)

This paper develops a probabilistic decision making framework for reliability management in the short-term operational planning context. We build upon our recent work, which proposed a probabilistic ... [more ▼]

This paper develops a probabilistic decision making framework for reliability management in the short-term operational planning context. We build upon our recent work, which proposed a probabilistic reliability management approach and criterion (RMAC) for the latest decision making opportunity of real-time system operation. Here, we transpose the RMAC to the preceding problem instance of short-term operational planning, wherein i) risk is aggravated by the uncertainty on power injections and weather conditions, and, ii) the problem scope concerns choosing `strategic' actions (e.g., starting additional generating units, granting outage requests for maintenance, etc.) to facilitate decision making during the forthcoming real-time system operation. To anticipate on the latter, we formalize the notion of a real-time `proxy' as a simplified model of the real-time decision making context, adequately accurate for the purpose of operational planning decision making. Stating a first proposal for such a proxy, we mathematically formulate the RMAC for short-term operational planning as a multi-stage stochastic decision making problem and demonstrate its main features by case studies on a modified version of the single area IEEE RTS-96 system. [less ▲]

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See detailSearching for Plausible N-k Contingencies Endangering Voltage Stability
Weckesser, Johannes ULg; Van Cutsem, Thierry ULg

in IEEE International Conference on Innovative Smart Grid Technologies IEEE ISGT Europe 2017, Turin 26-29 September 2017 (in press)

This paper presents a novel search algorithm using time-domain simulations to identify plausible N-k contingencies endangering voltage stability. Starting from an initial list of disturbances ... [more ▼]

This paper presents a novel search algorithm using time-domain simulations to identify plausible N-k contingencies endangering voltage stability. Starting from an initial list of disturbances, progressively more severe contingencies are investigated. After simulation of a N-k contingency, the simulation results are assessed. If the system response is unstable, a plausible harmful contingency sequence has been found. Otherwise, components affected by the contingencies are considered as candidate next event leading to N-(k+1) contingencies. This implicitly takes into account hidden failures of component protections. The performance of the proposed search algorithm is compared to a brute-force algorithm and demonstrated on the IEEE Nordic test system. [less ▲]

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See detailCombined Local and Centralized Voltage Control in Active Distribution Networks
Soleimani Bidgoli, Hamid ULg; Van Cutsem, Thierry ULg

in IEEE Transactions on Power Systems (in press)

A two-level real-time voltage control scheme is proposed to keep voltages within specified limits in distribution grids using Distributed Generation Units (DGUs). It combines a local and a centralized ... [more ▼]

A two-level real-time voltage control scheme is proposed to keep voltages within specified limits in distribution grids using Distributed Generation Units (DGUs). It combines a local and a centralized control of their reactive powers. The local control provides fast response after a disturbance, reducing its impact and enhancing voltage quality. The centralized control uses measurements collected throughout the network to bring the voltages inside tighter limits and balance the various DGU contributions. To this purpose, it adjusts in a coordinated way their reactive power set-points, taking into account the local controls. This discrete control solves at each time step a multi-time-step constrained optimization inspired of Model Predictive Control. The method effectiveness is demonstrated on a 75-bus test system hosting 22 DGUs. [less ▲]

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See detailElectric Power Network State Tracking from Multirate Measurements
Alcaide-Moreno, Boris; Fuerte-Esquivel, Claudio; Glavic, Mevludin ULg et al

in IEEE Transactions on Instrumentation and Measurement (in press)

This paper proposes a novel tracking state estimator to process both fast-rate synchronized phasor and slow rate SCADA measurements. The former are assumed to be in limited number. The latter are ... [more ▼]

This paper proposes a novel tracking state estimator to process both fast-rate synchronized phasor and slow rate SCADA measurements. The former are assumed to be in limited number. The latter are exploited as and when they arrive to the control center. In order to restore observability, after each execution of the tracking state estimator, forecasted SCADA measurements are used as pseudo-measurements in the next estimation. An event detection analysis allows assessing if the system is in quasi steady-state. If so, an innovation analysis is performed to identify and eliminate erroneous SCADA measurements. The system state is computed by Hachtel’s augmented matrix method. The option of exploiting time-tagged SCADA measurements is also considered. The method is illustrated through detailed dynamic simulations of a test system evolving towards voltage collapse, with and without emergency control. [less ▲]

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See detailCharacterization of superconductor magnetic properties in crossed magnetic fields
Vanderbemden, Philippe ULg

in Larbalestier, David; Cardwell, David (Eds.) Handbook of Superconducting Materials (2nd edition) (in press)

This chapter deals with the characterization of the magnetic properties of superconductors which are subjected to magnetic fields that have been applied along two orthogonal directions, which is commonly ... [more ▼]

This chapter deals with the characterization of the magnetic properties of superconductors which are subjected to magnetic fields that have been applied along two orthogonal directions, which is commonly referred to as a “crossed” magnetic field configuration. The purpose of this chapter is to describe the techniques that are useful to perform crossed field experiments, with an emphasis placed on practical aspects that are useful for designing the system and for understanding the measured data. This chapter is organized as follows. In Section 2, the key terms involved in the literature dealing with crossed field effects are defined. Section 3 deals with experimental methods and some key parameters will be outlined. In Section 4, practical conclusions will be drawn and next challenges in this area will be discussed. [less ▲]

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See detailImproving pedestrian detection using motion-guided filtering
Wang, Yi; Pierard, Sébastien ULg; Su, Song-Zhi et al

in Pattern Recognition Letters (in press)

In this letter, we show how a simple motion-guided nonlinear filter can drastically improve the accuracy of several pedestrian detectors. More specifically, we address the problem of how to pre-filter an ... [more ▼]

In this letter, we show how a simple motion-guided nonlinear filter can drastically improve the accuracy of several pedestrian detectors. More specifically, we address the problem of how to pre-filter an image so almost any pedestrian detector will see its false detection rate decrease. First, we roughly identify moving pixels by cumulating their temporal gradient into a motion history image (MHI). The MHI is then used in conjunction with a nonlinear filter to filter out background details while leaving untouched foreground moving objects. We also show how a feedback loop as well as a merging procedure between the filtered and the unfiltered frames can further improve results. We tested our method on 26 videos from 6 categories. The results show that for a given miss rate, filtering out background details reduces the false detection rate by a factor of up to 69.6 times. Our method is simple, computationally light, and can be implemented with any pedestrian detector. Code is made publicly available at: https://bitbucket.org/wany1601/pedestriandetection. [less ▲]

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See detailIPCAPS: an R package for iterative pruning to capture population structure
Chaichoompu, Kridsadakorn ULg; Abegaz Yazew, Fentaw; Tongsima, Sissades et al

in Bioinformatics : Application Notes (in press)

Resolving population genetic structure is challenging, especially when dealing with closely related populations. Although Principal Component Analysis (PCA)-based methods and genomic var- iation with ... [more ▼]

Resolving population genetic structure is challenging, especially when dealing with closely related populations. Although Principal Component Analysis (PCA)-based methods and genomic var- iation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic an- cestry, improvements can be made targeting fine-level population structure. This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-level population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipP- CA) framework to systematically assign individuals to genetically similar subgroups. Our tool is able to detect and eliminate outliers in each iteration to avoid misclassification. It can be extended to de- tect subtle subgrouping in patients as well. In addition, IPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated. [less ▲]

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See detailPulse-Based Control Using Koopman Operator Under Parametric Uncertainty
Sootla, Aivar; Ernst, Damien ULg

in IEEE Transactions on Automatic Control (in press)

In applications, such as biomedicine and systems/synthetic biology, technical limitations in actuation complicate implementation of time-varying control signals. In order to alleviate some of these ... [more ▼]

In applications, such as biomedicine and systems/synthetic biology, technical limitations in actuation complicate implementation of time-varying control signals. In order to alleviate some of these limitations, it may be desirable to derive simple control policies, such as step functions with fixed magnitude and length (or temporal pulses). In this technical note, we further develop a recently proposed pulse-based solution to the convergence problem, i.e., minimizing the convergence time to the target exponentially stable equilibrium, for monotone systems. In particular, we extend this solution to monotone systems with parametric uncertainty. Our solutions also provide worst-case estimates on convergence times. Furthermore, we indicate how our tools can be used for a class of non-monotone systems, and more importantly how these tools can be extended to other control problems. We illustrate our approach on switching under parametric uncertainty and regulation around a saddle point problems in a genetic toggle switch system. [less ▲]

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See detailA biased random key genetic algorithm applied to the electric distribution network reconfiguration problem
de Faria Jr., Haroldo; Resende, Mauricio; Ernst, Damien ULg

in Journal of Heuristics (in press)

This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization ... [more ▼]

This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization problems in power system analysis. Given a set of switches of an electric network that can be opened or closed, the objective is to select the best configuration of the switches to optimize a given network objective while at the same time satisfying a set of operational constraints. The good performance of BRKGAs on many combinatorial optimization problems and the fact that it has never been applied to solve DNR problems are the main motivation for this research. A BRKGA is a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. Solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval (0,1), thus enabling an indirect search of the solution inside a proprietary search space. The genetic operators do not need to be modified to generate only feasible solutions, which is an exclusive task of the decoder of the problem. Tests were performed on standard distribution systems used in DNR studies found in the technical literature and the performance and robustness of the BRKGA were compared with other GA implementations. [less ▲]

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See detailDynamic Equivalent of a Distribution Grid Hosting Dispersed Photovoltaic Units
Chaspierre, Gilles ULg; Panciatici, Patrick; Van Cutsem, Thierry ULg

in IREP’2017 Symposium: X Bulk Power Systems Dynamics and Control Symposium (in press)

This paper deals with the derivation of a simplified model of a distribution network hosting dispersed photovoltaic units. The model is aimed at short-term dynamic simulations of a transmission grid in ... [more ▼]

This paper deals with the derivation of a simplified model of a distribution network hosting dispersed photovoltaic units. The model is aimed at short-term dynamic simulations of a transmission grid in response to large disturbances. In a first step, a generic dynamic model of a photovoltaic unit is proposed, focusing on its interactions with the grid, in particular the controls triggered by voltage disturbances. The model takes into account various present and near-future grid codes. In a second step, a dynamic equivalent is derived accounting for the distribution network, the dispersed photovoltaic units, as well as static and dynamic (motor) loads. This equivalent is of the “grey-box” type and its parameters are tuned in the least-square sense to match the dynamic response of the original, unreduced system. Simulation results are reported on a detailed 913-bus distribution system subject to faults at transmission level. [less ▲]

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See detailAn Optimal Control Formulation of Pulse-Based Control Using Koopman Operator
Sootla, Aivar; Mauroy, Alexandre; Ernst, Damien ULg

E-print/Working paper (2018)

In many applications, and in systems/synthetic biology in particular, it is desirable to compute control policies that force the trajectory of a bistable system from one equilibrium (the initial point) to ... [more ▼]

In many applications, and in systems/synthetic biology in particular, it is desirable to compute control policies that force the trajectory of a bistable system from one equilibrium (the initial point) to another equilibrium (the target point), or in other words to solve the switching problem. It was recently shown that, for monotone bistable systems, this problem admits easyto-implement open-loop solutions in terms of temporal pulses (i.e., step functions of fixed length and fixed magnitude). In this paper, we develop this idea further and formulate a problem of convergence to an equilibrium from an arbitrary initial point. We show that this problem can be solved using a static optimization problem in the case of monotone systems. Changing the initial point to an arbitrary state allows to build closed-loop, event-based or open-loop policies for the switching/convergence problems. In our derivations we exploit the Koopman operator, which offers a linear infinite-dimensional representation of an autonomous nonlinear system. One of the main advantages of using the Koopman operator is the powerful computational tools developed for this framework. Besides the presence of numerical solutions, the switching/convergence problem can also serve as a building block for solving more complicated control problems and can potentially be applied to non-monotone systems. We illustrate this argument on the problem of synchronizing cardiac cells by defibrillation. Potentially, our approach can be extended to problems with different parametrizations of control signals since the only fundamental limitation is the finite time application of the control signal. [less ▲]

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See detailSepsis prediction in critically ill patients by platelet activation markers on ICU admission: a prospective pilot study
LAYIOS, Nathalie ULg; Delierneux, Céline ULg; Hego, Alexandre ULg et al

in Intensive Care Medicine Experimental (2017), 5(1), 32

Background: Platelets have been involved in both surveillance and host defense against severe infection. To date, whether platelet phenotype or other hemostasis components could be associated with ... [more ▼]

Background: Platelets have been involved in both surveillance and host defense against severe infection. To date, whether platelet phenotype or other hemostasis components could be associated with predisposition to sepsis in critical illness remains unknown. The aim of this work was to identify platelet markers that could predict sepsis occurrence in critically ill injured patients. Results: This single-center, prospective, observational, 7-month study was based on a cohort of 99 non-infected adult patients admitted to ICUs for elective cardiac surgery, trauma, acute brain injury and post-operative prolonged ventilation and followed up during ICU stay. Clinical characteristics and severity score (SOFA) were recorded on admission. Platelet activation markers, including fibrinogen binding to platelets, platelet membrane P-selectin expression, plasma soluble CD40L, and platelet-leukocytes aggregates were assayed by flow cytometry at admission and 48h later, and also at the time of sepsis diagnosis (Sepsis-3 criteria) and 7 days later for sepsis patients. Hospitalization data and outcomes were also recorded. Of the 99 patients, 19 developed sepsis after a median time of 5 days. SOFA at admission was higher; their levels of fibrinogen binding to platelets (platelet-Fg) and of D-dimers were significantly increased compared to the other patients. Levels 48h after ICU admission were no longer significant. Platelet-Fg % was an independent predictor of sepsis (P = 0.030). By ROC curve analysis cutoff points for SOFA (AUC=0.85) and Platelet-Fg (AUC=0.75) were 8 and 50%, respectively. The prior risk of sepsis (19%) increased to 50% when SOFA was above 8, to 46% when Platelet-Fg was above 50%, and to 87% when both SOFA and Platelet-Fg were above their cutoff values. By contrast, when the two parameters were below their cutoffs, the risk of sepsis was negligible (3.8%). Patients with sepsis had longer ICU and hospital stays and higher death rate. Conclusion: In addition to SOFA, platelet-bound fibrinogen levels assayed by flow cytometry within 24h of ICU admission help identifying critically ill patients at risk of developing sepsis. [less ▲]

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See detailA coupled electro-thermal Discontinuous Galerkin method
Homsi, Lina ULg; Geuzaine, Christophe ULg; Noels, Ludovic ULg

in Journal of Computational Physics (2017), 348

This paper presents a Discontinuous Galerkin scheme in order to solve the nonlinear elliptic partial differential equations of coupled electro-thermal problems. In this paper we discuss the fundamental ... [more ▼]

This paper presents a Discontinuous Galerkin scheme in order to solve the nonlinear elliptic partial differential equations of coupled electro-thermal problems. In this paper we discuss the fundamental equations for the transport of electricity and heat, in terms of macroscopic variables such as temperature and electric potential. A fully coupled nonlinear weak formulation for electro-thermal problems is developed based on continuum mechanics equations expressed in terms of energetically conjugated pair of fluxes and fields gradients. The weak form can thus be formulated as a Discontinuous Galerkin method. The existence and uniqueness of the weak form solution are proved. The numerical properties of the nonlinear elliptic problems i.e., consistency and stability, are demonstrated under specific conditions, i.e. use of high enough stabilization parameter and at least quadratic polynomial approximations. Moreover the prior error estimates in the H1-norm and in the L2-norm are shown to be optimal in the mesh size with the polynomial approximation degree. [less ▲]

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See detailContributions to deep reinforcement learning and its applications in smartgrids
François-Lavet, Vincent ULg

Doctoral thesis (2017)

Reinforcement learning and its extension with deep learning have led to a field of research called deep reinforcement learning. Applications of that research have recently shown the possibility to solve ... [more ▼]

Reinforcement learning and its extension with deep learning have led to a field of research called deep reinforcement learning. Applications of that research have recently shown the possibility to solve complex decision-making tasks that were previously believed extremely difficult for a computer. Yet, deep reinforcement learning requires caution and understanding of its inner mechanisms in order to be applied successfully in the different settings. As an introduction, we provide a general overview of the field of deep reinforcement learning. In the first part of this thesis, we provide an analysis of reinforcement learning in the particular setting of a limited amount of data and in the general context of partial observability. In this setting, we focus on the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data), and theoretically show that while potentially increasing the asymptotic bias, a smaller state representation decreases the risk of overfitting. An original theoretical contribution relies on expressing the quality of a state representation by bounding $L_1$ error terms of the associated belief states. We also discuss and empirically illustrate the role of other parameters to optimize the bias-overfitting tradeoff: the function approximator (in particular deep learning) and the discount factor. In addition, we investigate the specific case of the discount factor in the deep reinforcement learning setting case where additional data can be gathered through learning. In the second part of this thesis, we focus on a smartgrids application that falls in the context of a partially observable problem and where a limited amount of data is available (as studied in the first part of the thesis). We consider the case of microgrids featuring photovoltaic panels (PV) associated with both long-term (hydrogen) and short-term (batteries) storage devices. We propose a novel formalization of the problem of building and operating microgrids interacting with their surrounding environment. In the deterministic assumption, we show how to optimally operate and size microgrids using linear programming techniques. We then show how to use deep reinforcement learning to solve the operation of microgrids under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. [less ▲]

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See detailUsing IPCAPS to identify fine-scale population structure
Chaichoompu, Kridsadakorn ULg; Fentaw Abegaz, Yazew ULg; Tongsima, Sissades et al

Poster (2017, September 09)

Detailed reference viewed: 38 (6 ULg)