�ݺ�ߣshows by User: PantelisSopasakis

�ݺ�ߣshows by User: PantelisSopasakis / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: PantelisSopasakis / Wed, 14 Sep 2016 14:06:48 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: PantelisSopasakis Fast parallelizable scenario-based stochastic optimization /slideshow/fast-parallelizable-scenariobased-stochastic-optimization/66019425 sopasakis-eucco-2016-160914140648
Fast parallelizable scenario-based stochastic optimization: a forward-backward LBFGS method for stochastic optimal control problems with global convergence rate guarantees. (Talk at EUCCO 2016, Leuven, Belgium).]]>
Fast parallelizable scenario-based stochastic optimization: a forward-backward LBFGS method for stochastic optimal control problems with global convergence rate guarantees. (Talk at EUCCO 2016, Leuven, Belgium).]]> Wed, 14 Sep 2016 14:06:48 GMT /slideshow/fast-parallelizable-scenariobased-stochastic-optimization/66019425 PantelisSopasakis@slideshare.net(PantelisSopasakis) Fast parallelizable scenario-based stochastic optimization PantelisSopasakis Fast parallelizable scenario-based stochastic optimization: a forward-backward LBFGS method for stochastic optimal control problems with global convergence rate guarantees. (Talk at EUCCO 2016, Leuven, Belgium). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sopasakis-eucco-2016-160914140648-thumbnail.jpg?width=120&height=120&fit=bounds" /> Fast parallelizable scenario-based stochastic optimization: a forward-backward LBFGS method for stochastic optimal control problems with global convergence rate guarantees. (Talk at EUCCO 2016, Leuven, Belgium).

Fast parallelizable scenario-based stochastic optimization from Pantelis Sopasakis

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0 Accelerated reconstruction of a compressively sampled data stream /slideshow/accelerated-reconstruction-of-a-compressively-sampled-data-stream/65698795 sopasakis-eusipco-2016-160905121324
Recursive compressed sensing on a stream of data: The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed: the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. In this paper, we develop a novel Newton-type forward-backward proximal method to recursively solve the regularized Least-Squares problem (LASSO) online. We establish global convergence of our method as well as a local quadratic convergence rate. Our simulations show a substantial speed-up over the state of the art which may render the proposed method suitable for applications with stringent real-time constraints.]]>
Recursive compressed sensing on a stream of data: The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed: the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. In this paper, we develop a novel Newton-type forward-backward proximal method to recursively solve the regularized Least-Squares problem (LASSO) online. We establish global convergence of our method as well as a local quadratic convergence rate. Our simulations show a substantial speed-up over the state of the art which may render the proposed method suitable for applications with stringent real-time constraints.]]> Mon, 05 Sep 2016 12:13:24 GMT /slideshow/accelerated-reconstruction-of-a-compressively-sampled-data-stream/65698795 PantelisSopasakis@slideshare.net(PantelisSopasakis) Accelerated reconstruction of a compressively sampled data stream PantelisSopasakis Recursive compressed sensing on a stream of data: The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed: the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. In this paper, we develop a novel Newton-type forward-backward proximal method to recursively solve the regularized Least-Squares problem (LASSO) online. We establish global convergence of our method as well as a local quadratic convergence rate. Our simulations show a substantial speed-up over the state of the art which may render the proposed method suitable for applications with stringent real-time constraints. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sopasakis-eusipco-2016-160905121324-thumbnail.jpg?width=120&height=120&fit=bounds" /> Recursive compressed sensing on a stream of data: The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed: the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. In this paper, we develop a novel Newton-type forward-backward proximal method to recursively solve the regularized Least-Squares problem (LASSO) online. We establish global convergence of our method as well as a local quadratic convergence rate. Our simulations show a substantial speed-up over the state of the art which may render the proposed method suitable for applications with stringent real-time constraints.

Accelerated reconstruction of a compressively sampled data stream from Pantelis Sopasakis

]]> 304 3 https://cdn.slidesharecdn.com/ss_thumbnails/sopasakis-eusipco-2016-160905121324-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Smart Systems for Urban Water Demand Management /slideshow/smart-systems-for-urban-water-demand-management/65319765 ascona-sopasakis-160824134758
In this tutorial session we will discuss how dynamical modeling combined with time-series analysis and optimization can lead to an efficient management of complex water systems. We will introduce key performance indicators to evaluate the performance of the controlled system and formulate an economic model predictive control (EMPC) scheme to address the prescribed control objectives. We will also see how we can harness the computational power of graphics cards to accelerate complex computations involved in our control problems.]]>
In this tutorial session we will discuss how dynamical modeling combined with time-series analysis and optimization can lead to an efficient management of complex water systems. We will introduce key performance indicators to evaluate the performance of the controlled system and formulate an economic model predictive control (EMPC) scheme to address the prescribed control objectives. We will also see how we can harness the computational power of graphics cards to accelerate complex computations involved in our control problems.]]> Wed, 24 Aug 2016 13:47:58 GMT /slideshow/smart-systems-for-urban-water-demand-management/65319765 PantelisSopasakis@slideshare.net(PantelisSopasakis) Smart Systems for Urban Water Demand Management PantelisSopasakis In this tutorial session we will discuss how dynamical modeling combined with time-series analysis and optimization can lead to an efficient management of complex water systems. We will introduce key performance indicators to evaluate the performance of the controlled system and formulate an economic model predictive control (EMPC) scheme to address the prescribed control objectives. We will also see how we can harness the computational power of graphics cards to accelerate complex computations involved in our control problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ascona-sopasakis-160824134758-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this tutorial session we will discuss how dynamical modeling combined with time-series analysis and optimization can lead to an efficient management of complex water systems. We will introduce key performance indicators to evaluate the performance of the controlled system and formulate an economic model predictive control (EMPC) scheme to address the prescribed control objectives. We will also see how we can harness the computational power of graphics cards to accelerate complex computations involved in our control problems.

Smart Systems for Urban Water Demand Management from Pantelis Sopasakis

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0 Recursive Compressed Sensing /slideshow/recursive-compressed-sensing/60815967 rcs-160412145939
A very wide spectrum of optimization problems can be efficiently solved with proximal gradient methods which hinge on the celebrated forward-backward splitting (FBS) schema. But such first-order methods are only effective when low or medium accuracy is required and are known to be rather slow or even impractical for badly conditioned problems. Moreover, the straightforward introduction of second-order (Hessian) information is beset with shortcomings as, typically, at every iteration we need to solve a non-separable optimisation problem. In this talk we will follow a different route to the solution of such optimisation problems. We will recast non-smooth optimisation problems as the minimisation of a real-valued, continuously differentiable function known as the forward-backward envelope. We will then employ a semismooth Newton method to solve the equivalent optimisation problem instead of the original one. We will then apply the proposed semismooth Newton method to L1-regularised least squares (LASSO) problems which is motivated by an an interesting application: recursive compressed sensing. Compressed sensing is a signal processing methodology for the reconstruction of sparsely sampled signals and it offers a new paradigm for sampling signals based on their innovation, that is, the minimum number of coefficients sufficient to accurately represent it in an appropriately selected basis. Compressed sensing leads to a lower sampling rate compared to theories using some fixed basis and has many applications in image processing, medical imaging and MRI, photography, holography, facial recognition, radio astronomy, radar technology and more. The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed; the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. We will see how we can tailor the forward-backward Newton method to solve recursive compressed sensing problems at one tenth of the time required by other algorithms such as ISTA, FISTA, ADMM and interior-point methods (L1LS).]]>
A very wide spectrum of optimization problems can be efficiently solved with proximal gradient methods which hinge on the celebrated forward-backward splitting (FBS) schema. But such first-order methods are only effective when low or medium accuracy is required and are known to be rather slow or even impractical for badly conditioned problems. Moreover, the straightforward introduction of second-order (Hessian) information is beset with shortcomings as, typically, at every iteration we need to solve a non-separable optimisation problem. In this talk we will follow a different route to the solution of such optimisation problems. We will recast non-smooth optimisation problems as the minimisation of a real-valued, continuously differentiable function known as the forward-backward envelope. We will then employ a semismooth Newton method to solve the equivalent optimisation problem instead of the original one. We will then apply the proposed semismooth Newton method to L1-regularised least squares (LASSO) problems which is motivated by an an interesting application: recursive compressed sensing. Compressed sensing is a signal processing methodology for the reconstruction of sparsely sampled signals and it offers a new paradigm for sampling signals based on their innovation, that is, the minimum number of coefficients sufficient to accurately represent it in an appropriately selected basis. Compressed sensing leads to a lower sampling rate compared to theories using some fixed basis and has many applications in image processing, medical imaging and MRI, photography, holography, facial recognition, radio astronomy, radar technology and more. The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed; the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. We will see how we can tailor the forward-backward Newton method to solve recursive compressed sensing problems at one tenth of the time required by other algorithms such as ISTA, FISTA, ADMM and interior-point methods (L1LS).]]> Tue, 12 Apr 2016 14:59:39 GMT /slideshow/recursive-compressed-sensing/60815967 PantelisSopasakis@slideshare.net(PantelisSopasakis) Recursive Compressed Sensing PantelisSopasakis A very wide spectrum of optimization problems can be efficiently solved with proximal gradient methods which hinge on the celebrated forward-backward splitting (FBS) schema. But such first-order methods are only effective when low or medium accuracy is required and are known to be rather slow – or even impractical – for badly conditioned problems. Moreover, the straightforward introduction of second-order (Hessian) information is beset with shortcomings as, typically, at every iteration we need to solve a non-separable optimisation problem. In this talk we will follow a different route to the solution of such optimisation problems. We will recast non-smooth optimisation problems as the minimisation of a real-valued, continuously differentiable function known as the forward-backward envelope. We will then employ a semismooth Newton method to solve the equivalent optimisation problem instead of the original one. We will then apply the proposed semismooth Newton method to L1-regularised least squares (LASSO) problems which is motivated by an an interesting application: recursive compressed sensing. Compressed sensing is a signal processing methodology for the reconstruction of sparsely sampled signals and it offers a new paradigm for sampling signals based on their innovation, that is, the minimum number of coefficients sufficient to accurately represent it in an appropriately selected basis. Compressed sensing leads to a lower sampling rate compared to theories using some fixed basis and has many applications in image processing, medical imaging and MRI, photography, holography, facial recognition, radio astronomy, radar technology and more. The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed; the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. We will see how we can tailor the forward-backward Newton method to solve recursive compressed sensing problems at one tenth of the time required by other algorithms such as ISTA, FISTA, ADMM and interior-point methods (L1LS). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rcs-160412145939-thumbnail.jpg?width=120&height=120&fit=bounds" /> A very wide spectrum of optimization problems can be efficiently solved with proximal gradient methods which hinge on the celebrated forward-backward splitting (FBS) schema. But such first-order methods are only effective when low or medium accuracy is required and are known to be rather slow – or even impractical – for badly conditioned problems. Moreover, the straightforward introduction of second-order (Hessian) information is beset with shortcomings as, typically, at every iteration we need to solve a non-separable optimisation problem. In this talk we will follow a different route to the solution of such optimisation problems. We will recast non-smooth optimisation problems as the minimisation of a real-valued, continuously differentiable function known as the forward-backward envelope. We will then employ a semismooth Newton method to solve the equivalent optimisation problem instead of the original one. We will then apply the proposed semismooth Newton method to L1-regularised least squares (LASSO) problems which is motivated by an an interesting application: recursive compressed sensing. Compressed sensing is a signal processing methodology for the reconstruction of sparsely sampled signals and it offers a new paradigm for sampling signals based on their innovation, that is, the minimum number of coefficients sufficient to accurately represent it in an appropriately selected basis. Compressed sensing leads to a lower sampling rate compared to theories using some fixed basis and has many applications in image processing, medical imaging and MRI, photography, holography, facial recognition, radio astronomy, radar technology and more. The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed; the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. We will see how we can tailor the forward-backward Newton method to solve recursive compressed sensing problems at one tenth of the time required by other algorithms such as ISTA, FISTA, ADMM and interior-point methods (L1LS).

Recursive Compressed Sensing from Pantelis Sopasakis

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0 Distributed solution of stochastic optimal control problem on GPUs /slideshow/distributed-solution-of-stochastic-optimal-control-problem-on-gpus/56264504 cdc2015-151218061929
Stochastic optimal control problems arise in many applications and are, in principle, large-scale involving up to millions of decision variables. Their applicability in control applications is often limited by the availability of algorithms that can solve them efficiently and within the sampling time of the controlled system. In this paper we propose a dual accelerated proximal gradient algorithm which is amenable to parallelization and demonstrate that its GPU implementation affords high speed-up values (with respect to a CPU implementation) and greatly outperforms well-established commercial optimizers such as Gurobi.]]>
Stochastic optimal control problems arise in many applications and are, in principle, large-scale involving up to millions of decision variables. Their applicability in control applications is often limited by the availability of algorithms that can solve them efficiently and within the sampling time of the controlled system. In this paper we propose a dual accelerated proximal gradient algorithm which is amenable to parallelization and demonstrate that its GPU implementation affords high speed-up values (with respect to a CPU implementation) and greatly outperforms well-established commercial optimizers such as Gurobi.]]> Fri, 18 Dec 2015 06:19:29 GMT /slideshow/distributed-solution-of-stochastic-optimal-control-problem-on-gpus/56264504 PantelisSopasakis@slideshare.net(PantelisSopasakis) Distributed solution of stochastic optimal control problem on GPUs PantelisSopasakis Stochastic optimal control problems arise in many applications and are, in principle, large-scale involving up to millions of decision variables. Their applicability in control applications is often limited by the availability of algorithms that can solve them efficiently and within the sampling time of the controlled system. In this paper we propose a dual accelerated proximal gradient algorithm which is amenable to parallelization and demonstrate that its GPU implementation affords high speed-up values (with respect to a CPU implementation) and greatly outperforms well-established commercial optimizers such as Gurobi. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cdc2015-151218061929-thumbnail.jpg?width=120&height=120&fit=bounds" /> Stochastic optimal control problems arise in many applications and are, in principle, large-scale involving up to millions of decision variables. Their applicability in control applications is often limited by the availability of algorithms that can solve them efficiently and within the sampling time of the controlled system. In this paper we propose a dual accelerated proximal gradient algorithm which is amenable to parallelization and demonstrate that its GPU implementation affords high speed-up values (with respect to a CPU implementation) and greatly outperforms well-established commercial optimizers such as Gurobi.

Distributed solution of stochastic optimal control problem on GPUs from Pantelis Sopasakis

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0 HMPC for Upper Stage Attitude Control /slideshow/hmpc-for-upper-stage-attitude-control/50891495 hmpcupperstage-150724144346-lva1-app6891
HMPC for Upper Stage Attitude Control:]]>
HMPC for Upper Stage Attitude Control:]]> Fri, 24 Jul 2015 14:43:46 GMT /slideshow/hmpc-for-upper-stage-attitude-control/50891495 PantelisSopasakis@slideshare.net(PantelisSopasakis) HMPC for Upper Stage Attitude Control PantelisSopasakis HMPC for Upper Stage Attitude Control: <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hmpcupperstage-150724144346-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> HMPC for Upper Stage Attitude Control:

HMPC for Upper Stage Attitude Control from Pantelis Sopasakis

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0 Sloshing-aware MPC for upper stage attitude control /slideshow/sloshingaware-mpc-for-upper-stage-attitude-control/50596473 ecc15sloshingmpc-150716134550-lva1-app6891
We present a novel modeling methodology to derive a nonlinear dynamical model which adequately describes the effect of fuel sloshing on the attitude dynamics of a spacecraft. We model the impulsive thrusters using mixed logic and dynamics leading to a hybrid formulation. We design a hybrid model predictive control scheme for the attitude control of a launcher during its long coasting period, aiming at minimising the actuation count of the thrusters.]]>
We present a novel modeling methodology to derive a nonlinear dynamical model which adequately describes the effect of fuel sloshing on the attitude dynamics of a spacecraft. We model the impulsive thrusters using mixed logic and dynamics leading to a hybrid formulation. We design a hybrid model predictive control scheme for the attitude control of a launcher during its long coasting period, aiming at minimising the actuation count of the thrusters.]]> Thu, 16 Jul 2015 13:45:50 GMT /slideshow/sloshingaware-mpc-for-upper-stage-attitude-control/50596473 PantelisSopasakis@slideshare.net(PantelisSopasakis) Sloshing-aware MPC for upper stage attitude control PantelisSopasakis We present a novel modeling methodology to derive a nonlinear dynamical model which adequately describes the effect of fuel sloshing on the attitude dynamics of a spacecraft. We model the impulsive thrusters using mixed logic and dynamics leading to a hybrid formulation. We design a hybrid model predictive control scheme for the attitude control of a launcher during its long coasting period, aiming at minimising the actuation count of the thrusters. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ecc15sloshingmpc-150716134550-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> We present a novel modeling methodology to derive a nonlinear dynamical model which adequately describes the effect of fuel sloshing on the attitude dynamics of a spacecraft. We model the impulsive thrusters using mixed logic and dynamics leading to a hybrid formulation. We design a hybrid model predictive control scheme for the attitude control of a launcher during its long coasting period, aiming at minimising the actuation count of the thrusters.

Sloshing-aware MPC for upper stage attitude control from Pantelis Sopasakis

]]> 735 6 https://cdn.slidesharecdn.com/ss_thumbnails/ecc15sloshingmpc-150716134550-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

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0 Robust model predictive control for discrete-time fractional-order systems /slideshow/med-sopasakis/49557546 medsopasakis-150618142506-lva1-app6892
In this paper we propose a tube-based robust model predictive control scheme for fractional-order discrete- time systems of the Grunwald-Letnikov type with state and input constraints. We first approximate the infinite-dimensional fractional-order system by a finite-dimensional linear system and we show that the actual dynamics can be approximated arbitrarily tight. We use the approximate dynamics to design a tube-based model predictive controller which endows to the controlled closed-loop system robust stability properties]]>
In this paper we propose a tube-based robust model predictive control scheme for fractional-order discrete- time systems of the Grunwald-Letnikov type with state and input constraints. We first approximate the infinite-dimensional fractional-order system by a finite-dimensional linear system and we show that the actual dynamics can be approximated arbitrarily tight. We use the approximate dynamics to design a tube-based model predictive controller which endows to the controlled closed-loop system robust stability properties]]> Thu, 18 Jun 2015 14:25:06 GMT /slideshow/med-sopasakis/49557546 PantelisSopasakis@slideshare.net(PantelisSopasakis) Robust model predictive control for discrete-time fractional-order systems PantelisSopasakis In this paper we propose a tube-based robust model predictive control scheme for fractional-order discrete- time systems of the Grunwald-Letnikov type with state and input constraints. We first approximate the infinite-dimensional fractional-order system by a finite-dimensional linear system and we show that the actual dynamics can be approximated arbitrarily tight. We use the approximate dynamics to design a tube-based model predictive controller which endows to the controlled closed-loop system robust stability properties <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/medsopasakis-150618142506-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this paper we propose a tube-based robust model predictive control scheme for fractional-order discrete- time systems of the Grunwald-Letnikov type with state and input constraints. We first approximate the infinite-dimensional fractional-order system by a finite-dimensional linear system and we show that the actual dynamics can be approximated arbitrarily tight. We use the approximate dynamics to design a tube-based model predictive controller which endows to the controlled closed-loop system robust stability properties

Robust model predictive control for discrete-time fractional-order systems from Pantelis Sopasakis

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0 OpenTox API introductory presentation /slideshow/opentox-api-brief-presentation/39355728 opentoxapiathens2014-140921205304-phpapp02
Introduction to the OpenTox API, its underlying priciples and its capabilities.]]>
Introduction to the OpenTox API, its underlying priciples and its capabilities.]]> Sun, 21 Sep 2014 20:53:04 GMT /slideshow/opentox-api-brief-presentation/39355728 PantelisSopasakis@slideshare.net(PantelisSopasakis) OpenTox API introductory presentation PantelisSopasakis Introduction to the OpenTox API, its underlying priciples and its capabilities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/opentoxapiathens2014-140921205304-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to the OpenTox API, its underlying priciples and its capabilities.

OpenTox API introductory presentation from Pantelis Sopasakis

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0 Water demand forecasting for the optimal operation of large-scale water networks /slideshow/effinet-ifac-presentation/38125644 effinetifacpresentation-140819051639-phpapp01
Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs’ operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN.]]>
Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs’ operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN.]]> Tue, 19 Aug 2014 05:16:39 GMT /slideshow/effinet-ifac-presentation/38125644 PantelisSopasakis@slideshare.net(PantelisSopasakis) Water demand forecasting for the optimal operation of large-scale water networks PantelisSopasakis Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs’ operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/effinetifacpresentation-140819051639-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs’ operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN.

Water demand forecasting for the optimal operation of large-scale water networks from Pantelis Sopasakis

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0 Amiodarone administration /slideshow/amiodarone-administration/38125316 fractionalifacpresentation-140819050430-phpapp02
Controlled administration of Amiodarone by a fractional PID controller. ]]>
Controlled administration of Amiodarone by a fractional PID controller. ]]> Tue, 19 Aug 2014 05:04:29 GMT /slideshow/amiodarone-administration/38125316 PantelisSopasakis@slideshare.net(PantelisSopasakis) Amiodarone administration PantelisSopasakis Controlled administration of Amiodarone by a fractional PID controller. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fractionalifacpresentation-140819050430-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Controlled administration of Amiodarone by a fractional PID controller.

Amiodarone administration from Pantelis Sopasakis

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0 Drinking Water Networks: Challenges and opportunites /slideshow/drinking-water-networks-challenges-and-opportunites/38083050 dwnchallengesopportunitiesv2-140818033746-phpapp01
Operational control based on model predictive control for Drinking Water Networks and other challenges and opportunities for research and development. Presentation of the developments of the FP7-funded EU research project EFFINET (see http://effinet.eu)]]>
Operational control based on model predictive control for Drinking Water Networks and other challenges and opportunities for research and development. Presentation of the developments of the FP7-funded EU research project EFFINET (see http://effinet.eu)]]> Mon, 18 Aug 2014 03:37:46 GMT /slideshow/drinking-water-networks-challenges-and-opportunites/38083050 PantelisSopasakis@slideshare.net(PantelisSopasakis) Drinking Water Networks: Challenges and opportunites PantelisSopasakis Operational control based on model predictive control for Drinking Water Networks and other challenges and opportunities for research and development. Presentation of the developments of the FP7-funded EU research project EFFINET (see http://effinet.eu) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dwnchallengesopportunitiesv2-140818033746-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Operational control based on model predictive control for Drinking Water Networks and other challenges and opportunities for research and development. Presentation of the developments of the FP7-funded EU research project EFFINET (see http://effinet.eu)

Drinking Water Networks: Challenges and opportunites from Pantelis Sopasakis

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0 Controlled administration of Amiodarone using a Fractional-Order Controller /PantelisSopasakis/fractional-33026499 mainzposter-140402044458-phpapp02
Amiodarone is an antiarrhythmic drug that exhibits highly complex and non- exponential dynamics whose controlled administration has important implications for its clinical use especially for long-term therapies. Its pharmacokinetics has been accurately modelled using a fractional-order compartmental model. In this paper we design a fractional-order PID controller and we evaluate its dynamical characteristics in terms of the stability margins of the closed loop and the ability of the controlled system to attenuate various sources of noise and uncertainty.]]>
Amiodarone is an antiarrhythmic drug that exhibits highly complex and non- exponential dynamics whose controlled administration has important implications for its clinical use especially for long-term therapies. Its pharmacokinetics has been accurately modelled using a fractional-order compartmental model. In this paper we design a fractional-order PID controller and we evaluate its dynamical characteristics in terms of the stability margins of the closed loop and the ability of the controlled system to attenuate various sources of noise and uncertainty.]]> Wed, 02 Apr 2014 04:44:58 GMT /PantelisSopasakis/fractional-33026499 PantelisSopasakis@slideshare.net(PantelisSopasakis) Controlled administration of Amiodarone using a Fractional-Order Controller PantelisSopasakis Amiodarone is an antiarrhythmic drug that exhibits highly complex and non- exponential dynamics whose controlled administration has important implications for its clinical use especially for long-term therapies. Its pharmacokinetics has been accurately modelled using a fractional-order compartmental model. In this paper we design a fractional-order PID controller and we evaluate its dynamical characteristics in terms of the stability margins of the closed loop and the ability of the controlled system to attenuate various sources of noise and uncertainty�. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mainzposter-140402044458-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Amiodarone is an antiarrhythmic drug that exhibits highly complex and non- exponential dynamics whose controlled administration has important implications for its clinical use especially for long-term therapies. Its pharmacokinetics has been accurately modelled using a fractional-order compartmental model. In this paper we design a fractional-order PID controller and we evaluate its dynamical characteristics in terms of the stability margins of the closed loop and the ability of the controlled system to attenuate various sources of noise and uncertainty�.

Controlled administration of Amiodarone using a Fractional-Order Controller from Pantelis Sopasakis

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0 Model Predictive Control based on Reduced-Order Models /PantelisSopasakis/sop-berbem13-cdc sopberbem13cdc-131211183105-phpapp02
The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control. In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.]]>
The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control. In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.]]> Wed, 11 Dec 2013 18:31:05 GMT /PantelisSopasakis/sop-berbem13-cdc PantelisSopasakis@slideshare.net(PantelisSopasakis) Model Predictive Control based on Reduced-Order Models PantelisSopasakis The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control. In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sopberbem13cdc-131211183105-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control. In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.

Model Predictive Control based on Reduced-Order Models from Pantelis Sopasakis

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0 OpenTox API: Lessons learnt, limitations and challenges /slideshow/opentox-api-lessons-learnt/27712422 otapi2013-131029185817-phpapp01
A brief talk about challenges for the current OpenTox API.]]>
A brief talk about challenges for the current OpenTox API.]]> Tue, 29 Oct 2013 18:58:17 GMT /slideshow/opentox-api-lessons-learnt/27712422 PantelisSopasakis@slideshare.net(PantelisSopasakis) OpenTox API: Lessons learnt, limitations and challenges PantelisSopasakis A brief talk about challenges for the current OpenTox API. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/otapi2013-131029185817-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> A brief talk about challenges for the current OpenTox API.

OpenTox API: Lessons learnt, limitations and challenges from Pantelis Sopasakis

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0 Just Another QSAR Project under OpenTox /slideshow/just-another-qsar-projest-under-opentox/27204682 posterjaqpot-131015081516-phpapp02
Just Another QSAR Projest under OpenTox: RESTful web services compliant to the OpenTox API v1.2 for predictive toxicology applications based on QSAR/QSPR.]]>
Just Another QSAR Projest under OpenTox: RESTful web services compliant to the OpenTox API v1.2 for predictive toxicology applications based on QSAR/QSPR.]]> Tue, 15 Oct 2013 08:15:16 GMT /slideshow/just-another-qsar-projest-under-opentox/27204682 PantelisSopasakis@slideshare.net(PantelisSopasakis) Just Another QSAR Project under OpenTox PantelisSopasakis Just Another QSAR Projest under OpenTox: RESTful web services compliant to the OpenTox API v1.2 for predictive toxicology applications based on QSAR/QSPR. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/posterjaqpot-131015081516-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Just Another QSAR Projest under OpenTox: RESTful web services compliant to the OpenTox API v1.2 for predictive toxicology applications based on QSAR/QSPR.

Just Another QSAR Project under OpenTox from Pantelis Sopasakis

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0 ToxOtis: A Java Interface to the OpenTox Predictive Toxicology Network /slideshow/toxotis-a-java-interface-to-the-opentox-predictive-toxicology-network/27186042 fptoxotis-131014191202-phpapp01
The ToxOtis suite serves a double purpose in the quest for painless integration: First off, it is a Java interface to any OpenTox compliant web service and facilitates access control (Authentication and Authorization), the parsing of RDF (Resource Description Framework) documents that are exchanged with the web services, and the consumption of Model Building, Toxicity Prediction and other ancillary web services (e.g. computation of molecular similarity). Second, it facilitates the database management, the serialization of resources in RDF and provides all that is necessary to a web service provider to join the OpenTox network and offer predictive toxicology web services.]]>
The ToxOtis suite serves a double purpose in the quest for painless integration: First off, it is a Java interface to any OpenTox compliant web service and facilitates access control (Authentication and Authorization), the parsing of RDF (Resource Description Framework) documents that are exchanged with the web services, and the consumption of Model Building, Toxicity Prediction and other ancillary web services (e.g. computation of molecular similarity). Second, it facilitates the database management, the serialization of resources in RDF and provides all that is necessary to a web service provider to join the OpenTox network and offer predictive toxicology web services.]]> Mon, 14 Oct 2013 19:12:02 GMT /slideshow/toxotis-a-java-interface-to-the-opentox-predictive-toxicology-network/27186042 PantelisSopasakis@slideshare.net(PantelisSopasakis) ToxOtis: A Java Interface to the OpenTox Predictive Toxicology Network PantelisSopasakis The ToxOtis suite serves a double purpose in the quest for painless integration: First off, it is a Java interface to any OpenTox compliant web service and facilitates access control (Authentication and Authorization), the parsing of RDF (Resource Description Framework) documents that are exchanged with the web services, and the consumption of Model Building, Toxicity Prediction and other ancillary web services (e.g. computation of molecular similarity). Second, it facilitates the database management, the serialization of resources in RDF and provides all that is necessary to a web service provider to join the OpenTox network and offer predictive toxicology web services. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fptoxotis-131014191202-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The ToxOtis suite serves a double purpose in the quest for painless integration: First off, it is a Java interface to any OpenTox compliant web service and facilitates access control (Authentication and Authorization), the parsing of RDF (Resource Description Framework) documents that are exchanged with the web services, and the consumption of Model Building, Toxicity Prediction and other ancillary web services (e.g. computation of molecular similarity). Second, it facilitates the database management, the serialization of resources in RDF and provides all that is necessary to a web service provider to join the OpenTox network and offer predictive toxicology web services.

ToxOtis: A Java Interface to the OpenTox Predictive Toxicology Network from Pantelis Sopasakis

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0 Frobenious theorem /slideshow/frobenious-theorem/27168024 frobenioustheorem-131014074021-phpapp02
A tutorial on the Frobenious Theorem, one of the most important results in differential geometry, with emphasis in its use in nonlinear control theory. All results are accompanied by proofs, but for a more thorough and detailed presentation refer to the book of A. Isidori.]]>
A tutorial on the Frobenious Theorem, one of the most important results in differential geometry, with emphasis in its use in nonlinear control theory. All results are accompanied by proofs, but for a more thorough and detailed presentation refer to the book of A. Isidori.]]> Mon, 14 Oct 2013 07:40:21 GMT /slideshow/frobenious-theorem/27168024 PantelisSopasakis@slideshare.net(PantelisSopasakis) Frobenious theorem PantelisSopasakis A tutorial on the Frobenious Theorem, one of the most important results in differential geometry, with emphasis in its use in nonlinear control theory. All results are accompanied by proofs, but for a more thorough and detailed presentation refer to the book of A. Isidori. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/frobenioustheorem-131014074021-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> A tutorial on the Frobenious Theorem, one of the most important results in differential geometry, with emphasis in its use in nonlinear control theory. All results are accompanied by proofs, but for a more thorough and detailed presentation refer to the book of A. Isidori.

Frobenious theorem from Pantelis Sopasakis

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0 Set convergence /slideshow/set-convergence/27167888 setconvergence-131014073509-phpapp01
Note on set convergence: We give the definitions of inner and outer limits for sequences of sets in topological and normed spaces and we provide some important facts on set convergence on topological and normed spaces. We juxtapose the notions of the limit superior and limit inferior for sequences of sets and we outline some facts regarding the Painlevé-Kuratowski convergence of set-sequences.]]>
Note on set convergence: We give the definitions of inner and outer limits for sequences of sets in topological and normed spaces and we provide some important facts on set convergence on topological and normed spaces. We juxtapose the notions of the limit superior and limit inferior for sequences of sets and we outline some facts regarding the Painlevé-Kuratowski convergence of set-sequences.]]> Mon, 14 Oct 2013 07:35:09 GMT /slideshow/set-convergence/27167888 PantelisSopasakis@slideshare.net(PantelisSopasakis) Set convergence PantelisSopasakis Note on set convergence: We give the definitions of inner and outer limits for sequences of sets in topological and normed spaces and we provide some important facts on set convergence on topological and normed spaces. We juxtapose the notions of the limit superior and limit inferior for sequences of sets and we outline some facts regarding the Painlevé-Kuratowski convergence of set-sequences. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/setconvergence-131014073509-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Note on set convergence: We give the definitions of inner and outer limits for sequences of sets in topological and normed spaces and we provide some important facts on set convergence on topological and normed spaces. We juxtapose the notions of the limit superior and limit inferior for sequences of sets and we outline some facts regarding the Painlevé-Kuratowski convergence of set-sequences.

Set convergence from Pantelis Sopasakis

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0 Polytopes inside polytopes /PantelisSopasakis/polytopes-inside-polytopes polytopesinpolytopes-131012185558-phpapp01
Criteria for polytopes being subsets of other polytopes using ]]>
Criteria for polytopes being subsets of other polytopes using ]]> Sat, 12 Oct 2013 18:55:58 GMT /PantelisSopasakis/polytopes-inside-polytopes PantelisSopasakis@slideshare.net(PantelisSopasakis) Polytopes inside polytopes PantelisSopasakis Criteria for polytopes being subsets of other polytopes using <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/polytopesinpolytopes-131012185558-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Criteria for polytopes being subsets of other polytopes using

Polytopes inside polytopes from Pantelis Sopasakis

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0 https://cdn.slidesharecdn.com/profile-photo-PantelisSopasakis-48x48.jpg?cb=1523612823 http://dysco.imtlucca.it/sopasakis/ https://cdn.slidesharecdn.com/ss_thumbnails/sopasakis-eucco-2016-160914140648-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/fast-parallelizable-scenariobased-stochastic-optimization/66019425 Fast parallelizable sc... https://cdn.slidesharecdn.com/ss_thumbnails/sopasakis-eusipco-2016-160905121324-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/accelerated-reconstruction-of-a-compressively-sampled-data-stream/65698795 Accelerated reconstruc... https://cdn.slidesharecdn.com/ss_thumbnails/ascona-sopasakis-160824134758-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/smart-systems-for-urban-water-demand-management/65319765 Smart Systems for Urba...