Change search
Refine search result
1 - 21 of 21
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Atta, Khalid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Guay, Martin
    Queen's University.
    Lucchese, Riccardo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A geometric phasor extremum seeking control approach with measured constraints2019Conference paper (Refereed)
  • 2.
    Ebadat, Afrooz
    et al.
    ACCESS and the Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology, Sweden.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Bottegal, G
    TU Eindhoven, Department of Electrical Engineering, Eindhoven, The Netherlands.
    Wahlberg, Bo
    ACCESS and the Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology, Sweden.
    Johansson, K. H.
    ACCESS and the Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology, Sweden.
    Application-oriented input design for room occupancy estimation algorithms2018In: 2017 IEEE 56th Conference on Decision and Control, CDC, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 3417-3424Conference paper (Refereed)
    Abstract [en]

    We consider the problem of occupancy estimation in buildings using available environmental information. In particular, we study the problem of how to collect data that is informative enough for occupancy estimation purposes. We thus propose an application-oriented input design approach for designing the ventilation signal to be used while collecting the system identification datasets. The main goal of the method is to guarantee a certain accuracy in the estimated occupancy levels while minimizing the experimental time and effort. To take into account potential limitations on the actuation signals we moreover formulate the problem as a recursive nonlinear and nonconvex optimization problem, solved then using exhaustive search methods. We finally corroborate the theoretical findings with some numerical examples, which results show that computing ventilation signals using experiment design concepts leads to occupancy estimator performing 4 times better in terms of Mean Square Error (MSE).

  • 3.
    Guay, Martin
    et al.
    Department of Chemical Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada.
    Atta, Khalid
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An extremum-seeking control observer design technique for nonlinear systems2018In: 2017 IEEE 56th Conference on Decision and Control, CDC, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    In this study, we propose an extremum-seeking control approach for the design on nonlinear observers for a general class of detectable nonlinear systems. The extremum-seeking control approach provides a mechanism to compute an observer gain that minimizes the squared output error. The technique extends and generalizes an earlier extremum seeking control observer design approach that was limited to systems in observer normal form with a linear state to output map. The analysis provides conditions for the application of the technique and establishes a semi-global practical stability property of the error dynamics. Two simulation examples are presented to demonstrate the effectiveness of the technique

  • 4.
    Lucchese, Riccardo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A study of fine and coarse actuation capabilities in air-cooled server racks: control strategies and cost analysis2019In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865Article in journal (Refereed)
  • 5.
    Lucchese, Riccardo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Computing the allowable uncertainty of sparse control configurations2019In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771Article in journal (Refereed)
  • 6.
    Lucchese, Riccardo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Controlled Direct Liquid Cooling of Data Servers2019In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865Article in journal (Refereed)
  • 7.
    Lucchese, Riccardo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cooling Control Strategies in Data Centers for Energy Efficiency and Heat Recovery2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Data centers are facilities dedicated to the processing, storage, and relay of large amounts of digital information. As a whole, it is an energy intensive industry, characterized by a sizable carbon footprint and a short-term exponential growth rate. At a macroscopic level, their operation requires balancing the offer and demand of computational, cooling, and electrical power resources. The computational workload is influenced by external factors such as the end-users’ activity, while the overall run-time costs depend on the weather conditions and the fluctuating pricing of electricity. In this context, the adoption of optimizing control strategies and co-design methodologies that address simultaneously both the mechanical and control aspects, has the potential to unlock more sustainable designs. Improvements in the overall energetic efficiency open to larger-scale deployments in less favorable geographical locations. Recovery systems addressing the vast amounts of by-product heat can support other heat intensive processes such as district networks, wood drying, greenhouses, and food processing. This work focuses on how to adapt the provisioning of the cooling resources to the cooling demand, without negotiating the computational throughput. We devise top-down designs, that address unexplored control possibilities in existing deployments. We moreover apply a bottomup perspective, by modeling and studying co-designed cooling setups which bring significant simplifications to data center level optimal provisioning problems. The analysis aims at the different levels of the data center infrastructure hierarchy, and provides answers to centerpiece questions such as i) what are the optimal flow provisioning policies at different levels of the data centers?; ii) how to design simple but effective control strategies that address the complexity induced by the large scales?; iii) what are the exhaust heat properties that can be expected in air-cooled and liquid-cooled data centers?. Exploiting a model-centric approach we demonstrate the effectiveness of tailored control strategies in both achieving better cooling efficiency and a higher quality of the heat harvest. This thesis presents opportunities to simplify data center control structures while retaining or improving their performance. Furthermore, it lays modeling and control methodologies toward the holistic control-oriented treatment of the computing, cooling, and power distribution infrastructures. The results have a practical character and the model-based analysis establishes important development directions, confirming existing trends. Enabling intelligent data center management systems might not need to imply more complex tools; rather, a co-design effort might yield both simpler and effective control systems.

    The full text will be freely available from 2020-09-01 15:00
  • 8.
    Lucchese, Riccardo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Distributed Estimation of Network Cardinalities2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In distributed applications knowing the topological properties of the underlying communication network may lead to better performing algorithms. For instance, in distributed regression frameworks, knowing the number of active sensors allows to correctly weight prior information against evidence in the data. Moreover, continuously estimating the number of active nodes or communication links corresponds to monitoring the network connectivity and thus to being able to trigger network reconfiguration strategies. It is then meaningful to seek for estimators of the properties of the communication graphs that sense these properties with the smallest possible computational/communications overheads.

    Here we consider the problem of distributedly counting the number of agents in a network. This is at the same time a prototypical summation problem and an essential task instrumental to evaluating more complex algebraic expressions such as products and averages which are in turn useful in many distributed control, optimization and estimation problems such as least squares, sensor calibration, vehicle coordination and Kalman filtering.

    Being interested in generality, we consider computations in anonymous networks, i.e., in frameworks where agents are not ensured to have unique IDs and the network lacks a centralized authority. This setting implies that the set of distributedly computable functions is limited, that there is no size estimation algorithm with uniformly bounded computational complexity that can provide correct estimates with probability one, and thus that scalable size estimators are non-deterministic functions of the true network size. Natural questions are then: which one is the scheme that leads to topology estimators that are optimal in Mean Squared Error (MSE) terms? And what are the fundamental limitations of information aggregation for topology estimation purposes, i.e., what can be estimated and what not?

    Our focus is then to understand how to distributedly estimate cardinalities given devices with bounded resources (e.g., battery/energy constraints, communication bandwidth, etc.) and how considering different assumptions and trade-offs leads to different optimal strategies. We specifically consider the case of peer-to-peer networks where all the participants are required to: i) share the same final result (and thus the same view of the network) and ii) keep the communication and computational complexity at each node uniformly bounded in time.

    To this aim, we study four different estimation strategies that consider different tradeoffs between accuracy and convergence speed and characterize their statistical performance in terms of bias and MSE.

  • 9.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the allowable uncertainty in control configuration selection2019Conference paper (Other academic)
  • 10.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Johansson, Andreas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    ColdSpot: A thermal supervisor aimed at server rooms implementing a raised plenum cooling setup2019Conference paper (Refereed)
  • 11.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Johansson, Andreas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On energy efficient flow provisioning in air-cooled data servers2019In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 89, p. 103-112Article in journal (Refereed)
    Abstract [en]

    This study considers how to efficiently manage the thermal state of data servers by provisioning their cooling airflow dynamically. It introduces a thermal network framework that is tailored to capture the temperature dynamics of the on-board thermal inertiae. On top of this modeling, it devises LeakageCooling, a novel flow provisioning controller that balances the temperature dependent leakage dissipation within the electronic chips and the power consumed by the local fans to produce the cooling airflow. The prediction capabilities of the proposed modeling and the overall control performance are assessed over several relevant experimental scenarios on an Open Compute Windmill V2 server.

  • 12.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Johansson, Andreas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On server cooling policies for heat recovery: exhaust air properties of an Open Compute Windmill V2 platform2019Conference paper (Refereed)
  • 13.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lionello, Michele
    Università degli Studi di Padova.
    Rampazzo, Mirco
    Università degli Studi di Padova.
    Guay, Martin
    Queen's University.
    Atta, Khalid
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gradient-free optimization of data center cooling using Extremum Seeking2019Conference paper (Other academic)
  • 14.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lionello, Michele
    Università degli Studi di Padova.
    Rampazzo, Mirco
    Università degli Studi di Padova.
    Guay, Martin
    Queen's University.
    Atta, Khalid
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Newton-like phasor extremum seeking control with application to cooling data centers2019Conference paper (Refereed)
  • 15.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lionello, Michele
    Università degli Studi di Padova.
    Rampazzo, Mirco
    Università degli Studi di Padova.
    Johansson, Andreas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On economic cooling of contained server racks using an indirect adiabatic air handlerManuscript (preprint) (Other academic)
  • 16.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Olsson, Jesper
    Luleå University of Technology.
    Ljung, Anna-Lena
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Garcia-Gabin, Winston
    ABB Corporate Research, Västerås.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Energy savings in data centers: A framework for modelling and control of servers’ cooling2017In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, p. 9050-9057Article in journal (Refereed)
    Abstract [en]

    Aiming at improving the energy efficiency of air cooled servers in data centers, we devise a novel control oriented, nonlinear, thermal model of the servers that accounts explicitly for both direct and recirculating convective air flows. Instrumental to the optimal co-design of both geometries and cooling policies, we propose an identification methodology based on Computational Fluid Dynamics (CFD) for a generic thermal network of m fans and n electronic components. The performance of the proposed modelling framework is validated against CFD measurements with promising results. We formalize the minimum cooling cost control problem as a polynomially constrained Receding Horizon Control (RHC) and show, in-silico, that the resulting policy is able to efficiently modulate the cooling resources in spite of the unknown future computational and electrical power loads.

  • 17.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A tight bound on the Bernoulli trials network size estimator2016In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3474-3480, article id 7798790Conference paper (Refereed)
    Abstract [en]

    We consider the problem of finding exact statistical characterizations of the Bernoulli trials network size estimator, a simple algorithm for distributedly counting the number of agents in an anonymous communication network for which the probability of committing estimation errors scales down exponentially with the amount of information exchanged by the agents. The estimator works by cascading a local, randomized, voting step (i.e., the i.i.d. generation of some Bernoulli trials) with an average consensus on these votes. We derive a tight upper bound on the probability that this strategy leads to an incorrect estimate, and refine the offline procedure for selecting the Bernoulli trials success rate.

  • 18.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Average consensus via max consensus2015Article in journal (Refereed)
    Abstract [en]

    Since intuition states that it is simple and fast to compute maxima over networks, we aim at understanding the limits of computing averages over networks through computing maxima. We thus build on top of max-consensus based networks’ cardinality estimation protocols a novel estimation strategy that infers averages through computing maxima of opportunely and locally generated random initial conditions. We motivate the max-consensus strategy explaining why it satisfies practical requirements, we characterize completely its statistical properties, and we analyze when and under which conditions it performs favorably against classical linear consensus strategies in static Cayley graphs

  • 19.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Networks cardinality estimation using order statistics2015In: American Control Conference (ACC), 2015: Chicago, IL, 1-3 July 2015,, Piscataway, NJ: IEEE Communications Society, 2015, p. 3810-3817Conference paper (Refereed)
    Abstract [en]

    We consider a network of collaborative peers that aim at distributedly estimating the size of the network they belong to. We assume nodes to be endowed with unique identification numbers (IDs), and we study the performance of size estimators that are based on exchanging these IDs. Motivated by practical scenarios where the time-to-estimate is critical, we specifically address the case where the convergence time of the algorithm, i.e., the number of communications required to achieve the final estimate, is minimal. We thus construct estimators of the network size by exploiting statistical inference concepts on top of the distributed computation of order statistics of the IDs, i.e., of the M biggest IDs available in the network. We then characterize the statistical performance of these estimators from theoretical perspectives and show their effectiveness in practical estimation situations by means of numerical examples.

  • 20.
    Lucchese, Riccardo
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Delvenne, Jean-Charles
    Université Catholique de Louvain.
    Hendrickx, Julien M.
    Université Catholique de Louvain.
    Network cardinality estimation using max consensus: the case of Bernoulli trials2015In: IEEE 54th Annual Conference on Decision and Control (CDC): Osaka, Japan, 15-18 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 895-901, article id 7402342Conference paper (Refereed)
    Abstract [en]

    Interested in scalable topology reconstruction strategies with fast convergence times, we consider network cardinality estimation schemes that use, as their fundamental aggregation mechanism, the computation of bit-wise maxima over strings. We thus discuss how to choose optimally the parameters of the information generation process under frequentist assumptions on the estimand, derive the resulting Maximum Likelihood (ML) estimator, and characterize its statistical performance as a function of the communications and memory requirements. We then numerically compare the bitwise-max based estimator against lexicographic-max based estimators, and derive insights on their relative performances in function of the true cardinality.

  • 21.
    Pillonetto, Gianluigi
    et al.
    Department of Information Engineering, University of Padova, Padova, Italy.
    Schenato, Luca
    Department of Information Engineering, University of Padova, Padova, Italy.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Statistical bounds for Gaussian regression algorithms based on Karhunen-Loève expansions2018In: 2017 IEEE 56th Conference on Decision and Control, CDC, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 363-368Conference paper (Refereed)
    Abstract [en]

    We consider the problem of estimating functions in a Gaussian regression distributed and nonparametric framework where the unknown map is modeled as a Gaussian random field whose kernel encodes expected properties like smoothness. We assume that some agents with limited computational and communication capabilities collect M noisy function measurements on input locations independently drawn from a known probability density. Collaboration is then needed to obtain a common and shared estimate. When the number of measurements M is large, computing the minimum variance estimate in a distributed fashion is difficult since it requires first to exchange all the measurements and then to invert an M χ M matrix. A common approach is then to circumvent this problem by searching a suboptimal solution within a subspace spanned by a finite number of kernel eigenfunctions. In this paper we analyze this classical distributed estimator, and derive a rigorous probabilistic bound on its statistical performance that returns crucial information on the number of measurements and eigenfunctions needed to obtain the desired level of estimation accuracy.

1 - 21 of 21
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf