| 1. |
- Sandberg, Marcus, et al.
(författare)
-
A modelling methodology for assessing use of datacenter waste heat in greenhouses
- 2017
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Konferensbidrag (refereegranskat)abstract
- In Sweden, the number of datacenters establishments are steadily increasing thanks to green, stable and affordable electricity, free air cooling, advantageous energy taxes and well-developed Internet fiber infrastructures. Even though datacenters use a lot of energy, the waste heat that they create is seldom reused. A possible cause is that this waste heat is often low grade and airborne: it is therefore hard to directly inject it into a district heating system without upgrades, which require additional energy and equipment that generate extra costs. One option for reusing this heat without needs for upgrades is to employ it for heating up greenhouses. But assessing the feasibility of this approach by building physical prototypes can be costly, therefore using computer models to simulate real world conditions is an opportunity. However, there is a lack of computer modelling methodologies that can assess the possibility of using waste heat from datacenters in greenhouses in cold climates.The objective of this paper is therefore to propose such a methodology and discuss its benefits and drawbacks in comparison with other research studies. This methodology combines computational fluid dynamics, process modelling and control engineering principles into a computer model that constitutes a decision support system to study different waste heat and greenhouse or mushroom house scenarios.The paper validates the strategy through a case study in northern Sweden, where we assess the amount of produced waste heat by collecting temperature, relative humidity, and fan speed data for the air discharged from the datacenter.The resulting methodology, composed by conducting measurements and computer models, calculations can then be used for other datacenter operators or greenhouse developers to judge whether it is possible or not to build greenhouses using datacenter waste heat.
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| 2. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures
- 2018
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Ingår i: 2018 European Control Conference (ECC). - Piscataway, NJ : IEEE. - 9783952426982 - 9781538653036 ; , s. 2447-2453
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.
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| 3. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Calibrating distance sensors for terrestrial applications without groundtruth information
- 2017
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 17:12, s. 3698-3709
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move. The proposed calibration procedure exploits an approximated expectation maximization scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers, and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously. Tests in a controlled environment led to a reduction of the mean squared error of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.
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| 4. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions
- 2021
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that "fixed features shall have fixed relative distances and angles". The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.
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| 5. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors
- 2015
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 15:12, s. 31205-31223
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Tidskriftsartikel (refereegranskat)abstract
- We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.
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| 6. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Modeling and Calibrating Triangulation Lidars for Indoor Applications
- 2018
-
Ingår i: Informatics in Control, Automation and Robotics. - Cham : Springer. - 9783319550107 - 9783319550114 ; , s. 342-366
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Bokkapitel (refereegranskat)abstract
- We present an improved statistical model of the measurement process of triangulation Light Detection and Rangings (Lidars) that takes into account bias and variance effects coming from two different sources of uncertainty: (i) mechanical imperfections on the geometry and properties of their pinhole lens - CCD camera systems, and (ii) inaccuracies in the measurement of the angular displacement of the sensor due to non ideal measurements from the internal encoder of the sensor. This model extends thus the one presented in [2] by adding this second source of errors. Besides proposing the statistical model, this chapter considers: (i) specialized and dedicated model calibration algorithms that exploit Maximum Likelihood (ML)/Akaike Information Criterion (AIC) concepts and that use training datasets collected in a controlled setup, and (ii) tailored statistical strategies that use the calibration results to statistically process the raw sensor measurements in non controlled but structured environments where there is a high chance for the sensor to be detecting objects with flat surfaces (e.g., walls). These newly proposed algorithms are thus specially designed and optimized for inferring precisely the angular orientation of the Lidar sensor with respect to the detected object, a feature that is beneficial especially for indoor navigation purposes.
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| 7. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Statistical modeling and calibration of triangulation Lidars
- 2016
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Ingår i: ICINCO 2016. - : SciTePress. - 9789897581984 ; , s. 308-317
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Konferensbidrag (refereegranskat)abstract
- We aim at developing statistical tools that improve the accuracy and precision of the measurements returned by triangulation Light Detection and Rangings (Lidars). To this aim we: i) propose and validate a novel model that describes the statistics of the measurements of these Lidars, and that is built starting from mechanical considerations on the geometry and properties of their pinhole lens - CCD camera systems; ii) build, starting from this novel statistical model, a Maximum Likelihood (ML) / Akaike Information Criterion (AIC) -based sensor calibration algorithm that exploits training information collected in a controlled environment; iii) develop ML and Least Squares (LS) strategies that use the calibration results to statistically process the raw sensor measurements in non controlled environments. The overall technique allowed us to obtain empirical improvements of the normalized Mean Squared Error (MSE) from 0.0789 to 0.0046
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| 8. |
- Alhashimi, Anas, 1978-
(författare)
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Statistical Sensor Calibration Algorithms
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The use of sensors is ubiquitous in our IT-based society; smartphones, consumer electronics, wearable devices, healthcare systems, industries, and autonomous cars, to name but a few, rely on quantitative measurements for their operations. Measurements require sensors, but sensor readings are corrupted not only by noise but also, in almost all cases, by deviations resulting from the fact that the characteristics of the sensors typically deviate from their ideal characteristics.This thesis presents a set of methodologies to solve the problem of calibrating sensors with statistical estimation algorithms. The methods generally start with an initial statistical sensor modeling phase in which the main objective is to propose meaningful models that are capable of simultaneously explaining recorded evidence and the physical principle for the operation of the sensor. The proposed calibration methods then typically use training datasets to find point estimates of the parameters of these models and to select their structure (particularlyin terms of the model order) using suitable criteria borrowed from the system identification literature. Subsequently, the proposed methods suggest how to process the newly arriving measurements through opportune filtering algorithms that leverage the previously learned models to improve the accuracy and/or precision of the sensor readings.This thesis thus presents a set of statistical sensor models and their corresponding model learning strategies, and it specifically discusses two cases: the first case is when we have a complete training dataset (where “complete” refers to having some ground-truth informationin the training set); the second case is where the training set should be considered incomplete (i.e., not containing information that should be considered ground truth, which implies requiring other sources of information to be used for the calibration process). In doing so, we consider a set of statistical models consisting of both the case where the variance of the measurement error is fixed (i.e., homoskedastic models) and the case where the variance changes with the measured quantity (i.e., heteroskedastic models). We further analyzethe possibility of learning the models using closed-form expressions (for example, when statistically meaningful, Maximum Likelihood (ML) and Weighted Least Squares (WLS) estimation schemes) and the possibility of using numerical techniques such as Expectation Maximization (EM) or Markov chain Monte Carlo (MCMC) methods (when closed-form solutions are not available or problematic from an implementation perspective). We finally discuss the problem formulation using classical (frequentist) and Bayesian frameworks, and we present several field examples where the proposed calibration techniques are applied on sensors typically used in robotics applications (specifically, triangulation Light Detection and Rangings (Lidars) and Time of Flight (ToF) Lidars).
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| 9. |
- Bof, Nicoletta, et al.
(författare)
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Multiagent Newton–Raphson Optimization Over Lossy Networks
- 2019
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE. - 0018-9286 .- 1558-2523. ; 64:7, s. 2983-2990
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we study the problem of unconstrained convex optimization in a fully distributed multiagent setting, which includes asynchronous computation and lossy communication. In particular, we extend a recently proposed algorithm named Newton-Raphson consensus by integrating it with a broadcast-based average consensus algorithm, which is robust to packet losses. We show via the separation of time-scale principle that under mild conditions (i.e., persistency of the agents activation and bounded consecutive communication failures), the proposed algorithm is provably locally exponentially stable with respect to the optimal global solution. Finally, we complement the theoretical analysis with numerical simulations and comparisons based on real datasets.
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| 10. |
- Carli, Ruggero, et al.
(författare)
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Analysis of Newton-Raphson consensus for multi-agent convex optimization under asynchronous and lossy communications
- 2015
-
Ingår i: IEEE 54th Annual Conference on Decision and Control (CDC). - Piscataway, NJ : IEEE Communications Society. - 9781479978861 ; , s. 418-424
-
Konferensbidrag (refereegranskat)abstract
- We extend a multi-agent convex-optimization algorithm named Newton-Raphson consensus to a network scenario that involves directed, asynchronous and lossy communications. We theoretically analyze the stability and performance of the algorithm and, in particular, provide sufficient conditions that guarantee local exponential convergence of the node-states to the global centralized minimizer even in presence of packet losses. Finally, we complement the theoretical analysis with numerical simulations that compare the performance of the Newton-Raphson consensus against asynchronous implementations of distributed subgradient methods on real datasets extracted from open-source databases
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| 11. |
- Carli, Ruggero, et al.
(författare)
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Distributed quadratic programming under Asynchronous and Lossy Communications via Newton-Raphson Consensus
- 2015
-
Ingår i: 2015 European Control Conference (ECC). - Piscataway, NJ : IEEE Communications Society. - 9783952426937 ; , s. 2514-2520
-
Konferensbidrag (refereegranskat)abstract
- Quadratic optimization problems appear in several interesting estimation, learning and control tasks. To solve these problems in peer-to-peer networks it is necessary to design distributed optimization algorithms supporting directed, asynchronous and unreliable communication. This paper addresses this requirement by extending a promising distributed convex optimization algorithm, known as Newton-Raphson consensus, and originally designed for static and undirected communication. Specifically, we modify this algorithm so that it can cope with asynchronous, broadcast and unreliable lossy links, and prove that the optimization strategy correctly converge to the global optimum when the local cost functions are quadratic. We then support the intuition that this robustified algorithm converges to the true optimum also for general convex problems with dedicated numerical simulations.
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| 12. |
- Del Favero, S., et al.
(författare)
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Bayesian learning of probability density functions : A Markov chain Monte Carlo approach
- 2012
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Ingår i: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on. - : IEEE. ; , s. 1512-1517
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Konferensbidrag (refereegranskat)abstract
- The paper considers the problem of reconstructing a probability density function from a finite set of samples independently drawn from it.We cast the problem in a Bayesian setting where the unknown density is modeled via a nonlinear transformation of a Bayesian prior placed on a Reproducing Kernel Hilbert Space. The learning of the unknown density function is then formulated as a minimum variance estimation problem. Since this requires the solution of analytically intractable integrals, we solve this problem by proposing a novel algorithm based on the Markov chain Monte Carlo framework. Simulations are used to corroborate the goodness of the new approach.
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| 13. |
- Ebadat, Afrooz, et al.
(författare)
-
Blind identification strategies for room occupancy estimation
- 2015
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Ingår i: 2015 European Control Conference (ECC). - Piscataway, NJ : IEEE Communications Society. - 9783952426937 ; , s. 1315-1320
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Konferensbidrag (refereegranskat)abstract
- We propose and test on real data a two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels. The first tier is a blind identification step, based either on a frequentist Maximum Likelihood method, implemented using non-linear optimization, or on a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm. The second tier resolves the ambiguity of the unknown multiplicative factor, and returns the final estimate of the occupancy levels. The overall procedure addresses some practical issues of existing occupancy estimation strategies. More specifically, first it does not require the installation of special hardware, since it uses measurements that are typically available in many buildings. Second, it does not require apriori knowledge on the physical parameters of the building, since it performs system identification steps. Third, it does not require pilot data containing measured real occupancy patterns (i.e., physically counting people for some periods, a typically expensive and time consuming step), since the identification steps are blind.
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| 14. |
- Ebadat, Afrooz, et al.
(författare)
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Estimation of building occupancy levels through environmental signals deconvolution
- 2013
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Ingår i: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. - New York, NY, USA : ACM. - 9781450324311
-
Konferensbidrag (refereegranskat)abstract
- We address the problem of estimating the occupancy levelsin rooms using the information available in standardHVAC systems. Instead of employing dedicated devices, weexploit the significant statistical correlations between the occupancylevels and the CO2 concentration, room temperature,and ventilation actuation signals in order to identify adynamic model. The building occupancy estimation problemis formulated as a regularized deconvolution problem, wherethe estimated occupancy is the input that, when injected intothe identified model, best explains the currently measuredCO2 levels. Since occupancy levels are piecewise constant,the zero norm of occupancy is plugged into the cost functionto penalize non-piecewise constant inputs. The problemthen is seen as a particular case of fused-lasso estimator byrelaxing the zero norm into the `1 norm. We propose bothonline and offline estimators; the latter is shown to performfavorably compared to other data-based building occupancyestimators. Results on a real testbed show that the MSE ofthe proposed scheme, trained on a one-week-long dataset, is half the MSE of equivalent Neural Network (NN) or SupportVector Machine (SVM) estimation strategies.
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| 15. |
- Ebadat, Afrooz, et al.
(författare)
-
Multi-room occupancy estimation through adaptive gray-box models
- 2015
-
Ingår i: IEEE 54th Annual Conference on Decision and Control (CDC). - Piscataway, NJ : IEEE Communications Society. - 9781479978861 ; , s. 3705-3711, s. 3705-3711
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of estimating the occupancy level in buildings using indirect information such as CO2 concentrations and ventilation levels. We assume that one of the rooms is temporarily equipped with a device measuring the occupancy. Using the collected data, we identify a gray-box model whose parameters carry information about the structural characteristics of the room. Exploiting the knowledge of the same type of structural characteristics of the other rooms in the building, we adjust the gray-box model to capture the CO2 dynamics of the other rooms. Then the occupancy estimators are designed using a regularized deconvolution approach which aims at estimating the occupancy pattern that best explains the observed CO2 dynamics. We evaluate the proposed scheme through extensive simulation using a commercial software tool, IDA-ICE, for dynamic building simulation.
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| 16. |
- Ebadat, Afrooz, et al.
(författare)
-
Regularized Deconvolution-Based Approaches for Estimating Room Occupancies
- 2015
-
Ingår i: IEEE Transactions on Automation Science and Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-5955 .- 1558-3783. ; 12:4, s. 1157-1168
-
Tidskriftsartikel (refereegranskat)abstract
- We address the problem of estimating the number of people in a room using information available in standard HVAC systems. We propose an estimation scheme based on two phases. In the first phase, we assume the availability of pilot data and identify a model for the dynamic relations occurring between occupancy levels, CO2 concentration and room temperature. In the second phase, we make use of the identified model to formulate the occupancy estimation task as a deconvolution problem. In particular, we aim at obtaining an estimated occupancy pattern by trading off between adherence to the current measurements and regularity of the pattern. To achieve this goal, we employ a special instance of the so-called fused lasso estimator, which promotes piecewise constant estimates by including an l(1) norm-dependent term in the associated cost function. We extend the proposed estimator to include different sources of information, such as actuation of the ventilation system and door opening/closing events. We also provide conditions under which the occupancy estimator provides correct estimates within a guaranteed probability. We test the estimator running experiments on a real testbed, in order to compare it with other occupancy estimation techniques and assess the value of having additional information sources.
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| 17. |
- Eriksson, Martin, et al.
(författare)
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Monitoring and modelling open compute servers
- 2017
-
Ingår i: Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. - Piscataway, NJ : Institute of Electrical and Electronics Engineers Inc.. - 9781538611272 ; , s. 7177-7184, s. 7177-7184
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Konferensbidrag (refereegranskat)abstract
- Energy efficient control of server rooms in modern data centers can help reducing the energy usage of this fast growing industry. Efficient control, however, cannot be achieved without: i) continuously monitoring in real-time the behavior of the basic thermal nodes within these infrastructures, i.e., the servers; ii) analyzing the acquired data to model the thermal dynamics within the data center. Accurate data and accurate models are indeed instrumental for implementing efficient data centers cooling strategies. In this paper we focus on a class of Open Compute Servers, designed in an open-source fashion and currently deployed by Facebook. We thus propose a set of methods for collecting real-time data from these platforms and a control-oriented model describing the thermal dynamics of the CPUs and RAMs of these servers as a function of both manipulable and exogenous inputs (e.g., the CPU utilization levels and the air mass flow produced by the server's fans). We identify the parameters of this model from real data and make the results available to other researchers.
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| 18. |
- Fjällström, Eva, 1980-, et al.
(författare)
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Courses-Concepts-Graphs as a Tool to Measure the Importance of Concepts in University Programmes
- 2019
-
Ingår i: 2019 18th European Control Conference (ECC). - : IEEE. - 9783907144008 ; , s. 3076-3083
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Konferensbidrag (refereegranskat)abstract
- This paper investigates methods for quantitatively assessing the importance and relative importance of concepts taught in a university program. This assessment has many uses, e.g., to aid program design and inventory, and for communicating what concepts a course may rely on at a given point in the program. We propose to perform this quantitative assessment in two steps: first, representing the university program as an opportune graph with courses and concepts as nodes and connections between courses and concepts as edges; second, by quantitatively defining each concept's importance as its centrality as a node within the network. We thus perform two investigations, both leveraging a practical case - data collected from two engineering programs at two Swedish university: a) how to represent university programs in terms of graphs (here called Courses-Concepts Graph (CCG)), and b) how to reinterpret the most classical graph-theoretical node centrality indexes in the pedagogical term of concept centrality index.
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| 19. |
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| 20. |
- Fjällström, Eva, 1980-, et al.
(författare)
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Developing Concept Inventory Tests for Electrical Engineering : Extractable Information, Early Results, and Learned Lessons
- 2018
-
Ingår i: 2018 UKACC 12th International Conference on Control (CONTROL). - : IEEE. - 9781538628645 ; , s. 436-441
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Konferensbidrag (refereegranskat)abstract
- This paper suggests a method for developing, implementing and assessing a concept inventory test for electrical engineering students (CITE). The aim of this test is to help students better understand and learn core concepts, plus increase their awareness about links between the different courses and other themes of the program. Our and other experiences show that students often struggle to understand and use fundamental concepts, and how these relate to the various courses. This issue is probably due to the fact that traditional exams mainly focus on assessing procedural tasks (e.g., directly solving specific problems following step-by-step approaches). The investigated programs at Uppsala University (UU) and Luleå Uni-versity of Technology (LTU), nonetheless, have no tool for collecting quantitative data on how students develop conceptual knowledge throughout the programs, and thus no means to obtain an holistic view about their learning process. The here proposed methodology thus describes how to develop tests that would not only provide students with valuable feedback on their progression, but also equip teachers and program boards with high-end data for pedagogical and course development purposes. Besides illustrating the developmental methodology, the paper includes reactions and remarks from students on what the tests would provide and what would motivate them to take it.
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| 21. |
- Garin, Federica, et al.
(författare)
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Distributed estimation of diameter, radius and eccentricities in anonymous networks
- 2012
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Ingår i: Estimation and Control of Networked Systems, Vol 3. Part 1. - : IFAC. ; , s. 13-18
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Konferensbidrag (refereegranskat)abstract
- We consider how a set of collaborating agents can distributedly infer some of the properties of the communication network that they form. We specifically focus on estimating quantities that can characterize the performance of other distributed algorithms, namely the eccentricities of the nodes, and the radius and diameter of the network. We propose a strategy that can be implemented in any network, even under anonymity constraints, and has the desirable properties of being fully distributed, parallel and scalable. We analytically characterize the statistics of the estimation error, and highlight how the performance of the algorithm depends on a parameter tuning the communication complexity.
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| 22. |
- James, Weimer, et al.
(författare)
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Parameter-invariant detection of unknown inputs in networked systems
- 2013
-
Ingår i: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). - : IEEE conference proceedings. - 9781467357173 ; , s. 4379-4384
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Konferensbidrag (refereegranskat)abstract
- This work considers the problem of detecting unknown inputs in networked systems whose dynamics are governed by time-varying unknown parameters. We propose a strategy in opposition to the commonly employed approach of first estimating the unknown parameters and then using the estimates as the true parameter values for detection, e.g. maximum-likelihood approaches. The suggested detection scheme employs test statistics that are invariant to the unknown parameters and do not rely on parameter estimation. We specifically consider the case of severe lack of prior knowledge, i.e., the problem of detecting unknown inputs when nothing is known of the system but some primitive structural properties, namely that the system is a linear network, subject to Gaussian noise, and that a certain input signal is either present or not. The aim is thus to analyze the structure and performances of invariant tests in a limiting case, specifically where the amount of prior information is minimal. The developed test is proven to be maximally invariant to the unknown parameters and Uniformly Most Powerful Invariant (UMPI). Simulation results indicate that for arbitrary networked systems the parameterinvariant detector achieves a specified probability of false alarm while ensuring that the probability of detection is maximized.
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| 23. |
- Kask, Nathalie, et al.
(författare)
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Data-driven modelling of fatigue in pelvic floor muscles when performing Kegel exercises
- 2019
-
Ingår i: 2019 IEEE 58th Conference On Decision And Control (CDC). - : IEEE. - 9781728113982 ; , s. 5647-5653
-
Konferensbidrag (refereegranskat)abstract
- This paper studies how to describe, using a piece-wise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelSs parameters from field data. In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience. We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game. By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.
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| 24. |
- Kask, Nathalie, et al.
(författare)
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Data-driven modelling of fatigue in pelvic floor muscles when performing Kegel exercises
- 2020
-
Ingår i: 2019 IEEE 58th Conference on Decision and Control (CDC). - : IEEE. ; , s. 5647-5653
-
Konferensbidrag (refereegranskat)abstract
- This paper studies how to describe, using a piecewise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelřs parameters from field data. In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience. We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game. By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.
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| 25. |
- Knorn, Steffi, et al.
(författare)
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Analysing the effects of biofeedback using control-oriented formalisms : the case of gamified Kegel exercising
- 2020
-
Ingår i: European Control Conference 2020. - : IEEE. ; , s. 741-748
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Konferensbidrag (refereegranskat)abstract
- Biofeedback mechanisms affect the overall efficacy of the medical experience in different ways; while being often a suitable approach to improve and enable efficient treatments, they might also lead to unexpected, unplanned or even undesired effects. Motivated by these considerations, we discuss the problem of detecting whether specific designs of biofeedback mechanisms lead to unexpected, unplanned or undesired behaviors in the specific case of gamified pelvic floor muscles exercising. To solve the problem we use a control-theoretic framework, and ladder on the construction of a online estimator of the intentions of the patients in performing muscle activity. We propose thus to use a mixed model- and data-driven framework for which a parametric model is first opportunely identified and then used to forecast the behavior of the patient. This model, in conjunction with the data collected under the influence of biofeedback, is then used to investigate the resulting changes in patients’ intentions in real life settings, and report quantitative assessments on the performance of the overall scheme in field tests.
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| 26. |
- Knorn, Steffi, et al.
(författare)
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Data-driven modelling of pelvic floor muscles dynamics
- 2018
-
Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 51:27, s. 321-326
-
Tidskriftsartikel (refereegranskat)abstract
- This paper proposes individualized, dynamical and data-driven models that describe pelvic floor muscle responses in women that use vaginal dilation. Specifically, the models describe how the volume of an inflatable balloon inserted at the vaginal introitus dynamically affects the aggregated pressure exerted by the pelvic floor muscles of the person. The paper inspects the approximation capabilities of different model structures, such as Hammerstein-Wiener and NARX, for this specific application, and finds the specific model structures and orders that best describe the recorded measurement data. Hence, although the current dataset is drawn from a sample of healthy volunteers, this paper is an initial step towards better understanding women’s responses to vaginal dilation and facilitating individualised medical vaginal dilation treatment.
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| 27. |
- Knorn, Steffi, et al.
(författare)
-
Data-driven models of pelvic floor muscles dynamics subject to psychological and physiological stimuli
- 2019
-
Ingår i: IFAC Journal of Systems and Control. - : Elsevier BV. - 2468-6018. ; 8
-
Tidskriftsartikel (refereegranskat)abstract
- This paper proposes individualized, dynamical and data-driven models to describe pelvic floor muscle responses in women undergoing vaginal dilation. Specifically, the models describe how the aggregated pressure exerted by the pelvic floor muscles of women change due to physiological and psychological stimuli. Specifically, women experienced inflation of a balloon at the vaginal introitus while watching different short movies such as with or without sexual content. The paper inspects the approximation capabilities of different model structures, such as Hammerstein–Wiener and NARX, for this specific application, and finds the specific model structures and orders that best describe the recorded measurement data. Moreover, the manuscript explores the trade-offs between individualization and averaging of models. More precisely it numerically assesses how models obtained by assuming that each individual has the same response can be used to simulate the responses of different patients. Although the current dataset is drawn from a sample of healthy volunteers, this paper is an initial step towards better understanding women’s responses to vaginal dilation and sexual/nonsexual videos and facilitating individualized medical vaginal dilation treatment.
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| 28. |
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| 29. |
- Knorn, Steffi, et al.
(författare)
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Quantitative analysis of curricula coherence using directed graphs
- 2019
-
Ingår i: 12th IFAC Symposium on Advances in Control Education ACE 2019. - : Elsevier. ; 52:9, s. 318-323
-
Konferensbidrag (refereegranskat)abstract
- This paper investigates methods for quantitatively examining the connectivity and knowledge flow in a university program considering courses and concepts included in the program. The proposed method is expected to be useful to aid program design and inventory, and for communicating what concepts a course may rely on at a given point in the program. As a first step, we represent the university program as a directed graph with courses and concepts as nodes and connections between courses and concepts as directed edges. Then, we investigate the connectivity and the flow through the graph in order to gain insights into the structure of the program. We thus perform two investigations based on data collected from an engineering program at a Swedish university: a) how to represent (parts of) the university program as a graph (here called Directed Courses-Concepts Graph (DCCG)), and b) how to use graph theory tools to analyse the coherence and structure of the program.
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| 30. |
- Korsfeldt Larsén, Alexander, et al.
(författare)
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A computationally efficient model predictive control scheme for space debris rendezvous
- 2019
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Ingår i: 21st IFAC Symposium on Automatic Control in Aerospace ACA 2019. - : Elsevier. ; , s. 103-110
-
Konferensbidrag (refereegranskat)abstract
- We propose a non-linear model predictive scheme for planning fuel efficient maneuvers of small spacecrafts that shall rendezvous space debris. The paper addresses the specific issues of potential limited on-board computational capabilities and low-thrust actuators in the chasing spacecraft, and solves them by using a novel MatLab-based toolbox for real-time non-linear model predictive control (MPC) called MATMPC. This tool computes the MPC rendezvous maneuvering solution in a numerically efficient way, and this allows to greatly extend the prediction horizon length. This implies that the overall MPC scheme can compute solutions that account for the long time-scales that usually characterize the low-thrust rendezvous maneuvers. The so-developed controller is then tested in a realistic scenario that includes all the near-Earth environmental disturbances. We thus show, through numerical simulations, that this MPC method can successfully be used to perform a fuel-efficient rendezvous maneuver with an uncontrolled object, plus evaluate performance indexes such as mission duration, fuel consumption, and robustness against sensor and process noises.
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| 31. |
- Lucchese, Riccardo, et al.
(författare)
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A tight bound on the Bernoulli trials network size estimator
- 2016
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Ingår i: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509018376 ; , s. 3474-3480
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 32. |
- Lucchese, Riccardo, et al.
(författare)
-
Average consensus via max consensus
- 2015
-
Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 48:22, s. 58-63
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 33. |
- Lucchese, Riccardo, et al.
(författare)
-
Controlled Direct Liquid Cooling of Data Servers
- 2021
-
Ingår i: IEEE Transactions on Control Systems Technology. - : IEEE. - 1063-6536 .- 1558-0865. ; 29:6, s. 2325-2338
-
Tidskriftsartikel (refereegranskat)abstract
- We formulate a modeling and control framework aimed at direct liquid cooling of data servers. In our application scenario, the server's heat load is rejected into a liquid cooling circuit that extends to individual chips. We start with a comprehensive discussion of our modeling derivations. We then show how to dynamically provision the coolant, while 1) regulating the temperatures of any self-heating components within the safe operational envelope; 2) minimizing the coolant supply cost; and 3) increasing the server outflow temperature (a key performance objective toward heat recovery systems). We confirm experimentally the benefits of the proposed controlled cooling strategy over several realistic scenarios corresponding to different inlet coolant temperatures and computational loads.
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| 34. |
- Lucchese, Riccardo, et al.
(författare)
-
Distributed detection of topological changes in communication networks
- 2014
-
Ingår i: 19th IFAC World Congress on International Federation of Automatic Control. - : IFAC Papers Online. - 9783902823625 ; , s. 1928-1934, s. 1928-1934
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Konferensbidrag (refereegranskat)abstract
- Changes in the topology of communication networks, such as sudden appearance or disappearance of links or nodes, may signal malicious attacks or malfunctions. A topology change detector may thus be useful to trigger alarms or self-reconfiguration procedures. Here we present a novel approach that enjoys several desirable qualities such as fast convergence, intrinsically distributed computations, and scalability w.r.t. communication and computational requirements. We characterize the performance of this technique from analytical and practical points of view, providing theoretical results on its performance. We thus show how it is possible to tune and trade-off the accuracy of the change detection results with the communication requirements of the procedure.
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| 35. |
- Lucchese, Riccardo, et al.
(författare)
-
Energy savings in data centers : A framework for modelling and control of servers’ cooling
- 2017
-
Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 9050-9057
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 36. |
- Lucchese, Riccardo, et al.
(författare)
-
Network cardinality estimation using max consensus : the case of Bernoulli trials
- 2015
-
Ingår i: IEEE 54th Annual Conference on Decision and Control (CDC). - Piscataway, NJ : IEEE Communications Society. - 9781479978847 ; , s. 895-901
-
Konferensbidrag (refereegranskat)abstract
- 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.
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| 37. |
- Lucchese, Riccardo, et al.
(författare)
-
Networks cardinality estimation using order statistics
- 2015
-
Ingår i: American Control Conference (ACC), 2015. - Piscataway, NJ : IEEE Communications Society. - 9781479986859 ; , s. 3810-3817
-
Konferensbidrag (refereegranskat)abstract
- 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.
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| 38. |
- Mamikoglu, Umut, et al.
(författare)
-
Elbow Joint Angle Estimation by Using Integrated Surface Electromyography
- 2016
-
Ingår i: 24th Mediterranean Conference on Control and Automation (MED). - Piscataway, NJ : IEEE Communications Society. - 9781467383455 ; , s. 785-790
-
Konferensbidrag (refereegranskat)abstract
- Electromyography (EMG) signals represent the electrical activation of skeletal muscles and contain valuable information about muscular activity. Estimation of the joint movements by using surface EMG signals has great importance as a bio-inspired approach for the control of robotic limbs and prosthetics. However interpreting surface EMG measurements is challenging due to the nonlinearity and user dependency of the muscle dynamics. Hence it requires complex computational methods to map the EMG signals and corresponding limb motions. To solve this challenge we here propose to use an integrated EMG signal to identify the EMG-joint angle relation instead of using common EMG processing techniques. Then we estimate the joint angles for elbow flexion-extension movement by using an auto-regressive integrated moving average with exogenous input (ARIMAX) model, which takes integrated EMG measurements as input. The experiments showed that the suggested approach results in a 21.85% average increase in the estimation performance of the elbow joint angle compared to the standard EMG processing and identification.
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| 39. |
- Modolo, V., et al.
(författare)
-
Distributed formation control using robust asynchronous and broadcast-based optimization schemes
- 2018
-
Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 51:23, s. 385-390
-
Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of letting a network of mobile agents distributedly track and maintain a formation while using communication schemes that are asynchronous, broadcasts based, and prone to packet losses. To this purpose we revisit and modify an existing distributed optimization algorithm that corresponds to a distributed version of the Newton Raphson (NR) algorithm. The proposed scheme uses then robust asynchronous ratio consensus algorithms as building blocks, and employs opportune definitions for the local cost functions to achieve the desired coordination objective. In our algorithm, indeed, we code the position of the to-be-followed target as the minimum of a shared global cost, and capture the desired inter-robots behaviors through dedicated distances-based potential barriers. We then check the effectiveness of the strategy using field tests, and verify that the scheme achieves the desired goal of introducing robustness to changes in the agents positions due to unexpected disturbances. More precisely, if an agent breaks the formation, then the update mechanism embedded in our scheme make that agent move back to a meaningful position as soon as some packets are successfully received by the misplaced agent.
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| 40. |
- Mustafa, Mohammed Obaid, et al.
(författare)
-
Detecting broken rotor bars in induction motors with model-based support vector classifiers
- 2016
-
Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 52, s. 15-23
-
Tidskriftsartikel (refereegranskat)abstract
- We propose a methodology for testing the sanity of motors when both healthy and faulty data are unavailable. More precisely, we consider a model-based Support Vector Classification (SVC) method for the detection of broken bars in three phase asynchronous motors at full load conditions, using features based on the spectral analysis of the stator's steady state current (more specifically, the amplitude of the lift sideband harmonic and the amplitude at fundamental frequency). We diverge from the mainstream focus on using SVCs trained from measured data, and instead derive a classifier that is constructed entirely using theoretical considerations. The advantage of this approach is that it does not need training steps (an expensive, time consuming and often practically infeasible task), i.e., operators are not required to have both healthy and faulty data from a system for checking it. We describe what are the theoretical properties and fundamental limitations of using model based SVC methodologies, provide conditions under which using SVC tests is statistically optimal, and present some experimental results to prove the effectiveness of the suggested scheme.
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| 41. |
- Parisio, Alessandra, et al.
(författare)
-
A Scenario-based Predictive Control Approach to Building HVAC Management Systems
- 2013
-
Ingår i: IEEE International Conference on Automation Science and Engineering. - 9781479915156 ; , s. -435
-
Konferensbidrag (refereegranskat)abstract
- We present a Stochastic Model Predictive Control (SMPC) algorithm that maintains predefined comfort levels in building Heating, Ventilation and Air Conditioning (HVAC) systems while minimizing the overall energy use. The strategy uses the knowledge of the statistics of the building occupancy and ambient conditions forecasts errors and determines the optimal control inputs by solving a scenario-based stochastic optimization problem. Peculiarities of this strategy are that it does not make assumptions on the distribution of the uncertain variables, and that it allows dynamical learning of these statistics from true data through the use of copulas, i.e., opportune probabilistic description of random vectors. The scheme, investigated on a prototypical student laboratory, shows good performance and computational tractability.
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| 42. |
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| 43. |
- Parisio, Alessandra, et al.
(författare)
-
Control of HVAC Systems via Scenario-based Explicit MPC
- 2015
-
Ingår i: 2014 IEEE 53rd Annual Conference on Decision and Control : (CDC 2014). - Piscataway, NJ : IEEE Communications Society. ; , s. 5201-5207, s. 5201-5207
-
Konferensbidrag (refereegranskat)abstract
- Improving energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems is a primary objective for the society. Model Predictive Control (MPC) techniques for HVAC systems have recently received particular attention, since they can naturally account for several factors, such as weather and occupancy forecasts, comfort ranges and actuation constraints. Developing effective MPC based control strategies for HVAC systems is nontrivial, since buildings dynamics are nonlinear and affected by various uncertainties. Further, the complexity of the MPC problem and the burden of on-line computations can lead to difficulties in integrating this scheme into a building management system.We propose to address this computational issue by designing a scenario-based explicit MPC strategy, i.e., a controller that is simultaneously based on explicit representations of the MPC feedback law and accounts for uncertainties in the occupancy patterns and weather conditions by using the scenarios paradigm. The main advantages of this approach are the absence of a-priori assumptions on the distributions of the uncertain variables, the applicability to any type of building, and the limited on-line computational burden, enabling practical implementations on low-cost hardware platforms.We illustrate the practical implementation of the proposed explicit MPC controller on a room of a university building, showing its effectiveness and computational tractability.
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| 44. |
- Parisio, Alessandra, et al.
(författare)
-
Energy Management Systems for Intelligent Buildings in Smart Grids
- 2018
-
Ingår i: Intelligent Building Control Systems. - Cham : Springer. - 9783319684611 - 9783319684628 ; , s. 253-291
-
Bokkapitel (refereegranskat)abstract
- The next-generation electric grid needs to be smart and sustainable to simultaneously deal with the ever-growing global energy demand and achieve environmental goals. In this context, the role of residential and commercial buildings is crucial, due to their large share of primary energy usage worldwide. In this chapter, we describe energy management frameworks for buildings in a smart grid scenario. An Energy Management System (EMS) is responsible for optimally scheduling end-user smart appliances, heating systems, ventilation units, and local generation devices. We discuss the performance and the practical implementation of novel stochastic MPC schemes for HVAC systems, and illustrate how these schemes take into account several sources of uncertainties, e.g., occupancy and weather conditions. Furthermore, we show how to integrate local generation capabilities and storage systems into a holistic building energy management framework.
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| 45. |
- Parisio, Alessandra, et al.
(författare)
-
Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study
- 2014
-
Ingår i: 19th IFAC World Congress on International Federation of Automatic Control. - : Elsevier BV. - 9783902823625 ; , s. 599-605, s. 599-605
-
Konferensbidrag (refereegranskat)abstract
- Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.
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| 46. |
- Parisio, Alessandra, et al.
(författare)
-
Randomized Model Predictive Control for HVAC Systems
- 2013
-
Ingår i: BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. - New York, NY, USA : ACM. - 9781450324311
-
Konferensbidrag (refereegranskat)abstract
- Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and Indoor Air Quality (IAQ) levels, essentials for occupants well-being. Since performing this task implies high energy requirements, there is a need for improving the energetic efficiency of existing buildings. A possible solution is to develop effective control strategies for HVAC systems, but this is complicated by the inherent uncertainty of the to-be-controlled system. To cope with this problem, we design a stochastic Model Predictive Control (MPC) strategy that dynamically learns the statistics of the building occupancy and weather conditions and uses them to build probabilistic constraints on the indoor temperature and CO2 concentration levels. More specifically, we propose a randomization technique that finds suboptimal solutions to the generally non-convex stochastic MPC problem. The main advantage of this method is the absence of apriori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the proposed approach by means of numerical simulations and real tests on a student laboratory, and show its practical effectiveness and computational tractability.
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| 47. |
- Pillonetto, Gianluigi, et al.
(författare)
-
Distributed multi-agent Gaussian regression via finite-dimensional approximations
- 2019
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE. - 0162-8828 .- 1939-3539. ; 41:9, s. 2098-2111
-
Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of distributedly estimating Gaussian processes in multi-agent frameworks. Each agent collects few measurements and aims to collaboratively reconstruct a common estimate based on all data. Agents are assumed with limited computational and communication capabilities and to gather M noisy measurements in total on input locations independently drawn from a known common probability density. The optimal solution would require agents to exchange all the M input locations and measurements and then invert an M×M matrix, a non-scalable task. Differently, we propose two suboptimal approaches using the first E orthonormal eigenfunctions obtained from the Karhunen-Loève (KL) expansion of the chosen kernel, where typically E≪M. The benefits are that the computation and communication complexities scale with E and not with M, and computing the required statistics can be performed via standard average consensus algorithms. We obtain probabilistic non-asymptotic bounds that determine a priori the desired level of estimation accuracy, and new distributed strategies relying on Stein's unbiased risk estimate (SURE) paradigms for tuning the regularization parameters and applicable to generic basis functions (thus not necessarily kernel eigenfunctions) and that can again be implemented via average consensus. The proposed estimators and bounds are finally tested on both synthetic and real field data.
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| 48. |
- Pillonetto, Gianluigi, et al.
(författare)
-
Statistical bounds for Gaussian regression algorithms based on Karhunen-Loève expansions
- 2018
-
Ingår i: 2017 IEEE 56th Conference on Decision and Control, CDC. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509028733 - 9781509028740 ; , s. 363-368
-
Konferensbidrag (refereegranskat)abstract
- 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.
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| 49. |
- Simonazzi, E., et al.
(författare)
-
Detecting and modelling air flow overprovisioning / underprovisioning in air-cooled datacenters
- 2018
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781509066841 ; , s. 4893-4900, s. 4893-4900
-
Konferensbidrag (refereegranskat)abstract
- When cooling and exhaust air flows in air-cooled datacenters mix, the energetic efficiency of the cooling operations drops. One way to prevent this mixing of happening is by augmenting the air tightness of the hot and cold aisles; this, however, requires installing opportune hardware that may be expensive and require time consuming installations. Alternatively, one may try to minimize cooling and exhaust air flows mixing by opportunely controlling the speeds of the fans of the Computer Room Air Handling (CRAH) units so that the distribution of the air pressure field within the computer room is favorable. Implementing this type of flow control requires both detecting when there actually is some type of flow mixing somewhere, plus understanding how to operate the cooling infrastructure so that these mixings do not happen. To this aim, there is the need for models that can both help deciding whether these mixing events occur, plus designing automatic control strategies for reducing the risks that they will happen. In this manuscript, we propose an ad-hoc methodology for the data-driven derivation of control-oriented models that serve the purposes above. The methodology is built on classical Prediction Error Method (PEM) approaches to the system identification problem, and on laddering on the peculiarities of the physics of the phenomena under consideration. Moreover, we test and assess the methodology on a industrial-scale air-cooled datacenter with an installed capacity of 240 kW, and verify that the obtained models are able to capture the dynamics of the system in all its potential regimes.
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| 50. |
- Teixeira, André M. H., et al.
(författare)
-
Computer-aided curriculum analysis and design : existing challenges and open research directions
- 2020
-
Ingår i: 2020 IEEE Frontiers in Education Conference (FIE). - 9781728189628 - 9781728189611
-
Konferensbidrag (refereegranskat)abstract
- This Research-to-Practice Full Paper investigates the emerging perspectives for the 21st engineering curriculum, and discusses the crucial role that digital technologies will have in facilitating the management, evaluation, and development of such a curriculum. First, a vision for future engineering curricula is distilled from modern curricular perspectives and trends of future engineering professions, where the integration of non-cognitive competences and the increased individualization of study paths are central. Core requirements for future curricula are outlined, which pose significant barriers to curricular changes. Then, the role of technology in mitigating these barriers is discussed, by outlining key aspects of a data-driven digital approach to the management of future curricula. To illustrate the proposed approach, the paper presents a case example of a digital tool that analyzes curriculum coherency at the content level. The paper concludes with a discussion of future research directions regarding the conceptualization and management of future engineering curricula through digital technologies.
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