| 1. |
- Sandberg, Marcus, et al.
(författare)
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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
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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
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Ingår i: IEEE 54th Annual Conference on Decision and Control (CDC). - Piscataway, NJ : IEEE Communications Society. - 9781479978861 ; , s. 418-424
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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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