Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867724
Seyyed Shaho Alaviani, A. Kelkar, U. Vaidya
In this paper, a distributed resource allocation problem is considered where multiple agents want to allocate network resources among themselves while optimizing certain performance index. The first continuous-time distributed second-order gradient algorithm is proposed for resource allocation over static (non-switching) graphs under synchronous protocol. The algorithm is able to converge to the optimal solution of the problem with exponential convergence rate under suitable assumptions. Finally, a numerical example of a distributed estimation in wireless sensor networks is given by using the algorithm in order to demonstrate the results.
{"title":"A Distributed Second-Order Gradient Continuous-Time Algorithm for Resource Allocation","authors":"Seyyed Shaho Alaviani, A. Kelkar, U. Vaidya","doi":"10.23919/ACC53348.2022.9867724","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867724","url":null,"abstract":"In this paper, a distributed resource allocation problem is considered where multiple agents want to allocate network resources among themselves while optimizing certain performance index. The first continuous-time distributed second-order gradient algorithm is proposed for resource allocation over static (non-switching) graphs under synchronous protocol. The algorithm is able to converge to the optimal solution of the problem with exponential convergence rate under suitable assumptions. Finally, a numerical example of a distributed estimation in wireless sensor networks is given by using the algorithm in order to demonstrate the results.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130136747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867707
Xing-nan Zhou, Heran Shen, Zejiang Wang, Jun-ming Wang
Respecting vehicle dynamics, the sideslip angle is a vitally important state for assessing and maintaining the lateral stability. As a practical barrier, the direct sensory measurement for such a state is overly expensive and not always reliable. In view of this, a self-scheduled L1 robust observer is synthesized in this paper to estimate the sideslip angle. Three objectives are achieved via the proposed estimation algorithm. First, the peak-to-peak induced gain from the exogenous disturbances to the estimation error is attenuated. Second, exploiting the Modified Finsler’s Lemma, the proposed strategy is effectually robust against bounded tire cornering stiffness perturbations. Third, the time-varying vehicular longitudinal velocity is compensated for by a polytopic gain-scheduling scheme. The proposed robust observer is corroborated via the high-fidelity CARSIM simulation, and its performance is contrasted against a baseline method.
{"title":"Self-scheduled L1 Robust Vehicular Sideslip Angle Estimation","authors":"Xing-nan Zhou, Heran Shen, Zejiang Wang, Jun-ming Wang","doi":"10.23919/ACC53348.2022.9867707","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867707","url":null,"abstract":"Respecting vehicle dynamics, the sideslip angle is a vitally important state for assessing and maintaining the lateral stability. As a practical barrier, the direct sensory measurement for such a state is overly expensive and not always reliable. In view of this, a self-scheduled L1 robust observer is synthesized in this paper to estimate the sideslip angle. Three objectives are achieved via the proposed estimation algorithm. First, the peak-to-peak induced gain from the exogenous disturbances to the estimation error is attenuated. Second, exploiting the Modified Finsler’s Lemma, the proposed strategy is effectually robust against bounded tire cornering stiffness perturbations. Third, the time-varying vehicular longitudinal velocity is compensated for by a polytopic gain-scheduling scheme. The proposed robust observer is corroborated via the high-fidelity CARSIM simulation, and its performance is contrasted against a baseline method.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129006556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867694
Bjorn A. C. van de Ven, R. W. H. Sneijders, F. Hoekstra, H. Bergveld, M. Donkers
Accurately estimating the State-of-Charge (SoC) and temperature of lithium-ion cells inside a battery pack is critical for safe and reliable operation. This paper extends battery state estimation from single-cell SoC estimation towards a combined SoC and temperature estimation for a multi-cell pack. Combining the electrical and thermal models on a pack level allows for the inclusion of thermal interaction between the cells in a compact manner. The resulting coupled model is used in an extended Kalman filter with correlated noise and a forgetting factor, that is extended with model-residual-based tuning to accommodate differences in magnitude between the electrical and thermal parts of the coupled model. Combined estimation of temperature and SoC decreases the SoC error by around 50%, while the included thermal model adds an accurate estimate of the individual cell temperatures. This provides insight into the temperature distribution, without requiring a large number of temperature sensors.
{"title":"Combined Cell-Level Estimation of State-of-Charge and Temperature in Battery Packs","authors":"Bjorn A. C. van de Ven, R. W. H. Sneijders, F. Hoekstra, H. Bergveld, M. Donkers","doi":"10.23919/ACC53348.2022.9867694","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867694","url":null,"abstract":"Accurately estimating the State-of-Charge (SoC) and temperature of lithium-ion cells inside a battery pack is critical for safe and reliable operation. This paper extends battery state estimation from single-cell SoC estimation towards a combined SoC and temperature estimation for a multi-cell pack. Combining the electrical and thermal models on a pack level allows for the inclusion of thermal interaction between the cells in a compact manner. The resulting coupled model is used in an extended Kalman filter with correlated noise and a forgetting factor, that is extended with model-residual-based tuning to accommodate differences in magnitude between the electrical and thermal parts of the coupled model. Combined estimation of temperature and SoC decreases the SoC error by around 50%, while the included thermal model adds an accurate estimate of the individual cell temperatures. This provides insight into the temperature distribution, without requiring a large number of temperature sensors.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130617382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867283
Ji Wang, M. Krstić
We present an adaptive event-triggered boundary control scheme for a parabolic PDE-ODE system, where the reaction coefficient of the parabolic PDE, and the system parameter of a scalar ODE, are unknown. In the proposed controller, the parameter estimates, which are built by batch least-squares identification, are recomputed and the plant states are resampled simultaneously. As a result, both the parameter estimates and the control input employ piecewise-constant values. In the closed-loop system, the following results are proved: 1) the absence of a Zeno phenomenon; 2) finite-time exact identification of the unknown parameters under most initial conditions of the plant (all initial conditions except for a set of measure zero); 3) exponential regulation of the plant states to zero. A simulation example is presented to validate the theoretical result.
{"title":"Event-Triggered Adaptive Control of a Parabolic PDE-ODE Cascade","authors":"Ji Wang, M. Krstić","doi":"10.23919/ACC53348.2022.9867283","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867283","url":null,"abstract":"We present an adaptive event-triggered boundary control scheme for a parabolic PDE-ODE system, where the reaction coefficient of the parabolic PDE, and the system parameter of a scalar ODE, are unknown. In the proposed controller, the parameter estimates, which are built by batch least-squares identification, are recomputed and the plant states are resampled simultaneously. As a result, both the parameter estimates and the control input employ piecewise-constant values. In the closed-loop system, the following results are proved: 1) the absence of a Zeno phenomenon; 2) finite-time exact identification of the unknown parameters under most initial conditions of the plant (all initial conditions except for a set of measure zero); 3) exponential regulation of the plant states to zero. A simulation example is presented to validate the theoretical result.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130256145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867704
Dhanushki Hewawaduge, A. Zare
We study the effect of white-in-time additive stochastic base flow perturbations on the mean-square properties of the linearized Navier-Stokes equations. Such perturbations enter the linearized dynamics as multiplicative sources of uncertainty. We adopt an input-output approach to analyze the mean-square stability and frequency response of the flow subject to additive and multiplicative uncertainty. For transitional channel flows, we uncover the Reynolds number scaling of critical base flow variances and identify length scales that are most affected by base flow uncertainty. For small-amplitude perturbations, we adopt a perturbation analysis to efficiently compute the variance amplification of velocity fluctuations around the uncertain base state. Our results demonstrate the robust amplification of streamwise elongated flow structures in the presence of base flow uncertainty and that the wall-normal shape of base flow modulations can influence the amplification of various length scales.
{"title":"The effect of base flow uncertainty on transitional channel flows","authors":"Dhanushki Hewawaduge, A. Zare","doi":"10.23919/ACC53348.2022.9867704","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867704","url":null,"abstract":"We study the effect of white-in-time additive stochastic base flow perturbations on the mean-square properties of the linearized Navier-Stokes equations. Such perturbations enter the linearized dynamics as multiplicative sources of uncertainty. We adopt an input-output approach to analyze the mean-square stability and frequency response of the flow subject to additive and multiplicative uncertainty. For transitional channel flows, we uncover the Reynolds number scaling of critical base flow variances and identify length scales that are most affected by base flow uncertainty. For small-amplitude perturbations, we adopt a perturbation analysis to efficiently compute the variance amplification of velocity fluctuations around the uncertain base state. Our results demonstrate the robust amplification of streamwise elongated flow structures in the presence of base flow uncertainty and that the wall-normal shape of base flow modulations can influence the amplification of various length scales.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130466788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867221
D. Steeves, M. Krstić
Prescribed-time stabilization employs time-varying gains that grow and multiply states that decay. Such feedback structures have unprecedented properties of regulation in user-prescribed finite time, independent of the initial condition, and with zero asymptotic gains to process right-hand side disturbances (perfect disturbance rejection), regardless of the disturbance size. However, when the state measurement is itself subject to a disturbance, the multiplication with growing gains threatens to result in unbounded control inputs. In this paper we present results—for linear systems in the controllable canonical form and for nonlinear high-dimensional Euler-Lagrange systems that describe high-degree-of-freedom robotic manipulators—which carry no such risk: the sum of the state and the measurement disturbance is still driven to zero, the input remains bounded, and a particular ISS property relative to the disturbance is guaranteed. The price we pay for such strong and fairly unexpected results is a structural condition we impose on the disturbance, which is met in practical applications that rely on accelerometer, gyroscope, or encoder measurements.
{"title":"Prescribed-Time Stabilization Robust to Measurement Disturbances","authors":"D. Steeves, M. Krstić","doi":"10.23919/ACC53348.2022.9867221","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867221","url":null,"abstract":"Prescribed-time stabilization employs time-varying gains that grow and multiply states that decay. Such feedback structures have unprecedented properties of regulation in user-prescribed finite time, independent of the initial condition, and with zero asymptotic gains to process right-hand side disturbances (perfect disturbance rejection), regardless of the disturbance size. However, when the state measurement is itself subject to a disturbance, the multiplication with growing gains threatens to result in unbounded control inputs. In this paper we present results—for linear systems in the controllable canonical form and for nonlinear high-dimensional Euler-Lagrange systems that describe high-degree-of-freedom robotic manipulators—which carry no such risk: the sum of the state and the measurement disturbance is still driven to zero, the input remains bounded, and a particular ISS property relative to the disturbance is guaranteed. The price we pay for such strong and fairly unexpected results is a structural condition we impose on the disturbance, which is met in practical applications that rely on accelerometer, gyroscope, or encoder measurements.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131340448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867328
Qingsong Liu, Yaoyu Zhang
In this paper, we investigate online convex optimization (OCO) with squared l2 norm switching cost, which has great applicability but very little work has been done on it. Specifically, we provide a new theoretical analysis in terms of dynamic regret and lower bounds for the case when loss functions are strongly-convex and smooth or only smooth. We show that by applying the advanced Online Multiple Gradient Descent (OMGD) and Online Optimistic Mirror Descent (OOMD) algorithms that are originally proposed for classic OCO, we can achieve state-of-the-art performance bounds for OCO with squared l2 norm switching cost. Furthermore, we show that these bounds match the lower bound.
{"title":"Optimal Dynamic Regret for Online Convex Optimization with Squared l2 Norm Switching Cost","authors":"Qingsong Liu, Yaoyu Zhang","doi":"10.23919/ACC53348.2022.9867328","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867328","url":null,"abstract":"In this paper, we investigate online convex optimization (OCO) with squared l2 norm switching cost, which has great applicability but very little work has been done on it. Specifically, we provide a new theoretical analysis in terms of dynamic regret and lower bounds for the case when loss functions are strongly-convex and smooth or only smooth. We show that by applying the advanced Online Multiple Gradient Descent (OMGD) and Online Optimistic Mirror Descent (OOMD) algorithms that are originally proposed for classic OCO, we can achieve state-of-the-art performance bounds for OCO with squared l2 norm switching cost. Furthermore, we show that these bounds match the lower bound.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131424117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867488
Shih-Yen Wei, J. Spall
This paper is aimed at characterizing the mean square error and probabilistic uncertainty of a popular class of filtering algorithms in nonlinear systems. The state estimation error of the extended Kalman filter and the deterministic-gain Kalman filter are analyzed. We allow a vector state, but assume scalar measurements. A set of conditions for the mean square error to be upper-bounded is derived. Furthermore, the probabilistic bounds for the estimation error are computed via both the moment-based approach and the stochastic comparison analysis approach. The latter provides a formal means determining uncertainty bounds, such as statistical confidence regions.
{"title":"Uncertainty Quantification for the Extended and the Deterministic-Gain Kalman Filters","authors":"Shih-Yen Wei, J. Spall","doi":"10.23919/ACC53348.2022.9867488","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867488","url":null,"abstract":"This paper is aimed at characterizing the mean square error and probabilistic uncertainty of a popular class of filtering algorithms in nonlinear systems. The state estimation error of the extended Kalman filter and the deterministic-gain Kalman filter are analyzed. We allow a vector state, but assume scalar measurements. A set of conditions for the mean square error to be upper-bounded is derived. Furthermore, the probabilistic bounds for the estimation error are computed via both the moment-based approach and the stochastic comparison analysis approach. The latter provides a formal means determining uncertainty bounds, such as statistical confidence regions.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131661148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867461
H. Moring, J. Mathieu
The increasing penetration of renewable energy resources and the decreasing cost of battery energy storage in recent years has led to a growing interest in using batteries to provide grid services like frequency regulation. In this paper, we discuss the advantages and disadvantages of different battery degradation models and the impacts that model choice can have on the assumed cost of energy capacity loss due to operation. We also explore the effects of modeling degradation as an uncertain process by extending a two-stage, multi-period optimization problem for scheduling the operation of a battery providing multiple services with risk aversion. We use stochastic dual dynamic programming to derive a policy for the problem. Case study results show that using a stochastic degradation model with risk aversion produces a policy for more conservative battery use and longer lifespan in comparison to that obtained with a deterministic degradation model.
{"title":"Scheduling Battery Energy Storage Systems Under Battery Capacity Degradation Uncertainty","authors":"H. Moring, J. Mathieu","doi":"10.23919/ACC53348.2022.9867461","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867461","url":null,"abstract":"The increasing penetration of renewable energy resources and the decreasing cost of battery energy storage in recent years has led to a growing interest in using batteries to provide grid services like frequency regulation. In this paper, we discuss the advantages and disadvantages of different battery degradation models and the impacts that model choice can have on the assumed cost of energy capacity loss due to operation. We also explore the effects of modeling degradation as an uncertain process by extending a two-stage, multi-period optimization problem for scheduling the operation of a battery providing multiple services with risk aversion. We use stochastic dual dynamic programming to derive a policy for the problem. Case study results show that using a stochastic degradation model with risk aversion produces a policy for more conservative battery use and longer lifespan in comparison to that obtained with a deterministic degradation model.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129177716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867394
Kentaro Tsurumoto, W. Ohnishi, T. Koseki, Nard Strijbosch, T. Oomen
State estimation is essential for tracking conditions which can not be directly measured by sensors, or are too noisy. The aim of this poster is to present an approach to mitigate the phase delay without compromising the noise sensitivity, by using accessible future data. Such use of future data can be possible in cases like Iterative Learning Control, where full data of the previous trial is acquired beforehand. The effectiveness of the presented approach is verified through a motion system experiment, successfully showing the state estimation improvement in time domain. The presented non-causal approach improves the trade-offs between the phase delay of the estimation and the noise sensitivity of the state observer.
{"title":"A non-causal approach for suppressing the estimation delay of state observer","authors":"Kentaro Tsurumoto, W. Ohnishi, T. Koseki, Nard Strijbosch, T. Oomen","doi":"10.23919/ACC53348.2022.9867394","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867394","url":null,"abstract":"State estimation is essential for tracking conditions which can not be directly measured by sensors, or are too noisy. The aim of this poster is to present an approach to mitigate the phase delay without compromising the noise sensitivity, by using accessible future data. Such use of future data can be possible in cases like Iterative Learning Control, where full data of the previous trial is acquired beforehand. The effectiveness of the presented approach is verified through a motion system experiment, successfully showing the state estimation improvement in time domain. The presented non-causal approach improves the trade-offs between the phase delay of the estimation and the noise sensitivity of the state observer.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128759447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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