Pub Date : 2004-12-01DOI: 10.1109/CDC.2004.1428942
Shyam Sivakumar, Carolyn L. Beck
This paper describes a model reduction toolbox implemented in MATLAB for multidimensional systems. It is based on set of computationally tractable algorithms for systems represented by linear fractional transformations (LFTs) on structured operator sets.
{"title":"MRedTool - a MATLAB toolbox for model reduction of multi-dimensional systems","authors":"Shyam Sivakumar, Carolyn L. Beck","doi":"10.1109/CDC.2004.1428942","DOIUrl":"https://doi.org/10.1109/CDC.2004.1428942","url":null,"abstract":"This paper describes a model reduction toolbox implemented in MATLAB for multidimensional systems. It is based on set of computationally tractable algorithms for systems represented by linear fractional transformations (LFTs) on structured operator sets.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131132833","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 : 2004-12-01DOI: 10.1109/CDC.2004.1428840
Hong Wong, V. Kapila, Ravi Vaidyanathan
In this paper, 2-D optimal C-C-C class paths are determined for unmanned air vehicles performing target touring with kinematic and tactical constraints. Using vector calculus, a path-planning problem is decomposed to yield a parameter optimization problem. An efficient hybrid optimization algorithm is then used to solve the parameter optimization problem. Illustrative numerical simulations are given to demonstrate the efficacy of our approach.
{"title":"UAV optimal path planning using C-C-C class paths for target touring","authors":"Hong Wong, V. Kapila, Ravi Vaidyanathan","doi":"10.1109/CDC.2004.1428840","DOIUrl":"https://doi.org/10.1109/CDC.2004.1428840","url":null,"abstract":"In this paper, 2-D optimal C-C-C class paths are determined for unmanned air vehicles performing target touring with kinematic and tactical constraints. Using vector calculus, a path-planning problem is decomposed to yield a parameter optimization problem. An efficient hybrid optimization algorithm is then used to solve the parameter optimization problem. Illustrative numerical simulations are given to demonstrate the efficacy of our approach.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121875343","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 : 2004-12-01DOI: 10.1109/CDC.2004.1429325
A. Macchelli, A. Schaft, C. Melchiorri
In this paper, some new results concerning the boundary control of distributed parameter systems in port Hamiltonian form are presented. The classical finite dimensional port Hamiltonian formulation of a dynamical system has been generalized to the distributed parameter and multivariable case by extending the notion of finite dimensional Dirac structure in order to deal with an infinite dimensional space of power variables. Consequently, it seems natural that also finite dimensional control methodologies developed for finite dimensional port Hamiltonian systems can be extended in order to cope with infinite dimensional systems. In this paper, the control by interconnection and energy shaping methodology is applied to the stabilization problem of a distributed parameter system by means of a finite dimensional controller. The key point is the generalization of the definition of Casimir function to the hybrid case, i.e. when the dynamical system to be considered results from the power conserving interconnection of an infinite and a finite dimensional part. A simple application concerning the stabilization of the one-dimensional heat equation is presented.
{"title":"Port Hamiltonian formulation of infinite dimensional systems II. Boundary control by interconnection","authors":"A. Macchelli, A. Schaft, C. Melchiorri","doi":"10.1109/CDC.2004.1429325","DOIUrl":"https://doi.org/10.1109/CDC.2004.1429325","url":null,"abstract":"In this paper, some new results concerning the boundary control of distributed parameter systems in port Hamiltonian form are presented. The classical finite dimensional port Hamiltonian formulation of a dynamical system has been generalized to the distributed parameter and multivariable case by extending the notion of finite dimensional Dirac structure in order to deal with an infinite dimensional space of power variables. Consequently, it seems natural that also finite dimensional control methodologies developed for finite dimensional port Hamiltonian systems can be extended in order to cope with infinite dimensional systems. In this paper, the control by interconnection and energy shaping methodology is applied to the stabilization problem of a distributed parameter system by means of a finite dimensional controller. The key point is the generalization of the definition of Casimir function to the hybrid case, i.e. when the dynamical system to be considered results from the power conserving interconnection of an infinite and a finite dimensional part. A simple application concerning the stabilization of the one-dimensional heat equation is presented.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116474581","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 : 2004-12-01DOI: 10.1109/CDC.2004.1429577
Wen Yu, J. J. Rubio, A. Morales
Crude oil blending is an important unit in petroleum refining industry. Most of blend automation system is a real-time optimizer (RTO). RTO is a model-based optimization approach that uses current process information to update the model and predict the optimal operating policy. But in many oil fields, people hope to make decisions and conduct supervision control based on the history data, i.e., they want to know the optimal inlet flow rates without online analyzers. To overcome the drawback of the conventional RTO, in this paper we use neural networks to model the blending process by the history data. Then the optimization is carried out via the neural model. The contributions of this paper are: (1) we propose a new approach to solve the problem of blending optimization based on history data; (2) sensitivity analysis of the neural optimization is given; (3) real data of an oil field is used to show effectiveness of the proposed method.
{"title":"Optimization of crude oil blending with neural networks","authors":"Wen Yu, J. J. Rubio, A. Morales","doi":"10.1109/CDC.2004.1429577","DOIUrl":"https://doi.org/10.1109/CDC.2004.1429577","url":null,"abstract":"Crude oil blending is an important unit in petroleum refining industry. Most of blend automation system is a real-time optimizer (RTO). RTO is a model-based optimization approach that uses current process information to update the model and predict the optimal operating policy. But in many oil fields, people hope to make decisions and conduct supervision control based on the history data, i.e., they want to know the optimal inlet flow rates without online analyzers. To overcome the drawback of the conventional RTO, in this paper we use neural networks to model the blending process by the history data. Then the optimization is carried out via the neural model. The contributions of this paper are: (1) we propose a new approach to solve the problem of blending optimization based on history data; (2) sensitivity analysis of the neural optimization is given; (3) real data of an oil field is used to show effectiveness of the proposed method.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127088359","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 : 2004-12-01DOI: 10.1109/CDC.2004.1429242
A. Eryilmaz, R. Srikant
We consider the problem of scheduling packets from multiple flows over a Rayleigh fading wireless channel. Recently, there has been much interest in opportunistic scheduling, i.e., scheduling packets from a user who has the highest SNR (signal-to-noise ratio), to maximize the network's throughput. In this paper, we compare the throughput achievable under fair opportunistic scheduling (i.e., a modification of opportunistic scheduling to ensure fair resource allocation) with the throughput under time-division multiplexing (TDM) scheduling. Using large deviations to characterize the probability that the QoS constraint (an upper bound on delay) is violated, we numerically compare the performance of the two scheduling algorithms under various channel conditions. We show that the opportunistic scheduler outperforms the TDM scheduler when the number of users is small but the TDM scheduler performs better when the number of users exceeds a threshold which depends on the channel parameters.
{"title":"Scheduling with QoS constraints over Rayleigh fading channels","authors":"A. Eryilmaz, R. Srikant","doi":"10.1109/CDC.2004.1429242","DOIUrl":"https://doi.org/10.1109/CDC.2004.1429242","url":null,"abstract":"We consider the problem of scheduling packets from multiple flows over a Rayleigh fading wireless channel. Recently, there has been much interest in opportunistic scheduling, i.e., scheduling packets from a user who has the highest SNR (signal-to-noise ratio), to maximize the network's throughput. In this paper, we compare the throughput achievable under fair opportunistic scheduling (i.e., a modification of opportunistic scheduling to ensure fair resource allocation) with the throughput under time-division multiplexing (TDM) scheduling. Using large deviations to characterize the probability that the QoS constraint (an upper bound on delay) is violated, we numerically compare the performance of the two scheduling algorithms under various channel conditions. We show that the opportunistic scheduler outperforms the TDM scheduler when the number of users is small but the TDM scheduler performs better when the number of users exceeds a threshold which depends on the channel parameters.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127738772","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 : 2004-12-01DOI: 10.1109/CDC.2004.1428984
Peijuan Liu, R. Berry, M. Honig
We consider packet scheduling for the downlink in a wireless network, where each packet's service preferences are captured by a utility function that depends on the packet's delay. The goal is to schedule packet transmissions to maximize the total utility. We examine a simple gradient-based scheduling algorithm, the UR-rule, which is a type of generalized c/spl mu/-rule (Gc/spl mu/) that takes into account both a user's channel condition and derived utility. We study the performance of this scheduling rule for a draining problem. We formulate a "large system" fluid model for this draining problem where the number of packets increases while the packet-size decreases to zero, and give a complete characterization of the behavior of the UR scheduling rule in this limiting regime. We then give an optimal control formulation for finding the optimal scheduling policy for the fluid draining model. Using Pontryagin's minimum principle, we show that, when the user rates are chosen from a TDM-type of capacity region, the UR rule is in fact optimal in many cases. Finally, we consider non-TDM capacity regions and show that here the UR rule is optimal only in special cases.
{"title":"A fluid analysis of utility-based wireless scheduling policies","authors":"Peijuan Liu, R. Berry, M. Honig","doi":"10.1109/CDC.2004.1428984","DOIUrl":"https://doi.org/10.1109/CDC.2004.1428984","url":null,"abstract":"We consider packet scheduling for the downlink in a wireless network, where each packet's service preferences are captured by a utility function that depends on the packet's delay. The goal is to schedule packet transmissions to maximize the total utility. We examine a simple gradient-based scheduling algorithm, the UR-rule, which is a type of generalized c/spl mu/-rule (Gc/spl mu/) that takes into account both a user's channel condition and derived utility. We study the performance of this scheduling rule for a draining problem. We formulate a \"large system\" fluid model for this draining problem where the number of packets increases while the packet-size decreases to zero, and give a complete characterization of the behavior of the UR scheduling rule in this limiting regime. We then give an optimal control formulation for finding the optimal scheduling policy for the fluid draining model. Using Pontryagin's minimum principle, we show that, when the user rates are chosen from a TDM-type of capacity region, the UR rule is in fact optimal in many cases. Finally, we consider non-TDM capacity regions and show that here the UR rule is optimal only in special cases.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128223393","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 : 2004-12-01DOI: 10.1109/CDC.2004.1428604
I. Chattopadhyay, A. Ray
The signed real measure of regular languages has been introduced and validated in recent literature for quantitative analysis and synthesis of discrete-event supervisory (DES) control systems, where all events are assumed to be observable. This paper presents a modification of the language measure for supervisory control under partial observation and shows how to generalize the analysis when some of the events may not be observable at the supervisory level. Thus, for synthesis of DES control systems, the language measure of partially observable discrete-event processes is expressed in a closed form, which is structurally similar to that of completely observable discrete-event processes.
{"title":"A language measure for partially observed discrete event supervisory control systems","authors":"I. Chattopadhyay, A. Ray","doi":"10.1109/CDC.2004.1428604","DOIUrl":"https://doi.org/10.1109/CDC.2004.1428604","url":null,"abstract":"The signed real measure of regular languages has been introduced and validated in recent literature for quantitative analysis and synthesis of discrete-event supervisory (DES) control systems, where all events are assumed to be observable. This paper presents a modification of the language measure for supervisory control under partial observation and shows how to generalize the analysis when some of the events may not be observable at the supervisory level. Thus, for synthesis of DES control systems, the language measure of partially observable discrete-event processes is expressed in a closed form, which is structurally similar to that of completely observable discrete-event processes.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132100013","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 : 2004-12-01DOI: 10.1109/CDC.2004.1430338
Ji-Woong Lee, G. Dullerud
A dynamic team of multiple communicating decision makers perform Bayesian sequential multi-hypothesis testing with nonstationary observations. A member-by-member optimal decision rule is derived under the conditions that the observations are independent and uniformly distributed conditioned on each hypothesis, that the observation costs are adapted to the quality of observations, and that the cost for decision errors is sufficiently large and does not impose additional penalty for "vague" terminal decisions. The "vague" terminal decisions can be made "clear" by performing a finite number of member-by-member optimal sequential decisions successively.
{"title":"A dynamic decentralized sequential multi-hypothesis testing problem under uniformly distributed nonstationary observations","authors":"Ji-Woong Lee, G. Dullerud","doi":"10.1109/CDC.2004.1430338","DOIUrl":"https://doi.org/10.1109/CDC.2004.1430338","url":null,"abstract":"A dynamic team of multiple communicating decision makers perform Bayesian sequential multi-hypothesis testing with nonstationary observations. A member-by-member optimal decision rule is derived under the conditions that the observations are independent and uniformly distributed conditioned on each hypothesis, that the observation costs are adapted to the quality of observations, and that the cost for decision errors is sufficiently large and does not impose additional penalty for \"vague\" terminal decisions. The \"vague\" terminal decisions can be made \"clear\" by performing a finite number of member-by-member optimal sequential decisions successively.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133158830","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 : 2004-12-01DOI: 10.1109/CDC.2004.1429426
S. Bayraktar, Georgios Fainekos, George J. Pappas
Recent years have seen rapidly growing interest in the development of networks of multiple unmanned aerial vehicles (UAVs), as aerial sensor networks for the purpose of coordinated monitoring, surveillance, and rapid emergency response. This has triggered a great deal of research in higher levels of planning and control, including collaborative sensing and exploration, synchronized motion planning, and formation or cooperative control. In this paper, we describe our recently developed experimental testbed at the University of Pennsylvania, which consists of multiple, fixed-wing UAVs. We describe the system architecture, software and hardware components, and overall system integration. We then derive high-fidelity models that are validated with hardware-in-the-loop simulations and actual experiments. Our models are hybrid, capturing not only the physical dynamics of the aircraft, but also the mode switching logic that supervises lower level controllers. We conclude with a description of cooperative control experiments involving two fixed-wing UAVs.
{"title":"Experimental cooperative control of fixed-wing unmanned aerial vehicles","authors":"S. Bayraktar, Georgios Fainekos, George J. Pappas","doi":"10.1109/CDC.2004.1429426","DOIUrl":"https://doi.org/10.1109/CDC.2004.1429426","url":null,"abstract":"Recent years have seen rapidly growing interest in the development of networks of multiple unmanned aerial vehicles (UAVs), as aerial sensor networks for the purpose of coordinated monitoring, surveillance, and rapid emergency response. This has triggered a great deal of research in higher levels of planning and control, including collaborative sensing and exploration, synchronized motion planning, and formation or cooperative control. In this paper, we describe our recently developed experimental testbed at the University of Pennsylvania, which consists of multiple, fixed-wing UAVs. We describe the system architecture, software and hardware components, and overall system integration. We then derive high-fidelity models that are validated with hardware-in-the-loop simulations and actual experiments. Our models are hybrid, capturing not only the physical dynamics of the aircraft, but also the mode switching logic that supervises lower level controllers. We conclude with a description of cooperative control experiments involving two fixed-wing UAVs.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122465603","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 : 2004-12-01DOI: 10.1109/CDC.2004.1430359
L. Mihaylova, R. Boel
This paper considers the traffic flow estimation problem for the purposes of on-line traffic prediction, mode detection and ramp-metering control. The solution to the estimation problem is given within the Bayesian recursive framework. A particle filter (PF) is developed based on a freeway traffic model with aggregated states and an observation model with aggregated variables. The freeway is considered as a network of components, each component representing a different section of the traffic network. The freeway traffic is modeled as a stochastic hybrid system, i.e. each traffic section possesses continuous and discrete states, interacting with states of neighbor sections. The state update step in the recursive Bayesian estimator is performed through sending and receiving functions describing propagation of perturbations from upstream to downstream, and from downstream to upstream sections. Measurements are received only on boundaries between some sections and averaged within regular or irregular time intervals. A particle filter is developed with measurement updates each time when a new measurement becomes available, and with possibly many state updates in between consecutive measurement updates. It provides an approximate but scalable solution to the difficult state estimation and prediction problem with limited, noisy observations. The filter performance is validated and evaluated by Monte Carlo simulation.
{"title":"A particle filter for freeway traffic estimation","authors":"L. Mihaylova, R. Boel","doi":"10.1109/CDC.2004.1430359","DOIUrl":"https://doi.org/10.1109/CDC.2004.1430359","url":null,"abstract":"This paper considers the traffic flow estimation problem for the purposes of on-line traffic prediction, mode detection and ramp-metering control. The solution to the estimation problem is given within the Bayesian recursive framework. A particle filter (PF) is developed based on a freeway traffic model with aggregated states and an observation model with aggregated variables. The freeway is considered as a network of components, each component representing a different section of the traffic network. The freeway traffic is modeled as a stochastic hybrid system, i.e. each traffic section possesses continuous and discrete states, interacting with states of neighbor sections. The state update step in the recursive Bayesian estimator is performed through sending and receiving functions describing propagation of perturbations from upstream to downstream, and from downstream to upstream sections. Measurements are received only on boundaries between some sections and averaged within regular or irregular time intervals. A particle filter is developed with measurement updates each time when a new measurement becomes available, and with possibly many state updates in between consecutive measurement updates. It provides an approximate but scalable solution to the difficult state estimation and prediction problem with limited, noisy observations. The filter performance is validated and evaluated by Monte Carlo simulation.","PeriodicalId":254457,"journal":{"name":"2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123855191","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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