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检索条件"机构=Institute for Systems and Robotics ISR and Department Electrical Engineering and Computers"

共 148 条记录，以下是31-40 订阅

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A comparison of adaptive PID methodologies controlling a DC motor with a varying load

A comparison of adaptive PID methodologies controlling a DC ...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Luís Osório Jérôme Mendes Rui Araújo Tiago Matias Institute for Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

This work addresses the problem of controlling unknown and time varying plants for industrial applications. To deal with such problem several Self-Tuning Controllers with a Proportional Integral and Derivative (PID) structure have been chosen. The selected controllers are based on different methodologies, and some use implicit identification techniques (Single Neuron and Support Vector Machine) while the others use explicit identification (Dahlin, Pole placement, Deadbeat and Ziegler-Nichols) based in the Least Squares Method. The controllers were tested on a real DC motor with a varying load. The results have shown that all the tested methods were able to properly control an unknown plant with varying dynamics.

关键词： Vectors DC motors Support vector machines Heuristic algorithms Neurons Process control Adaptation models

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Mean-centric equilibrium: An equilibrium concept for learning in large-scale games

Mean-centric equilibrium: An equilibrium concept for learnin...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Brian Swensony Soummya Kar João Xavier Carnegie Mellon University Pittsburgh PA US Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Institute for Systems and Robotics (ISR) Technical University of Lisbon Portugal

The paper is concerned with learning in large-scale multi-agent games. The empirical centroid fictitious play (ECFP) algorithm is a variant of the well-known fictitious play algorithm that is practical and computationally tractable in large-scale games. ECFP has been shown to be an effective tool in learning consensus equilibria (a subset of the Nash equilibria) in certain games. However, the behavior of ECFP has only been characterized in terms of convergence of the networked-average empirical frequencies as opposed to the more traditional notion of learning mixed equilibria, namely the notion of convergence of individual empirical frequencies. The behavior of ECFP in terms of convergence in empirical frequencies is herein studied and the equilibrium concept of mean-centric equilibrium (MCE) is introduced. The concept of MCE is similar in spirit to that of Nash equilibrium (NE) but, in MCE each player is at equilibrium with respect to a centroid representing the aggregate behavior, as opposed to NE where players are at equilibrium with respect to the strategies of individual opponents. The MCE concept is well suited to large scale games where it is reflective of the fact that in many large scale games of interest, utilities are greatly affected by changes in the aggregate behavior but less susceptible to changes in the strategy of a particular opposing player. MCE is also well suited to large-scale games in that it can be learned using practical, low-information-overhead behavior rules (e.g. ECFP).

关键词： Games Convergence Joints Aggregates Vectors Cognition Economics

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Hand gesture recognition using color and depth images enhanced with hand angular pose data

Hand gesture recognition using color and depth images enhanc...

引用

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2012

作者： Trindade, Pedro Lobo, Jorge Barreto, Joao P. ISR - Institute of Systems and Robotics University of Coimbra Portugal ISR - Institute of Systems and Robotics and the Department of Electrical and Computer Engineering University of Coimbra Portugal

ISBN: (纸本)9781467325110

In this paper we propose a hand gesture recognition system that relies on color and depth images, and on a small pose sensor on the human palm. Monocular and stereo vision systems have been used for human pose and gesture recognition, but with limited scope due to limitations on texture, illumination, etc. New RGB-Depth sensors, that reply on projected light such as the Microsoft Kinect, have overcome many of those limitations. However, the point clouds for hand gestures are still in many cases noisy and partially occluded, and hand gesture recognition is not trivial. Hand gesture recognition is much more complex than full body motion, since we can have the hands in any orientation and can not assume a standing body on a ground plane. In this work we propose to add a tiny pose sensor to the human palm, with a minute accelerometer and magnetometer that combined provide 3D angular pose, to reduce the search space and have a robust and computationally light recognition method. Starting with the full depth image point cloud, segmentation can be performed by taking into account the relative depth and hand orientation, as well as skin color. Identification is then performed by matching 3D voxel occupancy against a gesture template database. Preliminary results are presented for the recognition of Portuguese Sign Language alphabet, showing the validity of the approach. © 2012 IEEE.

关键词： Image enhancement

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Fault Detection and Replacement of a Temperature Sensor in a Cement Rotary Kiln

Fault Detection and Replacement of a Temperature Sensor in a...

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IEEE Conference on Emerging Technologies & Factory Automation

作者： Tiago Matias Dulce Gabriel Francisco Souza Rui Araújo J. Costa Pereira Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Pólo II PT-3030-290 Coimbra Portugal. Department of Chemistry (DQ-UC) University of Coimbra PT-3004-535 Coimbra Portugal.

ISBN: (纸本)9781479908639

This paper proposes a method for fault detection and replacement of the sensor responsible by the measurement of the burning zone temperature in a rotary cement kiln. The control of the burning zone temperature is crucial for the control of kiln temperature and therefore for the control of the cement quality, pollutant emissions, and consumed energy. However the flying dust within the kiln can block the pyrometer sensor, causing faults in the temperature sensor. Exploring the analytical redundancy that usually exist in industrial processes, the proposed methodology uses a neural network trained using an online sequential extreme learning machine to online construct a model to estimate the burning zone temperature. Using the error between the measured and estimated temperatures, faults in the measurement can be detected and thus the replacement of the measured temperatures by the estimated output is made.

关键词： fault detection replacement measured temperatures

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Predicting Gas Emissions in a Cement Kiln Plant using Hard and Soft Modeling Strategies

Predicting Gas Emissions in a Cement Kiln Plant using Hard a...

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IEEE Conference on Emerging Technologies & Factory Automation

作者： Dulce Gabriel Tiago Matias J. Costa Pereira Rui Araújo Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Pólo II PT-3030-290 Coimbra Portugal. Department of Chemistry (DQ-UC) University of Coimbra PT-3004-535 Coimbra Portugal.

ISBN: (纸本)9781479908639

In this work, two alternative methodologies for modeling and predicting gas emissions of NO, NO_2 and SO_2 are presented. The first method involves hard modeling strategies with Parsimonious Multivariate Least Squares (PMLS) assuming simple polynomial functions as base model. The second is a soft modeling approach using Extreme Learning Machine (ELM). In this work we found that both methods have similar capabilities for data description, providing an in depth analysis of the system under study. Results also reveal further insights in predicting gas emissions and enlighten on which of the factors can be useful for prediction, and consequently for system characterization and emission abatement.

关键词： PMLS depth analysis system under study

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Bayesian 3D independent motion segmentation with IMU-aided RBG-D sensor

Bayesian 3D independent motion segmentation with IMU-aided R...

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2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2012

作者： Lobo, Jorge Ferreira, Joao Filipe Trindade, Pedro Dias, Jorge Department of Electrical and Computer Engineering University of Coimbra Portugal ISR - Institute of Systems and Robotics University of Coimbra Portugal Robotics Institute Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781467325110

In this paper we propose a two-tiered hierarchical Bayesian model to estimate the location of objects moving independently from the observer. Biological vision systems are very successful in motion segmentation, since they efficiently resort to flow analysis and accumulated prior knowledge of the 3D structure of the scene. Artificial perception systems may also build 3D structure maps and use optical flow to provide cues for ego- and independent motion segmentation. Using inertial and magnetic sensors and an image and depth sensor (RGB-D) we propose a method to obtain registered 3D maps, which are subsequently used in a probabilistic model (the bottom tier of the hierarchy) that performs background subtraction across several frames to provide a prior on moving objects. The egomotion of the RGB-D sensor is estimated starting with the angular pose obtained from the filtered accelerometers and magnetic data. The translation is derived from matched points across the images and corresponding 3D points in the rotation-compensated depth maps. A gyro-aided Lucas Kanade tracker is used to obtain matched points across the images. The tracked points can also used to refine the initial sensor based rotation estimation. Having determined the camera egomotion, the estimated optical flow assuming a static scene can be compared with the observed optical flow via a probabilistic model (the top tier of the hierarchy), using the results of the background subtraction process as a prior, in order to identify volumes with independent motion in the corresponding 3D point cloud. To deal with the computational load CUDA-based solutions on GPUs were used. Experimental results are presented showing the validity of the proposed approach. © 2012 IEEE.

关键词： Optical flows

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Hand gesture recognition using color and depth images enhanced with hand angular pose data

Hand gesture recognition using color and depth images enhanc...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Pedro Trindade Jorge Lobo João P. Barreto ISR-Institute of Systems and Robotics University of Coimbra Portugal ISR-Institute of Systems and Robotics and the Department of Electrical and Computer Engineering University of Coimbra Portugal

关键词： Gesture recognition Image color analysis Iterative closest point algorithm Skin Libraries Robot sensing systems

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Evolutionary fuzzy models for nonlinear identification

Evolutionary fuzzy models for nonlinear identification

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Jérôme Mendes Samuel Pinto Rui Araújo Francisco Souza Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

This paper proposes a new method for identification problems for industrial applications based on a Takagi-Sugeno (T-S) fuzzy model. The learning of the T-S model is performed from input/output data to approximate unknown nonlinear processes by a coevolationary genetic algorithm (GA). The proposed method is an automatic tool since it does not require any prior knowledge concerning the structure (e.g. the number of rules) and the database (e.g. antecedent fuzzy sets) of the T-S fuzzy model, and concerning the selection of the adequate input variables and their respective time delays. The proposed methodology is able to design all the parts of the T-S fuzzy prediction model and it is composed by five hierarchical levels. To validate and demonstrate the performance and effectiveness of the proposed algorithm, it is applied on Box-Jenkins benchmark problem.

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An online variable selection method using recursive least squares

An online variable selection method using recursive least sq...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Francisco Souza Rui Araújo Institute of Systems and Robotics (ISR-UC) Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

This paper proposes a method for online variable selection and model learning (AdaFSML-RLS) to be applied in industrial applications in the context of adaptive soft sensors. In the proposed method the model learning is made online and recursivelly, i.e it is not necessary to store the past values of data while learning the model. Furthermore, the proposed method has the capability of tracking the real time correlation coefficient between each variable and the target, allowing the knowledge about the importance of variables over the time. Moreover, in this method is not necessary to have any knowledge about the process or variables. The method was sucessfully applied in two datasets, an artificial dataset and in a real-world dataset.

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Exact Rate for Convergence in Probability of Averaging Processes via Generalized Min-Cut

Exact Rate for Convergence in Probability of Averaging Proce...

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IEEE Annual Conference on Decision and Control

作者： Dragana Bajovic Joao Xavier Jose M. F. Moura Bruno Sinopoli Department of Electrical and Computer Engineering Carnegie Mellon University Institute for Systems and Robotics (ISR) Instituto Superior Tecnico (IST) Technical University of Lisbon

ISBN: (纸本)9781467320658

We study the asymptotic exponential decay rate I for the convergence in probability of products W_kW_(k-1) ...W_1 of random symmetric, stochastic matrices W_k. Albeit it is known that the probability P that the product W_kW_(k-1)...W_1 is ∈ away from its limit converges exponentially fast to zero, i.e., P ～ e~(-kI), the asymptotic rate I has not been computed before. In this paper, assuming the positive entries of W_k are bounded away from zero, we explicitly characterize the rate I and show that it is a function of the underlying graphs that support the positive (non zero) entries of W_k. In particular, the rate I is given by a certain generalization of the min-cut problem. Although this min-cut problem is in general combinatorial, we show how to exactly compute I in polynomial time for the commonly used matrix models, gossip and link failure. Further, for a class of models for which I is difficult to compute, we give easily computable bounds: I ≤ I ≤ I, where I and I differ by a constant ratio. Finally, we show the relevance of I as a system design metric with the example of optimal power allocation in consensus+innovations distributed detection.

关键词： dividing and cutting machine iodine Converge power splitting Zero Gossip

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