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检索条件"机构=Control and Intelligent Processing Center of Excellence and Robotics Laboratory"

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Distributed form closure for convex planar objects through reinforcement learning with local information

Distributed form closure for convex planar objects through r...

引用

2004 IEEE/RSJ International Conference on intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)

作者： A.H. Elahibakhsh M.N. Ahmadabadi F.J. Sharifi B.N. Araabi School of Cognitive Sciences Institute for Studies on Theoretical Physics and Mathematics Tehran Iran Dept. of Electrical and Computer Eng. AI & Robotics Lab. Center of Excellence for Control and Intelligent Processing University of Tehran Tehran Iran

Many real world applications would involve grasp of large objects in unstructured environments. Agent-based approach to multi-robot grasp of objects would prove useful under the above circumstances. In this paper, the problem of form closure grasp for planar convex objects by multiple robots is tackled. Contrary to the previous approaches, no a priori information about the shape of the object is assumed, and the robots are not allowed to fully communicate among themselves. A distributed multi-agent based approach using Q-learning is proposed. The state space, action set and learning algorithm are formulated. The results are verified through simulations using a developed Q-learning test bed.

关键词： Learning Orbital robotics intelligent robots Shape control Robot sensing systems Testing Artificial intelligence Cognitive robotics robotics and automation Automatic control

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Clustering-based framework for comparing fMRI analysis methods

Clustering-based framework for comparing fMRI analysis metho...

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IEEE International Symposium on Biomedical Imaging

作者： G.-A. Hossein-Zadeh A.-M. Golestani H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering method in the new space. As a result, different methods can be compared by means of a cluster validity measure. The feature space is computed using a non-parametric method (principal component analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach.

关键词： Clustering algorithms Algorithm design and analysis Image analysis Clustering methods Magnetic analysis Testing Accuracy Reproducibility of results Radiology Magnetic resonance

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Multiresolution automatic segmentation of T1-weighted brain MR images

Multiresolution automatic segmentation of T1-weighted brain ...

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IEEE International Symposium on Biomedical Imaging

作者： M. Zeydabadi R.A. Zoroofi H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.

关键词： Image resolution Image segmentation Biomedical imaging Multiresolution analysis Image analysis Magnetic analysis Algorithm design and analysis Brain modeling Magnetic liquids Magnetic resonance

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MRSI brain tumor characterization using wavelet and wavelet packets feature spaces and artificial neural networks

MRSI brain tumor characterization using wavelet and wavelet ...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： A. Yazdan-Shahmorad H. Soltanian-Zadeh R.A. Zoroofi Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique for assessing biochemical fingerprint of tissue composition. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of MRSI. There are several technical reasons that make the brain easier than other organs to be examined with MRSI. This work presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types (malignant glioma, astrocytoma, and oligodendroglioma). After extracting features from MRSI data using wavelet and wavelet packets, we use artificial neural networks to determine the abnormal spectra and the type of abnormality. We evaluated the proposed methods using clinical and simulated MRSI data and biopsy results. The MRSI analysis results were correct 97% of the time when classifying the spectra of the clinical MRSI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determining tumor types using the clinical and simulated MRSI data, respectively.

关键词： Wavelet packets Neoplasms Artificial neural networks Biopsy Magnetic resonance Spectroscopy Magnetic resonance imaging Fingerprint recognition Surgery Cancer

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controlling the false positive detection rate in fuzzy clustering of fMRI data

Controlling the false positive detection rate in fuzzy clust...

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IEEE International Symposium on Biomedical Imaging

作者： H. Jahanian H. Soltanian-Zadeh G.A. Hossein-Zadeh Signal and Image Processing Group School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Elec. Eng. Department Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. controlling the false positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this paper, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space.

关键词： Fuzzy control Clustering methods Hemodynamics Clustering algorithms Brain Signal processing Image processing intelligent systems Process control control systems

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An extension of weighted strategy sharing in cooperative Q-learning for specialized agents

An extension of weighted strategy sharing in cooperative Q-l...

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International Conference on Neural Information processing

作者： S.M. Eshgh M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.

关键词： intelligent robots intelligent agent Process control control systems intelligent control Laboratories intelligent systems Physics Mathematics Entropy

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Certainty and expertness-based credit assignment for cooperative Q-Learning agents with an AND-type task

Certainty and expertness-based credit assignment for coopera...

引用

International Conference on Neural Information processing

作者： A. Harati M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence & AI and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.

关键词： Feedback intelligent agent intelligent robots Process control control systems intelligent control Artificial intelligence Robot kinematics Physics computing Reliability engineering

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A decision-making based approach for fault-handling in multi-agent systems

A decision-making based approach for fault-handling in multi...

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International Conference on Neural Information processing

作者： M.S. Mirian M.N. Ahmadabadi Z. Navabi Robotics and AI Laboratory Department of Elect. and Comp. Eng Faculty of Engineering University of Tehran Iran Robotics and AI Lab Control & Intelligent Processing Center of Excellence Dept. of Elect. and Comp. Eng. University of Tehran Iran

This paper focuses on distributed fault recovery in agent-based systems by providing help for faulty members. In the presented method, if one faulty agent requests for help or agents are informed of fault in one of their teammates, they first decide if they are able to help or not. In the case that they are able to help and several help requests exist, helper agents specify a sequence of help actions through another distributed decision-making phase. The introduced fault clearing method is totally distributed in the sense that each helper agent makes its decisions by itself and no central or special agent exists in the system. In fact, the decision making process and the required information are designed such that the agents cooperate implicitly to prevent the system performance loss. The developed ideas are implemented in a simulated distributed control system. As it is shown, the proposed distributed fault-clearing method through reconfiguring the agents' roles is very effective.

关键词： Decision making Multiagent systems Robustness Condition monitoring Process control Robots Artificial intelligence Hardware Distributed decision making System performance

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