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Pipelines 2009 Conference, Pipelines 2009: Infrastructure's Hidden Assets

作者： Valizadeh, Sima Moshiri, Behzad Salahshoor, Karim Control and Intelligent Processing Center of Excellence School of ECE University of Tehran Tehran Iran Department of Automation and Instrumentation Petroleum University of Technology Tehran Iran

ISBN: (纸本)9780784410691

A methodology for software based pipeline leak monitoring using feature extraction and classification is presented here. For feature extraction, time domain features of the measured flow, pressure and temperature of the pipe at its inlet and outlet are used. The time domain features are calculated for every time window. Information in the time domain signals is converted to matrix of features. The leak and no-leak signals are classified using k nearest neighbor and Bayesian classifiers. This approach is tested on a real multiphase pipeline with various leak positions and sizes. Also the ability of the classifiers for distinguishing operational changes from leakages is evaluated. Results shows that feature extraction and classification can be applied successfully to the problem of pipeline leak detection. © 2009 ASCE.

关键词： Feature extraction

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A computer model of language acquisition - syllable learning - based on hebbian cell assemblies and reinforcement learning

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World Academy of Science, Engineering and Technology 2009年 37卷 874-877页

作者： Fazeli, Sepideh Bahrami, Fariba Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Investigating language acquisition is one of the most challenging problems in the area of studying language. Syllable learning as a level of language acquisition has a considerable significance since it plays an important role in language acquisition. Because of impossibility of studying language acquisition directly with children, especially in its developmental phases, computer models will be useful in examining language acquisition. In this paper a computer model of early language learning for syllable learning is proposed. It is guided by a conceptual model of syllable learning which is named Directions Into Velocities of Articulators model (DIVA). The computer model uses simple associational and reinforcement learning rules within neural network architecture which are inspired by neuroscience. Our simulation results verify the ability of the proposed computer model in producing phonemes during babbling and early speech. Also, it provides a framework for examining the neural basis of language learning and communication disorders. © 2009 ***.

关键词： Reinforcement learning

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An imitation model based on Central Pattern Generator with application in robotic marionette behavior learning

An imitation model based on Central Pattern Generator with a...

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2009 IEEE/RSJ International Conference on intelligent Robots and Systems

作者： M. Ajallooeian M. Nili Ahmadabadi B. N. Araabi H. Moradi Control and Intelligent Processing Center of Excellence ECE Department University of Tehran Iran

Most of the central pattern generator (CPG) models are based on defining explicit dynamical systems and finding the appropriate parameters. In this paper, we propose a novel CPG model that is based on altering a nonlinear oscillator to obtain desired limit cycle behavior. This CPG model benefits from an explicit basin of attraction and also fast convergence behavior. The presented CPG model is used in an imitation model that tries to learn the proper periodical behavior by looking at a mentor. First, a mentor performs the desired periodical behavior. Then, a hand-eye coordination process, inspired from infant babbling, is initiated to extract proper motor actions from what is observed. The extracted motor actions are finally embedded into the CPG model for smooth reproduction. This imitation model is implemented on a robotic marionette behavior learning task. The outcome of the final performance of the robotic marionette is behaviorally understandable smooth actions.

关键词： Robot kinematics Oscillators Biological system modeling Limit-cycles intelligent robots Convergence Nonlinear equations USA Councils Circuits Muscles

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Acquisition of shared symbols in multi-agent cooperative tasks

Acquisition of shared symbols in multi-agent cooperative tas...

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IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)

作者： Siavash Kayal Abdol Hossein Aminaiee Caro Lucas Center of Excellence Control and Intelligent Processing ECE Faculty College of Engineering University of Tehran Iran Center of Excellence Control and Intelligent Processing ECE Faculty College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424448081

In this paper a novel mechanism for acquiring shared symbols in multi-agent cooperative task is introduced. Inspired by human communication, a technique is suggested in which learning the behaviors and learning how to communicate are decomposed. Decomposing the shared symbol acquisition into two separate learning phases not only simplifies the learning algorithm but also it speeds up the process. Moreover, utilizing the gained information about the environment in the behavior learning phase, agent communication is learned easily. A couple of simulations are conducted to support the idea. Simulation results show that agents could assign meaning to symbols and transfer information among themselves using the learned symbols. Roughly speaking, they could form a language.

关键词： Humans Computational modeling Artificial intelligence Protocols Process control Communication system control intelligent agent intelligent control Educational institutions Data mining

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Automatic segmentation of brain structures using geometric moment invariants and artificial neural networks

Automatic segmentation of brain structures using geometric m...

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21st International Conference on Information processing in Medical Imaging, IPMI 2009

作者： Jabarouti Moghaddam, Mostafa Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI United States

ISBN: (纸本)3642024971

We propose an automatic method for the segmentation of the brain structures in three dimensional (3D) Magnetic Resonance Images (MRI). The proposed method consists of two stages. In the first stage, we represent the shape of the structure using Geometric Moment Invariants (GMIs) in 8 scales. For each scale, an Artificial Neural Network (ANN) is designed to approximate the signed distance function of a desired structure. The GMIs along with the voxel intensities and coordinates are used as the input features of the ANN and the signed distance function as its output. In the second stage, we combine the outputs of the ANNs of the first stage and design another ANN to classify the image voxels into two classes, inside or outside of the structure. We introduce a fast method for moment calculations. The proposed method is applied to the segmentation of caudate, putamen, and thalamus in MRI where it has outperformed other methods in the literature. © 2009 Springer Berlin Heidelberg.

关键词： Neural networks

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Detection of Subsurface Cavities in a Power Plant Through Artificial Neural Network from Micro-Gravity Data

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地学前缘 2009年第S1期16卷 59-59页

作者： Alireza Hajian Caro Lucas Center of Excellence for Control and Intelligent Processing ECE DepartmentTehran University

Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam *** order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in *** we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training *** ANN was tested for both synthetic and real *** real data some suitable features are derivate from residual anomalies and applied to

关键词： artificial neural network power plant microgravity cavity

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Discrete Invasive Weed Optimization Algorithm: Application to Cooperative Multiple Task Assignment of UAVs

Discrete Invasive Weed Optimization Algorithm: Application t...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Mohsen Ramezani Ghalenoei Hossein Hajimirsadeghi Caro Lucas Control and Intelligent Processing Center of Excellence School of Electrical and Computer EngineeringUniversity of Tehran IEEE Control and Intelligent Processing Center of Excellence School of Electrical and Computer EngineeringUniversity of Tehranand School of Cognitive Sciences

This paper presents a novel discrete population based stochastic optimization algorithm inspired from weed *** performance in a discrete benchmark,timecost trade-off(TCT) problem,is evaluated and compared with five other evolutionary *** we use our proposed discrete invasive weed optimization(DIWO) algorithm for cooperative multiple task assignment of unmanned aerial vehicles(UAVs) and compare the solutions with those of genetic algorithms(GAs) which have shown satisfactory results in the previous *** task assignment problem is of great interest among researchers and many deterministic and stochastic methods have been devised to come up with the *** Carlo simulations show successful results that verify better performance of DIWO compared to GAs in both optimality of the solutions and computational time.

关键词： UAV TCT Application to Cooperative Multiple Task Assignment of UAVs Discrete Invasive Weed Optimization Algorithm

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Emotional control of inverted pendulum system: A soft switching from imitative to emotional learning

Emotional control of inverted pendulum system: A soft switch...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Mehrsan Javan-Roshtkhari Arash Arami Caro Lucas Control and Intelligent Processing Center of Excellence School of ECE University of Tehran Tehran Iran

Model-free control of unidentified systems with unstable equilibriums results in serious problems. In order to surmount these difficulties, firstly an existing model-based controller is used as a mentor for emotional-learning controller. This learning phase prepares the controller to behave like the mentor, while prevents any instability. Next, the controller is softly switched from model based to emotional one, using a FIS. Also the emotional stress is softly switched from the mentor-imitator output difference to the combination of objectives generated by a FIS which attentionally modulated stresses. For evaluating the proposed model free controller, a laboratorial inverted pendulum is employed.

关键词： control systems Learning Mathematical model intelligent robots Stress Power system modeling Laboratories Biological system modeling Cranes Brain modeling

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Application of Classifier Fusion for Protein Fold Recognition

Application of Classifier Fusion for Protein Fold Recognitio...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Sahar Jazebi Amir Tohidi Masoud Rahgozar Control and Intelligent Processing Center of Excellence School of ECE Engineering University of Tehran Iran

Protein data patterns which are discriminative can be used in many beneficial applications if they are defined correctly such as molecular medicine, agriculture, and microbial genome applications. Prediction of protein folding patterns by which the function of a protein whose structure is unknown can be determined, is much more complicated than that of protein structural classes. The classification rates achieved using different methods to solve this problem are not satisfactory and there is an urgent need to improve this classification rate. In this paper, a set of basic classifiers is used where each one is trained in different parameter systems all extracted from a common training dataset. Each individual classifier uses Probabilistic Neural Networks for classification in which the radial basis function parameter is optimized by Particle Swarm Optimization algorithm. Their outcomes are combined thru a weighted voting and Ordered Weighted Averaging (OWA) for final determination of classifying a query protein. The recognition rate achieved is 5-8% higher than the corresponding rates obtained by various existing Neural Networks.

关键词： Neural networks Particle swarm optimization Protein engineering Pattern recognition Voting Open wireless architecture Databases Support vector machines Support vector machine classification Fuzzy systems

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Cooperative coevolutionary invasive weed optimization and its application to Nash equilibrium search in electricity markets

Cooperative coevolutionary invasive weed optimization and it...

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World Congress on Nature & Biologically Inspired Computing (NaBIC)

作者： Hossein Hajimirsadeghi Amin Ghazanfari Ashkan Rahimi-Kian Caro Lucas Control and Intelligent Processing Center of Excellence ECE Department College of Engineering University of Tehran Tehran Iran

This paper presents a coevolutionary algorithm named cooperative coevolutionary invasive weed optimization (CCIWO) and investigates its performance for global optimization of functions with numerous local optima and also Nash equilibrium (NE) search for games. Ability of CCIWO for function optimization is tested through a set of common benchmarks of stochastic optimization, and reported results are compared with two other coevolutionary algorithms. In advance, a three-bus transmission-constrained electricity market model is studied, and CCIWO is employed to find NE for this complex system. Experimental results show efficiency of the proposed method to have more accurate solutions.

关键词： Nash equilibrium Electricity supply industry Stochastic processes Computational modeling Algorithm design and analysis Process control intelligent control Educational institutions Electronic mail Testing

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