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IEEE 7th International Conference on Computational Cybernetics, ICCC 2009

作者： Mahmoudi, Maryam Tayefeh Beheshti, Maedeh Lucas, Caro Taghiyareh, Fattaneh School of Electrical and Computer Eng. University of Tehran Tehran Iran Knowledge Engineering and Intelligent Systems Group Iran Telecom Research Center Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran Control and Intelligent Processing Lab. University of Tehran Tehran Iran School of Electrical and Computer Eng. College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424453115

Color image histograms are very useful tools for content based image retrieval (CBIR) that can be applied on features such as colour, texture and shape. As these kinds of histograms results with large variations between neighbouring bins, they seem so sensitive to any kind of changes such as noise, illumination. To overcome this problem, in this paper, fuzzy linking histogram approach based on OWA aggregation operator is proposed , which is capable of projecting 3-dimensional (L*a*b*) colour histograms into single-dimension. The proposed method have been evaluated and compared with five other related methods in retrieving similar images from the common dataset which is available on http://***/∼konkonst. The experimental results on 100 images within two categories of Cat and Sky reveals better performance of the proposed method in comparison with the other mentioned methods. ©2009IEEE.

关键词： Color

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Plug-in dual-mode-structure repetitive controller for CVCF PWM inverters

Plug-in dual-mode-structure repetitive controller for CVCF P...

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作者： Zhou, Keliang Wang, Danwei Zhang, Bin Wang, Yigang School of Electrical Engineering Wind Generation Research Center Southeast University Nanjing 210096 China Centre for Intelligent Machines Division of Control and Instrumentation Nanyang Technological University Singapore 639798 Singapore School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

Repetitive control (RC) is found to be a high-performance control scheme for constant-voltage constant-frequency (CVCF) pulsewidth-modulated (PWM) converters to eliminate output total harmonic distortion. In this paper, a dual-mode-structure RC (DMRC) is proposed to further improve the control performance of RC. The proposed DMRC controller comprises of two paralleled periodic signal generators: an odd-harmonic one and an even-harmonic one. Without additional complexity and loss of tracking accuracy, DMRC can achieve a faster error convergence rate than the conventional RC does. Moreover, the tracking accuracy of DMRC is higher than that of odd-harmonic RC. In the presence of nonlinear load disturbances and parameter uncertainties, an application example of a DMRC-controlled single-phase PWM inverter is provided to evaluate the proposed DMRC scheme. Systematical experimental results show the effectiveness and superior advantages of the DMRC approach. © 2009 IEEE.

关键词： controllers

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PH control of penicillin fermentation process using predictive approach

PH control of penicillin fermentation process using predicti...

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作者： Ashoori, Ahmad Moshiri, Behzad Ramezani, Amin Bakhtiari, Mohammad Reza Khaki-Sedigh, Ali Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Iranian Research Organization for Science and Technology Iran Department of Electrical Engineering K.N. Toosi University of Technology Tehran Iran Box No. 83 2205 Lower Mall Vancouver BC V6T 1Z4 United States

Bioprocesses which are involved in producing different pharmaceutical products may conveniently be classified according to the mode chosen for the process: cither batch, fed-batch or continuous. From the control engineer's viewpoint they are fed-batch processes, which present the greatest challenge to get a pure product with a high concentration. Complicated dynamics, nonline-arity and non-stationarity make controlling them a very delicate task. pH control of bioreactors has been an interesting problem from both implementation and controller design points of view. This is particularly true if the complex microbial interactions yield significant nonlinear behavior. When this occurs, conventional control strategies may not succeed and more advanced strategies need to be suggested. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The approach used here is to use quadratic cost function for pH regulation, while taking into account control signal fluctuations in the optimization block. The result of applying the obtained controller and also its sensitivity to disturbance have been displayed and compared with the results of an auto-tuned PID controller used in previous works. The merit of this method is its low computational cost of solving the optimization problem, while leading to a closed form controller as well.

关键词： Model predictive control

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OWA fuzzy linking histogram approach for image retrieval

OWA fuzzy linking histogram approach for image retrieval

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International Conference on Computational Cybernetics (ICCC)

作者： Maryam Tayefeh Mahmoudi Maedeh Beheshti Caro Lucas Fattaneh Taghiyareh Knowledge Engineering & Intelligent Systems Group Iran Telecom Research Center Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran School of computer engineering Iran University of Science and Technology Tehran Iran Control and Intelligent Processing Laboratory University of Tehran Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Color image histograms are very useful tools for content based image retrieval (CBIR) that can be applied on features such as colour, texture and shape. As these kinds of histograms results with large variations between neighbouring bins, they seem so sensitive to any kind of changes such as noise, illumination. To overcome this problem, in this paper, fuzzy linking histogram approach based on OWA aggregation operator is proposed, which is capable of projecting 3-dimensional (L*a*b*) colour histograms into single-dimension. The proposed method have been evaluated and compared with five other related methods in retrieving similar images from the common dataset which is available on http://***/~konkonst. The experimental results on 100 images within two categories of Cat and Sky reveals better performance of the proposed method in comparison with the other mentioned methods.

关键词： Open wireless architecture Joining processes Histograms Image retrieval Colored noise Color Content based retrieval Shape Noise shaping Lighting

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Polynomial kernel function and its application in locally polynomial neurofuzzy models

Polynomial kernel function and its application in locally po...

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International CSI computer Conference, CSICC

作者： A. Shirvani H. Chegini S. Setayeshi C. Lucas Tehran Science and Research Branch Computer Engineering Department Islamic Azad University Iran Faculty of Nuclear Engineering and Physics Amirkabir University of Technology슠 Tehran Iran Electrical and Computer Engineering Department Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran

Polynomials are one of the most powerful functions that have been used in many fields of mathematics such as curve fitting and regression. Low order polynomials are desired for their smoothness, good local approximation and interpolation. Being smooth, they can be used to locally approximate almost any derivable function. This means that when linear functions fail in approximation (e.g. where the first order Taylor expansion equals zero) polynomial functions can be used in local approximation, such that one can achieve better estimations at extremums. In this paper, application of polynomial kernel functions in locally linear neurofuzzy models is shown. Using polynomial kernels in local models, better local approximations in prediction of chaotic time series such as Mackey-Glass is achieved, and the capability of the neurofuzzy network is enhanced.

关键词： Polynomials Kernel Neurons Predictive models Power engineering and energy Chaos Application software Physics computing Power engineering computing Taylor series

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Comparison of rotary cement kiln identified models

Comparison of rotary cement kiln identified models

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International Conference on control and Automation (ICCA)

作者： G. Noshirvani A. Fatehi B. Araabi M. Shirvani M. Azizi ACER Markaz Branch Arak Iran Advanced Process Automation & Control Research group Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran Chemical Engineering Department and member of Cement Research Center University of Science and Technology Tehran Iran Saveh Cement Company Saveh Iran

Rotary cement kiln is the main part of a cement plant that clinker is produced in it. Clinker is the main ingredient of cement. Continual and prolonged operation of rotary cement kiln is vital in cement factories. However, prolonged operation of the kiln is not possible and periodic repairs of the refractory lining would become necessary, due to non-linear phenomena existing in the kiln, such as sudden falls of coatings in the burning zone and probability of damages to the refractory materials during production. This is the basic reason behind the needs for a comprehensive model which is severely necessary for better control of this process. Such a model can be derived based on the mathematic analysis with consultation of expert operator experiences. In this paper both linear and nonlinear model are identified for rotary kiln of Saveh white cement factory. The linear model is introduced using Box-Jenkins structure. The results of the obtained model were satisfactory compared to some other linear models and can be used for designing adaptive or robust controllers. Also, nonlinear system identification via Neural Network technique is performed and its result was compared to linear models.

关键词： Kilns Production facilities Coatings Building materials Process control Mathematical model Mathematics Programmable control Adaptive control Robust control

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An Empirical Study of DV-Hop Localization Algorithm in Random Sensor Networks

An Empirical Study of DV-Hop Localization Algorithm in Rando...

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International Conference on intelligent Computation technology and Automation, ICICTA

作者： Lingfei Wu Max Q.-H. Meng Zhenzhong Dong Huawei Liang Department of Automation University of Science and Technology Hefei Anhui China Center of Biomimetic Sensing and Control Research Institute of Intelligent Machines Chinese Academy and Sciences Hefei Anhui China Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China Department of Computer Science University of Science and Technology Hefei Anhui China

ISBN: (纸本)9781424447411

Node localization is an important problem for location-dependent applications of wireless sensor networks. Aiming at the positioning problem of wireless sensor networks node location, an improved DV-Hop localization algorithm is proposed in this paper. The proposed method firstly recalculates the hop-size and sends different correction along different directions instead of computing a single correction to be broadcasted into the networks. Then we empirically evaluate the difference between distance estimate and actual distance through a number of simulation experiments statistical results. We find empirical parameter to improve the accuracy of distance estimate. Simulation results show that the performance of the proposed scheme outweighs classical DV-Hop algorithms, especially in lower connectivity.

关键词： Wireless sensor networks intelligent sensors computer networks intelligent networks Machine intelligence Biomimetics intelligent robots Robotics and automation Broadcasting Computational modeling

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Real-Time Level Plant control Using Improved BELBIC

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IFAC Proceedings Volumes 2008年第2期41卷 4631-4635页

作者： M. Masoudinejad R. Khorsandi A. Fatehi C. Lucas S. Fakhimi Derakhshan M.R. Jamali Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Center of Excellence for Control and Intelligent Processing Electrical and Computer engineering Department University of Tehran Iran

Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW).

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

No abstract available

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A Neuro-Fuzzy controller for Rotary Cement Kilns

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IFAC Proceedings Volumes 2008年第2期41卷 13259-13264页

作者： Maryam Fallahpour Alireza Fatehi Babak N. Araabi Morteza Azizi Control & Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran Advanced Process Automation & Control (APAC) Research Group Electrical Engineering Faculty K. N. Toosi University of Technology Tehran Iran Saveh Cement Co. Saveh Iran

In this paper, we design a neurofuzzy controller to control several variables of a rotary cement kilns. The variables are back-end temperature, pre-heater temperature, oxygen content and CO2 gas content of the kiln. The fuzzy control system, as an advanced control option for the kilns, is intended to minimize the operator interaction in the control process. The proposed fuzzy controller uses a neural network to optimize TSK-type fuzzy controller. Since there is no generally applicable analytical model for cement kilns, we use the real data derived from Saveh cement factory for the plant identification. A model, which is very similar to the real plant, is identified then; and the identified model is used for control design and simulations. Extensive simulation studies justify the effectiveness and applicability of the proposed control scheme in intelligent control of cement plant.

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