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检索条件"机构=Faculty of Intelligent Science and Control Engineering"

共 408 条记录，以下是361-370 订阅

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Pusher reheating furnace control via fuzzy-neural model predictive control synthesis

Pusher reheating furnace control via fuzzy-neural model pred...

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2012 6th IEEE International Conference intelligent Systems, IS 2012

作者： Stojanovski, Goran Stankovski, Mile Rudas, Imre J. Jing, Juanwei Faculty of Electrical Eng. and Information Technologies Karpos 2 SS Cyril and Methodius University Skopje MK-1000 Macedonia Institute of Intelligent Engineering Systems Óbuda University Bécsi út 96/B H-1034 Budapest Hungary College of Information Science Engineering Institute of Control Theory Navigation Technology Northeastern University Shenyang Liaoning 100006 China

ISBN: (纸本)9781467327824

A design of fuzzy model-based predictive control for industrial furnaces has been derived and applied to the model of three-zone 25 MW RZS pusher furnace at Skopje Steelworks. The fuzzy-neural variant of Sugeno fuzzy model, as an adaptive neuro-fuzzy implementation, is employed as a predictor in a predictive controller. In order to build the predictive controller the adaptation of the fuzzy model using dynamic process information is carried out. Optimization procedure employing a simplified gradient technique is used to calculate predictions of the future control actions. © 2012 IEEE.

关键词： Optimization

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Sparse MEG Source Localization on Parameterized Cortical Medial Surface

Sparse MEG Source Localization on Parameterized Cortical Med...

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Iranian Conference of Biomedical engineering

作者： Hamid Khosravi Mahsa Mohammadkhani Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran Department of Computing Science University of Alberta Edmonton Alberta (AB) Canada

ISBN: (纸本)9781467331289

The inverse mapping in MagnetoEncephaloGraphy involves localizing neuronal activity of the gray matter tissue from measured signals around the head. The search space of the inverse mapping is conventionally represented in 3-D Cartesian coordinates including the cortical volume, or alternatively, 2-D manifold describing the cortical tissue. 3-D Cartesian coordinates does not suit sparse regularization of the inverse mapping in multi-resolution source domains. On the other hand, boundary surface of the cortex, represented as triangulated mesh, is used as the search space in some other inverse mapping techniques. However, this surface does not represent depth of the cortex. Moreover, to represent neuronal activity in embedded signal dictionaries, convoluted manifold should be gridded. In this paper we propose to use medial surface as the best surface descriptor of the cortical tissue. Furthermore, manifold gridding is constructed by defining bijective mapping between a flattened parameterized domain and convoluted surface. Signal bases are defined conventionally in the parameterized domain and mapped to the cortical volume by means of the bijective mapping. Medial surface of the cortex tissue, represented as triangulated mesh, is extracted by gradient-based search algorithm. Source localization, for simulated neuronal activity, is performed in real head model extracted from magnetic resonance images. Sparse estimated source of the inverse mapping are compared on volume, boundary surface, and medial surface domains. Simulation results confirm advantages of surface over volume domain. Performnace measures of source localization proves the medial surface as the best surface describing the cortex volume.

关键词： Magneto-encephalography (MEG) Inverse mapping Sparse regularization Multi-resolution Medial surface Mesh parameterization

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H∞ consensus of nonlinear multi-agent systems based on T-S fuzzy models

H∞ consensus of nonlinear multi-agent systems based on T-S ...

引用

Chinese control Conference (CCC)

作者： Zhao Yan Karimi Hamid Reza Li Bing Huijun Gao Shenzhen Graduate School Harbin Institute of Technology Shenzhen China Department of Engineering Faculty of Engineering and Science University of Agder Grimstad Norway Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin Heilongjiang China

In general, due to some limitations of nonlinear control methods, it is difficult to analyze control performance for nonlinear multi-agent network. The T-S fuzzy model-based approach is often introduced to help solve the performance analysis in nonlinear systems, but the problem of nonlinear follower agents approaching a time-varying leader is difficult to be formulated by using the general T-S fuzzy modeling method. In this paper, a novel T-S fuzzy modeling method is proposed, and the error dynamics between the states of agents and the leader signal, evolving according to an isolated unforced nonlinear agent model, is described by a set of T-S fuzzy models. Based on the model, leader-following consensus algorithm is conveniently designed so that under external disturbances, all the follower agents achieve consensus with the leader guaranteeing a prescribed disturbance attenuation level in H ∞ sense. Finally, simulations with chaotic dynamic systems and sinusoidal functions are presented, and by applying the obtained results to the initial nonlinear systems, the effectiveness of the obtained results is illustrated.

关键词： Multiagent systems Nonlinear systems Mathematical model Couplings Protocols Lead Attenuation

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Pusher Reheating Furnace control Via Fuzzy-Neural Model Predictive control Synthesis

Pusher Reheating Furnace Control Via Fuzzy-Neural Model Pred...

引用

IEEE International Conference intelligent Systems

作者： Goran Stojanovski Mile Stankovski Imre J. Rudas Juanwei Jing Faculty of Electrical Eng. & Information Technologies Karpos 2 SS Cyril & Methodius University Skopje MK-1000 R. of Macedonia Institute of Intelligent Engineering Systems óbuda University Bécsi út 96/B H-1034 Budapest Hungary College of Information Science and Engineering Institute of Control Theory & Navigation Technology Northeastern University Shenyang Liaoning 100006 P.R. of China

ISBN: (纸本)9781467322768

关键词： Fuzzy-neural models fuzzy model predictive control optimization set-point control time-delay processes. fuzzy modeling Predictive controllers Model predictive control dial steel machine Industrial furnaces Reheating furnaces Optimization Procedure Predictive control Pusher furnaces Steel plants

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A novel method for Centromere and length detection in microscopic images of human chromosomes

A novel method for Centromere and length detection in micros...

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2011 18th Iranian Conference of Biomedical engineering, ICBME 2011

作者： Jahani, Sahar Setarehdan, S. Kamaledin Faculty of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781467310055

Many genetic disorders or abnormalities that may occur in the future generations can be predicted through analyzing the shape and morphological characteristics of the human chromosomes. This is usually carried out by an expert, inspecting the Karyotype of the patients. A Karyotype is a particular table that presents the chromosome images in a standard format. To generate a Karyotype, it is necessary to identify each of the 23 pairs of the chromosomes within the microscopic images first. The main step to automate this procedure is the definition of some morphological features for each chromosome. The most common features used for chromosomes identification includes the location of the Centromere and the length of the chromosome. Many other important features, such as Centromeric Index, are usually extracted from the Centromere and length. In this paper, a novel and effective algorithm for Centromere locating and length calculation for the human chromosomes is presented. The proposed algorithm uses the fact that the centromere is the narrowest part of the chromosome. By defining a gray level mask (GLM), which is a linearly varying gray level image along the chromosome longitudinal direction and multiplying it to the binary version of the chromosome image, it is shown that the global minimum in the histogram of the resulted image indicates the location of the centromere. The data set used in this work was provided by the Tesi-Imaging srl in Milan, Italy. A mean value of the absolute error of 3.6 and 5.2 pixels was obtained in identification of the chromosome centromere and length respectively by the proposed method. © 2011 IEEE.

关键词： Chromosomes

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Automatic identification of overlapping/touching chromosomes in microscopic images using morphological operators

Automatic identification of overlapping/touching chromosomes...

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2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011

作者： Jahani, Sahar Setarehdan, S. Kamaledin Fatemizadeh, Emadedin Faculty of Biomedical Engineering Science and Research Branch Azad University Tehran Iran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer Eng. University of Tehran Tehran Iran Sharif University of Technology School of Electrical Engineering Iran

ISBN: (纸本)9781457715358

Karyotyping, is the process of classification of human chromosomes within the microscopic images. This is a common task for diagnosing many genetic disorders and abnormalities. Automatic Karyotyping algorithms usually suffer the poor quality of the images due to the non rigid nature of the chromosomes which makes them to have unpredictable shapes and sizes in various images. One of the main problems that usually need operator's interaction is the identification and separation of the overlapping/touching chromosomes. This paper presents an effective algorithm for identification of any cluster of the overlapping/touching chromosomes together with the number of chromosomes in the cluster, which is a very first step towards the development of a fully automatic Karyotyping system. The proposed algorithm which is based on the extraction of the number of endpoints within the skeleton of the image objects uses morphological operators. Two independent datasets obtained from the Tesi-Imaging srl in Milan, Italy and the Imam Hospital in Tehran, Iran was used to evaluate the performance of the algorithm. An accuracy of %96 and %99 were obtained on identification of the clusters of overlapping/touching chromosomes and single chromosomes respectively by the proposed algorithm. © 2011 IEEE.

关键词： Chromosomes

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intelligent control of human prosthetic eye movements system for the emotional support by a huggable pet-type robot via Gaussian RBF neural network based on sliding mode control

Intelligent Control of human prosthetic eye movements system...

引用

2011 2nd International Conference on control, Instrumentation and Automation, ICCIA 2011

作者： Farivar, Faezeh Kandroodi, Mojtaba Rostami Shoorehdeli, Mahdi Aliyari Department of Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Faculty of Electrical Engineering Department of Mechatronics Engineering K. N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781467316903

In this study, intelligent control of human eye movements system for the emotional support by a huggable pet type robot via Gaussian RBF neural network based on sliding mode control is presented. Despite active research and significant progress in the last three decades on control of human eye movements, it remains challenging issue due to its applications in prosthetic eyes and robotics. The geometry and model of human eye movements system are investigated and this system is considered as a nonlinear control system. The specified model may only be an academic exercise. It can have scientific importance in understanding of the human movement system in general. Also, it can be useful for robotics. By using Gaussian RBF Neural Networks Based on Sliding Mode control, the control goal can be achieved. The adaptation laws of designed controllers are obtained based on sliding mode control methodology without calculating the Jacobian of the system. The proposed method is capable to control of human eye movements system. intelligent methods such as artificial neural networks are proposed to control the human eye movements and numerical simulations are presented. It is concluded that the intelligent controls applied to control of human eye movements system are emulated from the neural controls in biological system. © 2011 IEEE.

关键词： Sliding mode control

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Automatic Identification of Overlapping/Touching Chromosomes in Microscopic Images Using Morphological Operators

Automatic Identification of Overlapping/Touching Chromosomes...

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Iranian Machine Vision and Image Processing (MVIP)

作者： Sahar Jahani S. Kamaledin Setarehdan Emadedin Fatemizadeh Faculty of Biomedical Engineering Science and Research Branch Azad University Tehran Iran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran School of Electrical Engineering Sharif University of Technology Iran

关键词： Biological cells Clustering algorithms Skeleton Object recognition Microscopy Humans Educational institutions

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A novel method for Centromere and length detection in microscopic images of human chromosomes

A novel method for Centromere and length detection in micros...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Sahar Jahani S. Kamaledin Setarehdan Faculty of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

关键词： Biological cells Histograms Humans Microscopy Shape Biomedical engineering Biomedical imaging

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A novel type of trigonometric neural network trained by Extended Kalman Filter

A novel type of trigonometric neural network trained by Exte...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： M. Norouzi M. Mansouri M. Teshnehlab M. Aliyari Shoorehdeli Faculty of Engineering Department of Computer Engineering Science and Research Branch Islamic Azad University Iran Intelligent System Laboratory (ISLAB) Faculty of Electrical Engineering Control Department K.N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Department of Mechatronics Engineering Advanced Process Automation &Control Laboratory (APAC) K.N. Toosi University of Technology Tehran Iran

In this study, a new type of trigonometric neural network is presented by adding frequency and phase to trigonometric activation functions. The proposed trigonometric neural network has more flexibility in comparison with conventional trigonometric neural networks and even other types of neural networks. Due to the low convergence rate and high posibility of trapping in a local minimum of backpropagation algorithm, Extended Kalman Filter algorithm is used to train the neural network's parameters which they appear in a nonlinear form. The Simulation of the suggested neural network based on the prediction of Mackey-Glass time series and identification of a nonlinear dynamic ystem reveals the efficiency of the proposed network. To show the efficiency of this method, the results are compared with the results of the others.

关键词： Biological neural networks Kalman filters Equations Neurons Vectors Covariance matrix Mathematical model

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