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Positivistic Tendencies Due to engineering Education

IFAC Proceedings Volumes 2003年第10期36卷 117-122页

作者： Mustafa Suphi Erden Department of Electrical and Electronics Engineering Computer Vision and Intelligent Systems Laboratory Middle East Technical University Ankara Turkey

In this research it is claimed that engineering education increases the positivistic tendencies of students regarding to the social problems. A theoretical background is given in the introduction and literature review. Two hypotheses are constructed and a micro-questionnaire survey is performed to test the hypotheses. In the survey positivistic tendencies of engineering students and social science students are compared. The results are depicted and discussed in detail. One of the hypotheses was approved by the results while the other was rejected. It is concluded that the rejected hypothesis was not in accordance with the claim since it overlooked some factors effecting positivism. The results of the survey support the claim by stating that ’engineering students are more positivist than social science students’.

关键词： Education engineering Education Social Science Education Positivism Ideology Questionnaire Survey

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AUTOMATIC FACIAL FEATURE EXTRACTION USING EDGE DISTRIBUTION AND GENETIC SEARCH

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International Journal of Computational Intelligence and Applications 2003年第1期3卷 89-100页

作者： GARY G. YEN NETHRIE NITHIANANDAN Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74074-5032 USA

A heuristic, automatic facial feature extraction approach is presented in this paper. The method is based on the edge density distribution of the image, containing non-occluded, nearly frontal view of one or more faces. In the preprocessing stage, a face is approximated to an ellipse, and genetic algorithm is applied to search for the best ellipse region match. In the feature extraction stage, genetic algorithm is applied to extract the facial features, such as the eyes (with eyebrows), nose and mouth, in the predefined subregions. The simulation results validates that the proposed method is capable of automatically extracting features from various video images effectively under natural lighting environments and in the presence of complex backgrounds, certain amount of artificial noise and of multi-face oriented with angles. This preliminary study advanced from a rich literature provides robust facial feature detection under certain variations in lighting conditions and backgrounds.

关键词： Facial recognition feature extraction genetic algorithm edge density

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Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design

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International Journal of Computational Intelligence and Applications 2003年第3期3卷 213-232页

作者： Gary G. Yen Haiming Lu Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078-503 USA

In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the training performance and network complexity. Instead of producing a single optimal solution, HRDGA provides a set of near-optimal neural networks to the designers so that they can have more flexibility for the final decision-making based on certain preferences. In terms of searching for a near-complete set of candidate networks with high performances, the networks designed by the proposed algorithm prove to be competitive, or even superior, to three other traditional radial-basis function networks for predicting Mackey–Glass chaotic time series.

关键词： Genetic algorithm multiobjective evolutionary algorithm neural network design RBF network hierarchical representation Pareto set

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Adaptive double self-organizing map and its application in gene expression data

Adaptive double self-organizing map and its application in g...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Ressom D. Wang P. Natarajan Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Maine Orono ME USA

This paper presents a novel clustering technique known as adaptive double self-organizing map (ADSOM). ADSOM has a flexible topology and perform clustering and cluster visualization simultaneously, thereby requiring no a priori knowledge about the number of clusters. ADSOM combines features of the popular self-organizing map (SOM) with two-dimensional position vectors, which serve as a visualization tool to accurately determine the number of clusters present in the data. ADSOM updates its free parameters during training and it allows convergence of its position vectors to a fairly consistent number of clusters provided that its initial number of nodes is greater than the expected number of clusters. A novel index is introduced based on hierarchical clustering of the final locations of position vectors. The index allows automatic detection of the number of clusters, thereby reducing human error that could be incurred from counting cluster visually. The reliance of ADSOM in identifying the number of clusters is proven by applying it to publicly available yeast gene expression data.

关键词： Gene expression Data visualization Subspace constraints Clustering algorithms Application software intelligent systems Laboratories Topology Convergence Humans

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Channel resource allocation/reallocation in cellular communication and linear programming

Channel resource allocation/reallocation in cellular communi...

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IEEE International Conference on systems, Man and Cybernetics

作者： R. Parra-Hernandez N. Dimopoulos Laboratory for Parallel and Intelligent Systems-LAPIS Department of Electrical and Computer Engineering University of Victoria Canada

An approach aiming to formulate the mobile network resource allocation problem is proposed here. In order to formulate the resource allocation problem, the way network resources are to be counted is defined. Then, the resource allocation problem is expressed as a multidimensional knapsack problem. The knapsack problem is relaxed and a constrained optimisation linear programming problem is obtained. A series of pseudo-utility values is obtained from the solution of the linear programming problem. The pseudo-utility values are used to establish an order by which the calls/terminals/users are to be attended by the network.

关键词： Resource management Linear programming Power control Multidimensional systems Constraint optimization Channel allocation intelligent networks Laboratories intelligent systems Land mobile radio cellular systems

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Constrained-learning in artificial neural networks

Constrained-learning in artificial neural networks

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IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： R. Parra-Hernandez Laboratory for Parallel and Intelligent Systems-LAPIS Department of Electrical and Computer Engineering University of Victoria Canada

The capacity to generalize is the most important characteristic in neural networks. However, the generalization capacity is lost when over-fitting occurs during the neural network training process; i.e., although the error after the training process is very small, when new data is presented to the neural network the error is large. An approach aiming to improve the neural network generalization capacity is presented in this work.

关键词： intelligent networks Artificial neural networks Neural networks Predictive models Signal processing Laboratories intelligent systems Artificial intelligence Parallel processing Fault tolerance

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Fuzzy logic-based gene regulatory network

Fuzzy logic-based gene regulatory network

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： H. Ressom D. Wang R.S. Varghese R. Reynolds Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Maine Orono ME USA

DNA microarray technology enables a parallel analysis of the expression of genes in an organism. The wealth of spatio-temporal data generated by this technology allows researchers to potentially reverse engineer the genetic network. Fuzzy logic has been proposed as a method to analyze the relationships between genes. This method can identify interacting genes that fit a known fuzzy model of gene interaction by testing all combinations of gene expression profiles. However, this approach is slow and computationally complex. This paper introduces improvements made in terms of reducing computation time and generalizing the gene regulatory model to accommodate co-activators and co-repressors. Improvement in computation time is achieved by using clustering as a pre-processing method, thereby reducing the total number of gene combinations analyzed. This will allow the algorithm to run in a shorter amount of time with minimal effect on the results. The proposed technique will pave the way towards the creation of a generalized gene interaction model that can accommodate any combination of genes.

关键词： Fuzzy logic Clustering algorithms DNA Reverse engineering Genetics Gene expression Differential equations intelligent systems Laboratories Organisms

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A SOM projection technique with the growing structure for visualizing high-dimensional data

A SOM projection technique with the growing structure for vi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Z. Wu G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional SOM structure with a growing self-organizing map. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. After the learning phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy visualization of the data. The projection method is demonstrated on two data sets with promising results and a significantly reduced network size.

关键词： Data visualization Neurons Data structures Shape intelligent systems intelligent control Control systems Laboratories Data engineering Data mining

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Multiple model fault tolerant control using Globalized Dual Heuristic Programming

Multiple model fault tolerant control using Globalized Dual ...

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IEEE International Symposium on intelligent Control (ISIC)

作者： P.G. DeLima G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

An advanced reconfigurable controller improved by a multiple model architecture is proposed as a tool to achieve fault tolerance in complex nonlinear systems. The most complete adaptive critic design, Globalized Dual Heuristic Programming (GDHP), constitutes a highly flexible nonlinear adaptive controller responsible for the generation of new control solutions for novel plant dynamics introduced by unknown faults. Working on a higher hierarchical level, the proposed supervisor makes use of two quality indexes to perform fault detection, identification and isolation based on the knowledge stored in a dynamic model bank. In the event of abrupt known faults, such knowledge is then used to greatly reduce the reconfiguration time of the GDHP controller. The synergy of the proposed supervisor and GDHP goes beyond, as solutions designed by the controller to previously unknown faults are autonomously added to the model bank. The fine interrelations among the algorithm's subsystems are illustrated in a rich numerical simulation of a MIMO nonlinear system subject to different fault scenarios.

关键词： Fault tolerance Programmable control Adaptive control Neural networks Nonlinear control systems Dynamic programming Stability Function approximation Nonlinear systems Fault detection

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Wavelet based estimation of the fractal dimension in fBm images

Wavelet based estimation of the fractal dimension in fBm ima...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： C. Parra K. Iftekharuddin D. Rendon Intelligent Systems and Image Processing Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

Fractional Brownian Motion (fBm) has been successfully exploited to model an important number of physical phenomena and non-stationary processes such as medical images. These mathematical models closely describe essential properties of natural phenomena, such as self-similarity, scale invariance and fractal dimension (FD). The use of wavelet analysis combined with fBm analysis may provide an interesting approach to compute key values for fBm processes, such as the fractal dimension FD. We propose two models to calculate the Hurst Coefficient H (and hence FD) for both one-dimensional and two-dimensional signals. The first approach is based on statistical properties of the signals, while the second one is based on their spectral characteristics. A formal extension of these two models to 2D processes is developed and implemented, and a comparison of both is presented. The proposed algorithms are tested using tomographic brain tumor data. Our simulation experiments offer promising results in the identification of such lesions.

关键词： Fractals Signal analysis Brownian motion Biomedical imaging Mathematical model Wavelet analysis Signal processing Testing Tomography Neoplasms

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