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Industrial Informatics, IEEE Transactions on

IEEE Transactions on Industrial Informatics focuses on knowledge-based factory automation as a means to enhance industrial fabrication and manufacturing processes. This embraces a collection of techniques that use information analysis, manipulation, and distribution to achieve higher efficiency, effectiveness, reliability, and/or security within the industrial environment. The scope of the Transaction includes reporting, defining, providing a forum for discourse, and informing ...


Nanobioscience, IEEE Transactions on

Basic and applied papers dealing both with engineering, physics, chemistry, and computer science and with biology and medicine with respect to bio-molecules and cells. The content of acceptable papers ranges from practical/clinical/environmental applications to formalized mathematical theory. TAB #73-June 2001. (Original name-IEEE Transactions on Molecular Cellular and Tissue Engineering). T-NB publishes basic and applied research papers dealing with the study ...


Systems, Man and Cybernetics, Part A, IEEE Transactions on

Systems engineering, including efforts that involve issue formnaulations, issue analysis and modeling, and decision making and issue interpretation at any of the life-cycle phases associated with the definition, development, and implementation of large systems. It will also include efforts that relate to systems management, systems engineering processes and a variety of systems engineering methods such as optimization, modeling and simulation. ...


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...


Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Applications, review, and tutorial papers within the scope of the Systems, Man and Cybernetics Society. Currently, this covers: (1) Integration of the theories of communication, control cybernetics, stochastics, optimization and system structure towards the formulation of a general theory of systems; (2) Development of systems engineering technology including problem definition methods, modeling, and stimulation, methods of systems experimentation, human factors ...



Most published Xplore authors for IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics

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Xplore Articles related to IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics

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A two-stage evolutionary process for designing TSK fuzzy rule-based systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1999

Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this design problem because they ...


An incremental-learning-by-navigation approach to vision-based autonomous land vehicle guidance in indoor environments using vertical line information and multiweighted generalized Hough transform technique

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1998

An incremental learning by navigation approach to vision based autonomous land vehicle (ALV) guidance in indoor environments is proposed. The approach consists of three stages: initial learning, navigation, and model updating. In the initial learning stage, the ALV is driven manually, and environment images and other status data are recorded automatically. Then, an offline procedure is performed to build an ...


Detection of incipient object slippage by skin-like sensing and neural network processing

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1998

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network used to detect incipient slippage and on a skin-like sensor sensible to normal ...


An approach by graphs for the recognition of temporal scenarios

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1998

This paper presents an approach by graphs for the recognition of temporal scenarios, that represent models of the dynamical behavior of a system. The aim of the presented work is to analyze the relative situation of a scenario and an effective behavior of the system, called a session. Different symbolic levels of recognition are proposed to qualify this status. All ...


A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1999

The performance of a pattern recognition system is dependent on, among other things, an appropriate data-preprocessing technique, In this paper, we describe a method to evaluate the performance of a variety of these techniques for the problem of odour classification using an array of gas sensors, also referred to as an electronic nose. Four experimental odour databases with different complexities ...


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Educational Resources on IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics

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IEEE-USA E-Books

  • A two-stage evolutionary process for designing TSK fuzzy rule-based systems

    Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK fuzzy rule-based systems from examples combining a generation stage based on a (/spl mu/, /spl lambda/)-evolution strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary knowledge base, and a refinement stage in which both the antecedent and consequent parts of the fuzzy rules in this previous knowledge base are adapted by a hybrid evolutionary process composed of a genetic algorithm and an evolution strategy to obtain the final Knowledge base whose rules cooperate in the best possible way. Some aspects make this process different from others proposed until now: the design problem is addressed in two different stages, the use of an angular coding of the consequent parameters that allows us to search across the whole space of possible solutions, and the use of the available knowledge about the system under identification to generate the initial populations of the Evolutionary Algorithms that causes the search process to obtain good solutions more quickly. The performance of the method proposed is shown by solving two different problems: the fuzzy modeling of some three-dimensional surfaces and the computing of the maintenance costs of electrical medium line in Spanish towns. Results obtained are compared with other kind of techniques, evolutionary learning processes to design TSK and Mamdani-type fuzzy rule- based systems in the first case, and classical regression and neural modeling in the second.

  • An incremental-learning-by-navigation approach to vision-based autonomous land vehicle guidance in indoor environments using vertical line information and multiweighted generalized Hough transform technique

    An incremental learning by navigation approach to vision based autonomous land vehicle (ALV) guidance in indoor environments is proposed. The approach consists of three stages: initial learning, navigation, and model updating. In the initial learning stage, the ALV is driven manually, and environment images and other status data are recorded automatically. Then, an offline procedure is performed to build an initial environment model. In the navigation stage, the ALV moves along the learned environment automatically, locates itself by model matching, and records necessary information for model updating. In the model updating stage, an offline procedure is performed to refine the learned model. A more precise model is obtained after each navigation-and-update iteration. Used environment features are vertical straight lines in camera views. A multiweighted generalized Hough transform is proposed for model matching. A real ALV was used as the testbed, and successful navigation experiments show the feasibility of the proposed approach.

  • Detection of incipient object slippage by skin-like sensing and neural network processing

    Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network used to detect incipient slippage and on a skin-like sensor sensible to normal and shear stresses. Normal and shear stresses components inside the sensor are the input data of the neural net. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being manipulated is not needed. To validate the method we worked on both simulated and experimental data. In the first case, the finite element method is used to solve the direct problem of elastic contact in its full nonlinearity by resorting to the lowest number of approximations regarding the real problem. Simulation has shown that the network learns and is robust to noise. Then an experimental test was carried out. Experimental results show that, in a simple case, the method is able to detect the insipiency of slippage between an object and the sensor.

  • An approach by graphs for the recognition of temporal scenarios

    This paper presents an approach by graphs for the recognition of temporal scenarios, that represent models of the dynamical behavior of a system. The aim of the presented work is to analyze the relative situation of a scenario and an effective behavior of the system, called a session. Different symbolic levels of recognition are proposed to qualify this status. All these levels, as well as most of the properties, are formulated in terms of graphs of temporal constraints. Different contexts are analyzed, where the session is either statically built, when considered as a history, or dynamically built, when information is treated in an incremental manner or on-line. In a second phase, each status is refined using a numeric estimation of the proximity between a scenario and a session. This estimation is performed by calculating an overlapping index or a temporal difference index between the volumes of the domains corresponding to the temporal graphs of the scenario and the session.

  • A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors

    The performance of a pattern recognition system is dependent on, among other things, an appropriate data-preprocessing technique, In this paper, we describe a method to evaluate the performance of a variety of these techniques for the problem of odour classification using an array of gas sensors, also referred to as an electronic nose. Four experimental odour databases with different complexities are used to score the data-preprocessing techniques. The performance measure used is the cross-validation estimate of the classification rate of a K nearest neighbor voting rule operating on Fisher's linear discriminant projection subspace.

  • Fuzzy function approximators with ellipsoidal regions

    This paper discusses two types of fuzzy function approximators that dynamically generate fuzzy rules with ellipsoidal regions: a function approximator based on Takagi-Sugeno type model with the center-of-gravity defuzzification and a function approximator based on a radial basis function network. Hereafter the former is called FACG and the latter is called FALC. In FACG, for each training datum the number of the training data that are within the specified distance is calculated and the training datum which has the maximum number of the training data is selected as the center of a fuzzy rule and the covariance matrix is calculated using the training data around the center. Then the parameters of the linear equation that defines the output value of the fuzzy rule are determined by the least-squares method using the training data around the center. In FALC, the training datum with the maximum approximation error is selected as the center of a fuzzy rule. Then using the training data around the center, the covariance matrix is calculated, and the parameters of a linear equation that determines the output value are calculated by the least-squares method. Performance of FACG and FALC is compared with that of multilayered neural networks and other fuzzy function approximators for the data generated by the Mackey-Glass differential equation and the data from a water purification plant.

  • Handwritten word recognition with character and inter-character neural networks

    An off-line handwritten word recognition system is described. Images of handwritten words are matched to lexicons of candidate strings. A word image is segmented into primitives. The best match between sequences of unions of primitives and a lexicon string is found using dynamic programming. Neural networks assign match scores between characters and segments. Two particularly unique features are that neural networks assign confidence that pairs of segments are compatible with character confidence assignments and that this confidence is integrated into the dynamic programming. Experimental results are provided on data from the U.S. Postal Service.

  • Tracking control of multi-input affine nonlinear dynamical systems with unknown nonlinearities using dynamical neural networks

    The purpose of this paper is to design and rigorously analyze a tracking controller, based on a dynamic neural network model for unknown but affine in the control, multi input nonlinear dynamical systems, Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. The controller derived is smooth. No a priori knowledge of an upper bound on the "optimal" weights and modeling errors is required. Simulation studies are used, to illustrate and clarify the theoretical results.

  • Reply to comments on 'A computational evolutionary approach to evolving game strategy and Cooperation'

    This is a reply to the comment by H.-C. Wu and C.-T. Sun on the paper "A Computational Evolutionary Approach to Evolving Game Strategies and Cooperation." Key design problems, limitations and potential applications are discussed.

  • New closed-form solution for kinematic parameter identification of a binocular head using point measurements

    This paper proposes a new closed-form solution for identifying the kinematic parameters of an active binocular head having four revolute joints and two prismatic joints by using three-dimensional (3-D) point (position) measurements of a calibration point. Since this binocular head is composed of off-the-shelf components, its kinematic parameters are unknown. Therefore, we can not directly apply those existing nonlinear optimization methods. Even if we want to use the nonlinear optimization methods, a closed-form solution can be first applied to obtain accurate enough initial values. Hence, this paper considers only methods that provide closed-form solutions, i.e., those requiring no initial estimates. Notice that most existing closed-form solutions require pose (i.e., both position and orientation) measurements. However, as far as we know, there is no inexpensive technique which can provide accurate pose measurements. Therefore, existing closed-form solutions based on pose measurements can not give us the required accuracy. As a result, we have developed a new method that does not require orientation measurements and can use only the position measurements of a calibration point to obtain highly accurate estimates of kinematic parameters using closed-form solutions. The proposed method is based on the complete and parametrically continuous (CPC) kinematic model, and can be applied to any kind of kinematic parameter identification problems with or without multiple end-effecters, providing that the links are rigid, the joints are either revolute or prismatic and no closed-loop kinematic chain is included.



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