The paper presents the concept of genetic algorithm based optimization for the EMG pattern recognition system controlling the hand prosthesis. The recognition of EMG signals for determining the hand movements is made ...
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ISBN:
(纸本)9783902661555
The paper presents the concept of genetic algorithm based optimization for the EMG pattern recognition system controlling the hand prosthesis. The recognition of EMG signals for determining the hand movements is made by a linear neural network discriminating between five predefined grasps. The input feature vector for the classification was established using AR model, and its coefficients became the features. The genetic algorithms are used to optimize the number of elements in the input feature vector and simultaneously maintain the recognition efficiency at the same level. Experimental results show a good efficiency in the optimization method maintaining the performance of the recognition system for the studied grasp movements' repertoire.
Abstract This paper deals with the problem of defining performance measures of doubly nonholonomic mobile manipulators composed of a nonholonomic mobile platform and a nonholonomic manipulator mounted on the platform....
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Abstract This paper deals with the problem of defining performance measures of doubly nonholonomic mobile manipulators composed of a nonholonomic mobile platform and a nonholonomic manipulator mounted on the platform. We introduce the kinematic theory of such mobile manipulators in frame of the endogenous configuration space approach, which assumes that: the kinematics of mobile manipulators is represented by a pair of driftless control systems driven by platform and manipulator controls, and sharing output function that describes position and orientation of the end-effector; the kinematic performance measures refer to the analytic Jacobian of the system; a locally controllable system yields nontrivial performance measures. Our results consist in a definition of the following local performance characteristics: the dexterity ellipsoid, the local dexterity, the energy efficiency and the motion efficiency. The usefulness of new local measures is demonstrated on the example of determining efficient configurations of a 3 d.o.f. planar nonholonomic manipulator fixed to a kinematic car type platform.
The aim of this paper is to describe a research roadmap for a multi-domain model-driven embedded systems design approach and the corresponding meta-model which is applicable to the domain of complex Industrial Automat...
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The aim of this paper is to describe a research roadmap for a multi-domain model-driven embedded systems design approach and the corresponding meta-model which is applicable to the domain of complex Industrial Automation and control Systems (IACS). The special requirements of the industrial automation sector are taken into account by this novel approach, utilizing existing model-driven techniques. This approach is currently being developed in the Framework Seven (FP7) Embedded Systems Design project MEDEIA funded by the European Commission.
We propose a six degree-of-freedom multi-body approach for modeling and simulation of biologically-inspired (or Biomimetic) autonomous underwater vehicles (BAUVs), i.e., artificial fish. The proposed approach is based...
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We propose a six degree-of-freedom multi-body approach for modeling and simulation of biologically-inspired (or Biomimetic) autonomous underwater vehicles (BAUVs), i.e., artificial fish. The proposed approach is based on considering the BAUV as comprised of multiple rigid bodies interlinked through joints; the external force and torque on each rigid body in the BAUV is expressed using quasi-steady aerodynamic theory and the joint constraints are imposed through an impulse-based technique. A BAUV simulation platform has been implemented based on the proposed modeling framework and has been applied to analyze a specific BAUV inspired by the electric ray. The hardware implementation of the electric ray inspired BAUV is also presented. Finally, sample simulation results and validation against experimental data collected from the electric ray inspired BAUV are also presented.
To provide appropriate guidance in minimally invasive surgical training (and potentially an additional safety measure in the operating room), we propose a model called a "No-Fly Zone" based on the situationa...
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To provide appropriate guidance in minimally invasive surgical training (and potentially an additional safety measure in the operating room), we propose a model called a "No-Fly Zone" based on the situational awareness enhancing system. By defining the configuration space of the instrument, a collision free region is defined. If an intrusion occurs into the no-fly region space, the system provides audio, visual, and haptic feedback to reinforce an appropriate maneuver. The proposed method is intended to refine surgical skills and to improve the patient safety. Usability experiment will be performed to test the system.
Grammatical Inference deals with the problem of learning structural models, such as grammars, from different sort of data patterns, such as artificial languages, natural languages, biosequences, speech and so on. This...
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Grammatical Inference deals with the problem of learning structural models, such as grammars, from different sort of data patterns, such as artificial languages, natural languages, biosequences, speech and so on. This article describes a new grammatical inference tool, Grammar-based Classifier System (GCS) dedicated to learn grammar from data. GCS is a new model of Learning Classifier Systems in which the population of classifiers has a form of a context-free grammar rule set in a Chomsky Normal Form. GCS has been proposed to address both regular language induction and the natural language grammar induction as well as learning formal grammar for DNA sequence. In all cases near-optimal solutions or better than reported in the literature were obtained.
Abstract The paper presents the concept of genetic algorithm based optimization for the EMG pattern recognition system controlling the hand prosthesis. The recognition of EMG signals for determining the hand movements...
Abstract The paper presents the concept of genetic algorithm based optimization for the EMG pattern recognition system controlling the hand prosthesis. The recognition of EMG signals for determining the hand movements is made by a linear neural network discriminating between five predefined grasps. The input feature vector for the classification was established using AR model, and its coefficients became the features. The genetic algorithms are used to optimize the number of elements in the input feature vector and simultaneously maintain the recognition efficiency at the same level. Experimental results show a good efficiency in the optimization method maintaining the performance of the recognition system for the studied grasp movements’ repertoire.
The self-adaptive model of a XCS-based ensemble machine solving data-mining tasks has been presented. The results of experiments have shown the ability of the architecture to adapt the parameters of single XCS: the mu...
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Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typic...
A new sampling scheme for 2D images is proposed, which is dedicated for fast processing and storing long sequences of industrial images. It was proved that the spectrum of non-bandlimited images can be approximated up...
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A new sampling scheme for 2D images is proposed, which is dedicated for fast processing and storing long sequences of industrial images. It was proved that the spectrum of non-bandlimited images can be approximated up to a desired accuracy when number of samples grows. A simple reconstruction scheme is also proposed. copyright by EURASIP.
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