832 resultados para Micro parallel kinematic manipulator


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Purpose – The purpose of this paper is to consider Turing's two tests for machine intelligence: the parallel-paired, three-participants game presented in his 1950 paper, and the “jury-service” one-to-one measure described two years later in a radio broadcast. Both versions were instantiated in practical Turing tests during the 18th Loebner Prize for artificial intelligence hosted at the University of Reading, UK, in October 2008. This involved jury-service tests in the preliminary phase and parallel-paired in the final phase. Design/methodology/approach – Almost 100 test results from the final have been evaluated and this paper reports some intriguing nuances which arose as a result of the unique contest. Findings – In the 2008 competition, Turing's 30 per cent pass rate is not achieved by any machine in the parallel-paired tests but Turing's modified prediction: “at least in a hundred years time” is remembered. Originality/value – The paper presents actual responses from “modern Elizas” to human interrogators during contest dialogues that show considerable improvement in artificial conversational entities (ACE). Unlike their ancestor – Weizenbaum's natural language understanding system – ACE are now able to recall, share information and disclose personal interests.

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Recent research in multi-agent systems incorporate fault tolerance concepts. However, the research does not explore the extension and implementation of such ideas for large scale parallel computing systems. The work reported in this paper investigates a swarm array computing approach, namely ‘Intelligent Agents’. In the approach considered a task to be executed on a parallel computing system is decomposed to sub-tasks and mapped onto agents that traverse an abstracted hardware layer. The agents intercommunicate across processors to share information during the event of a predicted core/processor failure and for successfully completing the task. The agents hence contribute towards fault tolerance and towards building reliable systems. The feasibility of the approach is validated by simulations on an FPGA using a multi-agent simulator and implementation of a parallel reduction algorithm on a computer cluster using the Message Passing Interface.

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Dormancy is a mechanism that regulates the timing of sprouting (germination) of affected plant parts as well as ensures that the food quality of edible parts is maintained in storage until the following growing season. In yam, however, little is known about the control of tuber initiation or tuber dormancy. The objective of this study was to determine the effects of selected plant growth regulators (PGRs) on tuber initiation and dormancy, using an in vitro system. In two replicated experiments, 2-chloroethylphosphonic acid (ethephon, an ethylene source), abscisic acid (ABA) and gibberellin (GA3) – and their inhibitors silver nitrate, fluridone and 2-chloroethyl-trimethylammonium chloride, respectively – were added at two concentrations to the culture medium prior to explant culture. Dates of micro-tuber initiation and sprouting (end of dormancy) and tuber number were recorded. In the control (no PGR) in Experiment 1, micro-tubers were initiated at the base of the stem after 176 days and sprouted 235 days later, that is 411 days after culturing. Most PGR treatments had only small effects (±30 days) on the duration of dormancy and the time of micro-tuber initiation. However, in GA3 micro-tuber initiation occurred after 76 days, about 100 days earlier than in the control, whereas fluridone affected the position of micro-tubers and duration of dormancy. With fluridone treatments, tubers were found at the base of the stem (normal position) and on lower and upper nodes. Lower node tubers sprouted within 225 days of culturing compared with about 420 days after culturing at other nodal positions and in other PGR treatments. These data suggest an important role for ABA and gibberellic acid in yam micro-tuber initiation and the induction of dormancy.

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A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

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A new technique is reported for micro-machining millimetre-wave rectangular waveguide components. S-parameter measurements on these structures show that they achieve lower loss than those produced using any other on-chip fabrication technique, have highly accurate dimensions, are physically robust, and are cheap and easy to manufacture.

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A novel technique for micro-machining millimeter and submillimeter-wave rectangular waveguide components is reported. These are fabricated in two halves which simply snap together, utilizing locating pins and holes, and are physically robust, and cheap, and easy to manufacture. In addition, S-parameter measurements on these structures are reported for the first time and display lower loss than previously reported micro-machined rectangular waveguides.

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The main objective is to develop methods that automatically generate kinematic models for the movements of biological and robotic systems. Two methods for the identification of the kinematics are presented. The first method requires the elimination of the displacement variables that cannot be measured while the second method attempts to estimate the changes in these variables. The methods were tested using a planar two-revolute-joint linkage. Results show that the model parameters obtained agree with the actual parameters to within 5%. Moreover, the methods were applied to model head and neck movements in the sagittal plane. The results indicate that these movements are well modeled by a two-revolute-joint system. A spatial three-revolute-joint model was also discussed and tested.

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The main objective is to generate kinematic models for the head and neck movements. The motivation comes from our study of individuals with quadriplegia and the need to design rehabilitation aiding devices such as robots and teletheses that can be controlled by head-neck movements. It is then necessary to develop mathematical models for the head and neck movements. Two identification methods have been applied to study the kinematics of head-neck movements of able-body as well as neck-injured subjects. In particular, sagittal plane movements are well modeled by a planar two-revolute-joint linkage. In fact, the motion in joint space seems to indicate that sagittal plane movements may be classified as a single DOF motion. Finally, a spatial three-revolute-joint system has been employed to model 3D head-neck movements.

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This paper shows that a wavelet network and a linear term can be advantageously combined for the purpose of non linear system identification. The theoretical foundation of this approach is laid by proving that radial wavelets are orthogonal to linear functions. A constructive procedure for building such nonlinear regression structures, termed linear-wavelet models, is described. For illustration, sim ulation data are used to identify a model for a two-link robotic manipulator. The results show that the introduction of wavelets does improve the prediction ability of a linear model.