This article proposes a new type of suspension for lunar rover. The suspension is mainly constructed by a positive quadrilateral levers mechanism and a negative quadrilateral levers mechanism. The suspension is design...
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This article proposes a new type of suspension for lunar rover. The suspension is mainly constructed by a positive quadrilateral levers mechanism and a negative quadrilateral levers mechanism. The suspension is designed based on following factors: Climbing up obstacles, adapting terrain, traveling smoothly, and distributing equally the load of cab to wheels. In the article, firstly the structure of the new suspension is described, secondly the kinematics of the levers is analyzed, and the relational equations of the suspension levers are established, so the distortion capability of the suspension is known. In order to test the capability of suspension, we design a prototype rover with the new suspension and take a test of climbing obstacles, and the result indicates that the prototype rover with new type of suspension has excellent capability to climb up obstacles with keeping cab smooth. Based on the shortcoming found in test, we optimize the levers mechanism, and then establish the rover models with the new type of suspension and with Rocker-Bogie suspension based on ADAMS, and then the capability compare on simulation is followed. The further researching work for this new developed suspension is being carried out now so as to improve its overall performances. China has been determined to carry out the lunar exploration project in the near future. The proposed new type of suspension would provide a valuable technical support to it.
Humans can generate accurate and appropriate motor commands in various and even uncertain environments. MOSAIC (MOdular Sellection And Identification for control) was formerly proposed for describing such human abilit...
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Humans can generate accurate and appropriate motor commands in various and even uncertain environments. MOSAIC (MOdular Sellection And Identification for control) was formerly proposed for describing such human ability, but it includes some complex and heuristic procedures which make the model's understandability hard. In this article, we present an alternative and probabilistic model of MOSAIC (p-MOSAIC) as a mixture of normal distributions, and an online EM-based learning method for its predictors and controllers. Theoretical consideration shows that the learning rule of p-MOSAIC corresponds to that of MOSAIC except for some points mostly related to the controller learning. Experimental studies using synthetic datasets have shown some practical advantages of p-MOSAIC. One is that the learning rule of p-MOSAIC makes the estimation of 'responsibility' stable. Another is that p-MOSAIC realizes accurate control and robust parameter learning in comparison to the original MOSAIC especially in noisy environments, due to the direct incorporation of the noise into the model
We introduce a genetic model based on stochastic crossover to solve the Hamiltonian cycle problem (DHCP) for random digraphs containing a random Hamiltonian cycle. The genetic model represents a new decision computati...
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We introduce a genetic model based on stochastic crossover to solve the Hamiltonian cycle problem (DHCP) for random digraphs containing a random Hamiltonian cycle. The genetic model represents a new decision computational method inspired by the remark that DHCP can be formulated as determining the compatibility of a quadratic system over the finite field GF(2). A (simple) genetic algorithm based on the stochastic crossover is experimentally compared with a randomized algorithm based on the Angulin and Valiant classic technique designed to find Hamiltonian cycles in random digraphs
In multi-objective optimization not only fast convergence is important, but it is also necessary to keep enough diversity so that the whole Pareto-optimal front can be found. In this work four variants of differential...
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In multi-objective optimization not only fast convergence is important, but it is also necessary to keep enough diversity so that the whole Pareto-optimal front can be found. In this work four variants of differential evolution are examined that differ in the selection scheme and in the assignment of crowding distance. The assumption is checked that the variants differ in convergence speed and amount of diversity. The performance is shown for 1000 consecutive generations, so that different behavior over time can be detected
This study presents a functional-link-based fuzzy neural network (FLFNN) structure for temperature control. The proposed FLFNN controller uses functional link neural networks (FLNN) that can generate a nonlinear combi...
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This study presents a functional-link-based fuzzy neural network (FLFNN) structure for temperature control. The proposed FLFNN controller uses functional link neural networks (FLNN) that can generate a nonlinear combination of the input variables as the consequent part of the fuzzy rules. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Simulation result of temperature control has been given to illustrate the performance and effectiveness of the proposed model
In this paper we consider the problem of analyzing streaming documents, in particular streaming news stories. The system is designed to extract statistics from the document, incorporate these into a graph-based model,...
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In this paper we consider the problem of analyzing streaming documents, in particular streaming news stories. The system is designed to extract statistics from the document, incorporate these into a graph-based model, and discard the document to reduce storage requirements. The model is defined in terms of a changing lexicon and sub-lexicons at each node in the graph, with the nodes of the graph representing topics. An approximation to the TFIDF term weighting is introduced. We illustrate the methodology on a dataset of news articles, and discuss the dynamic nature of the model
Automated planning is a combinatorial problem that is important to many NASA endeavors, including ground operations and control applications for unmanned and manned space flight. There is significant value to integrat...
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Automated planning is a combinatorial problem that is important to many NASA endeavors, including ground operations and control applications for unmanned and manned space flight. There is significant value to integrating planning and data mining to create better planners. We describe current work in this area, covering uses of data mining to speed up planners, improve the quality of plans returned by planners, and learn domain models for automated planners. The central contribution of this paper is a snap shot of the state of the art in integrating these technologies and a summary of challenges and open research issues
Intelligent planning algorithms such as the Partially Observable Markov Decision Process (POMDP) have succeeded in dialog management applications [10, 11, 12] because they are robust to the inherent uncertainty of hum...
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ISBN:
(纸本)1595936173
Intelligent planning algorithms such as the Partially Observable Markov Decision Process (POMDP) have succeeded in dialog management applications [10, 11, 12] because they are robust to the inherent uncertainty of human interaction. Like all dialog planning systems, however, POMDPs require an accurate model of the user (e.g., what the user might say or want). POMDPs are generally specied using a large probabilistic model with many parameters. These parameters are difficult to specify from domain knowledge, and gathering enough data to estimate the parameters accurately a priori is expensive. In this paper, we take a Bayesian approach to learning the user model simultaneously with dialog manager policy. At the heart of our approach is an efficient incremental update algorithm that allows the dialog manager to replan just long enough to improve the current dialog policy given data from recent interactions. The update process has a relatively small computational cost, preventing long delays in the interaction. We are able to demonstrate a robust dialog manager that learns from interaction data, out-performing a hand-coded model in simulation and in a robotic wheelchair application. Copyright 2007 ACM.
The emerging paradigm of grid computing provides a powerful platform for the optimisation of complex computer models, such as those used to simulate real-world logistics and supply chain operations. This paper introdu...
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The emerging paradigm of grid computing provides a powerful platform for the optimisation of complex computer models, such as those used to simulate real-world logistics and supply chain operations. This paper introduces a grid-based optimisation framework that provides a powerful tool for the optimisation of such computationally intensive objective functions. This framework is then used in the optimisation of maintenance scheduling strategies for fleets of aero-engines, a computationally intensive problem with a high-degree of stochastic noise
This paper presents a study regarding initial off-line tuning for the constant parameters of a self-tuning controller. The main characteristic of a self-tuning controller is the on-line adaptation of their parameters ...
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This paper presents a study regarding initial off-line tuning for the constant parameters of a self-tuning controller. The main characteristic of a self-tuning controller is the on-line adaptation of their parameters values to the process changes. However some parameters must be imposed off-line, their values remaining unchanged. Several study cases regarding choosing proper values for two of these initialization parameters - forgetting factor (parameter of the recursive least square parameters estimator) and control penalty factor (parameter of control law), particularized to the self-tuning excitation control of a synchronous generator are presented.
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