922 resultados para Indiana Dept. of Audit and Control


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提出了一种用于工业机器人时间最优轨迹规划及轨迹控制的新方法,它可以确保在关节位移、速度、加速度以及二阶加速度边界值的约束下,机器人手部沿笛卡尔空间中规定路径运动的时间阳短。在这种方法中,所规划的关节轨迹都采用二次多项式加余弦函数的形式,不仅可以保证各关节运动的位移、速度 、加速度连续而且还可以保证各关节运动的二阶加速度连续。采用这种方法,既可以提高机器人的工作效率又可以延长机器人的工作寿命以PUMA560机器人为对象进行了计算机仿真和机器人实验,结果表明这种方法是正确的有效的。它为工业机器人在非线性运动学约束条件下的时间最优轨迹规划及控制问题提供了一种较好的解决方案。

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A dynamic model and control system of an artificial muscle is presented. The artificial muscle is based on a contractile polymer gel which undergoes abrupt volume changes in response to variations in external conditions. The device uses an acid-base reaction to directly convert chemical to mechanical energy. A nonlinear sliding mode control system is proposed to track desired joint trajectories of a single link controlled by two antagonist muscles. Both the model and controller were implemented and produced acceptable tracking performance at 2Hz.

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Background. the skin neurogenic inflammation is mainly related to Substance P (SP) and Calcitonin Gene-related Peptide (CGRP). There is no data on their availability in the dynamics of skin nerve endings, concerning their release and replenishment after a nociceptive stimulus, so this was investigated. Materials and methods. 25 rats were randomly distributed in 5 groups. the animals of the control group (CG) determined the baseline levels of neuropeptides in the skin. the groups S0 and S30 did not receive any cutaneous stimulus at 30 and 60 minutes, respectively. in the group S1, an incision stimulus was made at 30 minutes. in the group S31, a nociceptive stimulus was performed by subdermal scratching at 30 minutes and, at 60 minutes, the incision stimulus was carried out in the same location (nociceptive hyperstimulation). the skin samples of the other animals were harvested from the back 1 minute after their death. SP, pro-CGRP and CGRP were quantified by Western Blotting. Results. the incision stimulus released SP, S1 compared to S0 (p < 0.05) detected in the first minute, and the replenishment time was more than 30 minutes. Also, it cleaved pro-CGRP, S1 compared to S31 (p < 0.05) in the first minute, and its replenishment time less than 30 minutes. Release of CGRP was not detected. Conclusion. the incision released SP already detected in the first minute; its replenishment time is more than 30 minutes. the incision decreased pro-CGRP, also detected in the first minute; and its replenishment time is less than 30 minutes.

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National Institute of Child Health and Human Development (HD051804)

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This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

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In this paper, a wireless sensor network mote hardware design and implementation are introduced for building deployment application. The core of the mote design is based on the 8 bit AVR microcontroller, Atmega1281 and 2.4 GHz wireless communication chip, CC2420. The module PCB fabrication is using the stackable technology providing powerful configuration capability. Three main layers of size 25 mm2 are structured to form the mote; these are RF, sensor and power layers. The sensors were selected carefully to meet both the building monitoring and design requirements. Beside the sensing capability, actuation and interfacing to external meters/sensors are provided to perform different management control and data recording tasks. Experiments show that the developed mote works effectively in giving stable data acquisition and owns good communication and power performance.

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The behaviour towards electron transfer of self-assembled monolayers of 2-mercaptobenzimidazole (MBI) and 2-mercaptobenzimidazole-5-sulfonate (MBIS) on Au(1 1 1) was examined by cyclic voltammetry. The influence of the monolayers was drastically dependent on the charge of the redox probe used. When [Ru(NH3)6]3+ is used, a post-adsorption peak characteristic of the adsorption of the redox probe is detected only at the MBIS modified electrode. Taking advantage of this difference, ac voltammetry has been used to determine the surface composition when mixed monolayers are formed by immersion of the gold substrate in mixtures of different molar fractions of MBI and MBIS. Results clearly indicate that the ionic strength of the immersion solution plays a key role in the surface composition when a charged surfactant is mixed with non-charged surfactant. © 2006 Elsevier B.V. All rights reserved.

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We develop an analytic framework for the analysis of robustness in social-ecological systems (SESs) over time. We argue that social robustness is affected by the disturbances that communities face and the way they respond to them. Using Ostrom's ontological framework for SESs, we classify the major factors influencing the disturbances and responses faced by five Indiana intentional communities over a 15-year time frame. Our empirical results indicate that operational and collective-choice rules, leadership and entrepreneurship, monitoring and sanctioning, economic values, number of users, and norms/social capital are key variables that need to be at the core of future theoretical work on robustness of self-organized systems. © 2010 by the author(s).

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The molecular networks regulating the G1-S transition in budding yeast and mammals are strikingly similar in network structure. However, many of the individual proteins performing similar network roles appear to have unrelated amino acid sequences, suggesting either extremely rapid sequence evolution, or true polyphyly of proteins carrying out identical network roles. A yeast/mammal comparison suggests that network topology, and its associated dynamic properties, rather than regulatory proteins themselves may be the most important elements conserved through evolution. However, recent deep phylogenetic studies show that fungal and animal lineages are relatively closely related in the opisthokont branch of eukaryotes. The presence in plants of cell cycle regulators such as Rb, E2F and cyclins A and D, that appear lost in yeast, suggests cell cycle control in the last common ancestor of the eukaryotes was implemented with this set of regulatory proteins. Forward genetics in non-opisthokonts, such as plants or their green algal relatives, will provide direct information on cell cycle control in these organisms, and may elucidate the potentially more complex cell cycle control network of the last common eukaryotic ancestor.