This paper discusses new optimal path planning algorithms based on a Gravitational Search Algorithm (GSA) and on a Particle Swarm Optimization (PSO) algorithm applied to multiple mobile robots on holonomic wheeled pla...
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ISBN:
(纸本)9781467355094
This paper discusses new optimal path planning algorithms based on a Gravitational Search Algorithm (GSA) and on a Particle Swarm Optimization (PSO) algorithm applied to multiple mobile robots on holonomic wheeled platforms. Four path planning objectives are aggregated in the definition of a set of separate optimization problems for each robot. Each optimization problem involves a single scalar objective function (o.f.) expressed as the weighted sum of four o.f.s. The GSA and the PSO algorithm solve the optimization problems aiming the minimization of the o.f.s, and the path planning algorithms transform the solutions to the optimization problems into optimal solutions as optimal paths. The GSA-and PSO-based path planning algorithms are validated by several experiments with multiple robots that participate in missions.
This paper proposes a data-driven algorithm that solves an optimal control problem by iteratively tuning the controller. The data-driven algorithm solves the optimization problem for a nonlinear process with a linear ...
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ISBN:
(纸本)9781479902255
This paper proposes a data-driven algorithm that solves an optimal control problem by iteratively tuning the controller. The data-driven algorithm solves the optimization problem for a nonlinear process with a linear controller, accounting for operational constraints and employing an interior-point barrier (IPB) algorithm. The search process in the IPB algorithm requires first-order information which is generated using identified models via neural networks in order to reduce the number of experiments. A case study which deals with the angular position control of a nonlinear aerodynamic system is included to validate the new algorithm by simulation results.
We present the Extended Minimum Estimated Expected Delay (EMEED) protocol. EMEED is designed for use in wireless Delay Tolerant Networks (DTNs) that consist of a large number of highly mobile nodes with non-uniform mo...
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We present the Extended Minimum Estimated Expected Delay (EMEED) protocol. EMEED is designed for use in wireless Delay Tolerant Networks (DTNs) that consist of a large number of highly mobile nodes with non-uniform mobility patterns. Under the EMEED protocol, any two nodes that are often in contact, either directly or through a multihop path, disseminate in the network the expected time they have to wait until they come into contact. Nodes route packets according to routing tables created using these expected times. When its main parameter, the contact radius, is equal to unity, the EMEED protocol operates similarly to the well known MEED protocol. However, using simulations, we show that for many mobility scenarios, when the contact radius is greater than unity, the EMEED protocol performs far better than MEED, in terms of throughput and delay, with only a modest increase in the control overhead.
Efficient design of wireless networks is a challenging task. Recently, the concept of cross-layer design in wireless networks has been investigated extensively. In this work, we present a cross-layer optimization fram...
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In this paper, a novel miniaturized UHF Radio-Frequency Identification (RFID) tag dipole antenna is proposed. Its original structure consists on a series of parallel arms connected to a perpendicular line at their end...
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This paper introduces a simulation and evaluation of guidance, navigation, and control algorithms applied to an autonomous hovercraft. A line-of-sight guidance law is adopted in conjunction with a neural network based...
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作者：
Wang, Yingxu
Schulich School of Engineering University of Calgary 2500 University Drive NW Calgary AB T2N 1N4 Canada Laboratory for Cognitive Informatics and Cognitive Computing
Dept. of Electrical and Computer Engineering Schulich School of Engineering 2500 University Drive NW Calgary AB T2N 1N4 Canada
A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is generated by physiological organs and what the logical model of the brain is beyond its neu ral architectures. Ac...
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ISBN:
(纸本)9781479907816
A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is generated by physiological organs and what the logical model of the brain is beyond its neu ral architectures. According to cognitive infonnatics and abstract in telligence, the exploration of the brain is a complicated recursive problem where contemporary denotational mathematics is needed to efficiently deal with it. Cognitive psychology and medical science were used to explain that the brain works in a ce rtain way based on empirical observations on related activ ities in usually overlapped brain areas. However, the lack of precise models and rigorous causality in brain studies has dissatisfied the formal expectations of researchers in computational intelligence and mathematics, because a computer, the logical counterpart of the brain, might not be explained in such a vague and empirical approach without the support of formal models and rigorous means. In order to fonnally explain the archi tectures and functions of the brain, as well as their intricate relations and interactions, systematic models of the brain are sought for revealing the principles and mechanisms of the brain at the neural, physiological, cognitive, and logical (abstract) levels. Cognitive and brain informatics investigate into the brain via not only inductive syntheses through these four cognitive levels from the bottom up in order to form theories based on empirical observations, but also deductive analyses from the top down in order to explain various functional and behavioral instances according to the abstract intelligence theory. This keynote lecture presents systematic models of th ebrain from the facets of cognitive informatics, abstract intelligence, brain informatics, neuroinformatics, and cognitive psychology. A logical model oft he brain is introduced that maps the cognitive functions of the brain onto its neural and physiological architectures. This work leads to a coherent abstr
Currently available techniques for surface reconstruction from multi-view images require a large number of images in order to assure high surface resolution and precise detail reconstruction. Obtaining such big number...
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Currently available techniques for surface reconstruction from multi-view images require a large number of images in order to assure high surface resolution and precise detail reconstruction. Obtaining such big number of images in some cases is impossible; moreover, it may impose high preparation cost. Therefore, to address this drawback, a novel method is presented. This method is based on the visual hull approach. It assumes to be able to reconstruct three dimensional manifolds without edge from images, the number of necessary images should be as small as possible, and the quality of reconstructed mesh should be good. Applying this method for four images from a dino dataset proved the proposed method works well.
In this paper we address the problem of balancing the processing load of MapReduce tasks running on heterogeneous clusters, i.e., clusters composed of nodes with different capacities and update cycles. We present a fu...
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ISBN:
(纸本)9781467357159
In this paper we address the problem of balancing the processing load of MapReduce tasks running on heterogeneous clusters, i.e., clusters composed of nodes with different capacities and update cycles. We present a fully decentralized algorithm, based on ratio consensus, where each mapper decides the amount of workload data to handle for a single user job using only job specific local information, i.e., information that can be collected from directly connected neighboring mappers, regarding their current workload usage and capacity. In contrast to other algorithms in the literature, the proposed algorithm can be deployed in heterogeneous clusters and can operate asynchronously in both directed and undirected communication topologies. The performance of the proposed algorithm is demonstrated via simulation experiments on large-scale strongly connected topologies.
Biomarker discovery and classification in medical applications both typically involve feature selection applied to a small-sample high-dimensional dataset. Recent work has proposed a framework to integrate a prior ove...
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Biomarker discovery and classification in medical applications both typically involve feature selection applied to a small-sample high-dimensional dataset. Recent work has proposed a framework to integrate a prior over an uncertainty class of parameterized feature-label distributions with training data to obtain optimal classifiers, MMSE classifier error estimates, and evaluate the MSE of error estimates. However, feature selection has not been investigated rigorously in this paradigm. In the present work, we begin to address optimal feature selection in a Bayesian framework via a sparsity inducing prior that assumes the number of “good” features is small. From modeling assumptions and this prior we derive expressions for the sample-conditioned probability mass over good feature sets. It thus becomes possible to find feature sets that are optimal relative to maximal posterior probability. Furthermore, one may provide this probability along with a given feature set, and thereby evaluate the validity and reliability of the results.
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