The photoelectric servo control system based on PC controllers is mainly used to control the speed and position of the load. This paper analyzed the mathematical modeling and the system identification
ISBN:
(纸本)9781510607729
The photoelectric servo control system based on PC controllers is mainly used to control the speed and position of the load. This paper analyzed the mathematical modeling and the system identification
The upper limb rehabilitation robot of end-traction type, because of complex working environment, it is very easy to collide with other objects and cause accidents. A loop-locked control algorithm for redundant upper ...
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ISBN:
(纸本)9781509043644
The upper limb rehabilitation robot of end-traction type, because of complex working environment, it is very easy to collide with other objects and cause accidents. A loop-locked control algorithm for redundant upper limb rehabilitation robot is proposed in this paper and the algorithm is based on the transformation of master-slave task. It achieves the switch of the task of obstacle avoidance and the task of the desired trajectory tracking by monitoring the minimum distance between the mechanical arm and the obstacle, thus it can solve the conflict between obstacle avoidance and desired trajectory tracking. There is error of task space unavoidably when we get each joint angular through numerical integration, based on this, this article introduces a loop-locked control about the actual and expected locations of end effector in order to overcome these drawbacks. In addition, the algorithm is also effective for many obstacles and dynamic obstacle. Finally, the algorithm is verified by the simulation experiment of planar redundant robot with three degrees of freedom. The results show that the distance between the manipulator arm and the obstacle is always closed to or greater than the threshold, and when mechanical arm is away from the obstacle, the end of the mechanical arm can track the desired trajectory with high accuracy. The algorithm is also effective for many obstacles and dynamic obstacle.
Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected no...
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Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected nodes in social and information networks is referred to as diffusion. In the literature, a number of diffusion models have been proposed for different applications like influential user identification and personalized recommendation. However, comprehensive studies to discover the hidden diffusion mechanisms governing the information diffusion using the data-driven paradigm are still lacking. This thesis research aims to design novel diffusion models with the structural and behaviorable dependency of neighboring nodes for representing social networks, and to develop computational algorithms to infer the diffusion models as well as the underlying diffusion mechanisms based on information cascades observed in real social networks. By incorporating structural dependency and diversity of node neighborhood into a widely used diffusion model called Independent Cascade (IC) Model, we first propose a component-based diffusion model where the influence of parent nodes is exerted via connected components. Instead of estimating the node-based diffusion probabilities as in the IC Model, component-based diffusion probabilities are estimated using an expectation maximization (EM) algorithm derived under a Bayesian framework. Also, a newly derived structural diversity measure namely dynamic effective size is proposed for quantifying the dynamic information redundancy within each parent component. The component-based diffusion model suggests that node connectivity is a good proxy to quantify how a node's activation behavior is affected by its node neighborhood. To model directly the behavioral dependency of node neighborhood, we then propose a co-activation pattern based diffusion model by integrating the latent class model into the IC Model where the co-activation patter
In the age of Big Data, efficient algorithms are in higher demand more than ever before. While Big Data takes us into the asymptotic world envisioned by our pioneers, it also challenges the classical notion of efficie...
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ISBN:
(数字)9781680831313
ISBN:
(纸本)9781680831306
In the age of Big Data, efficient algorithms are in higher demand more than ever before. While Big Data takes us into the asymptotic world envisioned by our pioneers, it also challenges the classical notion of efficient algorithms: algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today"s problems. It is not just desirable but essential that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. Scalable algorithms for Data and Network Analysis surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning. They also include spectral graph-theoretical methods, such as are used for computing electrical flows and sampling from Gaussian Markov random fields. These methods exemplify the fusion of combinatorial, numerical, and statistical thinking in network analysis. Scalable algorithms for Data and Network Analysis illustrates the use of these techniques by a few basic problems that are fundamental in analyzing network data, particularly for the identification of significant nodes and coherent clusters/communities in social and information networks. It also discusses some frameworks beyond graph-theoretical models for studying conceptual questions that arise in network analysis and social influences.
algorithms - Esa '99 : 7Th Annual European Symposium, Prague, Czech Republic, July 16-18, 1999 : Proceedings by Esa '99 (1999 : Prague, Czech Republic); Nešetřil, Jaroslav; published by Berlin ; New York : S...
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algorithms - Esa '99 : 7Th Annual European Symposium, Prague, Czech Republic, July 16-18, 1999 : Proceedings by Esa '99 (1999 : Prague, Czech Republic); Nešetřil, Jaroslav; published by Berlin ; New York : Springer
Algorithm Engineering : 3Rd International Workshop, Wae'99 London, Uk, July 19-21, 1999 : Proceedings by International Workshop on Algorithm Engineering (3Rd : 1999 : London, England); Vitter, Jeffrey Scott, 1955-...
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Algorithm Engineering : 3Rd International Workshop, Wae'99 London, Uk, July 19-21, 1999 : Proceedings by International Workshop on Algorithm Engineering (3Rd : 1999 : London, England); Vitter, Jeffrey Scott, 1955-; Zaroliagis, Christos D., 1963-; published by Berlin ; New York : Springer
algorithms and Data Structures : 6Th International Workshop, Wads'99, Vancouver, Canada, August 11-14, 1999 : Proceedings by Wads'99 (1999 : Vancouver, Canada); Dehne, F. (Frank), 1960-; published by Berlin ; ...
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algorithms and Data Structures : 6Th International Workshop, Wads'99, Vancouver, Canada, August 11-14, 1999 : Proceedings by Wads'99 (1999 : Vancouver, Canada); Dehne, F. (Frank), 1960-; published by Berlin ; New York : Springer
Combinatorial Pattern Matching : 10Th Annual Symposium, Cpm 99, Warwick University, Uk, July 22-24, 1999 : Proceedings by Cpm (Symposium) (10Th : 1999 : Warwick University); Crochemore, Maxime, 1947-; Paterson, Michael S; published by Berlin ; New York ; London : Springer
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