Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are difficult to solve by traditional methods. Particle swarm optimization has shown an effective perfo...
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Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are difficult to solve by traditional methods. Particle swarm optimization has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its performance, this paper proposes an improved particle swarm algorithm with dynamically changing velocity(DCV). Evolution speed and agglomeration degree coefficient are introduced into DCV to achieve a trade-off between exploration and exploitation abilities. The worst particles are recorded to make particles stay away from the best position in the evolution process. The velocity is updated according the position of the global best position, the worst position, particles previous best position, evolution speed and degree of agglomeration coefficient at each iteration. In order to verify the validity of the proposed algorithm in this paper, several typical functions are employed for testing, the results show that the algorithm proposed in this paper obtains a more promising performance than several other algorithms.
Nowadays parallel manipulators are used widely in bioengineering applications;this leads to many exciting expectations as well as challenges. The kinematic analysis of parallel manipulators with their differential kin...
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The large scale emergence of cloud platforms induce the tendency to virtualize application workloads that traditionally ran on physical machines. At the same time, cloud providers advertise unlimited resources availab...
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The large scale emergence of cloud platforms induce the tendency to virtualize application workloads that traditionally ran on physical machines. At the same time, cloud providers advertise unlimited resources available to the customers at any time for a fixed price. These factors create the opportunity for customers to easily scale up and down the infrastructure depending on the real time requirements, reducing the overall costs for providing the service. Cloud platforms today provide a threshold trigger mechanism that can trigger provisioning or de-provisioning of additional resources. This paper argues that the threshold approach is not enough for some real life application scaling requirements and introduces a predictive mechanism that allows accurate and proactive provisioning of workloads. The prediction algorithm is based on the observation that for some applications a usage pattern exists, and this usage pattern is repetitive. This paper presents the usage pattern identified in a large scale travel booking application and the execution of the algorithm on this data. The algorithm tested using IBM CloudBurst 2.1 deployment using a benchmark application and results are discussed.
The paper proposes the design of an integrated vehicle control system for in-wheel electric vehicle, which is able to track road geometry with a predefined reference velocity. In the design the lateral and longitudina...
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ISBN:
(纸本)9789633131862
The paper proposes the design of an integrated vehicle control system for in-wheel electric vehicle, which is able to track road geometry with a predefined reference velocity. In the design the lateral and longitudinal dynamics are combined using the in-wheel motors and the steering system. The design methodology of the hierarchical control is proposed. The required control signals are calculated by applying high-level controllers, which are designed using a robust control method. For the control design the model is augmented with weighting functions specified by the performance demands. The actuators generating the necessary control signals in order to achieve the requirements for which low-level tracking controllers are designed.
This work presents an extension of a design procedure for dynamic output feedback design for systems with nonlinearities satisfying quadratic constraints. In this work we used an axial gas compressor model described b...
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This work presents an extension of a design procedure for dynamic output feedback design for systems with nonlinearities satisfying quadratic constraints. In this work we used an axial gas compressor model described by the 3-state Moore-Greitzer compressor model (MG) that has some challenges for output feedback control design (Planovsky and Nikolaev 1990), (Rubanova 2013). The more general constraints for the investigation of the robustness with respect to parametric uncertainties and measurement noise are shown.
Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities...
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Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.
As ever more data is collected in the business processes of large enterprises, the decision makers need new business intelligence tools. Enterprise reporting tools have been available for some time, however, while rep...
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As ever more data is collected in the business processes of large enterprises, the decision makers need new business intelligence tools. Enterprise reporting tools have been available for some time, however, while reports aggregate business data, the large number of reports can become unmanageable. The European funded project Questor aims to create a revolutionary product that will eliminate the complexities inherent in the report management workflow. The final purpose is to make querying the report database as simple as addressing a question in natural language. This paper gives an overview over the concept of the Questor project, its software architecture, algorithms and results.
This work presents a complete navigation architecture for an autonomous aerial robot. The proposed scheme consist of: i) a low-level controller for establishing the attitude and position of the vehicle, ii) a Simultan...
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This work presents a complete navigation architecture for an autonomous aerial robot. The proposed scheme consist of: i) a low-level controller for establishing the attitude and position of the vehicle, ii) a Simultaneous Localization and Mapping (SLAM) system, based in bearing (angular) measurements, which gives the robot the ability for navigating in unknown environments, and iii) a high-level motion control system which generates online trajectories. The high-level motion control system (MCS), which represents the main contribution of this work, is inspired by the behavior-based control strategies. The MCS takes as input a very high level mission target (e.g. “explore as much as you can”) and generates online trajectories according to the mission, but at the same time minimizing uncertainty in the estimations in order to maintain the integrity of the robot. The proposed architecture is explained for simplified 3DOF dynamics, but it could be extended in a straightforward manner in order to be applied to full dynamics. Several simulations are included in order to show the performance of the proposed scheme.
In this paper, the state estimation problem for continuous-time linear systems with two types of sampling is considered. First, the optimal state estimator under periodic sampling is presented. Then the state estimato...
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In this paper, the state estimation problem for continuous-time linear systems with two types of sampling is considered. First, the optimal state estimator under periodic sampling is presented. Then the state estimator with event-based updates is designed, i.e., when an event occurs the estimator is updated linearly by using the measurement of output, while between the consecutive event times the estimator is updated by minimum mean-squared error criteria. The average estimation errors under both sampling schemes are compared quantitatively for first and second order systems, respectively. A numerical example is given to compare the effectiveness of two state estimators.
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