In recent years, increased interest in parallel robots has been observed. Their control with modern theory, such as the computed-torque method, has, however been restrained, essentially due to the difficulty in establ...
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In recent years, increased interest in parallel robots has been observed. Their control with modern theory, such as the computed-torque method, has, however been restrained, essentially due to the difficulty in establishing a simple dynamic model that can be calculated in real time. In this paper;a simple method based on the virtual work principle is proposed for modeling parallel robots. The mass matrix of the robot needed for decoupling control strategies, does not explicitly appear in the formulation;however it can be computed separately, based on kinetic energy considerations. The method is applied to the DELTA parallel robot, leading to a very efficient model that has been implemented in a real-time computed-torque control algorithm.
The objective of this study is to develop playability heuristics based on a lexical analysis of nouns and adjectives frequently used in online game reviews. Built on a previous lexical analysis of adjectives in online...
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The objective of this study is to develop playability heuristics based on a lexical analysis of nouns and adjectives frequently used in online game reviews. Built on a previous lexical analysis of adjectives in online game reviews, it is argued that nouns together with adjectives will likely provide more contextual information than adjectives alone, and therefore the patterns among these words can be used to develop playability heuristics. A revised lexical approach is adopted to analyze nouns and adjectives from 821,122 online reviews. Ninety seven factors emerge from this analysis. Based on the nouns and adjectives highly loaded on these factors, a new process is introduced and 90 playability heuristics are derived. This study significantly expands the current pool of playability heuristics that facilitate the computer game design process. The lexical method adopted in this study demonstrates its effectiveness in developing interface design guidelines based on a large number of online reviews on a system or product. It can be extended to other fields that are human-behavior centered.
Due to the decoding complexity of network coding, there have been concerns on adopting network coding in the practical P2P systems. To provide rapid decoding speed in practical network coding systems, various multi-th...
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Due to the decoding complexity of network coding, there have been concerns on adopting network coding in the practical P2P systems. To provide rapid decoding speed in practical network coding systems, various multi-threaded approaches which successfully exploit hardware supported TLP have been proposed. Among those parallel approaches, a dynamic partitioning method is known to be the best solution so far. However, the algorithm dynamically changes workload distribution and inherently contains some limits to utilize the SIMD instruction set which are designed to work on a fixed size of data. In this paper, we present a new data manipulation method to utilize SIMD instruction sets, which can be successfully integrated into the dynamic partitioning of thread-level workload distribution. With exploiting both SIMD and thread-level parallelism, we achieve the speed-up of 10.86 using eight running threads compared to the serial algorithm. (C) 2012 Elsevier Ltd. All rights reserved.
For a large number of degrees of freedom and/or large dimension systems, non-linear model based predictive control algorithms based on dual mode control can become intractable. This paper proposes an alternative which...
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For a large number of degrees of freedom and/or large dimension systems, non-linear model based predictive control algorithms based on dual mode control can become intractable. This paper proposes an alternative which deploys the closed-loop paradigm that has proved to be very effective for the case of linear time-varying or uncertain systems. The various attributes and computational advantages of the approach are shown to carry over to the non-linear case.
A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope...
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A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method. (C) 2009 Elsevier B.V. All rights reserved.
Standard pattern-matching methods used for deep packet inspection and network security can be evaded by means of TCP and IP fragmentation. To detect such attacks, intrusion detection systems must reassemble packets be...
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Standard pattern-matching methods used for deep packet inspection and network security can be evaded by means of TCP and IP fragmentation. To detect such attacks, intrusion detection systems must reassemble packets before applying matching algorithms, thus requiring a large amount of memory and time to respond to the threat. In the literature, only a few efforts proposed a method to detect evasion attacks at high speed without reassembly. The aim of this article is to introduce an efficient system for anti-evasion that can be implemented in real devices. It is based on counting Bloom filters and exploits their capabilities to quickly update the string set and deal with partial signatures. In this way, the detection of attacks and almost all of the traffic processing is performed in the fast data path, thus improving the scalability of intrusion detection systems.
The operational reliability of the space manipulator is closely related to the control method. However the existing control methods seldom consider the operational reliability from the system level. A method to constr...
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The operational reliability of the space manipulator is closely related to the control method. However the existing control methods seldom consider the operational reliability from the system level. A method to construct the operational reliability system control model based on particle filter for the space manipulator is presented in this paper. Firstly, the definition of operational reliability and the degree of operational reliability are given and the state space equations of the control system are established as well. Secondly, based on the particle filter algorithm, a method to estimate the distribution of the end position error and calculate the degree of operational reliability with any form of noise distribution in real time is established. Furthermore, a performance model based on quality loss theory is built and a performance function is obtained to evaluate the quality of the control process. The adjustment value of the end position of the space manipulator can be calculated by using the performance function. Finally, a large number of simulation results show that the control method proposed in this paper can improve the task success rate effectively compared to the simulation results using traditional control methods and control methods based on Bayesian estimation.
This paper considers the machine load balancing game with uniformly related machines. Players choose machines of different speeds to run their jobs striving to minimize job's delay, i.e., the job completion time o...
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This paper considers the machine load balancing game with uniformly related machines. Players choose machines of different speeds to run their jobs striving to minimize job's delay, i.e., the job completion time on a chosen machine. The social cost is the maximum delay over all machines. In the general case and the special case of 3 machines, we obtain upper estimates for the price of anarchy (PoA) and demonstrate when they coincide with the exact values. Moreover, sufficient conditions for PoA increase are established under new machine inclusion into the system. And finally, we propose a computing algorithm of the exact PoA value in the three-machine model.
Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of ...
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Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.
As is well known, traditional spectral clustering (SC) methods are developed based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the...
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As is well known, traditional spectral clustering (SC) methods are developed based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. But, for some high-dimensional and sparse data, such an assumption might be invalid. Consequently, the clustering performance of SC will be degraded sharply in this case. To solve this problem, in this paper, we propose a general spectral embedded framework, which embeds the true cluster assignment matrix for high-dimensional data into a nonlinear space by a predefined embedding function. Based on this framework, several algorithms are presented by using different embedding functions, which aim at learning the final cluster assignment matrix and a transformation into a low dimensionality space simultaneously. More importantly, the proposed method can naturally handle the out-of-sample extension problem. The experimental results on benchmark datasets demonstrate that the proposed method significantly outperforms existing clustering methods.
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