18262600
Parameters optimization of support vector regression based on immune particle swarm optimization algorithm
A novel Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Leave-One-Out Cross-Validation (LOOCV) standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sinx function with additive noise and spectra dataset, simulation results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.
35751769
CNP: A Protocol for Reducing Maintenance Cost of Structured P2P
With highly dynamic, structured P2P system needs very high maintenance cost. In this paper we propose a Clone Node Protocol to reduce the maintenance cost of structured P2P system by a mechanism of clone nodes. In order to verify the efficiency of CNP, we achieve a Clone Node Chord structure based on CNP, i.e. CNChord. Furthermore, we implement a bidirectional CNChord (BCNChord) in order to reduce query time of CNChord. Theoretical analysis and experimental results show that CNChord can greatly reduce the cost of maintaining P2P structure and BCNChord can effectively improve the query speed. In a word, CNP can effectively reduce the maintenance cost of structured P2P.
29802848
Improved Approximate Detection of Duplicates for Data Streams Over Sliding Windows
Detecting duplicates in data streams is an important problem that has a wide range of applications. In general, precisely detecting duplicates in an unbounded data stream is not feasible in most streaming scenarios, and, on the other hand, the elements in data streams are always time sensitive. These make it particular significant approximately detecting duplicates among newly arrived elements of a data stream within a fixed time frame. In this paper, we present a novel data structure, Decaying Bloom Filter (DBF), as an extension of the Counting Bloom Filter, that effectively removes stale elements as new elements continuously arrive over sliding windows. On the DBF basis we present an efficient algorithm to approximately detect duplicates over sliding windows. Our algorithm may produce false positive errors, but not false negative errors as in many previous results. We analyze the time complexity and detection accuracy, and give a tight upper bound of false positive rate. For a given space G bits and sliding window size W, our algorithm has an amortized time complexity of $ O + (\sqrt{G/W}) $. Both analytical and experimental results on synthetic data demonstrate that our algorithm is superior in both execution time and detection accuracy to the previous results.
15173958
Reanimating real humans: automatic reconstruction of animated faces from range data
Advances in 3D scanning technology have enabled automatic capture of complex 3D models such as human faces with highly detailed surfaces. However, the range data cannot be used easily for animatable face modeling due to the absence of functional animation structure and dense surface data. The paper presents an automatic facial model adaptation algorithm for reconstruction of animatable individualized 3D facial models from range data. A generic model that represents both the face shape and anatomical structure serves as the starting point for the adaptation algorithm. The global adaptation transforms the generic model to align it with the scan data in the 3D space based on measurements between a set of 3D landmarks. The local adaptation then deforms the skin mesh of the generic model to fit all of its vertices to the scan surface. The underlying muscle structure is automatically adapted and facial texture is transferred. The reconstructed 3D face resembles the shape and color of a real individual and can be animated immediately with muscle parameters.
39739560
Optimal Motion Generation of Flexible Macro-micro Manipulator Systems Using Estimation of Distribution Algorithm
In this paper, a new approach for motion generation and optimization of the flexible macro-micro manipulator system is proposed based on Estimation of Distribution Algorithm (EDA). The macro-micro manipulator system is a redundant system, of which inverse kinematics remains challenging, with no generic solution to date. Here, the manipulator system configurations, or the optimal joint motions, are generated using the EDA algorithm base on Gaussian probability model. Compared with simple genetic algorithms (SGA), this approach uses fewer parameters and the time for motion optimization is remarkably reduced. The proposed approach shows excellent performance on motion generation and optimization of a flexible macro-micro manipulator system, as demonstrated by the simulation results.
45332140
Semantic Web Development for Traditional Chinese Medicine
The present invention relates to an improved tool grinding mechanism for grinding single point diamond cutting tools to precise roundness and radius specifications. The present invention utilizes a tool holder which is longitudinally displaced with respect to the remainder of the grinding system due to contact of the tool with the grinding surface with this displacement being monitored so that any variation in the grinding of the cutting surface such as caused by crystal orientation or tool thickness may be compensated for during the grinding operation to assure the attainment of the desired cutting tool face specifications.
1781908
An EM-Based Time-Domain Channel Estimation Algorithm Using a priori Information
Based on the expectation-maximization (EM) algorithm, an iterative time-domain channel estimation approach capable of using a priori information is proposed for orthogonal frequency division multiplexing (OFDM) systems in this letter: it outperforms its noniterative counterpart in terms of estimation accuracy as well as bit error rate (BER) performance. Numerical simulations demonstrate that an SNR gain of 1 dB at BER = 10 -4 with only one iteration and estimation mean square error (MSE) which nearly coincides with the Cramer-Rao bound (CRB) in the low SNR region can be obtained, thanks to the efficient use of a priori information.
17190379
Real-Coded Quantum Evolutionary Algorithm Based on Immune Theory for Multi-modal Optimization Problems
A novel real-coded immune quantum evolutionary algorithm for multi-modal function optimization (MRIQEA) is proposed. By niching methods population is divided into subpopulations automatically, local search is carried by the immune mechanism, each subpopulation can obtain precise solutions, and then the population can maintain all optimal solutions. Because of the quantum evolutionary algorithm with intrinsic parallelism it can maintain quite nicely the population diversity than the classical evolutionary algorithm, because of the adaptive immune operator and real representation for the chromosome it can converge to all optimal solutions rapidly. The technique for improving the performance of MRIQEA has been described and its superiority is shown by some simulation experiments in this paper.