Large-scale service composition has become an important research topic in Service-Oriented computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and...
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Large-scale service composition has become an important research topic in Service-Oriented computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.
In the era of big data, data intensive applications have posed new challenges to the field of service composition. How to select the optimal composited service from thousands of functionally equivalent services but di...
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In the era of big data, data intensive applications have posed new challenges to the field of service composition. How to select the optimal composited service from thousands of functionally equivalent services but different Quality of Service(Qo S) attributes has become a hot research in service computing. As a consequence,in this paper, we propose a novel algorithm MR-IDPSO(Map Reduce based on Improved Discrete Particle Swarm Optimization), which makes use of the improved discrete Particle Swarm Optimization(PSO) with the Map Reduce to solve large-scale dynamic service composition. Experiments show that our algorithm outperforms the parallel genetic algorithm in terms of solution quality and is efficient for large-scale dynamic service composition. In addition,the experimental results also demonstrate that the performance of MR-IDPSO becomes more better with increasing number of candidate services.
An important aim in pattern recognition is to cluster the given shapes. This paper presents a shape recognition and retrieval algorithm. The algorithm first extracts the skeletal features using the medial axis transfo...
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An important aim in pattern recognition is to cluster the given shapes. This paper presents a shape recognition and retrieval algorithm. The algorithm first extracts the skeletal features using the medial axis transform. Then, the features are transformed into a string of symbols with the similarity among those symbols computed based on the edit distance. Finally, the shapes are identified using dynamic programming. Two public datasets are analyzed to demonstrate that the present approach is better than previous approaches.
With the advancement of industrial automation, there is an increasing focus on research concerning limited fault samples. Although meta-learning and other methods can address this issue, they often necessitate the inc...
With the advancement of industrial automation, there is an increasing focus on research concerning limited fault samples. Although meta-learning and other methods can address this issue, they often necessitate the incorporation of additional data and are unable to directly diagnose faults using only unlabeled data along with a small amount of labeled data. In response, this article proposes the use of simplicial complexes graph convolutional networks for fault diagnosis, which simultaneously account for both higher-order and lower-order topological structures among samples. This approach effectively addresses the challenge of limited samples by extracting relevant information from unlabeled data without the need to introduce new knowledge. Initially, simplices of varying dimensions are employed within a constructed simple graph to represent different relationships among samples. Subsequently, the simplicial complexes convolutional network is introduced to capture the higher-order information, while the graph convolutional network is utilized to obtain the lower-order information. The combined feature information is then input into a classifier for fault diagnosis. Finally, experiments conducted on two datasets characterized by small sample sizes or imbalanced samples demonstrate the method’s commendable diagnostic performance, as well as its robustness and practicality.
To simplify the design process of coding matrices for data forwarding in two-way wireless relay networks with multiple relay nodes, a distributed cooperative transmission scheme based on cyclic delay diversity (CDD) a...
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To simplify the design process of coding matrices for data forwarding in two-way wireless relay networks with multiple relay nodes, a distributed cooperative transmission scheme based on cyclic delay diversity (CDD) and conventional space-time codes is proposed in this paper. All relay nodes are divided into several groups firstly. In each group, the relay nodes adopt CDD ways to amplify-and-forward (AF) their received signals and thereby obtain the potential diversity gains. The forwarded signals among different groups are encoded into the form of one full-rate space-time code, which can solve the problem on limited error correction ability in conventional relay schemes only using CDD ways and then further reduce the bit error rate (BER) of the distributed wireless system. Consequently, the design process for two-way relay transmissions is simplified and this method can be extended to other complicated cases. Simulation results verify that the proposed scheme is able to achieve high diversity gains and good bit error performance.
Spectral efficiency and transmission reliability are among the main design considerations of modern wireless communication systems. In order to solve the problems of high decoding complexity, low spectrum utilization ...
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The U1 matrix and extreme U1 matrix were successfully used to study quadratic doubly stochastic operators by R. Ganikhodzhaev and F. Shahidi [Linear Algebra Appl., 2010, 432： 24-35], where a necessary condition for a...
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The U1 matrix and extreme U1 matrix were successfully used to study quadratic doubly stochastic operators by R. Ganikhodzhaev and F. Shahidi [Linear Algebra Appl., 2010, 432： 24-35], where a necessary condition for a U1 matrix to be extreme was given. S. Yang and C. Xu [Linear Algebra Appl., 2013, 438： 3905-3912] gave a necessary and sufficient condition for a symmetric nonnegative matrix to be an extreme U1 matrix and investigated the structure of extreme U1 matrices. In this paper, we count the number of the permutation equivalence classes of the n × n extreme U1 matrices and characterize the structure of the quadratic stochastic operators and the quadratic doubly stochastic operators.
A novel watermarking sharing system having the ability of sharing gray-level secret images with multi-user is proposed. Multiple-based number system is used to split the secret into n meaningless shares, each share is...
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We present a novel algorithm for point pattern matching by means of spectra of directed graphs. Given a feature point-set, we construct a weighted directed graph and skew-symmetric matrix associated with the graph. By...
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We present a novel algorithm for point pattern matching by means of spectra of directed graphs. Given a feature point-set, we construct a weighted directed graph and skew-symmetric matrix associated with the graph. By using spectral decomposition of the matrix, we give a spectral representation of the feature points with half of the eigenvectors. We theoretically analyze that our method can well deal with the matching problem under affine transformation. The expreiments applied to synthetic data and real-world images show the effectiveness of our method.
A novel scheme is developed to compute correctly the induced current based on the Electric field integral equation (EFIE) by the Method of moments (MOM) at resonant frequencies. As a First step, the inaccurate induced...
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A novel scheme is developed to compute correctly the induced current based on the Electric field integral equation (EFIE) by the Method of moments (MOM) at resonant frequencies. As a First step, the inaccurate induced current is obtained by solving the EFIE. Then, the scattered magnetic field due to the inaccurate induced current is calculated at any given point on the surface of the object. Finally, the accurate induced current at any given point is determined through the total magnetic field. The proposed approach is applied to the case of the infinitely Perfectly electric conducting (PEC) cylinder and PEC sphere to check its accuracy and efficiency. It is found that the numerical results match the analytical solution or the Combined field integral equation (CFIE) solution.
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