To improve the time and space efficiency of Frequent Pattern (FP) mining algorithm over uncertain dataset, the Uncertain Frequent Pattern Mining based on Max Probability (UFPM-MP) algorithm was proposed. First, the expected support number was estimated using maximum probability of the transaction itemset. Second, by comparing this expected support number to the minimum expected support number threshold, the candidate frequent itemsets were identified. Finally, the corresponding sub-trees were built for recursively mining frequent patterns. The UFPM-MP algorithm was tested on 6 classical datasets against the state-of-the-art algorithm AT (Array based tail node Tree structure)-Mine with positive results (about 30% improvement for sparse datasets, and 3-4 times more efficient for dense datasets). The expected support number estimation strategy effectively reduces the number of sub-trees and items of header table, and improves the algorithm's time and space efficiency; and when the minimum expected support threshold is low or there are lots of potential frequent patterns, time efficiency of the proposed algorithm performs more remarkably.
To solve the problems of Web service composition and verification, a formal model was proposed based on the framework of category theory. Process Algebra was introduced into the framework to describe the external behavior of service component, establishing a formal semantic model for the architecture of Web service system. The service network was described with category diagrams, in which Web services were used as categorical objects, and the interactive and composition relationships between services were used as morphisms. On the basis of the formal definitions of service interface, Web service and service composition, a further analysis and discussion about the semantics of service composition and interaction was undertaken. The concepts on Web service substitutability and service request satisfiability were formally defined. The application research shows that the proposed framework enhances semantic description capabilities of Web service architecture.
Spherical Voronoi diagram generating algorithm based on distance computation and comparison of Quaternary Triangular Mesh (QTM) has a higher precision relative to dilation algorithm. However, massive distance computation and comparison lead to low efficiency. To improve efficiency, Graphic Processing Unit (GPU) parallel computation was used to implement the algorithm. Then, the algorithm was optimized with respect to the access to GPU shared memory, constant memory and register. At last, an experimental system was developed by using C++ and Compute Unified Device Architecture (CUDA) to compare the efficiency before and after the optimization. The experimental results show that efficiency can be improved to a great extent by using different GPU memories reasonably. In addition, a higher speed-up ratio can be acquired when the data scale is larger.
The objective of Blind Source Separation (BSS) is to restore the unobservable source signals from their mixtures without knowing the prior knowledge of the mixing process. It is considered that the potential source signals are spatially uncorrelated but temporally correlated, i.e. they have non-vanishing temporal structure. A second-order statistics based BSS method was proposed for such sources. The robust prewhitening was firstly performed on the observed mixing signals, where the dimension of the sources was estimated based on the Minimum Description Length (MDL) criterion. Then, the blind separation was realized by implementing the Singular Value Decomposition (SVD) on the time-delayed covariance matrix of the whitened signals. The simulation on separation of a group of speech signals proves the effectiveness of the algorithm, and the performance of the algorithm was measured by Signal-to-Interference Ratio (SIR) and Performance Index (PI).
According to the influence of earliness and reworking penalties, the production order acceptance problem of hot-rolled bar was studied. A mathematical model with the objective of maximize gross profit of order was proposed. A hybrid algorithm with improved NEH (Nawaz-Enscore-Ham) algorithm and Modified Harmony Search (MHS) algorithm was proposed for the model. With the consideration of the constraints in the model, an initial solution was generated by the improved NEH algorithm and further optimized by MHS algorithm. Furthermore, the idea of Teaching-Learning-Based Optimization (TLBO) was introduced to the process of selection and updating for harmony vector to take control of the acceptance of new solutions. Meanwhile, in order to balance the breadth and depth of this algorithm's searching ability, the parameters were adjusted dynamically to improve the global optimization ability. The simulation experiments with practical production data show that the proposed algorithm can effectively improve total profit and acceptance rate, and validate the feasibility and effectiveness of the model and algorithm.
To deal with the problems of poor exploration capability and slow convergence speed in Biogeography-Based Optimization (BBO) algorithm, a hybrid quasi-oppositional learning based BBO algorithm named HQBBO was proposed. Firstly, the definition of heuristic hybrid quasi-oppositional point was given and its advantage in searching efficiency was proven theoretically. Then, the hybrid quasi-oppositional learning operator was brought forward to enhance the exploration capability and accelerate convergence speed. Meanwhile, the dynamic scaling strategy of searching domain and the elitism preservation strategy were utilized to boost optimization efficiency further. Simulation results on eight benchmark functions illustrate that the proposed algorithm outperforms the basic BBO algorithm and the oppositional BBO (OBBO) algorithm in terms of convergence accuracy and speed, which verifies the effectivity of hybird quasi-oppositional learning operator for improving the convergence speed and global exploring ability.
According to the data characteristics in sensor networks and the good performances of wavelet transforming in data stream compression, a novel mixed -entropy data compression algorithm based on interval wavelet transforming was proposed for sensor network. Theoretical analysis and simulation results show that, the new method can compress the data stream for sensor networks effectively, and reduce the energy costs of nodes in data transferring. So, it can prolong the lifetime of the whole networks to a greater degree combined with those traditional DC (Data Centric) routing algorithms such as DD (Directed Diffusion) protocol.