Abstract The finite volume method combines the advantage of the finite element method and the finite difference method, while overcoming their shortcomings. This paper describes the development of the finite volume method and the process of using finite volume method for fluid calculations. Finally, this paper introduces the process of using improved finite volume method for solving the flow equations of... The finite volume method combines the advantage of the finite element method and the finite difference method, while overcoming their shortcomings. This paper describes the development of the finite volume method and the process of using finite volume method for fluid calculations. Finally, this paper introduces the process of using improved finite volume method for solving the flow equations of NS. Read More Read Less

Abstract Data Mining is a new filed in data processing research. Support Vector Machine (SVM) is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won't work if its training set contains uncertain information. In order to solve the problem presented above, this... Data Mining is a new filed in data processing research. Support Vector Machine (SVM) is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won't work if its training set contains uncertain information. In order to solve the problem presented above, this paper discusses the constraining programming of fuzzy chance and the characteristic of fuzzy classification as well as its expression methods. The algorithm for classifying Support Vector Machine is also included in this paper. Read More Read Less

Abstract Mankind is confronted with an increasingly complex system, especially the major decision issues, such as organization and management, coordination, planning, forecasting, controlling and so on. Due to the complexity of the system, many fuzzy factors and the limitations of the existing complex giant system theory, there are still many problems to be solved. The research on the giant complex system... Mankind is confronted with an increasingly complex system, especially the major decision issues, such as organization and management, coordination, planning, forecasting, controlling and so on. Due to the complexity of the system, many fuzzy factors and the limitations of the existing complex giant system theory, there are still many problems to be solved. The research on the giant complex system based on the theory of fuzzy decision theory theoretically had been proving the reliability of the group decision and has established “the multijudgments information” fuzzy synthesis computational method. It has designed the corresponding multipurpose intelligent decision support system IDSS. Read More Read Less

Abstract The accurate modeling of micro-grid access to power system planning and design stage needs is the primary problem to solve. This paper modeled the micro grid photovoltaic power generation system, including silicon solar cell, photovoltaic inverters, battery energy storage system, and the micro power distribution system. In the use of power system analysis software (DIGSILENT) of actual power system simulation,... The accurate modeling of micro-grid access to power system planning and design stage needs is the primary problem to solve. This paper modeled the micro grid photovoltaic power generation system, including silicon solar cell, photovoltaic inverters, battery energy storage system, and the micro power distribution system. In the use of power system analysis software (DIGSILENT) of actual power system simulation, the simulation results verify the model's correctness. In the power grid fault disturbance, the light intensity of disturbance and the load disturbances, the simulation results show that the optical storage combined with micro network has fast dynamic response characteristics, and its network of gridconnected voltage influenced by the changes of the light and load is little, while more affected by the network fault influence. Read More Read Less

Abstract Shuffled frog leaping algorithm is a new kind of swarm intelligence optimization algorithm. Due to the local search of the basic shuffled frog leaping algorithm which is the only by the worst frog to search and jump, searching ability of which was limited; therefore it had the low precision, slow convergence speed and easy premature convergence etc. Therefore, in order... Shuffled frog leaping algorithm is a new kind of swarm intelligence optimization algorithm. Due to the local search of the basic shuffled frog leaping algorithm which is the only by the worst frog to search and jump, searching ability of which was limited; therefore it had the low precision, slow convergence speed and easy premature convergence etc. Therefore, in order to enhance the ability of the local searching, this paper presented a gravity attractor, all the frogs in the same memeplexe could find the best position under its guidance. Considering the transboundary problems in the frog searching for food process, we introduced a space zoomed factor which made the frog out of the searching space could also be put into the searching space, and the social position relation was not changed, thus it improve the search ability of the SFLA. Through the standard function for testing, and compared with the standard SFLA algorithm and szAPSO algorithm, the experimental results show that the improved algorithm not only improves the convergence speed of the algorithm, enhance the searching capability of the algorithm, but also has better stability. Read More Read Less

Abstract Traditional spatial data are generally high dimensional features, and in the clustering of high dimensional data can be directly applied to data processing because of Dimension effect and the data sparseness problem. For CLIQUE algorithm, which usually have the problem such as prone to non-axis direction of overclustering, boundary judgment of fuzzy clustering and smoothing clustering. In this paper, a... Traditional spatial data are generally high dimensional features, and in the clustering of high dimensional data can be directly applied to data processing because of Dimension effect and the data sparseness problem. For CLIQUE algorithm, which usually have the problem such as prone to non-axis direction of overclustering, boundary judgment of fuzzy clustering and smoothing clustering. In this paper, a fuzzy clustering algorithm based on fuzzy extension of the highdimensional spatial data is proposed. This algorithm not only considers effect of adjacent grid of data points in the sparse grid, but also extending the sparse grid region to avoid smoothing clustering phenomenon occurs. What's more, it can alleviate the problem of over-clustering and clustering fuzzy boundaries. Firstly, this passage gives a brief introduction to the characteristics of high dimensional spatial data as well as the Clustering methods. Based on the fuzzy extension a high-dimensional space data clustering analysis algorithms is put forward. The impact of the data samples around the point on the data points within the investigated grid is considered, sparse grid fuzzy extension, and at last, the problems of clustering fuzzy boundaries and to avoid excessive clustering produce meaningless clustering are solved. Read More Read Less

Abstract In our previously published researches we used different prediction algorithms to solve several problems in computer architecture and ubiquitous computing. During the time, we observed what is common among different solutions and also what can differentiate these solutions. Therefore, due to our experience in designing predictors, we are able now to propose a simple and efficient general solution for any... In our previously published researches we used different prediction algorithms to solve several problems in computer architecture and ubiquitous computing. During the time, we observed what is common among different solutions and also what can differentiate these solutions. Therefore, due to our experience in designing predictors, we are able now to propose a simple and efficient general solution for any problem that implies prediction. Since prediction is a widely used technique in many fields, our proposed design pattern can be very useful for software developers. Read More Read Less

Abstract The harvester has been widely used in forestry operations. However the existence of obstacles in forests, such as stones, animals, human and clustered trees, may leads to safety accident and decreases the operating efficiency of the harvester. Therefore it becomes vital to show the obstacles accurately to provide guidance for the harvester. In this paper, an improved image fusion algorithm... The harvester has been widely used in forestry operations. However the existence of obstacles in forests, such as stones, animals, human and clustered trees, may leads to safety accident and decreases the operating efficiency of the harvester. Therefore it becomes vital to show the obstacles accurately to provide guidance for the harvester. In this paper, an improved image fusion algorithm based on Contourlet transform and pulse coupled neural network (PCNN) is proposed to enhance the fused images clarity and capture more abundant information of the reality. As for comparison, algorithms, such as wavelet transform, Principal Component Analysis (PCA) and PCNN are simulated to evaluate the proposed algorithm. Visible and infrared thermal image are captured in forest in this experiment. The experimental results demonstrate that the proposed algorithm outperform the mentioned methods by the objective criteria of Entropy, Average gradient and Standard deviation. Read More Read Less

Abstract PID control is widely used in air conditioning system. The algorithm is simple but has a long response time, and the adjustment is not flexible. In this paper, we propose a method of optimizing PID controller parameters based on adaptive genetic algorithm. Specifically, the fitness function with penalty terms is added to the classical genetic algorithm and adaptive crossover and... PID control is widely used in air conditioning system. The algorithm is simple but has a long response time, and the adjustment is not flexible. In this paper, we propose a method of optimizing PID controller parameters based on adaptive genetic algorithm. Specifically, the fitness function with penalty terms is added to the classical genetic algorithm and adaptive crossover and mutation factor genes to make the PID parameters converge to global optimum quickly. In addition, the method can prevent overshoot and improve stability effectively. Simulation results indicate that our method achieved a 83% reduction in regulation time of response of an airconditioning unit compared with the original PID system. The method can be used for online tuning of PID parameters as well. Read More Read Less

Abstract Face recognition (PCA algorithm) based on principal component analysis method is a classic algorithm of digital information face recognition. This paper first analyzes the PCA algorithm and its basic principles. And in view of the shortage that it is affected by its subject facial expression changes and other factors, it designs the improved PCA algorithm, namely adding the image enhancement... Face recognition (PCA algorithm) based on principal component analysis method is a classic algorithm of digital information face recognition. This paper first analyzes the PCA algorithm and its basic principles. And in view of the shortage that it is affected by its subject facial expression changes and other factors, it designs the improved PCA algorithm, namely adding the image enhancement processing method, such as the local mean and standard deviation, to the original algorithm to increase the robustness of human face illumination and facial expression change during recognition. In addition, verify it on ORL face database and the results show that the improved PCA algorithm can get a higher resolution of the human face and up to 81.59% accuracy rate. The average growth of time-consuming is 0.378 seconds compared to traditional algorithms, and it is a fruitful improved algorithm. Read More Read Less

Abstract The most of natural language processing problems are data-intensive. An important step in the distributed orchestration of natural language processing software is a rational choice of the specific middleware. The middleware should solve the presented problem with minimal deployment, support and usage costs. It is necessary to run and use that software in the distributed cloud computing environment to achieve... The most of natural language processing problems are data-intensive. An important step in the distributed orchestration of natural language processing software is a rational choice of the specific middleware. The middleware should solve the presented problem with minimal deployment, support and usage costs. It is necessary to run and use that software in the distributed cloud computing environment to achieve such advantages such as consolidation, isolation, and efficient use of the existent infrastructure. It is often impossible to modify the existent natural language processing software to integrate it into the cloud computing environment because of licensing or organizational issues. This paper studies various popular distributed data processing tools and evaluates the selected natural language processing tools on a relatively large document collection in distributed way using the Gearman framework. The document collection is a 10,000 sentences from the Russian news subcorpus of the Leipzig corpora. The benchmarks are presented and discussed. Read More Read Less