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Self-monitoring the sensor statuses such as liveness, node density and residue energy is critical for maintaining the normal operation of the sensor network. When building the monitoring architecture, most existing work focuses on minimizing the number of monitoring nodes. However, with less monitoring points, the false alarm rate may increase as a consequence. In this paper, we study the fundamental...
A fault identification with fuzzy C-Mean clustering algorithm based on improved ant colony algorithm (ACA) is presented to avoid local optimization in iterative process of fuzzy C-Mean (FCM) clustering algorithm and the difficulty in fault classification. In the algorithm, the problem of fault identification is translated to a constrained optimized clustering problem. Using heuristic search of colony...
Focusing on the problem of resource allocation under large-scale, distributed, autonomous, heterogeneous and dynamic environments in grid computing, a heuristic algorithm combining fuzzy clustering with application preference is proposed. Fuzzy clustering method is applied according to a group of features, which describe the user's application preference, to realize reasonable pre-classification resource...
Ant Colony Optimization (ACO) metaheuristic is a recent population-based approach inspired by the observation of real ants colony and based upon their collective foraging behavior. In ACO, solutions of the problem are constructed within a stochastic iterative process, by adding solution components to partial solutions. Each individual ant constructs a part of the solution using an artificial pheromone,...
In this paper, we propose Clustering method and Ant Colony Optimization (ACO) for mobile robot. This paper describes the analysis and design of a new class of mobile robots. These small robots are intended to be simple and inexpensive, and will all be physically identical, thus constituting a homogeneous team of robots. They derive their usefulness from their group actions, performing physical tasks...
House environment of edible fungi in industrialized production pattern greatly influences the growth of edible fungi. Workers judge the suitability of the environment in accordance with the morphological characteristics (such as mushroom stem diameter, diameter of mushroom cap, ratio of mushroom cap to mushroom stem) of edible fungi, and such judgment is often subjective and can not be judged in time...
This paper presents a feasibility study for an intelligent cart system designed to be used in common airports. The framework provides novel methods to control carts using mobile software agents. In airport terminals, it is desirable that carts draw themselves together automatically after being used so that manual collection becomes less laborious. In order to avoid excessive energy consumption by...
Rapid and accurate search similar case is the key of establishing CBR engine design system. In order to enhance the search speed, a novel mixed FCM clustering is used to establish category index of CBR engine design system. Firstly, because date type of engine general parameters includes quantitative, Boolean and categorical data, categorized concept tree is used to quantify category parameters and...
Focused on the disadvantage of classical Euclidian distance in data clustering analysis, we propose an improved distance calculation formula, which describes the local compactness and global connectivity between data points. Furthermore, we improve ant-colony clustering algorithm by using the improved distance calculation formula. Theoretical analysis and experiments show that this method is more...
K-means clustering is one of the well-known distance-based clustering methods which partitions data into distinct groups. To implement an automatic attribute-scaled K-mean algorithm, the concept of classification has been integrated. Data points which belong to the same target class are considered similar in K-means clustering. In this paper, we explore and determine the optimal attribute-scaled vector...
Ant colony optimization (ACO) is a stochastic approach for solving combinatorial optimization problems like routing in computer networks. The idea of this optimization is based on the food accumulation methodology of the ant community. Zone based routing algorithms is build on the concept of individual node's position for routing of packets in mobile ad-hoc networks. Here the nodes' position can be...
We present a novel parallel auction algorithm implementation for solving the linear sum assignment problem. It is implemented using the message passing interface (MPI) on a computer cluster. Our approach enables dynamic computational load balancing over all processors throughout all steps of the algorithm's execution. We show that the performance of our approach is superior to existing approaches...
It has been crucial for credit card operators to conduct targeted marketing with effective customer segmentation recently years. The clustering analysis of data mining technology is the most effective tool for this, and the selection of indicators means a lot on the results of segmentation in the meanwhile. In this paper, we select two algorithms, AHP (Analytical Hierarchy Process) for indicator optimization,...
Clustering analysis is one of the fundamental technologies for conducting customer oriented services and the services management. Due to the continuously changes in the composition, need, experience and interest of customers, it may be difficult in refining stable and consistent aggregations of those customers. This paper presented a new algorithm for customer clustering analysis. It was originally...
Swarm intelligence exhibits a number of interesting properties such as flexibility, robustness, decentralization and self-organization. The instances of these algorithms on the domains of optimization, telecommunication network, knowledge discovery and robots are obviously increased. An ant colony algorithm is proposed aiming at the basic ant colony algorithms convergence slow and be prone to plunge...
Against the low efficiency of training on large-scale SVM, a reduction approach based on kernel distance clustering is proposed. The kernel distance's formulation is brought in to cluster the highly-dimensioned dataset, and the clustering step will reduce a large amount of unsupport vectors during training, thereby, the training time will decrease. The experiments show that this new training algorithm...
The Ant colony algorithm is an intelligent algorithm with its better robustness and parallelism, as well as the advantage combined easily with other algorithms. Each ant Agent does not need to have a comprehensive understanding to the every aspect of the system so the individual Agent is considered as the main object of study. In the paper, supermarket customer subdivision model was researched by...
This paper discusses the two important phases, which are data preprocessing and clustering analysis, in Web transactions clustering analysis, in order to gain an easily interpreted clustering result, we introduce the "Concept URL" in the data preprocessing phase; In the clustering analysis phase, A model of artificial ant is set up. Based on this model, we implement an ant-colony clustering...
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in...
In this paper, we study how resources within a large high performance computing (HPC) cluster can be dynamically partitioned to optimize client utility for multiple service classes. We model service effectiveness using both perceived service quality and resources required. Using empirical data obtained from A*STAR Computational Resource Center (A*CRC), we analyze how quality metrics and statistical...
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