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Reducing fuel consumption while coping with continually increasing customer demands with regard to driving dynamics, is a conflict of objectives in vehicle development. At the same time, hybrid vehicles offer a chance to meet this challenge. For calibrating the hybrid car's operational strategy, realistic driving cycles are of great importance. Studies have shown that the driver's driving style as...
Laplacian Eigenmaps (LE) is a typical nonlinear graph-based (manifold) dimensionality reduction (DR) method, applied to many practical problems such as pattern recognition and spectral clustering. It is generally difficult to assign appropriate values for the neighborhood size and heat kernel parameter for LE graph construction. In this paper, we modify graph construction by learning a graph in the...
There are many factors affect the stability of reservoir slopes, each of them is associated and coupled with others. Generally, the analysis of slopes stability can be achieved by the method of effect-factors analogy and cluster analysis. Traditional cluster analysis is difficult to obtain the stable global optimal solution, since the results are sensitive to the initial cluster center and the order...
The goal of image segmentation is to cluster pixels into salient image regions, it is the most significant step in image analysis. Thresholding is a simple but effective tool to separate objects from the background, which is one of the most popular algorithms. The artificial bee colony algorithm (ABC) is a recently presented meta-heuristic algorithm, which has been successfully applied to solve many...
In order to make good use of the limited energy, ant colony optimization (ACO) was applied to inter-cluster routing mechanism. An uneven clustering routing algorithm for Wireless Sensor Networks (WSNs) based on ant colony optimization (ACO) was proposed. The algorithm utilized the dynamic adaptability and optimization capabilities of the ant colony to get the optimum route between the cluster head...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
Adaptive data-driven dictionaries for sparse approximations provide superior performance compared to predefined dictionaries in applications involving representation and classification of data. In this paper, we propose a novel algorithm for learning global dictionaries particularly suited to the sparse representation of natural images. The proposed algorithm uses a hierarchical energy based learning...
Many high-end computing systems use an extremely large number of power-hungry commercial components to achieve high performance. Power reduction and energy conservation are important in these systems for the reason of minimizing operating cost. Two main mechanisms are commonly applied to power reduction in these systems: Dynamic Voltage/ Frequency Scaling (DV/FS) and server number controlling: Vary-On...
In many real-world applications, the accurate number of clusters in the data set may be unknown in advance. In addition, clustering criteria are usually high dimensional, nonlinear and multi-model functions and most existing clustering algorithms are only able to achieve a clustering solution that locally optimizes them. Therefore, a single clustering criterion sometimes fails to identify all clusters...
This paper address the problem of static task clustering and scheduling of a parallel program given as a Directed Acyclic Graph(DAG) with duplication. Scheduling of task graphs onto multiprocessors is known to be an NP-complete in most cases leading to solutions based on heuristics. In this paper, a novel task-duplication scheduling heuristic is presented. Our approach differs from other heuristics...
Spatial clustering is very relevant to its sample distribution, clustering geometry configuration and spatial structure. Hierarchy dividing of spatial clustering and its optimization has been studied based on K-means algorithm in this paper. It recommends a classical method of hierarchy dividing of spatial clustering and a new rule to optimize the k value of spatial clustering. The rule and its expansibility...
Robust biometric recognition is of paramount importance in security and surveillance applications. In face based biometric systems, data is usually collected using a video camera with high frame rate and thus the captured data has high redundancy. Selecting the appropriate instances from this data to update a classification model, is a significant, yet valuable challenge. Active learning methods have...
In this paper, an improved ant colony optimization based approach for image edge detection is proposed. The algorithm use ant colony clustering approach to extract edge feature. The approach set the heuristics information function and the initial cluster, thus avoiding the search blindness which carried out by traditional ant colony algorithm. And a series of simulation experiments demonstrate the...
In this paper we propose a web log mining-based network user behavior analysis scheme, which plays an important role in network structure optimization and website server configuration. Based on clustering and regression model, we studied the network user's visit model in a university by analyzing a large amount of web log data which is collected from the university campus network. The data analyzing...
In CBR system, the case base is becoming increasingly larger with the incremental learning which results in the decline of case retrieval efficiency and its weaker performance. Aiming at such weakness of CBR system, this article proposes a novel case retrieval method based on Hybrid Ant-Fish Clustering Algorithm (HA-FC). At beginning of algorithm, we get rough cluster sets utilizing the advantage...
Support vector machine (SV machine, SVM) is a genius invention with many merits, such as the non-existence of local minima, the largest separating margins of different clusters, as well as the solid theoretical foundation. However, it is also well-noted that SVMs are frequently with a large number of SVs. In this paper, we investigate the number of SVs in a benchmark problem, the parity problem experimentally...
The existence of fake tea from non-origin impacts on the credibility and sales of the origin Longjing tea seriously. In order to weaken this impact, we proposed a technology using ant colony clustering algorithm in discrimination the origin of Longjing tea. Then acquired and analyzed the characteristics of the origin tea comprehensively, the 16 parameters of the images and spectra from each sample...
In the recent years, forests of decision trees have seen an increasing interest from the Machine Learning community since they allow to aggregate the decisions from a set of decision trees into one robust answer. However, this approach suffers from two well-known limits: first, their performances depend on the number of trees and thus finding the right size and how to aggregate decisions could be...
The World Wide Web is a global information space. With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the...
Today's applications deal with multiple types of information: graph data to represent the relations between objects and attribute data to characterize single objects. Analyzing both data sources simultaneously can increase the quality of mining methods. Recently, combined clustering approaches were introduced, which detect densely connected node sets within one large graph that also show high similarity...
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