This work proposed a conceptually simple and computationally straightforward clustering algorithm based on the Cauchy-type distance for data clustering. It was demonstrated that the proposed approach does not require ...
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This work proposed a conceptually simple and computationally straightforward clustering algorithm based on the Cauchy-type distance for data clustering. It was demonstrated that the proposed approach does not require the priori number of clusters and the convergence of the proposed algorithm was proved. The experiment results showed that the proposed clustering algorithm was superior to other compared algorithms. Computational complexity was also provided. A real dengue gene expression dataset was used to demonstrate the effectiveness of the proposed method.
Though many building energy benchmarking programs have been developed during the past decades, they hold certain limitations. The major concern is that they may cause misleading benchmarking due to not fully consideri...
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Though many building energy benchmarking programs have been developed during the past decades, they hold certain limitations. The major concern is that they may cause misleading benchmarking due to not fully considering the impacts of the multiple features of buildings on energy performance. The existing methods classify buildings according to only one of many features of buildings the use type, which may result in a comparison between two buildings that are tremendously different in other features and not properly comparable as a result. This paper aims to tackle this challenge by proposing a new methodology based on the clustering concept. The clustering concept, which reflects on machine learning algorithms, classifies buildings based on a multi-dimensional domain of building features, rather than the single dimension of use type. Buildings with the greatest similarity of features that influence energy performance are classified into the same cluster, and benchmarked according to the centroid reference of the cluster. The proposed methodology contains four steps: feature selection, clustering algorithm adaptation, results validation, and interpretation. The experimentation was carried out with a comparison between the proposed methodology and the Energy Star approach. It was shown that the proposed methodology could account for the total building energy performance and was able to provide a more comprehensive approach to benchmarking. In addition, the multi-dimensional clustering concept enables energy benchmarking among different types of buildings, and inspires a new perspective to investigate building performance typology. (C) 2014 Elsevier B.V. All rights reserved.
In the scenario of the vehicle network, the unstable vehicle to vehicle (V2V) connectivity caused by a high-speed vehicle movement is improved through efficient clustering algorithm to some extent. However, in view of...
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In the scenario of the vehicle network, the unstable vehicle to vehicle (V2V) connectivity caused by a high-speed vehicle movement is improved through efficient clustering algorithm to some extent. However, in view of the insufficient consideration of the variation of cluster stability in existing clustering algorithms, the V2V connectivity needs to be further enhanced. To this end, in this paper, a V2V link duration scheme using platoon-optimized clustering algorithm is proposed, which consists of three aspects: system model, a platoon-optimized clustering algorithm, and the analysis of link duration. Specifically, the second-order nonlinear dynamic system for platoon is analyzed to initialize the network scene. Then, a platoon-optimized clustering algorithm is performed, which combines the platoon leader (PL) selection based on motion consistency and the updated algorithm based on the dynamically stable cluster to reduce the randomness caused by the dynamic change of network topology. In addition, the V2V link duration based on the proposed scheme is quantitatively implemented through specific mathematical formulas. Finally, the extensive simulation results show that the proposed scheme can effectively guarantee the cluster stability and prolong the V2V link duration.
As there are many uncertain factors in the geological hazard risk, great challenges are brought to the comprehensive evaluation of it. In order to improve the comprehensiveness and accuracy of geological hazard risk a...
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As there are many uncertain factors in the geological hazard risk, great challenges are brought to the comprehensive evaluation of it. In order to improve the comprehensiveness and accuracy of geological hazard risk assessment, a cloud fuzzy clustering algorithm is constructed in this paper, which can effectively estimate and evaluate uncertain variables. The weight value of the risk cloud droplets is calculated as the input. By setting up clustering conditions and function output conditions, the cluster weights of the inputs which meet requirements can be obtained by multiple clustering iterations. Through the introduction of time parameters, the influence of time factors on data importance and the risk severity of geological disaster emergencies are fully considered. The experimental results show that the calculated risk degree cluster weights are less than 1, which verifies the feasibility and practicability of the algorithm. The research in this paper shows that the clustering dynamic assessment of geological hazards can help to improve the accuracy of risk assessment and provide reference and help for the prevention and control of regional geological hazards.
Traditional wireless ad hoc network power-efficient design proceeds separately on access and clustering algorithms by assuming perfect distance (that is, no fading and channel impairments) at most. In this paper, we d...
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Traditional wireless ad hoc network power-efficient design proceeds separately on access and clustering algorithms by assuming perfect distance (that is, no fading and channel impairments) at most. In this paper, we discard the traditional layer-concept to tackle this important power-efficient wireless ad hoc networks under shadow fading, by identifying distance between a node pair as a sort of random distance to accommodate fading effect, which of course can be considered as a cross-layer design from traditional concept. By deriving the probability distribution of the distance between two nodes and the probability distribution of the distances between nodes and a randomly selected common reference node, the impacts of shadow fading on the link connectivity and node degree of the randomly constructed network topology are studied. Next, we propose a critical node first (CNF) based clustering algorithm to organize such a shadow faded random network topology into a power-efficient network architecture. By taking the shadow fading effects into considerations, our results show that the cluster-based network architecture generated by the proposed CNF-based clustering algorithm is power-efficient since the required number of exchanges of the cluster maintenance overheads is reduced.
In this paper, a motion video sequence extraction algorithm based on cumulative frame differential clustering is proposed for the same scene. Firstly, the difference results of the two frames are clustered, and then m...
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In this paper, a motion video sequence extraction algorithm based on cumulative frame differential clustering is proposed for the same scene. Firstly, the difference results of the two frames are clustered, and then median filtering technology and morphological method are used to extract and process the clustered results for obtaining moving foreground objects. During the process of video object image extraction, the color space clustering algorithm and filtering algorithm are introduced to reduce color noise in image and improve the accuracy of target object location and extraction efficiency. Finally, the framework of extraction system are designed and implemented. The high performance and accuracy of the proposed method are proved by quantitative analysis and extraction results comparison. The research in this paper has theoretical and practical value for promoting the development and application of key frame extraction technology and content-based video retrieval. The experimental results show that the algorithm has better performance, in terms of low computational complexity, real-time performance and better segmentation results.
Underwater acoustic sensor networks (UASNs) are important technical means to explore the ocean realm. As a strategic measure, clustering techniques balance the network energy and survival time obviously. This paper pr...
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Underwater acoustic sensor networks (UASNs) are important technical means to explore the ocean realm. As a strategic measure, clustering techniques balance the network energy and survival time obviously. This paper proposes a clustering algorithm for UASNs. First, an UASN structure of hierarchical 3D mesh is defined, and an energy consumption model is built. Second, the algorithm based on the designed framework is presented, including the basic clustering messages, the setup phase and the data transmission phase. Finally, experiment of the algorithm based on WOSS and MATLAB is implemented, and compared with DS-VBF, IAR, and GEDAR in terms of the average end-to-end delay, the survival rate, the number of survival nodes, the number of clusters, and the coverage ratio. Results demonstrate that a tradeoff between clustering performance and network survival is achieved and the algorithm is suitable for UASNs.
The sensor nodes deployed in wireless sensor networks are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. An energy efficient clustering algorithm wit...
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The sensor nodes deployed in wireless sensor networks are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. An energy efficient clustering algorithm with optimum parameters is designed for reducing the energy consumption and prolonging the system lifetime. An analytical clustering model with one-hop distance and clustering angle is given. The optimum one-hop distance and clustering angle are formulated by minimizing the energy consumption between inter-cluster and intra-cluster. Furthermore, the continuous working mechanism of each cluster head which acts as the local control center and will not be replaced by the candidate cluster head until its continuous working times reach the optimum values is given, and the optimum continuous working times of each cluster head can be obtained through the optimum one-hop distance and the clustering angle. With the mechanism, the frequency of updating cluster head and the energy consumption for establishing new cluster head can be reduced. The simulation results demonstrate that the clustering algorithm can effectively reduce the energy consumption and increase the system lifetime. (C) 2009 Elsevier GmbH. All rights reserved.
This paper proposes an efficient clustering algorithm for region merging. To speed up the search of the best pair of regions which is merged into one region, dissimilarity values of all possible pairs of regions are s...
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This paper proposes an efficient clustering algorithm for region merging. To speed up the search of the best pair of regions which is merged into one region, dissimilarity values of all possible pairs of regions are stored in a heap. Then the best pair can be found as the element of the root node of the binary tree corresponding to the heap. Since only adjacent pairs of regions are possible to be merged in image segmentation, this constraints of neighboring relations are represented by sorted linked lists. Then we can reduce the computation for updating the dissimilarity values and neighboring relations which are influenced by the merging of the best pair. The proposed algorithm is applied to the segmentations of a monochrome image and range images.
Statistical process control techniques have been widely used to improve processes by reducing variations and defects. In the present paper, we propose a multivariate control chart technique based on a clustering algor...
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Statistical process control techniques have been widely used to improve processes by reducing variations and defects. In the present paper, we propose a multivariate control chart technique based on a clustering algorithm that can effectively handle a situation in which the distribution of in-control observations is inhomogeneous. A simulation study was conducted to examine the characteristics of the proposed control chart and to compare them with Hotelling's T-2 multivariate control charts that are widely used in real-world processes. Moreover, an experiment with real data from the thin film transistor liquid crystal display (TFT-LCD) manufacturing process demonstrated the effectiveness and accuracy of the proposed control chart.
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