Recently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks quickly. It is also suitable for ...
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Recently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks quickly. It is also suitable for different kinds of clustering tasks. However, deciding the cutoff distance d(c) largely depends on human experience which greatly affects clustering results. In addition, the selection of cluster centers requires manual participation which affects the efficiency of the algorithm. In order to solve these problems, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (KNN-ADPC). A clusters merging strategy is proposed to automatically aggregate over-segmented clusters. Additionally, the K nearest neighbors are adopted to divide data points more reasonably. There is only one parameter in KNN-ADPC algorithm, and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC, and DPC-KNN.
Wireless sensor networks can be used to collect environmental data from the interested area using multi-hop communication. As sensor networks have limited and non-rechargeable energy resources, energy efficiency is a ...
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Wireless sensor networks can be used to collect environmental data from the interested area using multi-hop communication. As sensor networks have limited and non-rechargeable energy resources, energy efficiency is a very important issue in designing the topology, which affects the lifetime of sensor networks greatly. In this paper, the energy consumption is modeled and compared under the flat scheme and the clustering scheme, respectively. Motivated by the analysis, we propose an energy-efficient multi-level clustering algorithm called EEMC, which is designed to achieve minimum energy consumption in sensor networks. The cluster head election scheme is also considered in EEMC. EEMC terminates in O(log log N) iterations given N nodes. When the path loss exponent is 2, EEMC also achieves minimum latency. We focus on the case where sink node is remotely located and sensor nodes are stationary. Simulation results demonstrate that our proposed algorithm is effective in prolonging the network lifetime of a large-scale network, as well as low latency and moderate overhead across the network. (c) 2007 Elsevier B.V. All rights reserved.
Wireless Sensor Networks (WSNs) for reducing energy consumption and increasing sensors lifetime can use the clustering algorithms. We propose a new energy-efficient hierarchical clustering algorithm based on soft thre...
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Wireless Sensor Networks (WSNs) for reducing energy consumption and increasing sensors lifetime can use the clustering algorithms. We propose a new energy-efficient hierarchical clustering algorithm based on soft threshold cluster-head election and cluster member bounds for WSNs which called HCABS. Our simulation studies suggest that HCABS achieves longer lifespan and reduce energy consumption in WSNs as well as low latency and moderate overhead across the network.
Energy consumption and air quality index (AQI) prediction is important for efficient heating, ventilation, and air conditioning (HVAC) system operation and management. A data-mining approach is presented in this paper...
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Energy consumption and air quality index (AQI) prediction is important for efficient heating, ventilation, and air conditioning (HVAC) system operation and management. A data-mining approach is presented in this paper for modeling and short-term prediction of the complicated non-linear system. The multilayer perceptron (MLP) ensemble performs best among the data mining algorithms discussed in this paper. A clustering-based method from preprocessing input data to construct the prediction models is proposed to decreases the prediction errors and the computational cost. The effectiveness of the proposed method is validated through a practical case study with both modeling and short-term prediction. The analytical results showed that the method was capable of reducing the prediction errors for modeling and short-term prediction by 11.05% and 12.21%, respectively, comparing with the models built without clustering method. (C) 2014 Elsevier B.V. All rights reserved.
Container port congestion threatens the effectiveness and sustainability of the global supply chain because it stagnates cargo flows and triggers ripple effects across connected, multimodal freight transport networks....
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Container port congestion threatens the effectiveness and sustainability of the global supply chain because it stagnates cargo flows and triggers ripple effects across connected, multimodal freight transport networks. This study aims to develop a novel and tangible method to measure port congestion by investigating ship behaviors between different zones in port waters. Different port zones have varying ship densities because ships moor in the anchorage area randomly but dock at berths in an orderly and close fashion. This observation leads us to apply the density-based clustering method for port zone identification and differentiation. In order to ensure the method is globally applicable and accurate, we develop a new clustering algorithm, an iterative, multi-attribute DBSCAN (IMA-DBSCAN), which incorporates an iterative process, together with both spatial information and domain knowledge. The necessary input data for the algorithm is extracted from the Automatic Identification System (AIS), a satellite-based tracking system with real-time ship positioning and sailing data. An illustrative case suggests that our algorithm can rapidly and precisely identify anchorage areas and individual berths (even in a port with complicated geographic features), while other methods cannot. The algorithm is applied to measure congestion at 20 major container ports in the world. The results show a significant increase in congestion at the Port of Los Angeles from August to December 2020, which matches the realistic statistics and proves the efficiency and practical applicability of the proposed algorithm.
The natural frequencies and damping ratios of machine tool structure vary with the change of the machining position in the machining space. When the stiffness distribution of the whole machine structure is not uniform...
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The natural frequencies and damping ratios of machine tool structure vary with the change of the machining position in the machining space. When the stiffness distribution of the whole machine structure is not uniform, some position change will further lead to the change of weak components of the structure. In order to detail the position-dependent dynamics of the machine tool, the change of structure dynamics caused by the change of position is divided into two types: one is both the modal parameters and structural weakness change, and the other is that only the modal parameters change, while the weakness remains unchanged. The entire workspace can be divided into different subareas according to whether the weakness changes. In the same subarea, only the modal parameters change and the weakness remains unchanged. In the different subareas, the weakness of whole machine tool structure changes. The change of structural weakness influences the vibration characteristics of the machine tool and the dominant modes of vibration. Hence, the partition of machining space according to the change of structural weakness is helpful to more accurately analyze the position-dependent dynamics and vibration characteristics of the machine tool. Firstly, this paper presents the method of modal energy distribution to analysis position-dependent structural weakness and the principle of the clustering to divide the workspace. A simulation example is given to verify the effectiveness of the method. Then, the clustering partition of the workspace for a gantry machining center is conducted with the presented method. Finally, the cutting tests are performed to verify the change of the vibration dominant mode of machine tool at different subareas.
In order to make up for the defect that the traditional spectral clustering algorithm cannot determine the number of clusters and the time-consuming calculation, this paper studies and improves the spectral clustering...
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In order to make up for the defect that the traditional spectral clustering algorithm cannot determine the number of clusters and the time-consuming calculation, this paper studies and improves the spectral clustering algorithm. In complex community networks, the spectral clustering algorithm based on modularity optimization is chosen to find the number of communities. In addition, four types of user attribute information are integrated, and a more reasonable user similarity model is constructed. At the same time, the original non-parallelized spectral clustering algorithm is optimized, and its improved scheme is suitable for the application of distributed computing. Many Hadoop optimization strategies are proposed for virtual community discovery scenarios in large-scale communities. Finally, the experimental results show that the efficiency of the parallelized spectral clustering algorithm is greatly improved, which can be applied to the virtual community discovery in large-scale social networks.
A novel clustering algorithm named local reachability density peaks clustering (DPC) which uses local reachability density, improve the performance of the density peaks clustering algorithm (DPC) is proposed in this p...
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A novel clustering algorithm named local reachability density peaks clustering (DPC) which uses local reachability density, improve the performance of the density peaks clustering algorithm (DPC) is proposed in this paper. This algorithm enhances robustness by removing the cutoff distance dc which is a sensitive parameter from the DPC. In addition, anew allocation strategy is developed to eliminate the domino effect, which often occurs in DPC. The experimental results confirm that this algorithm is feasible and effective. (C) 2020 The Authors. Published by Atlantis Press SARL.
The process of K-medoids algorithm is that it first selects data randomly as initial centers to form initial clusters. Then, based on PAM (partitioning around medoids) algorithm, centers will be sequential replaced by...
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The process of K-medoids algorithm is that it first selects data randomly as initial centers to form initial clusters. Then, based on PAM (partitioning around medoids) algorithm, centers will be sequential replaced by all the remaining data to find a result has the best inherent convergence. Since PAM algorithm is an iterative ergodic strategy, when the data size or the number of clusters are huge, its expensive computational overhead will hinder its feasibility. The authors use the fixed-point iteration to search the optimal clustering centers and build a FPK-medoids (fixed point-based K-medoids) algorithm. By constructing fixed point equations for each cluster, the problem of searching optimal centers is converted into the solving of equation set in parallel. The experiment is carried on six standard datasets, and the result shows that the clustering efficiency of proposed algorithm is significantly improved compared with the conventional algorithm. In addition, the clustering quality will be markedly enhanced in handling problems with large-scale datasets or a large number of clusters.
A reliability model of wind farm located in mountainous land with complex terrain, which considers the cable and wind turbine (WT) failures, is proposed in this paper. Simple wake effect has been developed to be appli...
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A reliability model of wind farm located in mountainous land with complex terrain, which considers the cable and wind turbine (WT) failures, is proposed in this paper. Simple wake effect has been developed to be applied to the wind farm in mountainous land. The component failures in the wind farm like the cable and WT failures which contribute to the wind farm power output (WFPO) and reliability is investigated. Combing the wind speed distribution and the characteristic of wind turbine power output (WTPO), Monte Carlo simulation (MCS) is used to obtain the WFPO. Based on clustering algorithm the multi-state model of a wind farm is proposed. The accuracy of the model is analyzed and then applied to IEEE-RTS 79 for adequacy assessment.
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