Many advances and algorithms have been proposed to obtain complete Lyapunov functions for dynamical systems and to properly describe the chain-recurrent set, e.g. periodic orbits. Recently, a heuristic algorithm was p...
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ISBN:
(纸本)9789897583803
Many advances and algorithms have been proposed to obtain complete Lyapunov functions for dynamical systems and to properly describe the chain-recurrent set, e.g. periodic orbits. Recently, a heuristic algorithm was proposed to classify and reduce the over-estimation of different periodic orbits in the chain-recurrent set, provided they are circular. This was done to investigate the effect on further iterations of the algorithm to compute approximations to a complete Lyapunov function. In this paper, we propose an algorithm that classifies the different connected components of the chain-recurrent set for general systems, not restricted to (circular) periodic orbits. The algorithm is based on identifying clustering of points and is independent of the particular algorithm to construct the complete Lyapunov functions.
In this paper, k-means parallel clustering algorithm is studied. Firstly, this paper introduces the purpose and significance of k-means clustering algorithm. Secondly, we describe the process of clustering analysis, s...
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ISBN:
(纸本)9781450387828
In this paper, k-means parallel clustering algorithm is studied. Firstly, this paper introduces the purpose and significance of k-means clustering algorithm. Secondly, we describe the process of clustering analysis, six classical clustering algorithms, the composition and operation of Hadoop Environment, and the K-MEANS algorithm in the cluster environment. In the environment of large data, the time and space complexity of k-means algorithm becomes an obstacle of k-means algorithm. Based on the research of a lot of traditional k-means algorithms, a parallel k-means algorithm is proposed, and the formula of its speed-increasing ratio is given. Experimental results show that the algorithm is correct and effective, and has a good clustering effect.
Keeping the digital road maps up-to-date is of critical importance, because the quality of many road-dependent services relies on it, but traditional measurement methods are still time-consuming and expensive. With th...
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Keeping the digital road maps up-to-date is of critical importance, because the quality of many road-dependent services relies on it, but traditional measurement methods are still time-consuming and expensive. With the GPS technology and wireless communication technology maturing, the positioning data from floating car become a new data source for updating road maps. The paper presents a novel incremental clustering algorithm for automatically extracting the topology of the road network employing the floating car data. A trajectory is selected as a road Link and then the remaining trajectories are added in turn until all tracks are processed. Further, the algorithm determines whether to merge the trajectory or divide it into a new Link by judging the relations of the space position between the newly added trajectory and the existing Link. A partial curve matching method based on Frechet distance is employed to measure the partial similarity between a Trajectory and a Link and the time complexity of the proposed algorithm is reduced. Experiments show that the algorithm can quickly extract the geometric shape and topology of the road network with lightweight floating car data.
This paper presents a variable-categorized clustering algorithm (VCCA) using fuzzy logic for Internet of Things (IoT) local networks. The VCCA selects the cluster head (CH) that has the highest network capacity throug...
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This paper presents a variable-categorized clustering algorithm (VCCA) using fuzzy logic for Internet of Things (IoT) local networks. The VCCA selects the cluster head (CH) that has the highest network capacity through a classification process of cluster variables in accordance with the characteristics in order to configure a clustered network, which differs for different IoT applications. To achieve this, the VCCA employs a fuzzy inference system (FIS) that calculates an outcome through rule-based variable mapping for low complexity in the CH election and high scalability of cluster variables. In addition, experimental simulations using MATLAB are conducted to evaluate the performance of the VCCA. The simulation results show that the VCCA exhibits better network performance compared to the existing algorithms in terms of throughput, end-to-end latency, network lifetime, and energy consumption.
Fuzzy C-means clustering integration algorithm is a method to improve clustering quality by using integration ideas, but as the amount of data increases, its time complexity increases. A parallel FCM clustering integr...
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Fuzzy C-means clustering integration algorithm is a method to improve clustering quality by using integration ideas, but as the amount of data increases, its time complexity increases. A parallel FCM clustering integration algorithm based on MapReduce is proposed. The algorithm uses a random initial clustering centre to obtain differentiated cluster members. By establishing an overlapping matrix between clusters, the clustering labels are unified to find logical equivalence clusters. The cluster members share the classification information of the data objects by voting to obtain the final clustering result. The experimental results show that the parallel FCM clustering integration algorithm has good performance, and has high speedup and good scalability.
Wind power has the characteristic of daily similarity. Furthermore, days with wind power variation trends reflect similar meteorological phenomena. Therefore, wind power prediction accuracy can be improved and computa...
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Wind power has the characteristic of daily similarity. Furthermore, days with wind power variation trends reflect similar meteorological phenomena. Therefore, wind power prediction accuracy can be improved and computational complexity during model simulation reduced by choosing the historical days whose numerical weather prediction information is similar to that of the predicted day as training samples. This paper proposes a new prediction model based on a novel dilation and erosion (DE) clustering algorithm for wind power generation. In the proposed model, the days with similar numerical weather prediction (NWP) information to the predicted day are selected via the proposed DE clustering algorithm, which is based on the basic operations in mathematical morphology. And the proposed DE clustering algorithm can cluster automatically without supervision. Case study conducted using data from Yilan wind farm in northeast China indicate that the performance of the new generalized regression neural network (GRNN) prediction model based on the proposed DE clustering algorithm (DE clustering-GRNN) is better than that of the DPK-medoids clustering-GRNN, the K-means clustering-GRNN, and the AM-GRNN in terms of day-ahead wind power prediction. Further, the proposed DE clustering-GRNN model is adaptive. (C) 2019 Elsevier Ltd. All rights reserved.
The dissolution characteristics of glycerol derivatives in solvents at different temperatures and pressures were studied. The effects of solvent structure on gas absorption capacity and separation selectivity were ana...
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The dissolution characteristics of glycerol derivatives in solvents at different temperatures and pressures were studied. The effects of solvent structure on gas absorption capacity and separation selectivity were analyzed. The absorption thermodynamics and kinetics were discussed. The experimental results show that as the temperature increases, the solubility of glycerin derivatives decreases, and the separation selectivity between gases also decreases. At the same temperature, the more carbon atoms of the glycerin derivative, the easier the dissolution process in the ionic liquid. The thermodynamic parameters of each gas do not change much with increasing temperature. At the same time, the spectral clustering algorithm can be used to obtain the characteristics of the global optimal solution, which solves the problem that the traditional hybrid data clustering algorithm is easy to fall into the local optimal solution.
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.
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.
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