In order to improve the spectrum sensing performance, we propose a cooperative spectrum sensing method based on a feature and clustering algorithm in the case of a small number of secondary users participating in coop...
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In order to improve the spectrum sensing performance, we propose a cooperative spectrum sensing method based on a feature and clustering algorithm in the case of a small number of secondary users participating in cooperative spectrum sensing. This method introduces order decomposition and recombination and interval decomposition and recombination based on stochastic matrix, which can increases the secondary users logically. Firstly, the signal matrix collected by the secondary users is split and recombined, and the corresponding covariance matrix are calculated respectively to obtain the corresponding signal features. Based on these features, we construct them as a feature vector. Further, we will use the clustering algorithm to train and perform spectrum sensing based on the trained classifier. In the experimental and results analysis section, the method described in this paper was simulated and the experimental results were further analyzed.
Bike sharing has received a rapid development in recent China since 2016. However, the side effect from huge amount of sharing bikes poured into traffic networks has caused both side effect on traffic system and the r...
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Bike sharing has received a rapid development in recent China since 2016. However, the side effect from huge amount of sharing bikes poured into traffic networks has caused both side effect on traffic system and the restrictions from the government. In order to reduce the negative impact, recommended parking spots are made. In this paper, we build the time cost model and use clustering algorithm to compare the system performance before and after setting up the recommendation site by abstract model from real fact.
With the advent of the era of big data,data analysis and processing has become a hot topic of research,and data mining has become a top *** on the discussion of clustering algorithm,this paper proposes a hybrid cluste...
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ISBN:
(纸本)9781510876996
With the advent of the era of big data,data analysis and processing has become a hot topic of research,and data mining has become a top *** on the discussion of clustering algorithm,this paper proposes a hybrid clustering *** algorithm solves the dilemma of relying too much on the initial center and falling into local *** experimental results demonstrate the effectiveness and feasibility of the hybrid clustering algorithm.
Unmanned aerial vehicle(UUV)can load underwater camera,electronic scanning sonar and other *** military area,it can complete missions such as mine clearance,underwater reconnaissance,anti-submarine warfare and so *** ...
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Unmanned aerial vehicle(UUV)can load underwater camera,electronic scanning sonar and other *** military area,it can complete missions such as mine clearance,underwater reconnaissance,anti-submarine warfare and so *** be concrete,UUV is mainly divided into two categories: remote operated vehicle(ROV)and autonomous underwater vehicle(AUV).This paper focuses on cooperative search strategies in condition of multiple AUVs.
Aiming at the disadvantages of traditional fuzzy kernel clustering algorithm, which do not consider the different contribution degree of each dimension feature to clustering, and easily fall into local optimum, an imp...
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Aiming at the disadvantages of traditional fuzzy kernel clustering algorithm, which do not consider the different contribution degree of each dimension feature to clustering, and easily fall into local optimum, an improved fuzzy kernel clustering algorithm is proposed. The algorithm constructs a simple and effective fitness function that combines the advantages of the global search of the genetic algorithm to avoid the algorithm falling into a local optimum. A weight coefficient was also introduced for each dimension feature and weighted with the Relief algorithm. This algorithm is much better than the traditional fuzzy kernel clustering algorithm. The experimental results show its effectiveness.
clustering fusion is a large combination of different algorithms or the same algorithm using different parameters the members of quantitative clustering are fused by fusion function, and the final clustering results a...
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clustering fusion is a large combination of different algorithms or the same algorithm using different parameters the members of quantitative clustering are fused by fusion function, and the final clustering results are obtained. clustering fusion has become a research hotspot in the field of data mining. However, the traditional clustering fusion method the method usually involves all the cluster members produced. But in supervised classification learning, Great progress has been made in the selection of classification fusion, and the selectivity for unsupervised classification has been improved. clustering fusion has been paid more and more attention only in recent years. The study shows that the selective clustering fusion the combined method can improve the accuracy of clustering analysis. This paper aims at selective polymerization. Data dimensionality reduction, selection strategy, fusion function design and other algorithms in class fusion are studied. The selective clustering fusion algorithm is applied to the analysis of multiple clustering problems.
Data gathering in wireless sensor networks (WSNs) is one of the major sources for power consumption. Compression is often employed to reduce the number of packet transmissions required for data gathering. However, con...
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Data gathering in wireless sensor networks (WSNs) is one of the major sources for power consumption. Compression is often employed to reduce the number of packet transmissions required for data gathering. However, conventional data compression techniques can introduce heavy in-node computation, and thus, the use of compressive sensing (CS) for WSN data gathering has recently attracted growing attention. Among existing CS-based data gathering approaches, hierarchical compressive data gathering (HCDG) methods currently offer the most transmission-efficient architectures. When employing HCDG, clustering algorithms can affect the number of data transmissions. Most existing HCDG works use the random clustering (RC) method as a clustering algorithm, which can produce significant number of transmissions in some cases. In this paper, we present a compressibility-based clustering algorithm (CBCA) for HCDG. In CBCA, the network topology is first converted into a logical chain, similar to the idea proposed in PEGASIS [1], and then the spatial correlation of the cluster nodes' readings are employed for CS. We show that CBCA requires significantly less data transmission than the RC method with a little recovery accuracy loss. We also identify optimal parameters of CBCA via mathematical analysis and validate them by simulation. Finally, we used water level data collected from a real-world flood inundation monitoring system to drive our simulation experiments and showed the effectiveness of CBCA.
The clustering algorithm is a popular lightning data processing method. Traditional density-based algorithm can't function without input of initial parameter Min_ρ or neighborhood radius ε. Besides, in most rega...
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ISBN:
(纸本)9781538680988;9781538680971
The clustering algorithm is a popular lightning data processing method. Traditional density-based algorithm can't function without input of initial parameter Min_ρ or neighborhood radius ε. Besides, in most regarding studies, the initial parameter is set by experience, but not on the basis of selective and sensitive analysis. Due to poor universality, they can hardly be used in daily business. This work analyzed a thunderstorm in 31/7/2017 and found that the characteristic parameter ρ_0, extracted from the lightning data distribution, could help designate the initial parameter of the clustering algorithm. So the improved OPTICS algorithm could dynamically retrieve its initial parameter from the lightning data that is to be processed, enjoying good self-adaption. Our calculation proved the extracted characteristic parameter ρ_0 the optimum initial input which has satisfying clustering effect. As a result, the improved OPTICS algorithm applies well to the lighting data with different spatial and temporal distribution and this could be used in daily business.
This paper proposes a monitoring method of power dispatching automation master station based on clustering algorithm. Based on the application of history data, this method constructs multi-dimensional space vector, an...
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This paper proposes a monitoring method of power dispatching automation master station based on clustering algorithm. Based on the application of history data, this method constructs multi-dimensional space vector, and generates operation state knowledge base by clustering algorithm. Real time data can be monitored and classified by using the generated knowledge base. The validity of the method is verified by a period of basic data. The results show that the method has better ability and accuracy to monitor power dispatching automation master station system, and it can provide reference for the selection of monitoring method of power dispatching automation master station.
This paper introduces a new dynamic feature selection to classification algorithms, which is based on individual similarity and it uses a clustering algorithm to select the best features for an instance individually. ...
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ISBN:
(纸本)9783319686127;9783319686110
This paper introduces a new dynamic feature selection to classification algorithms, which is based on individual similarity and it uses a clustering algorithm to select the best features for an instance individually. In addition, an empirical analysis will be performed to evaluate the performance of the proposed method and to compare it with existing feature selection methods, applying to classification problems. The results shown in this paper indicate that the proposed method had better performance results than the existing methods compared, in most cases.
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