In the air-sea environment, unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs) and autonomous underwater vehicles (AUVs) constitute the autonomous marine systems (AMS). However, the challenge is that th...
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ISBN:
(纸本)9781728162072
In the air-sea environment, unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs) and autonomous underwater vehicles (AUVs) constitute the autonomous marine systems (AMS). However, the challenge is that the performance of underwater acoustic networks is much lower than that of overwater networks. Existing clustering algorithms cannot adapt to AMS well. Besides, existing routing protocols have not considered using the advantages of the overwater networks performance to improve the transmission performance of the underwater acoustic networks. In this paper, we propose a LEACH-based cross media clustering algorithm (LEACH-CM) and a vector-based cross media routing protocol (VBCM), both of them are applicable to the AMS environment. LEACH-CM can ensure the success rate of data transmission in a high-density node environment. VBCM can effectively select a link with a lower delay based on the estimated link delay. We also verify the effectiveness of LEACH-CM and VBCM through NS3 network simulation.
Since spectrum environment is increasingly complex and more devices accessed communication networks, traditional spectrum sensing algorithms have not adapted to an extreme volume of spectrum data. In this paper, a nov...
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ISBN:
(纸本)9781450365765
Since spectrum environment is increasingly complex and more devices accessed communication networks, traditional spectrum sensing algorithms have not adapted to an extreme volume of spectrum data. In this paper, a novel deep cooperative spectrum sensing scheme is proposed, which combined principal component analysis (PCA) with clustering algorithm. First, a multi-dimension feature matrix, consisting of energy vectors generated in fusion center, is reduced to a lower-dimension matrix according to the PCA algorithm. Subsequently, a clustering algorithm with K-means++ method is utilized to train the classifier by the lower-dimensional matrix. The simulation results show that the proposed scheme has shorter training duration about 64% of no PCA processing when the primary user power is 400 mW, and ensures spectrum sensing accuracy of secondary users. More importantly, the proposed scheme, compared with the others' cooperative spectrum sensing schemes, can significantly reduce the required hardware memory.
This paper focuses on clustering algorithm of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. Th...
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ISBN:
(纸本)9781424445165
This paper focuses on clustering algorithm of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an clasters that really exist in the empirical data.
This paper proposed a novel flower pollination clustering algorithm, which called NIPC algorithm for arranging data sets into clusters. It starts from the phenomenon of plant aggregation and growth with similar charac...
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ISBN:
(纸本)9781728140940
This paper proposed a novel flower pollination clustering algorithm, which called NIPC algorithm for arranging data sets into clusters. It starts from the phenomenon of plant aggregation and growth with similar characteristics inspired from insect pollination. In this context, the two position updating strategies allows plants to get the most comfortable position. The experiments show that it has a good effect on processing data sets with different sizes, shapes and density compared with other swarm intelligence algorithms.
In three-phase unbalanced management, static var generator (SVG) can be used as a current source to compensate unbalanced load and reduce three-phase unbalanced node voltage. Reasonable configuration of SVG compensati...
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ISBN:
(纸本)9781728143903
In three-phase unbalanced management, static var generator (SVG) can be used as a current source to compensate unbalanced load and reduce three-phase unbalanced node voltage. Reasonable configuration of SVG compensation points can not only improve the governance effect of three-phase imbalance, but also reduce the investment cost. Aiming at the problem of reasonable configuration of SVG compensation points, this paper proposes a negative sequence current based clustering algorithm to determine SVG compensation points, and establishes a mathematical model to solve the compensation capacity of SVG with the objective of negative sequence voltage, network loss and minimum investment cost. The feasibility and economy of the proposed method are verified by IEEE33-bus simulation.
clustering algorithm is one of the hotspots for studying routing protocol in wireless sensor networks, which can help to enlarge the network topology and utilize the capacity of channel efficiently. In this paper, we ...
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ISBN:
(纸本)9781424479351
clustering algorithm is one of the hotspots for studying routing protocol in wireless sensor networks, which can help to enlarge the network topology and utilize the capacity of channel efficiently. In this paper, we present a maximum degree and negotiation strategy based clustering algorithm to solve the cluster overlapping problem. It selects candidate according to maximum degree and determines the Cluster-Head according to the negotiation strategy. The negotiation strategy can also be applied to the weighted clustering algorithm. The simulation results demonstrate that the proposed algorithms can remove the cluster overlapping phenomenon of the original algorithms and prolong the lifetime of the wireless sensor networks.
clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a hig...
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ISBN:
(纸本)9781509018932
clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a higher practicability. RFSC, which is an improved algorithm of FSC algorithm, is less sensitive to the input parameters and faster. However, neither RFSC nor FSC can deal with uneven density data sets. In order to solve that problem, we propose an improved algorithm KFSC in this paper by dynamically controlling of the width of the kernel function. KFSC uses the idea of attractor of the DENCLUE and can customize their own personalized attraction for each point. The experimental results on synthetic data sets show that KFSC has a better performance on uneven density data sets than FSC and RFSC.
In this paper, an unsupervised clustering algorithm based on the Gaussian Mixture Model (UCGMM algorithm) for the coherent optical OFDM communication system is proposed to determine the constellation diagram. The purp...
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ISBN:
(纸本)9781510639607
In this paper, an unsupervised clustering algorithm based on the Gaussian Mixture Model (UCGMM algorithm) for the coherent optical OFDM communication system is proposed to determine the constellation diagram. The purpose of nonlinear equalization of communication systems is achieved. In a back to back transmission system, compared to the K-means algorithm and the without any clustering algorithm, the UCGMM algorithm can obtain gains of approximately 0.6dB and 2dB respectively. For the cases of simulation in optical fiber transmission, the transmission distance of UCGMM algorithm is extended by 45km relative to the K-means algorithm, and 75km relative to without any clustering algorithm. In both cases, the effectiveness of the proposed UCGMM algorithm in nonlinear equilibrium is proved.
Multispectral images such as multispectral chemical images or multispectral satellite images provide detailed data with information in both the spatial and spectral domains. Many segmentation methods for multispectral...
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Multispectral images such as multispectral chemical images or multispectral satellite images provide detailed data with information in both the spatial and spectral domains. Many segmentation methods for multispectral images are based on a per-pixel classification, which uses only spectral information and ignores spatial information. A clustering algorithm based on both spectral and spatial information would produce better results. In this work, spatial refinement clustering (SpaRef), a new clustering algorithm for multispectral images is presented. Spatial information is integrated with partitional and agglomeration clustering processes. The number of clusters is automatically identified. SpaRef is compared with a set of well-known clustering methods on compact airborne spectrographic imager (CASI) over an area in the Klompenwaard, The Netherlands. The clusters obtained show improved results. Applying SpaRef to multispectral chemical images would be a straight-forward step. (C) 2003 Elsevier B.V. All rights reserved.
Most clustering algorithms, such as k-means and fuzzy c-means (FCM), are used to cluster a set of objects based on a function of dissimilarities between objects. However, clustering on attribute variables of objects m...
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ISBN:
(纸本)9781424435968
Most clustering algorithms, such as k-means and fuzzy c-means (FCM), are used to cluster a set of objects based on a function of dissimilarities between objects. However, clustering on attribute variables of objects may give more cluster information. Thus, to have a clustering algorithm that can be designated to construct simultaneously an optimal partition of objects and also attribute variables into homogeneous block is important. This kind of clustering was called block clustering (see Duffy and Quiroz, 1991). Recently, Govaert and Nadif (2003) proposed a block classification EM (block CEM) algorithm and then proposed block fuzzy c-methods (block FCM) in 2006. In this paper, based on Huang and Ng's (1999) fuzzy k-modes (FKM) method, we propose a block FKM clustering algorithm. Several examples are used to make the comparisons between block FCM and the proposed block FKM.
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