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.
Sleep episodes are generally classified according to EEG, EMG, ECG, EOG and other signals. Many experts at home and abroad put forward many automatic sleep staging classification methods, however the accuracy of most ...
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ISBN:
(纸本)9783319707723;9783319707716
Sleep episodes are generally classified according to EEG, EMG, ECG, EOG and other signals. Many experts at home and abroad put forward many automatic sleep staging classification methods, however the accuracy of most methods still remain to be improved. This paper firstly improves the initial center of clustering by combining the correlation coefficient and the correlation distance and uses the idea of piecewise function to update the clustering center. Based on the improvement of K-means clustering algorithm, an automatic sleep stage classification algorithm is proposed and is adopted after the wavelet denoising, EEG data feature extraction and spectrum analysis. The experimental results show that the classification accuracy is improved and the sleep automatic staging algorithm is effective by comparison between the experimental results with the artificial markers and the original algorithms.
' In this paper, we present certain algorithms for clustering the vertices of fuzzy graphs(FGs) and intuitionistic fuzzy graphs(IFGs). These algorithms are based on the edge density of the given graph. We apply th...
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' In this paper, we present certain algorithms for clustering the vertices of fuzzy graphs(FGs) and intuitionistic fuzzy graphs(IFGs). These algorithms are based on the edge density of the given graph. We apply the algorithms to practical problems to derive the most prominent cluster among them. We also introduce parameters for intuitionistic fuzzy graphs.
A vehicular ad hoc network (VANET) basically consists of a group of vehicles that communicate with each other through a wireless transmission and requires no pre-existing management infrastructure. This communication,...
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ISBN:
(纸本)9783319465685;9783319465678
A vehicular ad hoc network (VANET) basically consists of a group of vehicles that communicate with each other through a wireless transmission and requires no pre-existing management infrastructure. This communication, as the main objective, streamlining traffic for drivers. This exchange of information is not always reliable because of several constraints such as the existence of malicious users aimed falsifying information to serve their own interests. In this paper, we will simulate the Black Hole attack in a VANET environment with a generated real world mobility model using MOVE Tool and SUMO and analyse the performance of this communication under this attack. And then we propose a clustering algorithm to detect and react against the black hole attacker node.
A multi-cluster-head based clustering routing algorithm is researched and realized in order to achieve better balance the energy consumption of wireless sensor network nodes as well as promote the stability and extend...
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ISBN:
(纸本)9781538639818
A multi-cluster-head based clustering routing algorithm is researched and realized in order to achieve better balance the energy consumption of wireless sensor network nodes as well as promote the stability and extend the service life of the network. By taking cluster as the basic unit, it divides the wireless sensor network into multiple clusters, each of which includes a main cluster head node, an assistant cluster head node, a cluster management node and several ordinary nodes. The article elaborates the energy consumption model of the wireless sensor network, the network topological structure of the multi-cluster-head based clustering routing algorithm and the method for realization. In addition, it conducts simulation and analysis on the multi-cluster-head based clustering routing algorithm. According to the results, the algorithm can achieve preferable balance on energy consumption of various nodes in the wireless sensor network, which effectively extends the service life and improves the stability of the wireless sensor network. It has good application prospects.
In order to prolong the network lifetime, a novel energy-efficient clustering algorithm is proposed to adapt the characteristic of heterogeneous wireless sensor networks. In the proved clustering protocol, taking comm...
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ISBN:
(纸本)9781538645024
In order to prolong the network lifetime, a novel energy-efficient clustering algorithm is proposed to adapt the characteristic of heterogeneous wireless sensor networks. In the proved clustering protocol, taking communication distance and energy consumption into account comprehensively, cluster-heads are elected more evenly in clustering stage by a value based on minimum energy consumption rather than the physical position center. Simulation results indicate that the proved algorithm has longer network lifetime and superior performance compared with the original algorithms in heterogeneous environments.
A clustering algorithm usually detects outliers as an aftermath of partitioning data points in a finite dimensional continuous dataset such as AGNES, k-means, and DBSCAN. This research makes use of the extreme anomalo...
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ISBN:
(纸本)9781450363518
A clustering algorithm usually detects outliers as an aftermath of partitioning data points in a finite dimensional continuous dataset such as AGNES, k-means, and DBSCAN. This research makes use of the extreme anomalous score which represents the outlierness of a data point based on the largest radius of a ball containing only that data point. The new clustering algorithm based on this score is proposed called the extreme anomalous score clustering algorithm (ESC). It searches for a cluster representative by combining two data points which are placed with the smallest extreme anomalous score. Then all extreme anomalous scores are updated and the algorithm stops when it reaches the number of clusters defined by a user. Otherwise, it continues to combine two data points having the smallest extreme anomalous scores. The experimental results on three groups of simulated datasets report the superior performance of ESC over AGNES, k-means, and DBSCAN based on the silhouette measurement and the homogeneity measurement.
A great deal of research has focused on using convolutional neural network for optical character recognition. However we encountered two typical problem in this field when applied convolutional neural network to handw...
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ISBN:
(纸本)9781538632215
A great deal of research has focused on using convolutional neural network for optical character recognition. However we encountered two typical problem in this field when applied convolutional neural network to handwritten Yi character recognition. First, since convolutional neural network is a kind of supervised deep learning model, the manual training data labeling is a very time consuming and labor intensive work. Second, because the theory is not well studied, the structure design and parameter adjustment of convolutional neural network depend heavily on experience, and our recognition accuracy was not satisfactory at the beginning. To address these two problems, in this paper, for one thing, we use entropy theory improved a density-based clustering algorithm, which is proved very effective in data labeling. For another, as to the problem of structure design and parameter adjustment, we compared performance of models with different scales and different parameters, and gave some experience about this problem. Finally we achieved 99.65% accuracy on the test set. We hope that this paper will inspire more researches on convolutional neural network applied to dataset-lacked optical character recognition problems.
In wireless sensor networks (WSN), energy efficiency is one of the major challenges because of the difficulty of charging nodes in monitored area. clustering sensor nodes is an effective topology control method to red...
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ISBN:
(纸本)9781538636282
In wireless sensor networks (WSN), energy efficiency is one of the major challenges because of the difficulty of charging nodes in monitored area. clustering sensor nodes is an effective topology control method to reduce energy consumption of sensor nodes. Studies of clustering algorithm usually focus on the whole lifetime but ignore the stable time (the time at which the first node dies) in WSN. This study proposes a clustering algorithm which aims to improve the stability and extend the lifetime of the network simultaneously by balancing and reducing the energy consumption for each node in WSN. The proposed algorithm is based on an improved Non-dominated sorting genetic algorithm-II (NSGA-II) which is a multi-objective optimization algorithm to achieve several goals. Five objective functions are used to optimize energy consumption and load balance. In the improved NSGA-II, a weight value is adopted to evaluate the clustering solutions after the crowding distance to sort the individuals in every generation more reasonably. According to the simulation results, the proposed algorithm achieves longer stable period and longer lifetime than LEACH & clustering algorithm based on traditional NSGA-II.
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