For technical problems of low accuracy of automatic decoding for Morse signal, an automatic decoding method for time-frequency spectrum of manual or mechanical Morse signal is put forward, which based on time frequenc...
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ISBN:
(纸本)9783319502090;9783319502083
For technical problems of low accuracy of automatic decoding for Morse signal, an automatic decoding method for time-frequency spectrum of manual or mechanical Morse signal is put forward, which based on time frequency analysis method and machine learning technology. It generates time frequency image based on STFT, which used for extraction of Morse signal based on adaptive image enhancement later. K-means clustering algorithm have been introduced to identify the dots, dashes and interval between them. Error-correction algorithm put forward to improve the accuracy of decoding. Simulation experiment and engineering practice on Morse signal demonstrate the effectiveness and feasibility of this algorithm.
In this article, we have devised modified genetic algorithm (MfGA) based fuzzy C-means algorithm, which segment magnetic resonance (MR) images. In FCM, local minimum point can be easily derived for not selecting the c...
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ISBN:
(纸本)9789811031533;9789811031526
In this article, we have devised modified genetic algorithm (MfGA) based fuzzy C-means algorithm, which segment magnetic resonance (MR) images. In FCM, local minimum point can be easily derived for not selecting the centroids correctly. The proposed MfGA improves the population initialization and crossover parts of GA and generate the optimized class levels of the multilevel MR images. After that, the derived optimized class levels are applied as the initial input in FCM. An extensive performance comparison of the proposed method with the conventional FCM on two MR images establishes the superiority of the proposed approach.
Feature extracting and screening get more important and necessary because of data analysis will become very slow and difficult with the increasing of data *** reduce the dimension of features,we propose a new way of f...
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ISBN:
(纸本)9781509046584
Feature extracting and screening get more important and necessary because of data analysis will become very slow and difficult with the increasing of data *** reduce the dimension of features,we propose a new way of feature screening in this *** improved clustering algorithm is employed to screen the features preliminarily,and then the genetic algorithm synergistically combined with the random forest is cascaded to screen the features *** validate the way feasible,1588 tobacco leaves belonging to 41 grades are used to be classified in the *** results show that both the recognition rate and the speed can be *** demonstrates that the presented cascaded screening approach can raise not only the recognition rate but also the speed because the feature dimension is decreasing effectively.
In this paper, a low-power, high-security wireless routing network based on clustering routing algorithm is proposed for the existing problems of the current wireless routing network, such as high energy consumption, ...
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In this paper, a low-power, high-security wireless routing network based on clustering routing algorithm is proposed for the existing problems of the current wireless routing network, such as high energy consumption, algorithm optimization and routing network insecurity. This paper proposes an artificial intelligence algorithm and its improved algorithm to deal with the edge of the image. Firstly, it introduces the basic establishment method of the clustered routing algorithm, and then introduces the specific design method of the router network. At the same time, it introduces the reasonable selection method of the cluster head and the possible problems in the data synthesis, and gives a specific description on how to reduce the power consumption and improve the safety in the design process. Finally, it makes a detailed analysis and test for the wireless router network design obtained by us, which proves the scientific nature of our algorithm and also proves the correctness and rationality of our design.
With the development of web service technology, identifying and discovering the users with similar preferences have an important significance to service selection and service optimization in the service environment. I...
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ISBN:
(纸本)9781538607527
With the development of web service technology, identifying and discovering the users with similar preferences have an important significance to service selection and service optimization in the service environment. In order to divide the users into groups based on their preference similarity in the process of service selection, a combination-based clustering algorithm, named AAK, is presented in this paper. The method combines the K-means algorithm with the Affinity Propagation ( AP) algorithm to cluster the users with similar preferences. In the clustering process, the algorithm makes full use of the advantages of the two algorithms, including the high partition accuracy of K-means algorithm and the independence in the prior knowledge of AP algorithm, which breaks the limitation of using a single clustering algorithm. Then a parallel execution model of the algorithm is built and implemented by a high order MapReduce sequence linking technology. Finally AAK algorithm is compared with its serial model and the other combination-based clustering methods on Matlab platform and Hadoop platform. The experimental results show that AAK algorithm can be applied to distinguish user group with different preferences and has a good effectiveness and efficiency.
In recent years, name disambiguation has become one of the hot topics to be resolved in natural language processing and information extracting. This paper introduces the meaning of research and theories of name disamb...
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ISBN:
(纸本)9781538604120
In recent years, name disambiguation has become one of the hot topics to be resolved in natural language processing and information extracting. This paper introduces the meaning of research and theories of name disambiguation and briefly reviews the feature extraction, similarity calculation, clustering algorithm and evaluation method which enables readers to form a preliminary understanding on name disambiguation and lay a good foundation for further research.
Ultra dense networks (UDN) are treated as a promising technology to meet the challenges of the future wireless communications where interference plays an important role in the network performance. Interference alignme...
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ISBN:
(纸本)9781509039449
Ultra dense networks (UDN) are treated as a promising technology to meet the challenges of the future wireless communications where interference plays an important role in the network performance. Interference alignment (IA) has been considered to be a resultful technique for achieving the optimal capacity scaling. However, in practical communication system, mitigating all interference via IA requires heavy signaling overhead and high iteration complexity. In this paper, we propose a dynamic clustering algorithm based on graph partitioning with low complexity. Our work focuses on dividing the whole network into a number of clusters under size constraint and realizes the maximum intra-cluster interference and minimum inter-cluster interference. In addition, neighbor selecting scheme based on neighbor interference ratio (NIR) in proposed algorithm can get the proper cluster result in the random spatial network model. Furthermore, proposed algorithm is compared with other traditional algorithms in complexity and performance. The simulation results show that proposed algorithm reduces the complexity of clustering process significantly and achieves average 7% higher performance gain than existing clustering algorithms.
clustering technology gradually becomes one of the most important technologies in vehicular ad hoc network (VANET) due to improve the stability and scalability of routing protocols. To overcome the large packet loss r...
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ISBN:
(纸本)9781538636282
clustering technology gradually becomes one of the most important technologies in vehicular ad hoc network (VANET) due to improve the stability and scalability of routing protocols. To overcome the large packet loss rate and low stability of cluster head (CH) in the existing algorithms in intersection scenario, in this paper, we propose a new cluster member (CM) data updating mechanism. It is updating dynamically according to the road section of CM and the road section of CH, instead of updating periodically. Additionally, in order to select a more valid CH, we utilize the base station (BS) to collect the information of vehicles on the road sections around it, and then use the BS to carry out a new CH election by using the relative position between vehicles and the relative distance from the vehicle to the BS. Finally, a new concept of the road section queue is proposed to improve the stability of cluster in intersection scenario. The simulation results by employing SUMO and OMENT++ show that the proposed clustering algorithm can improve the packet loss rate, overhead and cluster stability significantly in intersection scenario.
clustering analysis has been applied in many fields as a key technology in data analysis and processing. A lot of clustering algorithms which have their own advantages and disadvantages have been presented by many sch...
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ISBN:
(纸本)9781509050369;9781509050352
clustering analysis has been applied in many fields as a key technology in data analysis and processing. A lot of clustering algorithms which have their own advantages and disadvantages have been presented by many scholars. A clustering algorithm framework based on the principal component analysis is presented in this paper. Three steps are taken in the clustering algorithm such as following. Firstly, the data set are dealt with by the way of the principal component analysis(PCA), then the main components are selected to construct the new data set according to the analysis results, finally, the new data set is clustered by the way of DBSCAN. In the algorithm, the dimensions of the new data set are reduced. On account of the lower dimension data set, the amount of calculation is greatly reduced so as to improve the efficiency of the algorithm. The analyzing results of the algorithm simulation and conclusion are also given in this paper. The numerical simulations show that our algorithm is suitable for the occasion on which the clusters have the obvious difference between each other and this clustering algorithm can produce perfect clustering effect for the data set which is extracted by the principal component.
At present, China's market economy reform is in the continuous development stage, and the market demand faced by Chinese enterprises is becoming more and more complicated. Only the enterprises reasonably integrate...
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At present, China's market economy reform is in the continuous development stage, and the market demand faced by Chinese enterprises is becoming more and more complicated. Only the enterprises reasonably integrate and absorb the available effective resources, the operation cost of the enterprise can be reduced to the greatest extent, and then, a position in a difficult market can be obtained. And as a solid support for the corporate assets, the fixed assets have a very important significance. Based on this, in this paper, the enterprise fixed assets management based on K-MEANS clustering algorithm was researched. Firstly, the design of K-MEANS clustering algorithm was introduced, and then, the problems of fixed asset management were introduced. Finally, the specific process of the research of enterprise fixed assets management based on K-MEANS clustering algorithm was introduced in detail. The test results showed that the improved clustering algorithm could meet the higher requirements of clustering results.
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