The synthetic aperture radar (SAR) auto target recognition (ATR) system developed at Lincoln Laboratory is a standard system for target detection/recognition. It has three main stages: a prescreener, a discriminator a...
详细信息
The synthetic aperture radar (SAR) auto target recognition (ATR) system developed at Lincoln Laboratory is a standard system for target detection/recognition. It has three main stages: a prescreener, a discriminator and a classifier. The clustering algorithm between the prescreener stage and the discriminator stage is used to cluster the multiple detections of a single target to form a region of interest (ROI). This paper introduces the steps of the common clustering algorithm and analyzes its disadvantages. We improve the common clustering algorithm from two aspects of the read sequence of image data and the calculation means of clustering quasi-center coordinates. The clustering results based on two actual images testify efficiency of clustering algorithm improvement.
In today's data age, the big data processing analysis framework plays an important role in mass information processing, along with the increasing of massive data. "Sharing Data" is proposed to enhance th...
详细信息
In today's data age, the big data processing analysis framework plays an important role in mass information processing, along with the increasing of massive data. "Sharing Data" is proposed to enhance the performance of data processing through structured data scheduling. However, such approach makes the higher communication cost and buffer cost for the extra data copy and buffering. Hence, in the big data analysis environment, this paper uses based on the correlation of data, Dynamic Cluster Scheduling algorithm(DCSA) is proposed for parallel optimization of big data tasks. Firstly, a dynamic data queue based on the server's request database is generated. The priority of data item and size of data item are as the considerations of dynamic data queue for data clustering association. And then the weights are introduced, the dynamic data item is made equalization to provide the basis for the multi-channel optimal scheduling. Secondly, according to the relevance of the data items, the mechanism of data optimized placement is used to make the data which are aggregated in the same frame. After the placement is completed, the dynamic data is uniformly scheduled to minimize the cost at the time of migration, with the local characteristics of the data item as constraints. Through the target iteration, the optimal scheduling scheme is adjusted, and finally to achieve multi-channel optimal scheduling. Experiments show that the proposed method enables dynamic data to achieve optimal scheduling.
Based on the full analysis of the advantages and disadvantages of the traditional K - means and BIRCH clustering algorithms, an improved incremental clustering algorithm based on the core tree is proposed. The optimal...
详细信息
Based on the full analysis of the advantages and disadvantages of the traditional K - means and BIRCH clustering algorithms, an improved incremental clustering algorithm based on the core tree is proposed. The optimal global parameters Eps and MinPts are adaptively calculated according to the KNN distribution and mathematical statistics to avoid manual intervention in the clustering process so as to realize the full automation of the clustering process. By improving the seed selection method for regional query, no missing operation is needed to effectively improve the efficiency of clustering. The algorithm can helps financial users to make reasonable financial investment strategies in coastal areas, to a certain extent, reduce the financial investment risk, with strong practical significance.
Through data analysis method to obtain the internal connection of the data set,and extract different characteristics from it,and provide a strategy for the self-learning and acquisition of the expert system rule *** a...
详细信息
Through data analysis method to obtain the internal connection of the data set,and extract different characteristics from it,and provide a strategy for the self-learning and acquisition of the expert system rule *** article analyzes the composition and operating principle of the expert system,and selects the clustering algorithm to design the process of self-learning rules,and then obtains the rule base of the expert system,trying to solve the problem of difficulty in obtaining the empirical reasoning rule base.A circuit fault diagnosis example is selected to verify the feasibility of the *** simulation tests,it is proved that the algorithm can realize self-learning rules,and an expert system rule base is initially formed,and fault information can be reasoned and diagnosed to simulate or replace empirical rules to a certain extent.
In order to accurately identify the traffic state of the expressway, this paper preprocesses the ETC monitoring data of the expressway based on the traffic flow parameters of the expressway. A fuzzy C-means clustering...
详细信息
ISBN:
(纸本)9781450384971
In order to accurately identify the traffic state of the expressway, this paper preprocesses the ETC monitoring data of the expressway based on the traffic flow parameters of the expressway. A fuzzy C-means clustering model was established to cluster the traffic volume, time average vehicle speed and time occupancy rate data of specific road sections. In order to avoid outliers becoming cluster centers, the data density (DKC) value was used to improve the model. Taking the traffic volume, time average speed and time occupancy rate of a typical section of Suzhou Ring Expressway as an example, clustering calculation is performed to classify the traffic state of this section.
For classical clustering algorithms, it is difficult to find clusters that have non-spherical shapes or varied size and density. In view of this, many methods have been proposed in recent years to overcome this proble...
详细信息
For classical clustering algorithms, it is difficult to find clusters that have non-spherical shapes or varied size and density. In view of this, many methods have been proposed in recent years to overcome this problem, such as introducing more representative points per cluster, considering both interconnectivity and closeness, and adopting the density-based method. However, the density defined in DBSCAN is decided by minPts and Eps, and it is not the best solution to describe the data distribution of one cluster. In this paper, a deviation factor model is proposed to describe the data distribution and a novel clustering algorithm based on artificial immune system is presented. The experimental results show that the proposed algorithm is more effective than DBSCAN, k-means, etc.
The division of network community is an important part of network research. Based on the clustering algorithm, this study analyzed the partition method of network community. Firstly, the classic Louvain clustering alg...
详细信息
The division of network community is an important part of network research. Based on the clustering algorithm, this study analyzed the partition method of network community. Firstly, the classic Louvain clustering algorithm was introduced, and then it was improved based on the node similarity to get better partition results. Finally, experiments were carried out on the random network and the real network. The results showed that the improved clustering algorithm was faster than GN and KL algorithms, the community had larger modularity, and the purity was closer to 1. The experimental results show the effectiveness of the proposed method and make some contributions to the reliable community division.
The traditional method does not calculate the moving distance parameter when detecting coverage vulnerabilities, so its coverage performance is poor. The clustering algorithm can effectively calculate the moving dista...
详细信息
The traditional method does not calculate the moving distance parameter when detecting coverage vulnerabilities, so its coverage performance is poor. The clustering algorithm can effectively calculate the moving distance parameter. Therefore, a method to detect the coverage hole in wireless sensor network based on clustering algorithm was proposed. Firstly, the parameter of coverage hole in wireless sensor network were calculated, including the moving distance parameters, circular intersection area parameters and redundancy parameters. Secondly, the algorithm of hole edge intersection was used to judge edge nodes of coverage holes in wireless sensor network. By judging whether there was a hole edge intersection on the sensing circle, the edge nodes of coverage holes in wireless sensor network could be determined. Thirdly, the mobile nodes were deployed in the way of airdrop, and then they were distributed evenly. The density of the initially deployed mobile nodes should meet the highest coverage requirement in wireless sensor network. After determining the edge nodes of coverage holes in wireless sensor network, the deployed mobile nodes were used to find the edge nodes of coverage holes in wireless sensor networks by random walk. Meanwhile, the coverage holes were approximated by the set of edge nodes. Finally, it was able to detect the coverage holes in wireless sensor network by clustering algorithm, including the detection of hole shape and the judgment of holes size. In order to verify the gap coverage performance of the coverage hole detection method based on clustering algorithm, the traditional method was compared with the proposed method. Experimental results show that the gap coverage performance of the proposed method is better than that of traditional method. The gap coverage performance of this method is 389 T, while the traditional method is only 351 T. so this method is more suitable for the detection of coverage hole in wireless sensor network.
Ozone is an active gas in the atmosphere. Its content is quite low, but it plays an important role in protecting the health of human beings and other living things on earth. Ozone circulates in the atmosphere, and its...
详细信息
The joint combat of multiple fighter formations is an important means of attack in modern warfare. We can respond in a timely manner if the enemy's flight formations can be predicted in advance. That is significan...
详细信息
ISBN:
(纸本)9781728185750
The joint combat of multiple fighter formations is an important means of attack in modern warfare. We can respond in a timely manner if the enemy's flight formations can be predicted in advance. That is significant to our combat deployment and countermeasures. Based on the needs and characteristics of aircraft formation analysis and prediction, this paper uses a density-based clustering algorithm to automatically identify flight formations, and uses a large amount of real aircraft data for testing. The test results of the examples show that the proposed method can be more accurate to identify aircraft flying in formation.
暂无评论