Modern medical science strongly depends on imaging technologies for accurate diagnose and treatment planning. Raw medical images generally require post-processing - like edge and contrast enhancement, and noise remova...
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ISBN:
(纸本)9781450363143
Modern medical science strongly depends on imaging technologies for accurate diagnose and treatment planning. Raw medical images generally require post-processing - like edge and contrast enhancement, and noise removal - for visualization. In this paper, a clustering-based contrast enhancement technique is presented for computed tomography (CT) images.
Poor understanding and low clustering efficiency of massive data is a problem under the context of big data. To solve this problem, Canopy + K-means clustering algorithm is proposed, and the MapReduce programming mode...
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Poor understanding and low clustering efficiency of massive data is a problem under the context of big data. To solve this problem, Canopy + K-means clustering algorithm is proposed, and the MapReduce programming model is used to make full use of the computing and storage capacity of Hadoop cluster. Large quantities of buyers on taobao are taken as application context to do case study through Hadoop platform's data mining set Mahout. General procedure for miming with Mahout is also given. clustering algorithm based on MapReduce shows preferable clustering quality and operation speed. Comparison is made between Canopy + K-means algorithm and K-means algorithm in respect of runtime, speed-up ratio and extendibility. Test is conducted for these two clustering algorithms on clusters with different numbers of nodes in context of dataset of various scales. The experimental results show that Canopy + K-means algorithm has faster operation speed than K-means algorithm, but both of them show good speed-up ratio under Hadoop environment and Canopy + K-means algorithm is even much better K-means algorithm.
Real-time monitoring of surface water quality is an intractable problem. A Soft-sensor method based on fuzzy neural network (FNN) is proposed to solve this problem in this paper. Firstly, the river data was analyzed b...
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ISBN:
(纸本)9789881563972
Real-time monitoring of surface water quality is an intractable problem. A Soft-sensor method based on fuzzy neural network (FNN) is proposed to solve this problem in this paper. Firstly, the river data was analyzed by principal component analysis (PCA) to obtain related variables such as dissolved oxygen (DO) and ammonia nitrogen (NH3-N). Secondly, a multi-input soft-sensor method based on FNN is designed. The training data is preprocessed by Hierarchical clustering and K-means algorithm (H-K algorithm), which improves the accuracy of the soft-sensor method. Finally, the soft-sensor method is packaged and applied to Beijing Tonghui River. The results indicate that the FNN based soft-sensor can predict surface water quality simultaneously with suitable prediction accuracy.
For the development of the Underwater Internet of Things, reliable transmission of underwater wireless sensor networks to monitor the marine environment is important. However, for ocean monitoring, the reliability of ...
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ISBN:
(纸本)9781728103501
For the development of the Underwater Internet of Things, reliable transmission of underwater wireless sensor networks to monitor the marine environment is important. However, for ocean monitoring, the reliability of data transmission is difficult to guarantee because of node mobility. In addition, energy consumption must be reduced during data transmission because node energy is limited. To entirely address these problems, this paper proposes a self-organising routing algorithm based on a joint clustering and routing strategy for ocean monitoring (JCR-OM) to increase reliable data transmission in underwater wireless sensor networks. Firstly, the reliable communication distance of the node is calculated in a multilayer current model by using a force analysis of the anchor node. Then, in cluster head selection, the reliable transmission distance and a backoff strategy are introduced to improve the impact of node mobility on data transmission. In intercluster routing selection, a greedy strategy is used to construct a routing strategy with minimum communication cost. The simulation results verify that JCR-OM can improve data transmission and prolong network lifetime.
with the rapid development of the Internet, the problem of cyber security has become increasingly serious. Trojan viruses and botnet programs play an important role in the Internet black industry chain. How to deal wi...
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ISBN:
(纸本)9781728140346
with the rapid development of the Internet, the problem of cyber security has become increasingly serious. Trojan viruses and botnet programs play an important role in the Internet black industry chain. How to deal with cyber security incidents quickly and efficiently is a problem that needs to be solved. It has been found that the malware infected users are widely distributed and have diverse types, which cause great difficulties for cybersecurity experts to handle the threats. Based on practical cybersecurity incident handling experience, this paper proposes a User-Event Threat Matrix Model to define the infected users, and implement a classification method based on machine learning algorithm. So that we could find out the high-risk infected users and pay more attention to them.
Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether ...
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Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether GPCR structures and GPCR conformational dynamics could be described in terms of conserved hydrogen-bond networks, and alterations of these hydrogen-bond networks along the reaction coordinate of the GPCR. To enable efficient analyses of the hydrogen-bond networks of GPCRs we implemented graph-based algorithms, and applied these algorithms to static GPCR structures from structural biology, and from molecular dynamics simulations of two opioid receptors. We find that static GPCR structures tend to have a conserved, core hydrogen-bond network which, when protein and water dynamics are included with simulations, extends to comprise most of the interior of an inactive receptor. In an active receptor, the dynamic protein-water hydrogen-bond network spans the entire receptor, bridging all functional motifs. Such an extensive, dynamic hydrogen-bond network might contribute to the activation mechanism of the GPCR.
In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min-Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is t...
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In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min-Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is to formulate an accurate and useful learning model that is able to perform online clustering and to explain its predictions. The online clustering capability is inherited from the FMM hyperboxes, while the explanatory capability arises from the underlying decision trees of ECT. Four different mean measures, namely harmonic, geometric, arithmetic, and root mean square, are incorporated into FMM for computing its hyperbox centroids. A series of benchmark and real-world data sets are used for evaluating the FMM-ECT performance. The results are analyzed and compared with those from other models. The outcomes indicate that FMM-ECT is able to achieve comparable clustering performances, with the advantage of providing explanations of its predictions using a decision tree. (c) 2017 Elsevier B.V. All rights reserved.
In order to solve the problem that the sorting threshold of traditional frequency-hopping signal needs to be manually adjusted, Faster-RCNN and clustering algorithm is proposed. In this paper, the Faster-RCNN is first...
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ISBN:
(纸本)9781538692981
In order to solve the problem that the sorting threshold of traditional frequency-hopping signal needs to be manually adjusted, Faster-RCNN and clustering algorithm is proposed. In this paper, the Faster-RCNN is firstly used to identify and locate all frequency-hopping points in the time-frequency spectrum diagram, and then AlexNet is used to obtain the number of frequency-hopping signal. Experimental results show that the Faster-RCNN can be effectively used for automatic signal sorting when the number of frequencyhopping signal is small.
Aiming at the shortcomings of the K-Means algorithm in the traditional K-Means algorithm, the DBSCAN algorithm is used to divide the order set according to the density, and obtain the batch number K value and the init...
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ISBN:
(纸本)9783030374297;9783030374280
Aiming at the shortcomings of the K-Means algorithm in the traditional K-Means algorithm, the DBSCAN algorithm is used to divide the order set according to the density, and obtain the batch number K value and the initial cluster center point. Based on this, the improved K-Means algorithm is used for optimization. Based on the real environment and instance data, the established batch assignment batch model is simulated. The experimental results show that the density-based K-Means clustering algorithm can effectively shorten the picking time and improve the warehouse logistics operation.
In order to solve the shortcomings of traditional industrial control network intrusion detection schemes, such as insensitivity to detection samples and inaccurate judgment of internal anomalies. An industrial control...
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ISBN:
(纸本)9781728121659
In order to solve the shortcomings of traditional industrial control network intrusion detection schemes, such as insensitivity to detection samples and inaccurate judgment of internal anomalies. An industrial control network intrusion detection scheme based on FCM algorithm and supervised Kohonen is proposed. FCM algorithm, FCM-GRNN network algorithm, FCM-BP network algorithm, FCM-Kohonen network algorithm and FCM-S_ Kohonen network algorithm are built on MATLAB software platform to test DARPA data samples. The accuracy of clustering results of different types of intrusion is counted according to five algorithms. The scheme can detect NORMAL, U2R, R2L, DoS and PRB network attacks more accurately, and the overall average classification accuracy rate is more than 95%.
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