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检索条件"主题词=Clustering algorithm"

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Feature Enhanced Speckle Reduction In Ultrasound Images: algorithms For Scan Modelling, Speckle Filtering, Texture Analysis And Feature Improvement

Feature Enhanced Speckle Reduction In Ultrasound Images: Alg...

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作者： Prerna Singh University of Canterbury

学位级别：博士

This thesis aims to contribute to the advancement of research in the field of ultrasound image analysis by developing several novel algorithms in three key areas: (i) modelling, analysis and validation of synthetic ultrasound images, (ii) characterization and reduction of speckle artifacts, and (iii) enhancement of image features. These three areas have been identified based on existing gaps in research and their importance in advanced ultrasound image analysis frameworks for segmentation and classification. Synthetic models of ultrasound image formation that can generate noise-free ground truth data with intensity and texture characteristics of real ultrasound images, are valuable for machine learning applications and performance evaluation of speckle reduction techniques. This thesis develops a complete framework for synthetic ultrasound image generation incorporating algorithms for image acquisition, sampling and speckle simulation. The framework allows us to simulate image acquisition in both sector and linear scans with varying axial and lateral resolutions. Speckle artifacts appear in the form of granular noise in ultrasound images, degrading their diagnostic quality. This thesis presents novel algorithms for speckle reduction while preserving edges, fine details, and contrast of the image. The first despeckling framework presented in the thesis uses a novel application of clustering algorithms based on a transformation to wavelet sub-bands, and is inspired by the success of such methods for synthetic aperture radar imagery. The second despeckling framework uses a modified adaptive Wiener filter along with the Canny edge detection and an enhanced steerable pyramid transformation algorithm. Additionally, a coherence component extraction method is used to enhance the overall texture and edge features even in the darker portions of the image. The filtering operations used in a majority of speckle reduction methods induce blurring that affects edges and other fine

关键词： Medical ultrasound scans Synthetic ultrasound image modelling Speckle simulation Interpolation Feature extraction and selection Gray level co-occurrence matrix Local binary patterns Image quality measures Speckle filtering clustering algorithm Steerable pyramid transform Contrast limited adaptive histogram equalization

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Using Pattern Analysis and Machine Learning to Categorise users of Online Directories based on their Surfing Habits

Using Pattern Analysis and Machine Learning to Categorise us...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Athinarayanan, S. Joel, M. Robinson Jumlesha, Shaik Susmitha, K. Charitha, Limgamgunta School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Cse Chennai Tamil Nadu Avadi600062 India Aits Department of Computer Science and Engineering Andra Pradesh Tirupati India Kings Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350313987

The major goal is to group users together, including in student organizations. Assume that the notion is required to cluster consumers in educational institutions. A cluster of websites or web pages organized by user interest makes up an online use directory. Web mining is the process of using strategies to extract websites from the web proxy log file. We are employing the Apriori algorithm and the Fuzzy clustering technique for it. Typically, data cleaning, a preprocessing step, is followed by aggregation of the data after cleanliness in web use mining. We do a pattern analysis following the data's classification. The outcomes of the studies are contrasted with another intriguing technique that is made public. In comparison to other efforts, the outcomes produced by the suggested system exhibit better performance. The technique we utilised, called WDUM or web directories user mining, produces better results than earlier techniques. We compare user growth and penetration performance to the threshold values. Thus, after preprocessing, the log files are retrieved, and the Apriori algorithm and fuzzy logic classification are applied. These outcomes demonstrate the greatest methods for capturing user interest. Finally, the projected user clicked the prediction findings using machine learning techniques. © 2023 IEEE.

关键词： clustering algorithm Machine learning and Web proxy log Pattern analysis Web Usage Mining

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Optimal Design of Topological Structure for Mountainous Wind Farm Collection System 8

Optimal Design of Topological Structure for Mountainous Wind...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Chen, Guoyu Gao, Heng Li, Ming Dalian University of Technology State Key Laboratory of Structural Analysis for Industrial Equipment Dalian China

ISBN: (纸本)9798350328813

In wind farm projects, the investment cost of the collection system is high, and optimizing the topological structure of the collection system can save a significant amount of cost. This paper addresses the optimization problem of the collection system in mountainous wind farms and proposes a feasible optimization method, including methods for acquiring geographical information data and establishing topographic maps, subdomain partitioning considering terrain and cable load flow impact, three-dimensional shortest path search method, and cost-optimal collection cable connection method. Case studies demonstrate that for mountainous wind farm projects with significant terrain influences, this method exhibits good optimization performance. © 2023 IEEE.

关键词： A-star algorithm clustering algorithm collection system minimum cost tree algorithm mountainous wind farm optimized design

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Research on PCB Small Target Defect Detection Based on Improved YOLOv5 3

Research on PCB Small Target Defect Detection Based on Impro...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Liang, Mou Wu, Jigang Cao, Hong Hunan University of Science and Technology Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment Xiangtan China

ISBN: (纸本)9781665492812

As global automation accelerates, the importance of the PCB as a core component of electronic products grows with each passing day. The smallest hazards in PCBs can cause huge losses, so testing the quality of PCBs is an important step in the production process. To address the high level of integration, miniaturization, and multilayering of PCB production technology, we are using a new and improved model based on YOLOv5 to detect PCB defects. This new model solves the problems of difficult feature extraction, the similarity between features, and poor detection performance of PCB defects. In this paper, we use 10,668 images of PCB data containing six different defects. Experimental results show that the improved model in this paper has a detection accuracy of 99.0% and a detection speed of 0.016s compared to other defect detection algorithms of the same type. © 2022 IEEE.

关键词： Attention mechanism clustering algorithm Decoupled-head PCB defect detection YOLOv5

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Online clustering for Wind Speed Forecasting Based on Combination of RBF Neural Network and Persistence Method

Online Clustering for Wind Speed Forecasting Based on Combin...

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2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Xiao Qin Cong Jiang Jun Wang Department of Automation Tsinghua University Beijing China. Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong S Department of Control Science and Engineering Tongji University Shanghai China

This paper proposes an online clustering algorithm for wind speed forecasting. The algorithm combines the persistence method and the RBF neural network, and chooses an appropriate method according to different wind conditions. Computer simulations demonstrate that this algorithm can more accurately predict wind speed than either of the single methods and therefore is more effective for wind speed forecasting.

关键词： Wind speed prediction Combination forecasting On-line prediction clustering algorithm

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THE REASEARCH OF HIERARCHY MODEL FOR AD HOC NETWORK MONITORING BASED ON clustering

THE REASEARCH OF HIERARCHY MODEL FOR AD HOC NETWORK MONITORI...

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2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology（IC-BNMT2009）

作者： Zhou Yanping,Jin Yuehui,Cui Yidong,Que Xirong State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876

Ad hoc network has dynamic topology and large-scale nodes,for the purpose of monitoring and measurement in Ad hoc network,this paper proposes a new frame method of hierarchy model for monitoring,i.e.,clusterhead selection *** mechanism designed a weight formula to handle the comprehensive update considering several factors such as distance,mobile rate,energy during the model establishment period,and three kinds of partial update means in real-time attributes *** enhances load balance and structure stability in monitor hierarchy model and reduces calculation cost for unnecessary *** simulation results show that the model using clusterhead selection mechanism was stable and efficient,which could apply to muti-scale networks,achieve conveniently and flexible deployment in Ad hoc network measurement system.

关键词： Ad hoc network measurement monitor hierarchy model clustering algorithm

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Automatic clustering using particle swarm optimization with various validity indices

Automatic clustering using particle swarm optimization with ...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Chih-Wei Wang Jen-Ing G. Hwang Department of Computer Science and Information Engineering Fu Jen Catholic University Taipei Taiwan

ISBN: (纸本)9781467311830

Data clustering partitions a dataset into clusters where each cluster contains similar data. clustering algorithms usually require users to set the number of clusters, e.g., k-means or fuzzy c-means. However, it is difficult to determine a meaningful number of clusters if users lack prior knowledge of the data. Data clustering may use a validity index to grade the clustering quality. Most validity indices are based on clustering compactness and separation, but other criteria are also used for clustering. Therefore, no individual validity index is applicable to data with different properties. This paper presents a novel dynamic clustering based on particle swarm optimization. The proposed algorithm is compared with other dynamic clustering algorithms based on particle swarm optimization using artificial and real data sets. The experimental results showed that our proposed algorithm not only determines the appropriate number of clusters with correct cluster centers but can also be applied to data with different properties using various validity indices.

关键词： data clustering particle swarm optimization Validity index clustering algorithm dynamic clustering

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Performance Comparison of clustering Methods for Gene Family Data

Performance Comparison of Clustering Methods for Gene Family...

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2011 International Conference on Frontiers in Computer Education(ICFCE 2011)

作者： Dan Wei Qingshan Jiang Cognitive Science Department Xiamen University XiamenChina Fujian Key Laboratory of the Brain-like Intelligent Systems (Xiamen University) Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

clustering gene sequences into families is important for understanding and predicting gene functionMany clustering algorithms and alignment-free similarity measures have been used to analyze gene familyThe clustering results can be influenced by the similarity measure and clustering algorithm usedWe compare the results from running four commonly used clustering methods, including K-means, single-linkage clustering, completelinkage clustering and average-linkage clustering, on three alignment-free similarity measuresWe try to find out which method should provide the best clustering result based on real-world gene family datasetsExperiment results show that average-linkage clustering with our similarity measure, DMk, performed best.

关键词： Gene family clustering algorithm Similarity Measure

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32nd International Conference on Information Networking (ICOIN)

作者： Chang, Chun-De Lin, Hui-Tang Natl Cheng Kung Univ Inst Comp & Commun Engn Tainan Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan

ISBN: (纸本)9781538622902

The Internet plays an important role in people's lives nowadays. However, Internet security is a major concern. Among the various threats facing the Internet and Internet users are so-called botnet attacks. A typical botnet is composed of a botmaster, a Command and Control (C&C) server and many compromised devices called bots. A botmaster can control these bots via the C&C server to launch various attacks, such as DDOS attacks, phishing, spam distribution, and so on. Among all botnets, Domain Generation algorithm (DGA) botnets are particularly resilient to traditional detection by associating the C&C server to one of the generated domains in each bot. Accordingly, this study presents a robust approach for detecting DGA botnets based on an inspection of the DNS traffic in a system. In the proposed approach, the DNS records are filtered to remove known benign or malicious domains and are then clustered using a modified Chinese Whispers algorithm. The nature of each group (i.e., malicious or benign) is then identified by means of a Sequence Similarity Module and a Query Sequence Similarity Module. It is shown that the proposed method successfully detects various types of botnet in a real-world, large scale network.

关键词： Botnet Domain Flux DGA clustering algorithm

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clustering Analysis on the Introduction of Talents in Colleges

Clustering Analysis on the Introduction of Talents in Colleg...

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2018第三届应用数学与数据科学国际会议

作者： Fang Dan Chen Xinhui Xi Xin School of Data Sciences Zhejiang University of Finance and Economics

With the development of economy and technology,introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole ***,the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of "Double top" are put forward in the same *** of clustering analysis have been widely used in the actual *** this study,an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is ***,preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics,and use Principal Component Analysis(PCA),Weighted Principal Component Analysis(Weighted-PCA) and Random Forest(RF) to reduce the dimensions of the ***,use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed *** classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative *** addition,the experiment divides talents into high-end talents and mid-end *** looking into the analysis of the characteristics of the clustering results,some targeted advices on the talents introduction in colleges can be provided.

关键词： clustering Analysis Dimensionality Reduction clustering algorithm

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