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检索条件"主题词=clustering Algorithm"

共 1902 条记录，以下是361-370 订阅

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Comparative Analysis of Micro/Minichannel Flow Boiling Pattern Recognition and Classification using a Combined CNN-clustering algorithms Approach 14

Comparative Analysis of Micro/Minichannel Flow Boiling Patte...

引用

14th International Conference on Pattern Recognition Systems (ICPRS)

作者： Harris, Mohammad Angelopoulou, Anastasia Wu, Hongwei Zhang, Wenbin Univ Hertfordshire Sch Phys Engn & Comp Sci Coll Lane Campus Hatfield Herts England Univ Westminster Sch Comp Sci & Engn 115 New Cavendish St London England Nottingham Trent Univ Sch Sci & Technol Clifton Lane Nottingham England

ISBN: (纸本)9798350375657

Microchannel heat sinks have attracted considerable attention in thermal management applications owing to their high heat transfer capabilities and compact size. Amongst the cooling techniques, flow boiling in microchannels has emerged as a promising method for efficient heat dissipation. However, the intricate flow patterns in microchannels present challenges for accurate classification, pattern recognition, and inefficient data handling practices. This paper presented a comparative analysis of flow boiling classification techniques for pattern recognition in microchannel heat sinks. Three different clustering algorithm-driven convolutional neural networks (CNNs) were analysed and compared alongside a base CNN to establish a data pipeline capable of agile flow boiling pattern recognition. The Gaussian Mixture Model clustering-based CNN exhibited the best performance, achieving an overall mean accuracy of 88% for the test set validation. Thus, this study lays the groundwork for improving the performance of flow boiling pattern recognition in microchannel heat sinks.

关键词： pattern recognition image analysis clustering algorithm flow boiling thermal management case study

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Using hierarchical clustering algorithm to detect community structure in Traditional Chinese Medicine Formula network 27

Using hierarchical clustering algorithm to detect community ...

引用

27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Wang, Qian Li, Hong Wang, Tao Wang, Chong-Jun Yin, Xuri Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210046 Jiangsu Peoples R China Bengbu Automobile NCO Acad PLA Dept Transportat & Serv Bengbu 233011 Peoples R China

ISBN: (纸本)9781509001637

Traditional Chinese medicine (TCM) is a holistic medical approach and the formula's composition discipline is still a mystery. Detecting a formula's structure and herb communities/clusters in TCM Formula networks (TCMF) is a mainly existing problem in data mining of the data sets. In this paper, we devise a novel community similarity calculating method in the process of clustering, which is called Random Walk Hierarchical clustering (RWHC) algorithm, to identify herb communities by using clustering algorithms based on the formula network of atrophic lung disease. And we also use classic NG modularity function to evaluate the experimental results. The studies suggest that the TCM network clustering approach provides a new research paradigm for mining TCM data from an experience-based medicine, will accelerate TCM drug discovery, and also improve current drug discovery strategies.

关键词： Herb communities Community discovering Random walk clustering algorithm Formula network

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clustering based distributed multi-robot task allocation algorithm in large-scale systems 39

Clustering based distributed multi-robot task allocation alg...

引用

39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Meng, Fanyi Wang, Dong Liu, Zhong Lian, Jie Wang, Hongwei Dalian Univ Technol Sch Control Sci & Engn Dalian Peoples R China Grad Sch Chinese Aeronaut Estab Yangzhou Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This paper proposes a consistency bundle algorithm based on clustering grouping for large-scale task allocation problems. Large scale task allocation problems often result in incomplete network coverage, exponential increase in communication frequency, and communication obstruction. Therefore, this article first uses clustering algorithms to group robots based on the number of tasks, transforming large-scale problems into small-scale problems;Secondly, use the consistency bundle algorithm to solve the task allocation problem for each group separately;Finally, the algorithm was used for simulation experiments on large-scale task allocation problems, and the results showed that the proposed algorithm can solve the problem while effectively reducing the number of communications.

关键词： multi-robot task allocation problem clustering algorithm consensus-based bundle algorithm

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A GOP-Level Bitrate clustering Recognition algorithm for Wireless Video Transmission 2

A GOP-Level Bitrate Clustering Recognition Algorithm for Wir...

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2nd International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Shi, Wen-Juan Li, Song Sun, Yan-Jing Cao, Qi Zuo, Hai-Wei Yancheng Teachers Univ Sch New Energy & Elect Engn Yancheng 224051 Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China

ISBN: (纸本)9781614997221;9781614997214

The wireless video transmission process, due to the complexity and variability of wireless communication channels, requires to adjust the bitrates to match the dynamic wireless channel. An analysis of the video frame quality could be used as an important basis to adjust the wireless video bitrates. This paper aims at detecting and recognizing transmission bitrates based on video frame quality for wireless networks. According to the distribution characteristics of video frame quality, this paper proposes a GOP-level bitrate clustering recognition algorithm (GLBCR) by video coding GOP structural feature and temporal continuity of video frames to recognize different bitrates for wireless video. GLBCR uses PSNR between each pair of original and terminal decoding frame as the feature to quantify the degradation of video frame quality. The algorithm extracts the PSNR values of all I-frames by the peak detector function, then uses PSNR similarity measure to recursively split the frame interval into subintervals. Finally, the different video bitrates can be recognized by GLBCR. The proposed algorithm is evaluated by using the LIVE mobile video quality assessment (VQA) database. The results show that the proposed algorithm can recognize the change of video bitrates by analyzing video frame quality, it is well consistent with the real bitrate changes in wireless video transmission with small amount of calculation.

关键词： bitrate clustering recognition wireless video PSNR video frame quality clustering algorithm

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Group Mobility Based clustering algorithm for Mobile Ad Hoc Networks 17

Group Mobility Based Clustering Algorithm for Mobile Ad Hoc ...

引用

17th Asia-Pacific Network Operations and Management Symposium APNOMS 2015

作者： Cai, Mengqing Rui, Lanlan Liu, Danmei Huang, Haoqiu Qiu, Xuesong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9784885522963

Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propose a distributed clustering algorithm based on the group mobility and a revised group mobility metric which is derived from the instantaneous speed and direction of nodes. Our dynamic, distributed clustering approach use Gauss Markov group mobility model for mobility prediction that enables each node to anticipate its mobility relative to its neighbors. In particular, it is suitable for reflecting group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. We also take the residual energy of nodes and the number of neighbor nodes into consideration. The proposed clustering scheme aims to form stable clusters by reducing the clustering iterations even in a highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two well-known clustering approaches, the MOBIC and DGMA, in terms of average number of clusterhead changes.

关键词： MANETs clustering algorithm node mobility group mobility model

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A New Hierarchical clustering algorithm

A New Hierarchical Clustering Algorithm

引用

ICIIBMS 2015 Int C Intelligent Inform BioMedical

作者： Nazari, Zahra Kang, Dongshik Asharif, M. Reza Sung, Yulwan Ogawa, Seiji Univ Ryukyus Grad Sch Engn & Sci Nishihara Okinawa Japan Tohoku Fukushi Univ Kansei Fukushi Res Ctr Sendai Miyagi Japan

ISBN: (纸本)9781479985623

The purpose of data clustering algorithm is to form clusters (groups) of data points such that there is high intra-cluster and low inter-cluster similarity. There are different types of clustering methods such as hierarchical, partitioning, grid and density based. Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. A hierarchical clustering method can be thought of as a set of ordinary (flat) clustering methods organized in a tree structure. These methods construct the clusters by recursively partitioning the objects in either a top-down or bottom-up fashion. In this paper we present a new hierarchical clustering algorithm using Euclidean distance. To validate this method we have performed some experiments with low dimensional artificial datasets and high dimensional fMRI dataset. Finally the result of our method is compared to some of existing clustering methods.

关键词： clustering algorithm hierarchical clustering algorithm agglomerative hierarchical clustering fMRI data clustering

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Effective User Invitation Models for Online Survey Using clustering algorithm 5

Effective User Invitation Models for Online Survey Using Clu...

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Fifth International Conference on Instrumentation & Measurement, Computer, Communication, and Control (IMCCC)

作者： Shao, Sen Wu, Shaochun Zou, Guobing Chen, Liang Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

ISBN: (纸本)9781467377232

With the continuous and rapid development of online questionnaire survey, the low response rate has plagued operating companies. To solve this problem, this paper proposed an effective user invitation model by our improved clustering algorithm, which analyzed large-scale historical user behavior characteristic data, including users' quality data, users' preferential data and users' similarity data. Extensive experiments with large-scale data from an online survey company have been conducted to validate the feasibility and effectiveness of our proposed approach. Experimental results demonstrate that the questionnaire response rate is increased and our approach can be easily deployed in real-world online survey application for effective personalized survey recommendation.

关键词： online questionnaire survey clustering algorithm user invitation model response rate

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An Improved Fast Search clustering algorithm Based on Kernel Density

An Improved Fast Search Clustering Algorithm Based on Kernel...

引用

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Zhang, Ruisheng Ma, Huiyi Liu, Qidong Zhao, Zhili Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9781509018932

clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a higher practicability. RFSC, which is an improved algorithm of FSC algorithm, is less sensitive to the input parameters and faster. However, neither RFSC nor FSC can deal with uneven density data sets. In order to solve that problem, we propose an improved algorithm KFSC in this paper by dynamically controlling of the width of the kernel function. KFSC uses the idea of attractor of the DENCLUE and can customize their own personalized attraction for each point. The experimental results on synthetic data sets show that KFSC has a better performance on uneven density data sets than FSC and RFSC.

关键词： clustering algorithm density-based clustering kernel function uneven density

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Medical Image clustering algorithm Based on Graph Entropy 12

Medical Image Clustering Algorithm Based on Graph Entropy

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12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhan, Yu Pan, Haiwei Han, Qilong Xie, Xiaoqin Zhang, Zhiqiang Zhai, Xiao Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9781467376822

Recently, a variety of medical imaging technologies have been used widely in clinical diagnosis. As a large number of medical images are produced everyday, it becomes a hot issue of data mining on medical image in current that how to make full use of these medical images and cluster efficiently to help doctors to diagnose. In this paper, we propose a medical image clustering method. Firstly, medical image dataset is represented as a weighted, undirected and completed graph. Secondly, the graph is sparsified and pruned. This model can describe the similarity between medical images very well. Last, weighted and undirected graph clustering method based on graph entropy is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity and clustering results.

关键词： medical image entropy-based sparsification clustering algorithm

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A Stable K-hop clustering algorithm for Routing in Mobile Ad Hoc Networks 11

A Stable <i>K</i>-hop Clustering Algorithm for Routing in Mo...

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11th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Guizani, Badreddine Ayeb, Bechir Koukam, Abderrafiaa Fac Sci Unite Rech PRINCE CS Dept Monastir 5000 Tunisia Univ Technol Belfort Lab Syst & Transports F-90010 Montbeliard Belfort France

ISBN: (纸本)9781479953448

Flat routing protocols proposed for MANETs suffer from scalability problem. For this purpose, clustering schemes are proposed to improve the efficiency of routing by organizing the MANET into a hierarchical structure. In this context, we propose SKCA, a stable K-hop clustering algorithm with the view to provide a stable cluster topology and reduce the control overhead. The algorithm proposes a new maintenance function that attempts to reduce cluster topology changes and increases cluster lifetime. Besides, SKCA introduces a novel two-round cluster-head election that reduces the diffusion of the cluster information in the K-hop neighborhood. Using simulation, the performances of SKCA is compared to KCMM algorithm. Also, we study the performances of a cluster-based link state routing protocol in presence of our clustering algorithm SKCA.

关键词： clustering algorithm Mobile Ad Hoc Networks Cluster Stability Cluster-based routing

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