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检索条件"主题词=K-means clustering algorithm"

共 398 条记录，以下是111-120 订阅

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Study on the Path Optimized method Based on an Improved clustering Ant Colony algorithm for CNC Laser Drilling

Study on the Path Optimized method Based on an Improved Clus...

引用

International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Guo, Ente Wu, Ting Zhang, Libing Huang, Fengli Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Peoples R China

ISBN: (纸本)9783038351153

In order to improve the machining efficiency of the CNC laser drilling, this paper presents the path optimized method based on an improved ant colony algorithm of the k-means clustering approach. The mathematical model of the path optimization is constructed, and the path optimized method based on the improved clustering ant colony algorithm is designed and actualized. The optimized path method for CNC laser drilling based on the improved clustering ant colony algorithm is tested, and the simulative and experimental result have shown that the proposed method is better performance, and the machining efficiency is greatly improved.

关键词： path optimization laser drilling ant colony algorithm k-means clustering algorithm

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Outdoor Location Fingerprint Based on Optimized k-means clustering

Outdoor Location Fingerprint Based on Optimized K-means Clus...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Liu, Jingjing Chuai, Gang Gao, Weidong Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China

ISBN: (纸本)9781665439442

In the indoor scene, location fingerprint has been developed to improve the locating speed by using clustering algorithm to process the fingerprint database. In the outdoor scene, location fingerprint is still at the stage of constructing a fingerprint database due to the vast region and its large data, so its locating speed needs to be faster. In the view of the slow speed, this paper proposes optimized k-means algorithm by I-CFSFDP algorithm to process the outdoor fingerprint database, aiming to greatly increase the locating speed without sacrificing the accuracy of locating. In order to verify the performance of the algorithm, the standard propagation model is applied to calculate the sampling point's reference signal received power (RSRP), and universal kriging algorithm is used to interpolate the database, ensuring the authenticity of outdoor environment simulation. The result shows that the locating speed of outdoor fingerprint database can be greatly improved without decreasing accuracy, after being processed by I-CFSFDP optimized k-means.

关键词： outdoor locating location fingerprint RSRP universal kriging interpolation algorithm k-means clustering algorithm

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Research on Interference from 5G System to NGSO Satellite Constellation Based on k-means clustering 1

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5th International Conference on Space Information Networks (SINC)

作者： Li, Linghui Li, Wei Ren, Zixuan Jin, Jin kuang, Linling Tsinghua Univ Beijing Peoples R China State Radio Monitoring Ctr Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (数字)9789811619670

ISBN: (纸本)9789811619663;9789811619670

In the co-frequency coexistence problem between the ground 5G system and NGSO (non-geostationary Orbit) constellation system, the interference from the 5G system to NGSO satellites is a typical scenario. Due to the massive number of satellites in the NGSO satellite constellation system, the location, beam direction and beam coverage of satellites are constantly changing. There are issues that the interference calculation amount is large and the actual distribution of the 5G system is difficult to obtain. To address these issues, we analyze the system model and interference principle, put forward the method of 5G system radiation energy to reduce the calculation amount, and present the location analysis method of 5G system based on k-means clustering to reflect the actual distribution of 5G system. Based on this, the interference from the Taiyuan 5G system to the O3b system satellite constellation is simulated. Compared with the existing simulation methods, the proposed method has less computation and is more in line with the actual distribution characteristics of the specific urban 5G system.

关键词： NGSO satellite constellation 5G k-means clustering algorithm

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A clustering-based approach for quality level verification of sanitation procedures in workplaces

A clustering-based approach for quality level verification o...

引用

IEEE International Workshop on Metrology for Industry 4.0 & IoT (IEEE MetroInd4.0 and IoT)

作者： Santucci, Francesca Faramondi, Luca Setola, Roberto Massenzi, Marco Orlando, Francesco Univ Campus Biomed Roma Unit Automat Control Rome Italy Teleconsys SpA Rome Italy

ISBN: (纸本)9781665419802

With the COVID-19 pandemic outbreak, sanitizing procedures have become fundamental in work environments, where surfaces and objects are frequently touched by multiple people, enhancing the risk of exposure to the disease. To assure safe working conditions, it is of primary importance to assess the adherence of the sanitation activity to the recommended protocols with a certain level of accuracy. In this work, we propose a methodology able to estimate the accuracy level of sanitation procedures by applying clustering techniques on multiple features extracted from wrist-mounted accelerometric sensors measurements.

关键词： sanitation quality level distance-based clustering features extraction k-means clustering algorithm

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Applying Hybrid kEPSO clustering to Web Pages 10

Applying Hybrid KEPSO Clustering to Web Pages

引用

48th ACM Annual Southeast Regional Conference (SE)

作者： Moh, Teng-Sheng Sabnis, Ameya San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9781450300643

Various optimization methods are used along with the standard clustering algorithms to make the clustering process simpler and quicker. In this paper we propose a new hybrid technique of clustering known as k-Evolutionary Particle Swarm Optimization (kEPSO) based on the concept of Particle Swarm Optimization (PSO). The proposed algorithm uses the k-means algorithm as the first step and the Evolutionary Particle Swarm Optimization (EPSO) algorithm as the second step to perform clustering. The experiments were performed using the clustering benchmark data. This method was compared with the standard k-means and EPSO algorithms. The results show that this method produced compact results and performed faster than other clustering algorithms. Later, the algorithm was used to cluster web pages. The web pages were clustered by first cleaning the unnecessary data and then labeling the obtained web pages to categorize them.

关键词： clustering algorithms k-means clustering algorithm Particle Swarm Optimization Data clustering Evolutionary Particle Swarm Optimization algorithm

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Segmentation of Rapeseed Color Drone Images Using k-means clustering 19

Segmentation of Rapeseed Color Drone Images Using K-Means Cl...

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3rd International Conference on Computer Science and Application Engineering (CSAE)

作者： Yang, kang Liu, Changhua Wu, Xiaoming Li, Hao Wuhan Polytech Univ Wuhan Peoples R China Chinese Acad Agr Sci Oil Crops Res Inst Wuhan Peoples R China

ISBN: (纸本)9781450362948

Aiming at the problem of unsatisfactory segmentation effect of aerial image of rapeseed flowers in the process of florescence recognition. This paper proposes a method combining k-means algorithm and color segmentation algorithm to segment rapeseed image. Firstly, the k-means algorithm is used to first process the rapeseed image in Lab space. Then, the clustering results were processed once again in HSV space using color segmentation algorithm. Finally, the segmented rapeseed was subjected to morphological treatment to complete the effective segmentation of rapeseed and rapeseed flowers. Sixty different aerial rapeseed images were selected for segmentation experiments. The results show that this method can not only segment rapeseed well, but also effectively avoid the influence of illumination. The results of this experiment can provide reference for the later study of the flowering period of rapeseed.

关键词： Drone rapeseed image k-means clustering algorithm Color segmentation algorithm Image segmentation

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Hybrid clustering-Time Series-Bayesian Neural Network Short-Term Load Forecasting Method 48

Hybrid Clustering-Time Series-Bayesian Neural Network Short-...

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48th North American Power Symposium (NAPS)

作者： Ghofrani, M. Carson, D. Ghayekhloo, M. Univ Washington Sch Sci Technol Engn & Math STEM Bothell WA 98011 USA Qazvin Islamic Azad Univ Dept Elect & Comp Engn Qazvin Iran

ISBN: (纸本)9781509032709

This paper offers a hybrid short-term load forecasting (STLF) model using a Bayesian neural network (BNN) with a pre-processing stage consisting of a k-means clustering algorithm and time series analysis. The data clusters are time series analyzed to provide the most accurate data sets for each hour of the day. The final forecast is provided from the BNN output. California load data is used to determine the accuracy and processing speed of the proposed method. Additionally a comparison between BNN and other intelligent algorithms is provided using the same pre-processing stage to further gauge performance benchmarks.

关键词： Bayesian neural network k-means clustering algorithm short-term load forecasting

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Structure pattern analysis using term rewriting and clustering algorithm

Structure pattern analysis using term rewriting and clusteri...

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作者： Fu, Xuezheng Georgia State University

学位级别：Ph.D.

Biological data is accumulated at a fast pace. However, raw data are generally difficult to understand and not useful unless we unlock the information hidden in the data. knowledge/information can be extracted as the patterns or features buried within the data. Thus data mining, aims at uncovering underlying rules, relationships, and patterns in data, has emerged as one of the most exciting fields in computational science. In this dissertation, we develop efficient approaches to the structure pattern analysis of RNA and protein three dimensional structures. The major techniques used in this work include term rewriting and clustering algorithms. Firstly, a new approach is designed to study the interaction of RNA secondary structures motifs using the concept of term rewriting. Secondly, an improved k-means clustering algorithm is proposed to estimate the number of clusters in data. A new distance descriptor is introduced for the appropriate representation of three dimensional structure segments of RNA and protein three dimensional structures. The experimental results show the improvements in the determination of the number of clusters in data, evaluation of RNA structure similarity, RNA structure database search, and better understanding of the protein sequence-structure correspondence. keywords. Data mining, knowledge discovery, Term rewriting, k-means clustering algorithm, Validation measure, Stability, and Bioinformatics.

关键词： Data mining knowledge discovery Term rewriting k-means clustering algorithm Validation measure Stability Bioinformatics

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Study on Multi-UAV Task clustering and Task Planning in Cooperative Reconnaissance 6

Study on Multi-UAV Task clustering and Task Planning in Coop...

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhao Junwei Zhao Jianjun Naval Aeronaut & Astronaut Univ Grad Students Brigade Yantai Peoples R China Naval Aeronaut & Astronaut Univ Ordnance Sci & Technol Dept Yantai Peoples R China

ISBN: (纸本)9781479949557

For multi-UAV cooperative reconnaissance to enemy's multi-task points, because of multi-task, reasonable clustering is needed and the task clustering model should be established. In this paper, the task planning model is established according to task clustering of each UAV, and the sequence of task execution is determined. Reasonable task clustering optimization index is put forward. Task allocation is proposed based on improved k-means clustering algorithm of simulated annealing. The shortest path task planning is designed using the simulated annealing algorithm, which makes multi-UAV relatively balanced in the assignments, the task group in the group centralized distribution, inter-group distribution scattered and the total cruise time shortest. Simulation results show that the task clustering is well achieved and the optimum task planning program is obtained. The validity of the model and algorithm is verified and the algorithm has certain theoretical and practical value.

关键词： cooperative reconnaissance task clustering Task Planning Task allocation k-means clustering algorithm

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Precision of Rough Set clustering

引用

6th International Conference on Rough Sets and Current Trends in Computing

作者： Lingras, Pawan Chen, Min Miao, Duoqian St Marys Univ Dept Math & Comp Sci Halifax NS B3H 3C3 Canada Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9783540884231

Conventional clustering algorithms categorize an object into precisely one cluster. In many applications, the membership of some of the objects to a cluster can be ambiguous. Therefore, an ability to specify membership to multiple clusters can be useful in real world applications. Fuzzy clustering makes it possible to specify the degree to which a given object belongs to a cluster. In Rough set representations, an object may belong to more than one cluster, which is more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The unsupervised nature of fuzzy and rough algorithms means that there is a choice about the level of precision depending on the choice of parameters. This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.

关键词： Rough sets k-means clustering algorithm precision

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