An Adaptive Fuzzy Clustering Technique for Traffic Prediction of Packet-switched Networks

2001 ◽  
Vol 5 (3) ◽  
pp. 180-188
Author(s):  
Yau-Hwang Kuo ◽  
◽  
Mong-Fong Horng ◽  
Jung-Hsien Chiang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
High Accuracy ◽  
Traffic Prediction ◽  
Prediction Errors ◽  
Traffic Patterns ◽  
Switched Networks ◽  
Real Video ◽  
And Performance ◽  
Prediction Approach

Traffic prediction is significant to QoS design because it assists efficient management of network resources to improve the reliability and performance of the next generation Internet. The unavoidable traffic variation caused by diverse Internet services complicates traffic prediction, particularly in a multi-hop network. To simplify the complicated statistical analysis used in traditional approaches, an adaptive traffic prediction approach featuring robustness, high accuracy and high adaptability is proposed in this paper. The proposed approach bases on a novel fuzzy clustering algorithm to generalize and unveil the hidden structure of traffic patterns. The unveiled structure represents the characteristics of the target traffic. Therefore, it can be referenced to predict traffic in a limited time period by fuzzy matching. To track the variation of target traffic, the proposed approach adopts an incremental and dynamic on-line clustering procedure so that the prediction can maintain high accuracy under traffic variation. To verify the performance of the proposed approach and investigate its properties, the periodical, Poisson and real video traffic patterns have been used to experiment. The experimental results showed an excellent performance of the developed adaptive predictor. The prediction errors, in average, are near 2.2%, 13.6% and 7.62% for periodical, Poisson and real video traffics, respectively.

scholarly journals Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Naresh Kumar Nagwani ◽  
Shirish V. Deo
Keyword(s):  
Compressive Strength ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Prediction Errors ◽  
Concrete Compressive Strength ◽  
Independent Variables ◽  
Second Stage ◽  
Regression Techniques ◽  
Hard Clustering ◽  
Compressive Strength Of Concrete

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithmC-means performs better thanK-means algorithm.

Fuzzy clustering algorithm based on w-mean distance

2013 ◽  
Vol 32 (7) ◽  
pp. 1978-1982
Author(s):  
Rui-li ZHANG ◽  
Ji-fu ZHANG
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Clustering Algorithm

Model of Trust Monitoring and Updating Based on Dynamic Weighted Allocation

2019 ◽  
Vol 12 (1) ◽  
pp. 79-85
Author(s):  
Bin Hu ◽  
Yuemin Wu ◽  
Min Sun ◽  
Zheng Bang Liu ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Information Entropy ◽  
Network Environment ◽  
Mathematical Methods ◽  
Efficient Operation ◽  
Open Network ◽  
Dynamic Trust ◽  
Effective Synthesis ◽  
Effectiveness And Efficiency ◽  
And Performance

Backgrounds: In order to guarantee safe and efficient operation interaction in open network environment, a new dynamic trust monitoring and updating model based on behavior context is proposed in this paper. Methods: Setting four behavior attributes such as security, availability, reliability and performance. Then utilizing the fuzzy clustering and information entropy mathematical methods to carry out the effective synthesis on such attributes. Conclusion: The effectiveness and efficiency of the schema are verified by simulation.

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

scholarly journals Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 840
Author(s):  
Willem Q. M. van de Koot ◽  
Larissa J. J. van Vliet ◽  
Weilun Chen ◽  
John H. Doonan ◽  
Candida Nibau
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Plant Density ◽  
Important Determinant ◽  
Carbon Sink ◽  
High Accuracy ◽  
Analysis Pipeline ◽  
Colony Density ◽  
Water Table Management ◽  
And Performance

Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

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