A Categorical Model for Airport Capacity Estimation Using Hierarchical Clustering

2017 ◽  
Vol 4 (4) ◽  
pp. 245-273
Author(s):  
Gokcin Cinar ◽  
Hernando Jimenez ◽  
Dimitri N. Mavris
Keyword(s):  
Hierarchical Clustering ◽  
Capacity Estimation ◽  
Categorical Model ◽  
Airport Capacity

Studies of capacity estimation of the airport with two parallel runways

2005 ◽  
Vol 109 (1098) ◽  
pp. 395-401 ◽  
Author(s):  
Yihua Cao ◽  
Yong Chen ◽  
Yong Zhou
Keyword(s):  
Mathematical Model ◽  
Historical Data ◽  
Analytical Models ◽  
Capacity Estimation ◽  
Capacity Curves ◽  
Airport Capacity ◽  
The Mathematical Model

Abstract The analytical capacity models of an airport with two runways system are developed in this paper and the theoretical capacity curves yielded by this model are analysed. The statistic airport capacity estimation methodology based on historical data is introduced as well. Both analytical models and statistic strategy are applied to estimate the capacity of the two runways system of a typical airport. Two sets of airport capacity curves obtained in different ways are compared and analysed. The result of the analysis indicates that the mathematical model works effectively in a fairly accurate way in the estimation of airport capacity.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

Femtocell Selection Scheme for Reducing Unnecessary Handover and Enhancing Downlink QoS in Cognitive Femtocell Networks

2017 ◽  
Author(s):  
Hoang Nhu Dong ◽  
Hoang Nam Nguyen ◽  
Hoang Trong Minh ◽  
Takahiko Saba
Keyword(s):  
Cellular Networks ◽  
Estimation Methods ◽  
Mobility Prediction ◽  
Capacity Estimation ◽  
Femtocell Networks ◽  
Selection Scheme ◽  
Indoor Communications ◽  
Performance Results ◽  
Cognitive Femtocell

Femtocell networks have been proposed for indoor communications as the extension of cellular networks for enhancing coverage performance. Because femtocells have small coverage radius, typically from 15 to 30 meters, a femtocell user (FU) walking at low speed can still make several femtocell-to-femtocell handovers during its connection. When performing a femtocell-to-femtocell handover, femtocell selection used to select the target handover femtocell has to be able not only to reduce unnecessary handovers and but also to support FU’s quality of service (QoS). In the paper, we propose a femtocell selection scheme for femtocell-tofemtocell handover, named Mobility Prediction and Capacity Estimation based scheme (MPCE-based scheme), which has the advantages of the mobility prediction and femtocell’s available capacity estimation methods. Performance results obtained by computer simulation show that the proposed MPCE-based scheme can reduce unnecessary femtocell-tofemtocell handovers, maintain low data delay and improve the throughput of femtocell users. DOI: 10.32913/rd-ict.vol3.no14.536

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Alifia Puspaningrum ◽  
Nahya Nur ◽  
Ozzy Secio Riza ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Hierarchical Clustering ◽  
Gabor Filter ◽  
Hierarchical Cluster ◽  
Experimental Result ◽  
Main Process ◽  
Percentile Method ◽  
Cluster Analysis Method ◽  
2D Gabor Filter

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

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