scholarly journals METODE NILAI JARAK GUNA KESAMAAN ATAU KEMIRIPAN CIRI SUATU CITRA (KASUS DETEKSI AWAN CUMULONIMBUS MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS)

2017 ◽  
Vol 7 (2) ◽  
pp. 21 ◽  
Author(s):  
Dwi Nugraheny

One commonality or similarity matching phase characteristics of an image is by using the method of distance measurement. Distance is an important aspect in the development of methods of grouping and regression. Before the grouping of data or object to the detection process, first determined the size of the proximity distance between data elements. In this study, there will be a comparison of several methods including distance measurement using Euclidean distance, Manhattan/ City Block Distance, Mahalanobis which will be implemented in the case of cumulonimbus image clouds detection using Principal Component Analysis (PCA). The average percentage of accuracy of image similarity value Cumulonimbus clouds using the Euclidean distance method was 93 percent and the distance Manhattan/ City Block Distance is 90 percent, while the Mahalanobis distance method was 50 percent.

Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.


2016 ◽  
Vol 19 (03) ◽  
pp. 382-390 ◽  
Author(s):  
Martina Siena ◽  
Alberto Guadagnini ◽  
Ernesto Della Rossa ◽  
Andrea Lamberti ◽  
Franco Masserano ◽  
...  

Summary We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through principal-component analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesian-clustering algorithm. Considering the cluster that includes the EOR field under evaluation, an intercluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components (PCs) and (b) the fraction of variance associated with each PC is taken as weight of the Euclidean distance that we determine. As a test bed, we apply our approach on three fields operated by Eni. These include light-, medium-, and heavy-oil reservoirs, where gas, chemical, and thermal EOR projects were, respectively, proposed. Our results are (a) conducive to the compilation of a broad and extensively usable database of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.


2005 ◽  
Vol 3 (4) ◽  
pp. 731-741 ◽  
Author(s):  
Petr Praus

AbstractPrincipal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.


2019 ◽  
pp. 1478-1492
Author(s):  
Sovik Mukherjee

The chapter brings out a brief note on the tourist attractions, hotels and lodges, NGOs/travel agencies operating in that region, railway/bus stations, land use profile, etc. in the Sundarban area of West Bengal in conjunction with exploring the potential of ecotourism using GIS and some secondary source data. Moving onto the analysis part, by making use of geo-spatial data, the attributes of ecotourism potential in the Sundarbans has been explored. The author makes use of the Euclidean distance mechanism and principal component analysis to rank the ecotourism sites in Sunderbans (i.e., based on the construction of ecotourism potential index [EPI]). The novelty of the chapter lies in comparing the ranks obtained by constructing the EPI following the principal component analysis and the Euclidean distance function. It needs to be mentioned here that these tourist spots have been selected based on the information collected on the inflow of both domestic and foreign tourists to these spots. The chapter concludes by discussing the future scope of research in this regard.


2021 ◽  
Vol 12 (4) ◽  
pp. 93-104
Author(s):  
Diego Vipa Amâncio ◽  
Gilberto Coelho ◽  
Rosângela Francisca de Paula Vitor Marques ◽  
Laíla Luana Campos ◽  
Renato Antônio da Silva

Population growth and industrialization are correlated with the contamination of water resources by the release of untreated effluents into water sources. The objective of this work was to characterize heavy metals in sub-basins of the rivers Capivari and Mortes and the variability using principal component analysis (PCA). Three points were sampled at GD1 (P - I at Ingai – Minduri River, P - II at Capivari River and P - III at Ingai – Luminarias River) and three points at GD2 (P - IV at Mortes River, P - V at Peixe River and P - VI at Ribeirao dos Tabuoes). The monitoring period was from April 2015 to February 2016. Analysis of Aluminum, Bromine, Copper, Hexavalent Chromium, Iron, Manganese, Nickel and Zinc were evaluated. We compared the results with the Maximum Allowed Value in agreement with class 2, according to DN COPAM CERH 01/08. We also observed variables above the allowed value due to the discharge of domestic and industrial effluents, interference from precipitation and the contact between livestock and water sources. The principal components analysis (PCA) revealed that on average, the principal component 1 corresponds to 62.2% of the total variability of the data considering GD 1, and, in GD 2, PC 1 is responsible for a higher average percentage of the total variability of the data, corresponding to 73.4%, hence being more representative.


Author(s):  
Sovik Mukherjee

The chapter brings out a brief note on the tourist attractions, hotels and lodges, NGOs/travel agencies operating in that region, railway/bus stations, land use profile, etc. in the Sundarban area of West Bengal in conjunction with exploring the potential of ecotourism using GIS and some secondary source data. Moving onto the analysis part, by making use of geo-spatial data, the attributes of ecotourism potential in the Sundarbans has been explored. The author makes use of the Euclidean distance mechanism and principal component analysis to rank the ecotourism sites in Sunderbans (i.e., based on the construction of ecotourism potential index [EPI]). The novelty of the chapter lies in comparing the ranks obtained by constructing the EPI following the principal component analysis and the Euclidean distance function. It needs to be mentioned here that these tourist spots have been selected based on the information collected on the inflow of both domestic and foreign tourists to these spots. The chapter concludes by discussing the future scope of research in this regard.


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