scholarly journals GEOTECNOLOGIAS APLICADAS A AQUICULTURA: ESTUDO DE CASO DO POTENCIAL AQUÍCOLA DO MUNICÍPIO DE CONCEIÇÃO DO ARAGUAIA-PA.

2021 ◽  
Vol 23 (1) ◽  
pp. 218-238
Author(s):  
Victor Tiago Da Silva Catuxo ◽  
Breno Gustavo Bezerra Costa ◽  
Kátia Cristina De Araújo Silva
Keyword(s):  
Nearest Neighbor ◽  
Weighted Overlay ◽  
Natural Neighbor

O presente estudo no município de Conceição do Araguaia no estado do Pará visando observar de forma espacial o estabelecimento de empreendimentos aquícolas e a possibilidade de ampliação da atividade, utilizando análise espacial ponderada. Foi verificado a distribuição espacial dos piscicultores existentes e suas respectivas produções, através de ferramentas geoestatísticas, entre as quais: Averagem Nearest Neighbor (ANN) e kernel, para análise de densidade e o método Natural Neighbor (NN) para avaliar as áreas mais produtivas. Para análise de ponderação a áreas propícias a construção de viveiros escavados utilizou a metodologia Weighted Overlay, que sobrepõem várias camadas com pesos atribuídos, de acordo com a sua participação no processo construção aquícola. Na análise de ponderação foram utilizadas imagens de satélite processadas de forma a representar relevos, hidrografia e uso do solo, em alguns mapas foi utilizado informações de instituições governamentais. Os resultados mostraram que a dispersão dos piscicultores se deu de forma agregada. Os aglomerados formados dentro de um raio de 5,0 km, constituíram 6 áreas, mas apenas duas com potencial para crescimento. As ponderações de áreas propicias, mostraram que 27,43% é regular e 16,65% são ideais, muito embora a atividade se desenvolveu nas áreas regulares.

Experiment, evaluate methods of interpolation of terain surface for different types of terrain

2020 ◽  
Vol 61 (2) ◽  
pp. 116-125
Author(s):  
Yen Quoc Phan ◽  
Nga Thu Thi Nguyen ◽  
Keyword(s):  
Nearest Neighbor ◽  
Polynomial Interpolation ◽  
Spline Functions ◽  
Mountainous Terrain ◽  
Mean Square ◽  
Data Set ◽  
Natural Neighbor ◽  
Distance Weighting ◽  
Different Types ◽  
Inverse Distance

Surface modeling is done by many classic and modern algorithms such as Polynomial Interpolation, Delaunay Triangulation, Nearest Neighbor, Natural Neighbor, Kriging, Inverse Distance Weighting (IDW), Spline Functions, etc. The important issue is to experiment, evaluate and select algorithms suitable to the reality of the data and the study area. The paper used three algorithms IDW, Kriging and Natural Neighbor to model the terrain on two map sheets representing different types of terrain. From there, compare the results and evaluate the accuracy of the methods based on random test data from the data set which is extracted from the original map. In addition, checking the contour determined from the algorithm compared to the original contour were also carried out on the entire map sheet. Results show that: Natural Neighbor algorithm gives better results on both experimental areas, then IDW and Kriging algorithms, the root mean Square Error of 15.2922, 16.4754 and 17.9949 m respectively for average high terrain and 13.9728, 15.2466, 15.7613 meters with high mountainous terrain

Observation of Superlattice Dislocations on Cube Planes in Ni3Al

1973 ◽  
Vol 31 ◽  
pp. 174-175 ◽  
Author(s):  
J. M. Oblak ◽  
W. H. Rand
Keyword(s):  
Nearest Neighbor ◽  
Antiphase Boundary ◽  
Nearest Neighbors ◽  
High Energy ◽  
Cross Slip ◽  
Octahedral Plane ◽  
Trapping Mechanism ◽  
Slip Traces

The energy of an a/2 <110> shear antiphase. boundary in the Ll2 expected to be at a minimum on {100} cube planes because here strue ture is there is no violation of nearest-neighbor order. The latter however does involve the disruption of second nearest neighbors. It has been suggested that cross slip of paired a/2 <110> dislocations from octahedral onto cube planes is an important dislocation trapping mechanism in Ni3Al; furthermore, slip traces consistent with cube slip are observed above 920°K.Due to the high energy of the {111} antiphase boundary (> 200 mJ/m2), paired a/2 <110> dislocations are tightly constricted on the octahedral plane and cannot be individually resolved.

Application of Lattice Imaging Techniques to Amorphous Films

1975 ◽  
Vol 33 ◽  
pp. 8-9
Author(s):  
S. R. Herd ◽  
P. Chaudhari
Keyword(s):  
Nearest Neighbor ◽  
Random Network ◽  
Imaging Techniques ◽  
Network Models ◽  
Accurate Method ◽  
Direct Transmission ◽  
Lattice Imaging ◽  
Transmission Electron ◽  
Lattice Planes ◽  
Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

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