Skyline Minimum Vector

2010 ◽  
Author(s):  
Su Min Jang ◽  
Choon Seo Park ◽  
Jae Soo Yoo
Keyword(s):  
Minimum Vector

Cache Valley virus: isolations from mosquitoes in Saskatchewan, 1972–1974

1979 ◽  
Vol 25 (6) ◽  
pp. 760-764 ◽  
Author(s):  
J. O. Iversen ◽  
R. J. Wagner ◽  
M. K. Leung ◽  
L. B. Hayles ◽  
J. R. McLintock
Keyword(s):  
Infection Rate ◽  
Culex Tarsalis ◽  
Cache Valley Virus ◽  
Cache Valley ◽  
Culiseta Inornata ◽  
Minimum Vector

Eighteen isolations of Cache Valley virus (Bunyaviridae) were obtained from a total of 113 694 mosquitoes collected in Saskatchewan during the summers of 1972 to 1974. Most of the isolations were from mosquitoes collected during August. Culiseta inornata, the most abundant mosquito (38% of total collected), had the highest minimum vector-infection rate (0.83 isolations per 1000 mosquitoes). The virus was also isolated from Culex tarsalis and Aedes texans. It is indicated in the isolations that the prairie grasslands of the province are enzootic for Cache Valley virus.

scholarly journals Modifikasi Penaksir Robust dalam Pelabelan Outlier Multivariat

2018 ◽  
Vol 14 (1) ◽  
pp. 46
Author(s):  
Erna Tri Herdiani
Keyword(s):  
Minimum Volume ◽  
Data Set ◽  
Minimum Volume Ellipsoid ◽  
Minimum Vector

Outlier adalah suatu observasi yang polanya tidak mengikuti mayoritas data. Outlier dalam kasus multivariat sangat sulit untuk dideteksi, khususnya ketika dimensi lebih dari 2. Kesulitan ini meningkat ketika data set berukuran besar, yakni jumlah variabel menjadi besar. Metode-metode pendeteksian outlier telah lama berkembang dan beberapa digunakan untuk pelabelan outlier sehingga data dapat dipisahkan antara data yang dicurigai sebagai outlier dan data set pada umumnya. Metode-metode tersebut adalah minimum volume ellipsoid disingkat MVE, minimun covariance determinant disingkat MCD, dan minimum vector variance disingkat MVV. Dari ketiga metode tersebut MVV memiliki waktu perhitungan yang paling cepat. Berdasarkan algoritma MVV, kriteria mengurutkan data menggunakan jarak mahalanobis, maka pada paper ini akan dimodifikasi kriteria pengurutan data dengan menghindari penulisan dalam bentuk invers dari matriks variansi kovariansi. Hasil yang diperoleh adalah metode MVV menjadi lebih cepat dengan menggunakan kriteria baru dengan kecermatan yang sama dengan MVV sebelumnya serta akan diaplikan untuk data real dan data simulasi.

Modular Product Design, Considering Functions and Properties

2012 ◽  
Author(s):  
Israel Aguilera Navarrete ◽  
Alejandro A. Lozano Guzmán
Keyword(s):  
Product Design ◽  
Data Clustering ◽  
Design Problem ◽  
Modular Design ◽  
Recursive Method ◽  
Clustering Method ◽  
Data Presentation ◽  
Modular Product ◽  
Technical Solutions ◽  
Minimum Vector

In traditional machine, equipment and devices design, technical solutions are practically independent, thus increasing designs cost and complexity. Overcoming this situation has been tackled just using designers experience. In this work, a data clustering method which allows this data presentation in a more systematic way using a matrix arrangement, is shown. From this matrix, data can be reorganized in clusters with a hierarchical structure, in such a way that modular design is now more tractable. Proposed method is based on a Euclidean algorithm which allows finding the shortest vectorial distance among technical solutions. Taking product properties as vector dimensions, a recursive method for moving matrix rows and columns is applied. As a result of this procedure, the minimum vector distances are found thus being possible to identify the best technical solutions for the design problem raised. The proposed modular procedure is shown with a 30 inches oven door design.

scholarly journals An Alternative Hotelling T^2 Control Chart Based on Minimum Vector Variance (MVV)

2011 ◽  
Vol 5 (4) ◽  
Author(s):  
Sharifah Soaad Syed Yahaya ◽  
Hazlina Ali ◽  
Zurni Omar
Keyword(s):  
Control Chart ◽  
Minimum Vector

scholarly journals Outlier Detection Using Minimum Vector Variance Algorithm with Depth Function and Mahalanobis Distance

2021 ◽  
Vol 17 (3) ◽  
pp. 418-427
Author(s):  
Puji Puspa Sari ◽  
Erna Tri Herdiani ◽  
Nurtiti Sunusi
Keyword(s):  
Data Analysis ◽  
Outlier Detection ◽  
Mahalanobis Distance ◽  
Statistical Approach ◽  
Depth Function ◽  
Approach Method ◽  
Minimum Vector ◽  
Data Sorting

Outliers are observations where the point of observation deviates from the data pattern. The existence of outliers in the data can cause irregularities in the results of data analysis. One solution to this problem is to detect outliers using a statistical approach. The statistical approach method used in this study is the Minimum Vector Variance (MVV) algorithm which has robust characteristics for outliers. The purpose of this research is to detect outliers using the MVV algorithm by changing the data sorting criteria using the Robust Depth Mahalanobis to produce maximum detection. The results obtained from this study are that RDMMVV is superior to the observed value in showing the outliers and the location of the outliers in the data plot compared to DMMVV and MMVV.

scholarly journals Minimum vector rank and complement critical graphs

2014 ◽  
Vol 27 ◽  
Author(s):  
Xiaowei Li ◽  
Michael Nathanson ◽  
Rachel Phillips
Keyword(s):  
Critical Graphs ◽  
Minimum Vector

scholarly journals On the minimum vector rank of multigraphs

2010 ◽  
Vol 20 ◽  
Author(s):  
Lon Mitchell ◽  
Sivaram Narayan ◽  
Ian Rogers
Keyword(s):  
Minimum Vector
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