Methods For Large Data Research Articles

Transformer Topology Identification Method Based on GPU Acceleration

Traditional transformer topology identification methods have poor accuracy and efficiency and are easy to be interfered. Aiming at these problems, this paper proposes a transformer topology identification based on GPU acceleration, which obtains and analyses the data from large quantities of smart meters and effectively identify the corresponding relationship between transformers and meters, via high performance computing technology and large data methods. Data analysis is implemented by GPU parallel accelerated grey correlation analysis method, which effectively improves the efficiency of the method. Numerical experiments show that the method proposed in this paper has high accuracy and computational efficiency, which has the value and potential of engineering applications.

Parallelization of MAFFT for large-scale multiple sequence alignments.

SummaryWe report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences.Availability and implementationThis feature is available in MAFFT versions 7.355 or later at https://mafft.cbrc.jp/alignment/software/mpi.html.Supplementary information Supplementary data are available at Bioinformatics online.

The Application of Modified Least Trimmed Squares with Genetic Algorithms Method in Face Recognition

Severely occluded face images are the main problem in low performance of face recognition algorithms. In this paper, we apply a new algorithm, a modified version of the least trimmed squares (LTS) with a genetic algorithms introduce by <!--[if supportFields]><i><span
 style='font-size:9.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family:
 "Times New Roman";color:black;mso-ansi-language:EN-US;mso-fareast-language:
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 style='mso-spacerun:yes'> </span>ADDIN EN.CITE
 <EndNote><Cite><Author>Rahim
 Abdul</Author><Year>2016</Year><RecNum>303</RecNum><DisplayText>[1]</DisplayText><record><rec-number>303</rec-number><foreign-keys><key
 app="EN" db-id="f5s5xeavm025z9expeavfwr2x2szra2tx55s"
 timestamp="1507074147">303</key></foreign-keys><ref-type
 name="Journal
 Article">17</ref-type><contributors><authors><author>Rahim
 Abdul, Nur Azimah</author><author>Ramli, Norazan
 Mohamed</author><author>Md Ghani, Nor Azura</author></authors></contributors><titles><title>The
 Performance of Modified Least Trimmed Squares-Based Methods for Large Data Sets
 Based on Monte Carlo Simulations</title><secondary-title>Advanced
 Science Letters</secondary-title></titles><periodical><full-title>Advanced
 Science
 Letters</full-title></periodical><pages>4359-4363</pages><volume>22</volume><number>12</number><keywords><keyword>Genetic
 Algorithm</keyword><keyword>Large Data</keyword><keyword>Least
 Trimmed Squares</keyword></keywords><dates><year>2016</year><pub-dates><date>//</date></pub-dates></dates><urls><related-urls><url>http://www.ingentaconnect.com/content/asp/asl/2016/00000022/00000012/art00091</url><url>https://doi.org/10.1166/asl.2016.8155</url></related-urls></urls><electronic-resource-num>10.1166/asl.2016.8155</electronic-resource-num></record></Cite></EndNote><span
 style='mso-element:field-separator'></span></span></i><![endif]-->[1]<!--[if supportFields]><i><span
 style='font-size:9.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family:
 "Times New Roman";color:black;mso-ansi-language:EN-US;mso-fareast-language:
 EN-US;mso-bidi-language:AR-SA'><span style='mso-element:field-end'></span></span></i><![endif]-->. We focused on the application of modified LTS with genetic algorithm method for face image recognition. This algorithm uses genetic algorithms to construct a basic subset rather than selecting the basic subset randomly. The modification in this method lessens the number of trials to obtain the minimum of the LTS objective function. This method was then applied to two benchmark datasets with clean and occluded query images. The performance of this method was measured by recognition rates. The AT&T dataset and Yale Dataset with different image pixel sizes were used to assess the method in performing face recognition. The query images were contaminated with salt and pepper noise. The modified LTS with GAs method is applied in face recognition framework by using the contaminated images as query image in the context of linear regression. By the end of this study, we can determine this either this method can perform well in dealing with occluded images or vice versa.