Wszystkie pola: Mallik, S. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Power flow analysis and control of distributed FACTS devices in power system
Autorzy:: Anand, V.
Mallik, S. K.
Powiązania:: https://bibliotekanauki.pl/articles/141190.pdf
Data publikacji:: 2018
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: D-FACTS
UPFC
power flow control
stability
THD
FACTS
Opis:: The deployment of a distributed power-flow controller (DPFC) in a single- machine infinite-bus power system with two parallel transmission lines are considered for the analysis in this paper. This paper presents the network analysis of the DPFC for power flow control. The performance is evaluated on a given test system with a single line-to- ground fault. The improvement in the stability as well as power quality is evident from the results. Thus the DPFC has the ability to enhance the stability and power quality of the system.
Źródło:: Archives of Electrical Engineering; 2018, 67, 3; 545--561
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Integrated statistical and rule-mining techniques for dna methylation and gene expression data analysis
Autorzy:: Mallik, S.
Mukhopadhyay, A.
Maulik, U.
Powiązania:: https://bibliotekanauki.pl/articles/1396742.pdf
Data publikacji:: 2013
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: statistical analysis
gene marker
methylation
genetic algorithm
DNA
Opis:: For determination of the relationships among significant gene markers, statistical analysis and association rule mining are considered as very useful protocols. The first protocol identifies the significant differentially expressed/methylated gene markers, whereas the second one produces the interesting relationships among them across different types of samples or conditions. In this article, statistical tests and association rule mining based approaches have been used on gene expression and DNA methylation datasets for the prediction of different classes of samples (viz., Uterine Leiomyoma/class-formersmoker and uterine myometrium/class-neversmoker). A novel rule-based classifier is proposed for this purpose. Depending on sixteen different rule-interestingness measures, we have utilized a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training set of data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to estimate its class-label through weighted-sum method. We have run this classifier on the combined dataset using 4-fold cross-validations, and thereafter a comparative performance analysis has been made with other popular rulebased classifiers. Finally, the status of some important gene markers has been identified through the frequency analysis in the evolved rules for the two class-labels individually to formulate the interesting associations among them.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 2; 101-115
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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