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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.2018-02-23T02:38:46ZAbel–Tauber process and asymptotic formulas
http://repository.iitgn.ac.in/handle/123456789/3472
Abel–Tauber process and asymptotic formulas
Banerjee, D.; Chakraborty, K.; Kanemitsu, S.; Maji, Bibekananda
The Abel-Tauber process consists of the Abelian process of forming the Riesz sums and the subsequent Tauberian process of differencing the Riesz sums, an analogue of the integration-differentiation process. In this article, we use the Abel-Tauber process to establish an interesting asymptotic expansion for the Riesz sums of arithmetic functions with best possible error estimate. The novelty of our paper is that we incorporate the Selberg type divisor problem in this process by viewing the contour integral as part of the residual function. The novelty also lies in the uniformity of the error term in the additional parameter which varies according to the cases. Generalization of the famous Selberg Divisor problem to arithmetic progression has been made by Rieger [Zum Teilerproblem von Atle Selberg. Math. Nachr. 30 (1965), 181-192], Marcier [Sums of the form Σ g(n)/f(n). Canad. Math. Bull. 24 (1981), 299-307], Nakaya [On the generalized division problem in arithmetic progressions. Sci. Rep. Kanazawa Univ. 37 (1992), 23-47] and around the same time Nowak [Sums of reciprocals of general divisor functions and the Selberg division problem, Abh. Math. Sem. Univ. Hamburg 61 (1991), 163-173] studied the related subject of reciprocals of an arithmetic function and obtained an asymptotic formula with the Vinogradov-Korobov error estimate with the main term as a finite sum of logarithmic terms. We shall also elucidate the situation surrounding these researches and illustrate our results by rich examples.
2018-02-01T00:00:00ZShort-term wind power forecasting using wavelet-based neural network
http://repository.iitgn.ac.in/handle/123456789/3471
Short-term wind power forecasting using wavelet-based neural network
Abhinava, Rishabh; Pindoriya, Naran M.; Wu, Jianzhong; Long, Chao
Wind power generation highly depends on the atmospheric variables which itself depend on the time of the day, months and seasons. The intermittency of wind hinders the accuracy of wind forecasting, which is important for safe operation and reliability of future power grid. One way to address this problem is to consider all these atmospheric variables which can be obtained from Numerical Weather Prediction (NWP) models. However, using NWP parameters increases the complexity of the forecast model and it requires a large amount of historic data. Additionally, different models are required for different seasons or months. This paper presents a wavelet-based neural network (WNN) forecast model which is robust enough to predict the wind power generation in short-term with significant accuracy, and this model is applicable to all seasons of the year. With reduced complexity, the model requires less historic data as compared to that in available literatures
2017-11-01T00:00:00ZReview of the book: Democracy and Its Institutions by André Béteille
http://repository.iitgn.ac.in/handle/123456789/3470
Review of the book: Democracy and Its Institutions by André Béteille
Mehta, Mona G.
2017-02-01T00:00:00ZHigh yield synthesis of chemically modified magnesium diboride nanosheets by chelation assisted chemical exfoliation
http://repository.iitgn.ac.in/handle/123456789/3469
High yield synthesis of chemically modified magnesium diboride nanosheets by chelation assisted chemical exfoliation
Saraswat, Rohit; James, Asha Liza; Jasuja, Kabeer
2018-01-29T00:00:00Z