Results 11 to 20 of about 389,923 (287)
Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm
Energies, 2020 In today’s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity.
Eshan Karunarathne +3 more
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Minimizers of Sparsity Regularized Huber Loss Function [PDF]
Journal of Optimization Theory and Applications, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Deniz Akkaya, Mustafa Ç. Pınar
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Кібернетика та комп'ютерні технології, 2020 Introduction. Due to the spread of COVID-19 in the world, mathematical modeling of epidemiological processes is an important and relevant scientific problem.
P. Knopov, O. Bogdanov
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IEEE Access, 2021 The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints.
Meisam Mahdavi +4 more
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Core loss resistance impact on sensorless speed control of an induction motor using hybrid adaptive sliding mode observer [PDF]
Archives of Electrical Engineering, 2023 Induction motors (IMs) experience power losses when a portion of the input power is converted to heat instead of driving the load. The combined effect of copper losses, core losses, and mechanical losses results in IM power losses.
Tadele Ayana +2 more
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Making Risk Minimization Tolerant to Label Noise [PDF]
, 2015 In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in presence of label noise.
Ghosh, Aritra +2 more
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Energies, 2018 This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered.
Zahir Sahli +3 more
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Optimal Minimization of the Covariance Loss
IEEE Transactions on Information Theory, 2023 Let $X$ be a random vector valued in $\mathbb{R}^{m}$ such that $\|X\|_{2} \le 1$ almost surely. For every $k\ge 3$, we show that there exists a sigma algebra $\mathcal{F}$ generated by a partition of $\mathbb{R}^{m}$ into $k$ sets such that \[\|\operatorname{Cov}(X) - \operatorname{Cov}(\mathbb{E}[X\mid\mathcal{F}]) \|_{\mathrm{F}} \lesssim \frac{1 ...
Vishesh Jain +2 more
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Noise Tolerance under Risk Minimization [PDF]
, 2012 In this paper we explore noise tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an ${\bf unobservable}$ training set which is noise-free.
Manwani, Naresh, Sastry, P. S.
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Large losses–-probability minimizing approach [PDF]
Applicationes Mathematicae, 2004 The probability minimizing problem of large losses of portfolio in discrete and continuous time models is studied. This gives a generalization of quantile hedging presented in [3].
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