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This paper considers a parallel machine earliness/tardiness (ET) scheduling problem with different penalties under the effects of position based learning and linear and nonlinear deterioration.
Let players who lose all their clothes keep playing with different penalties if they lose, such as having to answer a truth or do a dare.
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Fig. 6 Weighted SP recovery based on the diagnosed supports with different penalty values.
First, we study the generality of the proposed approach with different penalty functions.
Compared to existing work [23], we provide two main extensions: First, we study the generality of the proposed approach with different penalty functions.
In Fig. 6, we compare the performance of downlink channel estimation with different penalty values in weighting matrix in the proposed support diagnosis algorithm.
In order to guarantee comparability between the S4VDPCA and the RSPCA with different penalty functions, the BIC used in Lee et al. (2010) to choose an optimal penalization parameter within the RSPCA algorithm is replaced by the GIC of Equation (10).
However, qualitative examination of the LUCA versions reconstructed with different gain penalties indicates that, even with a gain penalty of 1 (equal weights assigned to a gain and a loss), the set of 572 genes assigned to LUCA might be nearly sufficient to sustain a functioning organism.
Unlike LMS-based methods, the proposed NLMS-based methods have avoided the uncertain signal scaling and normalized the power of input signal with different sparse penalties.
To solve this problem, we propose several improved adaptive sparse channel estimation methods using normalized LMS algorithm with different sparse penalties, which normalizes the power of input signal.
To solve this problem, we propose several improved adaptive sparse channel estimation methods using normalized LMS NLMSS) algorithm, which normalizes the power of input signal, with different sparse penalties, i.e., ℓp-norm (0 ≤ p ≤ 1).
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