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Background: Drivers drowsiness is one of the prime reasons for road accidents. Electroencephalogram (EEG) signals provide crucial information regarding drowsy state due to neurological changes in the brain. But the complex nature of EEG signals makes it difficult to study these changes. A detailed analysis of the EEG signal can be done if it is decomposed into multi-modes. Method : In this paper, adaptive variational mode decomposition (AVMD) is used for accurate analysis and synthesis of EEG signals. The number of modes ( ${J}$ ) and quadratic penalty factor ( $\alpha $ ) is selected adaptively to find out representative information from EEG signals. Selection of ${J}$ and $\alpha $ is done by minimizing the reconstruction error using the Jaya optimization algorithm. Features are extracted from the adaptively decomposed modes. Entropy-based features selected by statistical analysis are classified with different classification algorithms. Eight performance parameters are evaluated to test the system’s effectiveness. Results: The reconstruction error of $4.035\times 10^{-09}$ and $1.564\times 10^{-09}$ for the alert and drowsy state shows that the proposed method gives a better synthesis of signals. An accuracy, sensitivity, specificity, F-1 score, Kappa, false-positive rate, error, and precision of 97.19%, 97.01%, 97.46%, 0.976, 94.23%, 2.54%, 2.81%, and 98.18% shows that the proposed method provides representative modes for analysis. Conclusion: The comparison shows that AVMD is superior over conventional and existing methods by about 7% and 1%, respectively. The solution provided in this paper takes a step ahead for efficient synthesis and analysis of EEG signals to detect the drowsy state of drivers.

Electric vehicles (EV) offer a sustainable solution to the emission problems from transportation but require appropriate charging management facilities. This article presents a decentralized charging management scheme for EV scheduling in a multiaggregator scenario and is compared with a centralized scheduling (CES) strategy in terms of performance. Mobility-aware scheduling is implemented, where the EV arrival at charging stations is modeled using a survey conducted for the city of Jaipur, India, to evaluate the travel patterns. A new approach for modeling the willingness of the consumer has been presented that evaluates the willingness based on scheduling parameters, which include the charging cost, the charging duration, and the arrival/scheduling time of the vehicle. A penalty function has also been developed for the inclusion of the customer willingness into the profit equation. Results indicate superior performance of decentralized scheduling (DES) as compared with CES. Furthermore, the developed willingness indicator accommodates the preference of the EV driver, and the penalty factor represents the EV user's agreement with the scheduling in the profits. DES in collaborative scheduling provided the highest profits for the aggregators.

Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry's law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cut-off conditions are met. Ten low- and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance.

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency., Comment: Accepted to ISIT 2014

In this paper, the authors proposed a heuristic constraint-handing technique for considering equality and inequality constraints of economic dispatch (ED) problems. The proposed constraint-handling technique is able to improve the solution quality even if it might spend more CPU time than any penalty factor approaches.

In order to operate a power system at optimum economy, the production costs and transmission losses must be coordinated. A straightforward derivation of the co-ordination conditions is given. A widely adopted method of finding transmission penalty factors used so-called "B-constants." These constants are not easily modified to consider changes in the transmission system or deviation from "base-case" system conditions. By starting with the equilibrium equation of a power system written in terms of admittance constants, a concise expression for the total losses of a power transmission system which does not neglect shunt paths to neutral or the effect of off-nominal turns ratios is developed. Following this same approach, an easily modified, approximate expression for the penalty factor is formed. Any errors incurred by using this approximate penalty factor are small when compared with the inaccuracy of the original data on production costs. Several test cases on hypothetical systems are given. The generation schedules for economic dispatch, using approximate penalty factors, compare quite favorably in actual hourly costs with the costs of schedules obtained by exact penalty factors.

As systems expand, the proper co-ordination of incremental fuel costs and incremental transmission losses in the economic operation of power systems becomes increasingly important. This penalty factor computer-slide rule combination offers the advantages of simplicity, ease of operation, and accurate representation of incremental fuel costs.