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The authors discuss their use of a multistage process to determine admissions to Korea Advanced Institute of Science and Technology. They use logistic regression with past data to estimate the enrollment probability of each applicant and then model the admission decision problem as a Markov decision process.

In lithium metal batteries (LMBs), electrolytes composed of salts and organic solvents play a significant role in transporting Li+ions and creating the surface film on Li-metal anodes. Herein, the effect of methyl acetate (MA) as a co-solvent is reported, which enables the facilitated Li+transport and formation of a robust solid electrolyte interphase (SEI) on the Li-metal anode. The symmetrical Li//Li cell tests show remarkable cycle stability of MA-based electrolytes at 3 mA/cm2without obvious voltage fluctuation. At 5 mA/cm2, the Li//Li cells in MA-based electrolytes can still run up to 110 h with lower overpotential, compared to the cell cycled with MA-free electrolytes. Furthermore, the LMBs consisting of the Li anode and LiNi0.8Co0.15Al0.05O2(NCA) cathode deliver the high capacity (∼200 mA h/g), good cycling stability up to 300 cycles, excellent rate capability (10 C), and low self-discharging rates (8.5%) with MA-based electrolytes. Especially, the capacity of the Li//NCA cells with MA30 electrolytes at −35 °C is as high as 144 mA h/g, which is higher than that of the cells in MA-free electrolytes. It demonstrates that the MA is beneficial for the LMB operation at high rate and low temperature.

In this paper, the lagged cross-correlation of two probability density functions constructed by kernel density estimation is proposed, and by maximizing the proposed function, adaptive filtering algorithms for supervised and unsupervised training are also introduced. From the results of simulation for blind equalization applications in multipath channels with impulsive and slowly varying direct current (DC) bias noise, it is observed that Gaussian kernel of the proposed algorithm cuts out the large errors due to impulsive noise, and the output affected by the DC bias noise can be effectively controlled by the lag τ intrinsically embedded in the proposed function.

In this paper, a new blind signal processing scheme for equalization in fading and impulsive-noise channel environments is introduced based on probability density function matching method and a set of Dirac-delta functions. Gaussian kernel of the proposed blind algorithm has the effect of cutting out the outliers on the difference between the desired level values and impulse-infected outputs. And also the proposed algorithm has relatively less sensitivity to channel eigenvalue ratio and has reduced computational complexity compared to the recently introduced correntropy algorithm. According to these characteristics, simulation results show that the proposed blind algorithm produces superior performance in multi-path communication channels corrupted with impulsive noise.

A method of width adaptation in the radial basis function network (RBFN) using stochastic gradient (SG) algorithm is introduced. Using Taylor's expansion of error signal and differentiating the error with respect to the step-size, the optimal time-varying step-size of the width in RBFN is derived. The proposed approach to adjusting widths in RBFN achieves superior learning speed and the steady-state mean square error (MSE) performance in nonlinear channel environment. The proposed method has shown enhanced steady-state MSE performance by more than 3 dB in both nonlinear channel environments. The results confirm that controlling over step-size of the width in RBFN by the proposed algorithm can be an effective approach to enhancement of convergence speed and the steady-state value of MSE.

A new blind equalization algorithm that is based on maximizing the probability that the constant modulus errors concentrate near zero is proposed. The cost function of the proposed algorithm is to maximize the probability that the equalizer output power is equal to the constant modulus of the transmitted symbols. Two blind information-theoretic learning (ITL) algorithms based on constant modulus error signals are also introduced: One for minimizing the Euclidean probability density function distance and the other for minimizing the constant modulus error entropy. The relations between the algorithms and their characteristics are investigated, and their performance is compared and analyzed through simulations in multi-path channel environments. The proposed algorithm has a lower computational complexity and a faster convergence speed than the other ITL algorithms that are based on a constant modulus error. The error samples of the proposed blind algorithm exhibit more concentrated density functions and superior error rate performance in severe multi-path channel environments when compared with the other algorithms.

Blind equalization techniques have been used in broadcast and multipoint communications. In this paper, two criteria of minimizing Euclidian distance between two probability density functions (PDFs) for adaptive blind equalizers are presented. For PDF calculation, Parzen window estimator is used. One criterion is to use a set of randomly generated desired symbols at the receiver so that PDF of the generated symbols matches that of the transmitted symbols. The second method is to use a set of Dirac delta functions in place of the PDF of the transmitted symbols. From the simulation results, the proposed methods significantly outperform the constant modulus algorithm in multipath channel environments.