Variable Selection for Robust Mixture Regression Model with Skew Scale Mixtures of Normal Distributions

In this paper, we propose a robust mixture regression model based on the skew scale mixtures of normal distributions (RMR-SSMN) which can accommodate asymmetric, heavy-tailed and contaminated data better. For the variable selection problem, the penalized likelihood approach with a new combined penalty function which balances the SCAD and l2 penalty is proposed. The adjusted EM algorithm is presented to get parameter estimates of RMR-SSMN models at a faster convergence rate. As simulations show, our mixture models are more robust than general FMR models and the new combined penalty function outperforms SCAD for variable selection. Finally, the proposed methodology and algorithm are applied to a real data set and achieve reasonable results.

Animal Exercise:A New Evaluation Method

At present,Animal Exercise courses rely too much on teachers’subjective ideas in teaching methods and test scores,and there is no set of standards as a benchmark for reference.As a result,students guided by different teachers have an uneven understanding of the Animal Exercise and cannot achieve the expected effect of the course.In this regard,the authors propose a scoring system based on action similarity,which enables teachers to guide students more objectively.The authors created QMonkey,a data set based on the body keys of monkeys in the coco dataset format,which contains 1,428 consecutive images from eight videos.The authors use QMonkey to train a model that recognizes monkey body movements.And the authors propose a new non-standing posture normalization method for motion transfer between monkeys and humans.Finally,the authors utilize motion transfer and structural similarity contrast algorithms to provide a reliable evaluation method for animal exercise courses,eliminating the subjective influence of teachers on scoring and providing experience in the combination of artificial intelligence and drama education.

Enhanced Detection of Rolling Element Bearing Fault Based on Stochastic Resonance

Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance（SR） is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.

Evaluation of Burden Descent Model for Burden Distribution in Blast Furnace

Mathematical models for burden descending process have been applied to obtain whole burden structures in blast furnace,whereas the accuracy of those burden descent models has not been sufficiently investigated.Special evaluation method based on timeline burden profiles was established to quantitatively evaluate the error between experimental and modeled burden structures.Four existing burden descent models were utilized to describe the burden structure of a 1/20 scaled warm blast furnace.Input modeling conditions including initial burden profile,descending volumes in each time interval,and normalized descending velocity distribution were determined via special image processing technology.Modeled burden structures were evaluated combined with the published experimental data.It is found that all the models caught the main profile of the burden structure.Furthermore,the improved nonuniform descent model（Model IV）shows the highest level of precision especially when burden descends with unstable velocity distribution tendency.Meanwhile,the traditional nonuniform descent model（Model III）may also be desirable to model the burden descending process when the burden descending velocity presents a linear tendency.Finally,the uniform descent model（Model I）might be the first option for roughly predicting burden structure.

Electrochemical response of Nd^3＋ ions containing lithium borate glasses

The present investigation was to understand the electrical properties of 27.5 Li2O-（72.5–x） B2O（3-x）Nd2O3 with x=0.5, 1, 1.5 and 2 glasses. We analyzed the impedance data for a wide frequency range to get inside the conduction phenomenon. The conductivity of the glasses decreased due to the decrease in the mobility of mobile Li~＋ ions. This decrease was due to polymerization of glass network caused by the Nd^3＋ ions. Modulus formalism confirmed that the Li~＋ ions overcame the same barrier no matter whether it took part in the conduction process or relaxation process. Scaling of the electrochemical data showed that conduction process in the ion conducting glasses was composition dependent and not the temperature dependent.