Analysis of the Leading Modes of Autumn Precipitation over the Yangtze River Basin

Based on daily precipitation data from 109 stations in the Yangtze River Basin(YRB)over the past 36 years(1980-2015),the Empirical Orthogonal Function(EOF)is employed to analyze changes in autumn precipitation.We used the monthly mean re-analysis datasets of atmospheric circulation and sea surface temperature(SST)to investigate the possible causes of the two leading modes,based on which the predictive equations were constructed and tested.The results of the EOF analysis show that the variance contribution of the first mode is 31.07%,and the spatial distribution shows a uniform variation over the whole region.The variance contribution of the second mode is 15.02%,and the spatial distribution displays a north-south dipole pattern in the YRB.The leading mode shows a dominant interannual variation,which is mainly due to the West Pacific subtropical high and anticyclones over the Philippine islands.The SST field corresponds to the positive phase of the eastern Pacific El Niño and the tropical Indian Ocean dipole.The second mode may be related to the Indian Ocean-East Asian teleconnection and early withdrawal of the summer monsoon.The SST field corresponds to a weaker central Pacific El Niño.Through a stepwise regression analysis,SST anomalies in some areas during summer show a good predictive effect on the autumn precipitation mode in the YRB region.

Rapid testing and prediction of soil–water characteristic curve of subgrade soils considering stress state and degree of compaction

The subgrade soil is generally in saturated or unsaturated condition. To analyze complex thermo-hydro-mechanical-chemical (THMC) behaviors of subgrade, it is essential to determine the soil–water characteristic curve (SWCC) that represents the relationship between matric suction and moisture content. In this study, a full-automatic rapid stress-dependent SWCC pressure-plate extractor was developed. Then, the influences of overburden stress and degree of compaction on the SWCC of subgrade soil such as high liquid limit silt (MH) and low liquid limit clay (CL) were analyzed. Accordingly, a new model taking into account the influences of overburden stress and degree of compaction based on the well-known Van Genuchten (VG) SWCC fitting model was presented and validated. The results show that with the increase of the degree of compaction and overburden stress, the saturated moisture content of subgrade soil decreases, while the air-entry value increases and the transition section curve becomes flat. The influences of the degree of compaction and overburden stress on the SWCC of MH is greater than that of CL. Meanwhile, there was a satisfactory agreement between the prediction and measurement, indicating a good performance of the new model for predicting the SWCC.

Method for Evaluating Bolt Competitive Failure Life Under Composite Excitation

In this study,the competitive failure mechanism of bolt loosening and fatigue is elucidated via competitive failure tests on bolts under composite excitation.Based on the competitive failure mechanism,the mode prediction model and“load ratio-life prediction curve”(ξ-N curve)of the bolt competitive failure are established.Given the poor correlation of theξ-N curve,an evaluation model of the bolt competitive failure life is proposed based on Miner’s linear damage accumulation theory.Based on the force analysis of the thread surface and simulation of the bolt connection under composite excitation,a theoretical equation of the bolt competitive failure life is established to validate the model for evaluating the bolt competitive failure life.The results reveal that the proposed model can accurately predict the competitive failure life of bolts under composite excitation,and thereby,it can provide guidance to engineering applications.

Reentry Attitude Tracking Control for Hypersonic Vehicle with Reaction Control Systems via Improved Model Predictive Control Approach

This paper studies the reentry attitude tracking control problem for hypersonic vehicles(HSV)equipped with reaction control systems(RCS)and aerodynamic surfaces.The attitude dynamical model of the hypersonic vehicles is established,and the simplified longitudinal and lateral dynamic models are obtained,respectively.Then,the compound control allocation strategy is provided and the model predictive controller is designed for the pitch channel.Furthermore,considering the complicated jet interaction effect of HSV during RCS is working,an improved model predictive control approach is presented by introducing the online parameter estimation of the jet interaction coefficient for dealing with the uncertainty and disturbance.Moreover,considering the strong coupling effect between the yaw channel and roll channel,a coupled model predictive controller is designed by introducing the feedback of sideslip angle into the roll control channel to eliminate the coupling effect.Finally,the comparison simulations using the classical control method,MPC and IMPC approach are given to demonstrate the effectiveness and efficiency of the presented IMPC scheme.

Interannual Variability and Scenarios Projection of Sea Ice in Bohai Sea Part Ⅰ: Variation Characteristics and Interannual Hindcast

The Bohai Sea is one of the southernmost areas for sea ice formation in the northern hemisphere.Sea ice disasters in this body of water severely affect marine activities and the safety of coastal residents.In this study,we analyze the variation characteristics of the sea ice in the Bohai Sea and establish an annual regression model based on predictable mode analysis method.The results show the following:1)From 1970 to 2018,the average ice grade is(2.6±0.8),with a maximum of 4.5 and a minimum of 1.0.Liaodong Bay(LDB)has the heaviest ice conditions in the Bohai Sea,followed by Bohai Bay(BHB)and Laizhou Bay(LZB).Interannual variation is obvious in all three bays,but the linear decreasing trend is significant only in BHB.2)Three modes are obtained from empirical orthogonal function analysis,namely,single polarity mode with the same sign of anomaly in all of the three bays and strong interannual variability(82.0%),the north–south dipole mode with BHB and LZB showing an opposite sign of anomalies to that in LDB and strong decadal variations(14.5%),and a linear trend mode(3.5%).Critical factors are analyzed and regression equations are established for all the principal components,and then an annual hindcast model is established by synthesizing the results of the three modes.This model provides an annual spatial prediction of the sea ice in the Bohai Sea for the first time,and meets the demand of operational sea ice forecasting.

Defining Embedding Distortion for Intra Prediction Mode-Based Video Steganography

In this paper,an effective intra prediction mode-based video strganography is proposed.Secret messages are embedded during the intra prediction of the video encoding without causing large embedding impact.The influence on the sum of absolute difference(SAD)in intra prediction modes(IPMs)reversion phenomenon is sharp when modifying IPMs.It inspires us to take the SAD prediction deviation(SPD)to define the distortion function.What is more,the mapping rule between IPMs and the codewords is introduced to further reduce the SPD values of each intra block.Syndrome-trellis code(STC)is used as the practical embedding implementation.Experimental results demonstrate that our proposed steganographic scheme presents high undetectability compared with existing IPMs-based steganographic approaches.It also outperforms these schemes on stego video quality.

Variable structure control with sliding mode prediction for discrete-time nonlinear systems

A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.

Optimal Mode Decision Method for Interframe Prediction in H.264/AVC

Studies show that encoding technologies in H.264/AVC,including prediction and conversion,are essential technologies.However,these technologies are more complicated than the MPEG-4,which is a standard method and widely adopted worldwide.Therefore,the amount of calculation in H.264/AVC is significantly up-regulated compared to that of the MPEG-4.In the present study,it is intended to simplify the computational expenses in the international standard compression coding system H.264/AVC for moving images.Inter prediction refers to the most feasible compression technology,taking up to 60%of the entire encoding.In this regard,prediction error and motion vector information are proposed to simplify the computation of inter predictive coding technology.In the initial frame,motion compensation is performed in all target modes and then basic information is collected and analyzed.After the initial frame,motion compensation is performed only in the middle 8×8 modes,and the basic information amount shifts.In order to evaluate the effectiveness of the proposed method and assess the motion image compression coding,four types of motion images,defined by the international telecommunication union(ITU),are employed.Based on the obtained results,it is concluded that the developed method is capable of simplifying the calculation,while it is slightly affected by the inferior image quality and the amount of information.

Improvement of the prediction accuracy of polar motion using empirical mode decomposition

Previous studies revealed that the error of pole coordinate prediction will significantly increase for a prediction period longer than 100 days, and this is mainly caused by short period oscillations. Empirical mode decomposition （EMD）, which is increasingly popular and has advantages over classical wavelet decomposition, can be used to remove short period variations from observed time series of pole co- ordinates. A hybrid model combing EMD and extreme learning machine （ELM）, where high frequency signals are removed and processed time series is then modeled and predicted, is summarized in this paper. The prediction performance of the hybrid model is compared with that of the ELM-only method created from original time series. The results show that the proposed hybrid model outperforms the pure ELM method for both short-term and long-term prediction of pole coordinates. The improvement of prediction accuracy up to 360 days in the future is found to be 24.91% and 26.79% on average in terms of mean absolute error （MAE） for the xp and yp components of pole coordinates, respectively.

A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints

In lightweight automotive vehicles,the application of self-piercing rivet(SPR)joints is becoming increasingly widespread.Considering the importance of automotive service performance,the fatigue performance of SPR joints has received considerable attention.Therefore,this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR joints.The dataset comprises three specimen types:cross-tensile,cross-peel,and tensile-shear.To ensure data consistency,a finite element analysis was employed to convert the external loads of the different specimens.Feature selection was implemented using various machine-learning algorithms to determine the model input.The Gaussian process regression algorithm was used to predict fatigue life,and its performance was compared with different kernel functions commonly used in the field.The results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue life.Among the data points,95.9%fell within the 3-fold error band,and the remaining 4.1%exceeded the 3-fold error band owing to inherent dispersion in the fatigue data.To predict the failure location,various tree and artificial neural network(ANN)models were compared.The findings indicated that the ANN models slightly outperformed the tree models.The ANN model accurately predicts the failure of joints with varying dimensions and materials.However,minor deviations were observed for the joints with the same sheet.Overall,this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.

Real-time predictive sliding mode control method for AGV with actuator delay

In this paper,a predictive sliding mode control method based on multi-sensor fusion is proposed to solve the problem of insufficient accuracy in trajectory tracking caused by actuator delay.The controller,based on the kinematics model,uses an inner and outer two-layer structure to achieve decoupling of position control and heading control.A reference positional change rate is introduced into the design of controller,making the automatic guided vehicle(AGV)capable of real-time predictive control ability.A stability analysis and a proof of predictive sliding mode control theory are provided.The experimental results show that the new control algorithm can improve the performance of the AGV controller by referring to the positional change rate,thereby improving the AGV operation without derailing.

To gel or not to gel：A prior prediction of gelation in solvent mixtures

The gelation behaviours of low molecular weight gelators 1,3：2,5：4.6-tris（3,4-dichlorobenzylidene）-Dmannitol（G1） and 2,4-（3.4-dichlorobenzylidene）-N-（3-aminopropyl）-D-gluconamide（G2） in 34 solvents have been studied.We found that sample dissolved at low concentrations may become a gel or precipitate at higher concentrations.The Hansen solubility parameters（HSPs） and a Teas plot were employed to correlate the gelation behaviours with solvent properties,but with no success if the concentration of the tests was not maintained constant.Instead,on the basis of the gelation results obtained for the G1 and G2 in single solvents,we studied the gelation behaviours of G1 and G2 in23 solvent mixtures and found that the tendency of a gelator to form a gel in mixed solvents is strongly correlated with its gelation behaviours in good solvents.If the gelation occurs in a good solvent at higher concentrations,it will take place as well in a mixed solvent（the good solvent plus a poor solvent） at a certain volume ratio.In contrast,if the gelator forms a precipitate in a good solvent at higher concentrations,no gelation is to be observed in the mixed solvents.A gelation rule for mixed solvents is thus proposed,which may facilitate decision making with regard to solvent selection for gel formation in the solvent mixtures in practical applications.

An Improved Intra-Prediction Algorithm for H.264

H.264 improves the coding efficiency by adopting rate distortion optimization (RDO); however,it yields high computa-tional complexity. In order to reduce the intra-frame coding time,a fast and effective intra-prediction mode selection algorithm is proposed. The minimum sum of absolute differences between sub-block and its marginal weighted-pixels are used for selecting the candidates of prediction modes to speed up the intra-prediction process with an early termination criterion. Experimental results show that the proposed method can reduce the encoding time by more than 63% comparing with JM (Joint Model) software and is better than the previous algorithms,with negligible loss of coding performance.