Electronic Research Archive最新文献

In this paper, an adaptive neural network learning based nonsynchronous control method is developed for hidden Markov jump systems with unmodeled nonlinear dynamics. In particular, the system modes are not directly accessible and the limited mode information can be partly estimated by the nonsynchronous controller. More precisely, the mode information with partly accessible transition rates is utilized based on the transition probability matrix. Moreover, the unmodeled nonlinear dynamics are more general in practical applications. Based on the designed mode-dependent controllers with mode observation, sufficient conditions are first exploited by means of the Lyapunov method, such that the desired control performance could be ensured in the mean-square sense. Then, the nonsynchronous mode-dependent controllers are further determined in terms of convex optimization. In the end, our proposed control strategy is applied to a robotic manipulator with varying loads to validate the feasibility with simulation results.

The acquisition of antibiotic resistance due to the consumption of food contaminated with resistant strains is a public health problem that has been increasing in the last decades. Mathematical modeling is contributing to the solution of this problem. In this article we performed the qualitative analysis of a mathematical model that explores the competition dynamics in vivo of ceftiofur-resistant and sensitive commensal enteric Escherichia coli (E. coli) in the absence and during parenteral ceftiofur therapy within the gut of cattle, considering the therapeutic effects (pharmacokinetics (PK)/pharmacodynamics (PD)) in the outcome of infection. Through this analysis, empirical properties obtained through in vivo experimentation were verified, and it also evidenced other properties of bacterial dynamics that had not been previously shown. In addition, the impact of PD and PK has been evaluated.

Midori is a lightweight block cipher designed by Banik et al. and presented at the ASIACRYPT 2015 conference. According to the block size, it consists of two algorithms, denoted as Midori-64 and Midori-128. Midori generates 8-bit S-Boxes from 4-bit S-Boxes and applies almost MDS matrices instead of MDS matrices. In this paper, we introduce the meet-in-the-middle fault attack model in the 4-round cell-oriented fault propagation trail and reduce the key space in the last round by $ 2^{45.71} $ and $ 2^{39.86} $ for Midori-64 and Midori-128, respectively. For Midori-64, we reduce the time complexity from $ 2^{80} $ to $ 2^{28} $, $ 2^{32} $ and $ 2^{56} $ for the different single fault injection approaches. For Midori-128, we provide a 4-round fault attack method, which slightly increases the complexity compared to previous attacks. Our results indicate that the first and last four rounds of Midori must be protected to achieve its security.

Accurately predicting lane-changing behaviors (lane keeping, left lane change and right lane change) in real-time is essential for ensuring traffic safety, particularly in mixed-traffic environments with both autonomous and manual vehicles. This paper proposes a fused model that predicts vehicle lane-changing behaviors based on the road traffic environment and vehicle motion parameters. The model combines the ensemble learning XGBoost algorithm with the deep learning Bi-GRU neural network. The XGBoost algorithm first checks whether the present environment is safe for the lane change and then evaluates the likelihood that the target vehicle will make a lane change. Subsequently, the Bi-GRU neural network is used to accurately forecast the lane-changing behaviors of nearby vehicles using the feasibility of lane-changing and the vehicle's motion status as input features. The highD trajectory dataset was utilized for training and testing the model. The model achieved an accuracy of 98.82%, accurately predicting lane changes with an accuracy exceeding 87% within a 2-second timeframe. By comparing with other methods and conducting experimental validation, we have demonstrated the superiority of the proposed model, thus, the research achievement is of utmost significance for the practical application of autonomous driving technology.

We study the Benjamin-Bona-Mahony model with finite distributed delay in 3D, which depicts the dispersive impact of long waves. Based on the well-posedness of model, the family of pullback attractors for the evolutionary processes generated by a global weak solution has been obtained, which is unique and minimal, via verifying asymptotic compactness in functional space with delay $ C_V $ and topological space $ Vtimes C_V $, where the energy equation method and a retarded Gronwall inequality are utilized.

A two-degree-of-freedom vehicle wheel-rail impact vibration system model is developed, and the equivalent impact stiffness and damping of the rail are fitted applying ABAQUS, taking into account the high and low irregularity generated by the welded joints of the rail. A wheel-rail periodic interface with fixed impact was selected as the Poincaré map, and the fourth-order Runge-Kutta numerical method with variable step size was used to solve the system response. The dynamic characteristics of the system are investigated using a combination of the Bifurcation diagram, Phase plane diagram, the Poincaré map, the Time-domain diagram and the Frequency-domain diagram. It is verified that the vehicle wheel-rail impact vibration system has Hopf bifurcation, Neimark-Sacker bifurcation, Period-doubling bifurcation and Boundary crisis, and there are rich and complex nonlinear dynamic behavior changes. The research on the bifurcation and chaos characteristics of vehicle wheel-rail impact vibration systems can provide a reference for improving the stability of vehicle operation in engineering practice as well as the prediction and control of chaos in vehicle vibration reduction design.