Appropriate maintenance technologies that facilitate model consistency in distributed simulation systems are relevant but generally unavailable. To resolve this problem, we analyze the main factors that cause model inconsistency. The analysis methods used for traditional distributed simulations are mostly empirical and qualitative, and disregard the dynamic characteristics of factor evolution in model operational running. Furthermore, distributed simulation applications (DSAs) are rapidly evolving in terms of large-scale, distributed, service-oriented, compositional, and dynamic features. Such developments present difficulty in the use of traditional analysis methods in DSAs, for the analysis of factorial effects on simulation models. To solve these problems, we construct a dynamic evolution mechanism of model consistency, called the connected model hyper-digraph (CMH). CMH is developed using formal methods that accurately specify the evolutional processes and activities of models (i.e., self-evolution, interoperability, compositionality, and authenticity). We also develop an algorithm of model consistency evolution (AMCE) based on CMH to quantitatively and dynamically evaluate influencing factors. Experimental results demonstrate that non-combination (33.7% on average) is the most influential factor, non-single-directed understanding (26.6%) is the second most influential, and non-double-directed understanding (5.0%) is the least influential. Unlike previous analysis methods, AMCE provides good feasibility and effectiveness. This research can serve as guidance for designers of consistency maintenance technologies toward achieving a high level of consistency in future DSAs.

Improving simulation performance using activity tracking has attracted attention in the modeling field in recent years. The reference to activity has been successfully used to predict and promote the simulation performance. Tracking activity, however, uses only the inherent performance information contained in the models. To extend activity prediction in modeling, we propose the activity enhanced modeling with an activity meta-model at the meta-level. The meta-model provides a set of interfaces to model activity in a specific domain. The activity model transformation in subsequence is devised to deal with the simulation difference due to the heterogeneous activity model. Finally, the resource-aware simulation framework is implemented to integrate the activity models in activity-based simulation. The case study shows the improvement brought on by activity-based simulation using discrete event system specification (DEVS).

Exponential stability and robust exponential stability relating to switched systems consisting of stable and unstable nonlinear subsystems are considered in this study. At each switching time instant, the impulsive increments which are nonlinear functions of the states are extended from switched linear systems to switched nonlinear systems. Using the average dwell time method and piecewise Lyapunov function approach, when the total active time of unstable subsystems compared to the total active time of stable subsystems is less than a certain proportion, the exponential stability of the switched system is guaranteed. The switching law is designed which includes the average dwell time of the switched system. Switched systems with uncertainties are also studied. Sufficient conditions of the exponential stability and robust exponential stability are provided for switched nonlinear systems. Finally, simulations show the effectiveness of the result.

We investigate the optimization of linear impulse systems with the reinforcement learning based adaptive dynamic programming (ADP) method. For linear impulse systems, the optimal objective function is shown to be a quadric form of the pre-impulse states. The ADP method provides solutions that iteratively converge to the optimal objective function. If an initial guess of the pre-impulse objective function is selected as a quadratic form of the pre-impulse states, the objective function iteratively converges to the optimal one through ADP. Though direct use of the quadratic objective function of the states within the ADP method is theoretically possible, the numerical singularity problem may occur due to the matrix inversion therein when the system dimensionality increases. A neural network based ADP method can circumvent this problem. A neural network with polynomial activation functions is selected to approximate the pre-impulse objective function and trained iteratively using the ADP method to achieve optimal control. After a successful training, optimal impulse control can be derived. Simulations are presented for illustrative purposes.

We develop a new kind of underwater inductive coupling power transfer (ICPT) system to evaluate wireless power transfer in autonomous underwater vehicle (AUV) docking applications. Parameters that determine the performance of the system are systematically analyzed through mathematical methods. A circuit simulation model and a finite element analysis (FEA) simulation model are developed to study the power losses of the system, including copper loss in coils, semiconductor loss in circuits, and eddy current loss in transmission media. The characteristics of the power losses can provide guidelines to improve the efficiency of ICPT systems. Calculation results and simulation results are validated by relevant experiments of the prototype system. The output power of the prototype system is up to 45 W and the efficiency is up to 0.84. The preliminary results indicate that the efficiency will increase as the transmission power is raised by increasing the input voltage. When the output power reaches 500 W, the efficiency is expected to exceed 0.94. The efficiency can be further improved by choosing proper semiconductors and coils. The analysis methods prove effective in predicting the performance of similar ICPT systems and should be useful in designing new systems.

Multimedia broadcast multicast service (MBMS) with inherently low requirement for network resources has been proposed as a candidate solution for using such resources in a more efficient manner. On the other hand, the Next Generation Mobile Network (NGMN) combines multiple radio access technologies (RATs) to optimize overall network performance. Handover performance is becoming a vital indicator of the quality experience of mobile user equipment (UE). In contrast to the conventional vertical handover issue, the problem we are facing is how to seamlessly transmit broadcast/multicast sessions among heterogeneous networks. In this paper, we propose a new network entity, media independent broadcast multicast service center (MIBM-SC), to provide seamless handover for broadcast/multicast sessions over heterogeneous networks, by extensions and enhancements of MBMS and media independent information service (MIIS) architectures. Additionally, a network selection scheme and a cell transmission mode selection scheme are proposed for selecting the best target network and best transmission mode. Both schemes are based on a load-aware network capacity estimation algorithm. Simulation results show that the proposed approach has the capability to provide MBMS over heterogeneous networks, with improved handover performance in terms of packet loss rate, throughput, handover delay, cell load, bandwidth usage, and the peak signal-to-noise ratio (PSNR).