We investigate the controllability for a one-dimensional wave equation in domains with moving boundary. This model characterizes small vibrations of a stretched elastic string when one of the two endpoints varies. When the speed of the moving endpoint is less than , by Hilbert uniqueness method, sidewise energy estimates method, and multiplier method, we get partial Dirichlet boundary controllability. Moreover, we will give a sharper estimate on controllability time that only depends on the speed of the moving endpoint.

This paper addresses the study of the controllability for a one-dimensional wave equation in domains with moving boundary. This equation characterizes the motion of a string with a fixed endpoint and the other one moving. When the speed of the moving endpoint is less than , by the Hilbert Uniqueness Method, the exact controllability of this equation is established. Also, the explicit dependence of the controllability time on the speed of the moving endpoint is given.

Traditional mobile ad hoc network (MANET) routing protocols assume that contemporaneous end-to-end communication paths exist between data senders and receivers. In some mobile ad hoc networks with a sparse node population, an end-to-end communication path may break frequently or may not exist at anytime. Many routing protocols have been proposed in the literature to address the problem, but few were evaluated in a realistic "opportunistic" network setting. We use simulation and contact traces (derived from logs in a production network) to evaluate and compare five existing protocols: direct-delivery, epidemic, random, PRoPHET, and Link-State, as well as our own proposed routing protocol. We show that the direct delivery and epidemic routing protocols suffer either low delivery ratio or high resource usage, and other protocols make tradeoffs between delivery ratio and resource usage.

Adaptive inverse control (AIC) theory is introduced to inhibit interferences such as side wind during hovering control for small-sized unmanned helicopter. Based on this theory, an AIC control block diagram is improved to accommodate with the characteristics of helicopter hovering model and robust design. According to structure and fundamental theory of small-sized unmanned helicopter, a hover flight experimental platform is built. Also, the translation equations and the rotation equations are derived by the Kane Equation. Recursive least square (RLS) method is used to identify the parameters and design the controller. The experimental results show that, in terms of interference elimination, AIC-RLS method is better than PID controller. The realization of open-air hovering control on flight experimental platform demonstrates the method presented in this paper is effective.

Taking the small unmanned four-rotor as a research object, the dynamic model of the small four-rotor was built according to Newton-Euler formaLism. Adaptive Inverse Control (AIC) Theory was used in the control of the small unmanned four-rotor for the first time and the attitude stabiLization control of the small unmanned four-rotor was reaLized on the experimental platform. The simulation results and platform control experiment indicate that AIC is robust in the attitude control of the small unmanned four-rotor.

Flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem, and provides a closer approximation to real world scheduling situations. This paper present a hybrid genetic algorithm (GA) combined with chaotic local search to solve the FJSP with MAKESPAN criterion. A small percentage of elitist individuals are introduced into the initial population to fasten GA's convergence speed, efficient crossover and mutation operators are adopted to avoid infeasible solutions and to hasten the emergency of optimum solution. During the local search process, Logistic chaotic sequence is adopted to explore better neighborhood solutions around the best individual of the current generation. Representative flexible job shop scheduling benchmark problems are solved in order to test the effectiveness and efficiency of the proposed algorithm.

We evaluate mobility predictors in wireless networks. Handoff prediction in wireless networks has long been considered as a mechanism to improve the quality of service provided to mobile wireless users. Most prior studies, however, were based on theoretical analysis, simulation with synthetic mobility models, or small wireless network traces. We study the effect of mobility prediction for a large realistic wireless situation. We tackle the problem by using traces collected from a large production wireless network to evaluate several major families of handoff-location prediction techniques, a set of handoff-time predictors, and a predictor that jointly predicts handoff location and time. We also propose a fallback mechanism, which uses a lower-order predictor whenever a higher-order predictor fails to predict. We found that low-order Markov predictors, with our proposed fallback mechanisms, performed as well or better than the more complex and more space-consuming compression-based handoff-location predictors. Although our handoff-time predictor had modest prediction accuracy, in the context of mobile voice applications we found that bandwidth reservation strategies can benefit from the combined location and time handoff predictor, significantly reducing the call-drop rate without significantly increasing the call-block rate. We also developed a prediction-based routing protocol for mobile opportunistic networks. We evaluated and compared our protocol's performance to five existing routing protocols, using simulations driven by real mobility traces. We found that the basic routing protocols are not practical for large-scale opportunistic networks. Prediction-based routing protocols trade off the message delivery ratio against resource usage and performed well and comparable to each other.

Wireless local area networks (WLANs) are emerging as a popular technology for access to the Internet and enterprise networks. In the long term, the success of WLANs depends on services that support mobile network clients. Although other researchers have explored mobility prediction in hypothetical scenarios, evaluating their predictors analytically or with synthetic data, few studies have been able to evaluate their predictors with real user mobility data. As a first step towards filling this fundamental gap, we work with a large data set collected from the Dartmouth College campus-wide wireless network that hosts more than 500 access points and 6,000 users. Extending our earlier work that focuses on predicting the next-visited access point (i.e., location), in this work we explore the predictability of the time of user mobility. Indeed, our contributions are two-fold. First, we evaluate a series of predictors that reflect possible dependencies across time and space while benefiting from either individual or group mobility behaviors. Second, as a case study we examine voice applications and the use of handoff prediction for advance bandwidth reservation. Using application-specific performance metrics such as call drop and call block rates, we provide a picture of the potential gains of prediction. Our results indicate that it is difficult to predict handoff time accurately, when applied to real campus WLAN data. However, the findings of our case study also suggest that application performance can be improved significantly even with predictors that are only moderately accurate. The gains depend on the applications’ ability to use predictions and tolerate inaccurate predictions. In the case study, we combine the real mobility data with synthesized traffic data. The results show that intelligent prediction can lead to significant reductions in the rate at which active calls are dropped due to handoffs with marginal increments in the rate at which new calls are blocked.

Many network services may be improved or enabled by successful predictions of users' future mobility. The success of predictions depend on how much accuracy can be achieved on real data and on the sensitivity of particular applications to this achievable accuracy. We investigate these issues for the case of advanced bandwidth reservation using real WLAN traces collected on the Dartmouth College campus [1].

Location is an important feature for many applications, and wireless networks may serve their clients better by anticipating client mobility. As a result, many location predictors have been proposed in the literature, though few have been evaluated with empirical evidence. This paper reports on the results of the first extensive empirical evaluation of location predictors using a two-year trace of the mobility patterns of more than 6,000 users on Dartmouth's campus-wide Wi-Fi wireless network. The surprising results provide critical evidence for anyone designing or using mobility predictors. We implemented and compared the prediction accuracy of several location predictors drawn from four major families of domain-independent predictors, namely, Markov-based, compression-based, PPM, and SPM predictors. We found that low-order Markov predictors performed as well or better than the more complex and more space-consuming compression-based predictors