In peer-to-peer (P2P) networks, free-riders and redundant streams including overlapped and folded streams dramatically degrade playback quality and network performance, respectively. Although a locality-aware P2P live video can reduce the topological complexity, it cannot effectively avoid redundant streams while denying free-riders. In this paper, we first model free-rider, redundant streams and a distance-driven P2P system. Based on that model, a distance-driven alliance algorithm is proposed to construct not only an alliance that directly prevents any utility gains of free-riders through inter-user constraints but also a small-world network or a multicast tree that effectively reduces redundant streams. Finally, simulations confirm its advantages in functionality and performance over several existing strategies and distance-driven P2P live video systems.

In this paper, a Wireless Access in Vehicular Environments (WAVE)-based localization technology is proposed to supplement the gaps of output frequency for Global Position System (GPS), saving dedicated localization facility. A subcarrier-based reflection filter is used to mitigate multi-path effect. A fuzzy vector space and its fuzzy distance of normalized Received Signal Strength Indication (RSSI) sequence are formulated to solve the scarce variability problem of the position feature while offsetting common hardware errors and traffic noises. Based on them, the vehicle coordinates are calculated through integrating position feature with fuzzy distance, precise and stable result is achieved. Experiments confirmed its constant centimeter-level accuracy error, millisecond-level execute time as well as ignorable effects of grid size, speed, traffic and redweather. Performances meet the requirements of enforcement, autopilot and navigation.

The geological condition above tunnel face is essential to excavation and support. In order to solve the shortcoming of ground borehole method that cannot obtain continuous information while destroying soil stability, this paper presents a numeric algorithm to continuously recover the topology of each soil stratum. Specifically, firstly, a Silo-Wedge tunnel face model and its topology& mechanics parameter algorithms were proposed based on Terzaghi theory and Mohr stress circle. Through considering the friction effect of cutter-head and designing suitable parabolic arch element contour, the calculated shape and lateral stress ratio closely fits model tests. Secondly, a inter-stratum load transferring algorithm was designed based on differential element force equilibrium model. Through involving arch effect, shearing resistance and friction force, the result become more practical. Thirdly, a bottom-to-top two virtual stratum model was proposed to recursively calculate the thickness change of soil stratum corresponding to sensed pressure deviation. Through designing the load contribution weight of stratum, complex polynomial solving problem is avoided. Finally, a back propagation (BP) neural network was improved based on proposed model characteristics, calculation error and learning time are reduced. Finally, effectiveness was tested.

The stability state of tunnel face is the basis of excavation and support, but it is difficult to detect owing to its large buried depth. At present, the existing localizing algorithms of instability zone usually gave range rather than a certain value due to numerious parameters extracting from tests, while the algorithms of limit support pressure (LSP) contain large error owing to involving complex nonlinear polynomial solution and simplification. In this paper, we proposed a parabolic-arch topology model and differential-element mechanical model for all disturbed zones. Through designing suitable arch geometry contour, each disturbed zone is accurately positioned based on Terzaghi theory, and lateral stress ratio is derived from Mohr stress circle, while LSP is calculated through limit equilibrium principle. In practice, in order to copy with the calculation deviation stemming from humid and miscellaneous soil, we design a neural network(NN), and based on the reversed effect of two uncertain parameter in LSP algorithm, we improve its back propagation(BP) algorithm, reducing adjusting counts. Finally, the rationality and effectiveness are evaluated.