With the increasing speed of railway vehicles, deciding how to reasonably distribute impact energy to each vehicle has been a widespread concern in safety protection systems. This article formulates a three-dimensional train-track coupling dynamics model using MAthematical DYnamic MOdels (MADYMO) multibody dynamics software. A train-to-train collision is then simulated using this model. A hybrid solution methodology that combines the non-dominated sorting genetic algorithm II (NSGA-II), modified best and worst method with cloud model theory and grey relational analysis is proposed. The optimization parameters and objectives are determined based on the EN15227 crashworthiness requirements for railway vehicles. An empirical case of an existing train with eight vehicles that have been in operation in China is applied to verify this dynamics model derived from a high-speed train and solution methodology. Analysis and discussion are conducted to monitor the robustness of the results and the practical implications for rail transportation are summarized. The results prove that the obtained optimal solution by this research has better crashworthiness than an existing solution.

A critical problem to peer-to-peer video-on-demand (P2P VoD) systems is to provide efficient user interactivity support. In this paper, we study intra- and inter-video operations separately, aiming to exploit the locality of reference in user access patterns and reduce the latency of these VoD operations. We first introduce the concepts of available, request and delivered locality in intra-video user access patterns and prove that high available locality exists in different videos by both simulation and theoretical analysis. Moreover, with a relaxed definition of data chunk holder, intra-video locality can facilitate a high likelihood of a peer seeking within a video, finding a holder of the requested data among its neighbors. Exploiting this property, an aggressive cached publish scheme is designed to build shortcuts over the DHT network so as to reduce the lookup delay. This scheme may be simple but it is practical and easy to implement. Inter-video locality is exploited via learning association rules from the collective viewing history. A fast association rule learning algorithm is proposed to infer the relations between videos in a distributed manner based on partial knowledge. Both search and content prefetch are incorporated to achieve low inter-video jump delay with minimal overhead. Our simulations demonstrate that the proposed schemes can reduce the buffer and lookup delay for seeking within a video and provide an efficient prediction-based prefetch scheme for inter-video access.

In this paper, we seek to provide reliable and fast content discovery in peer-to-peer (P2P) video-on-demand (VoD) system to enable user interactivity under peer dynamics. We first identify two characteristics of content discovery in P2P-VoD: real-time constraints and limited local cache. Tapping on these properties, we propose a hybrid content discovery mechanism: SUpeRchunk- based eFficient search Network (SURFNet). SURFNet divides movies into superchunks and chunks. Stable peers that are likely to have longer lifespans in the system are used to construct an AVL tree to provide superchunk-level data availability information. Peers storing the same superchunk are grouped into a holder-chain, which is then attached to a node in the AVL tree. This structured overlay is further extended by a gossip-based unstructured network for chunk-level information exchange and data transmission. Since the AVL tree consists of only stable peers, it provides a reliable backbone even in highly dynamic environment. Our analysis and simulation results show that SURFNet supports nearly-constant and logarithmic search time for seeking within a video and jumping to a different video, respectively.

A critical problem to P2P VoD systems is to provide efficient user interactivity support. In this paper, we study intra- and inter-video operations separately and aim to reduce the latency of these VoD operations by exploiting the locality of reference in user access patterns. With a relaxed definition of data chunk holder, intra-video locality can facilitate a high likelihood of a peer seeking within a video, to find a holder of the requested data among its neighbors. Tapping on this property, an aggressive cached publish scheme is designed to build shortcut over the DHT network so as to reduce the lookup delay. Inter-video locality is exploited via learning association rules from the collective viewing history. A fast association rule learning algorithm is proposed to infer the relations between videos in a distributed manner based on partial knowledge. Both search and content prefetch are incorporated to achieve low inter-video jump delay with minimal overhead. Our simulations demonstrate that the proposed schemes can reduce the lookup delay for seeking within a video and provide an efficient prediction-based prefetch scheme for inter-video access.

It is crucial to provide reliable and fast search in P2P VoD system to enable user interactivity under peer dynamics. We propose a hybrid content discovery mechanism: superchunk based fast search network (SURFNet), based on our observations of current P2P VoD systems. SURFNet divides movies into superchunks and chunks, and takes advantages of stable peers to construct an AVL tree, which provides superchunk-level data availability information and aggregates superchunk holders into holder-chains. This structured overlay is further extended by a gossip-based unstructured network for chunk-level information exchange and data transmission. Our analysis and simulation results show that SURFNet supports efficient search and reduces seek latency significantly.