Air Force Oil Research Institute

Beijing, China

Air Force Oil Research Institute

Beijing, China
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Zhao J.,Beijing Institute of Technology | Chen X.-G.,Beijing Institute of Technology | Liu C.-T.,Beijing Institute of Technology | Yu X.-M.,Air Force Oil Research Institute | Yue B.,Air Force Oil Research Institute
Binggong Xuebao/Acta Armamentarii | Year: 2012

In order to detect the running state of oil pipeline in real time, reduce the interference between nodes of wireless sensor network (WSN) and improve network throughput, two multi-channel WSN models for the nodes distributed in a line were proposed; the models of channel assignment for single path and dual path network and their network throughputs were are discussed; the corresponding distributed MAC protocol was presented also. Field experiment results show that the single-path channel wireless sensor network not only reduces the energy consumption but also increases the network throughput; the dual path channel wireless sensor network has some fault tolerance and improves network throughput greatly, although it requires higher energy consumption.


Zhao J.,Beijing Institute of Technology | Chen X.-G.,Beijing Institute of Technology | Liu C.-T.,Beijing Institute of Technology | Yu X.-M.,Air Force Oil Research Institute | Yue B.,Air Force Oil Research Institute
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | Year: 2011

By investigating the working process of tanker trucks in a military airport, this paper proposes a centralized channel assignment algorithm and a power rating control method for fast-moving nodes to improve the real-time property and energy efficiency of the information collection, detect the state of mobile devices promptly, reduce interference between wireless networks and to increase network capacity in oil security systems. Considering the fact that IEEE 802.11 wireless LAN standard defines a number of non-overlapping channels which could be used at the same time, two wireless network interfaces operating on different channels could be installed to every node. Through the negotiation on the control channel and switching the data channel dynamically, the balance of network loads could be fulfilled so as to increase the total bandwidth. Simulation and application results demonstrate that, only three data channels is needed to realize the centralized channel assignment and the power rating control, and the network capacity and energy efficiency could be improved effectively.


Xie Y.,Beijing Institute of Technology | Chen X.,Beijing Institute of Technology | Yu X.,Air Force oil Research Institute | Yue B.,Air Force oil Research Institute | Guo J.,Air Force oil Research Institute
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | Year: 2011

Outlier is one of the common data fault types in data collection applications based on wireless sensor networks (WSNs), which greatly influences data quality. A fast support vector data description-based (FSVDD) approach for outlier detection in WSNs is proposed in this paper. The basic idea of the approach is as follows: firstly, the minimal spherical boundary containing normal samples is obtained using FSVDD; then the category of unknown samples is determined with this boundary. The proposed method adopts training set reduction strategy and SMO (Sequential Minimal Optimization) algorithm based on second order approximation to accelerate the training of SVDD. Simulations with both synthetic and real data show that the proposed approach achieves higher speed and requires less memory while maintaining classification accuracy, and is suitable for WSNs with limited resources.


Xie Y.-X.,Beijing Institute of Technology | Chen X.-G.,Beijing Institute of Technology | Yu X.-M.,Air Force Oil Research Institute | Yue B.,Air Force Oil Research Institute | Guo J.,Air Force Oil Research Institute
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | Year: 2010

In order to ensure the security of oil supply, the feasible strategy for fault diagnosis of node in wireless sensor network (WSN) is investigated and a fault diagnosis method based on variable precision rough set (VPRS) and RBF neural network for WSN's nodes is proposed in this paper. The procedure of the method is as follows. The sink gets node fault symptoms and forms initial decision table firstly. Then the VPRS theory is used as the front-end processing system to remove redundant and insignificant attribute symptoms for getting a relative minimum condition attribute set, which plays a major role in fault diagnosis. Finally, the relative minimum condition attribute set is input into RBF neural network to identify faults. Simulation results show that the proposed method can accurately and quickly arrive at the decision about the fault diagnosis of node with significant uncertainty in WSN. It also has strong robustness and applicability.

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