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991.
《Expert systems with applications》2014,41(7):3143-3156
Previous research on scheduling and solar power issues of wireless sensor networks (WSNs) assumes that the sensors are deployed in a general environment. While monitoring the stream environment, sensors are attached to the stream side to collect the sensed data and transmit the data back to the sink. The stream environment can be scaled in several similar environments. This type of geographic limitation not only exists in a stream environment but also on streets, roads, and trails. This study presents an effective node-selection scheme to enhance the efficiency of saving power and coverage of solar-powered WSNs in a stream environment. Analysis of the sensor deployment in the stream environment permits sensors to be classified into different segments, and then allows the selection of active nodes for building inter-stream connections, inter-segment connections, and intra-segment connections. Based on these connections, the number of active nodes and transmitted packets is minimized. Simulation results show that this scheme can significantly increase the energy efficiency and maintain the monitoring area in solar-powered WSNs. 相似文献
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For the flexural reinforcement of bridge and building structure, synthetic materials whose dynamic properties are superior and those containing the merit of corrosion‐proof are widely used as the substitute for a steel plate. Since FRP plate has improved bond strength owing to the fibers externally adhering to the plate, many researches regarding the bond strength improvement have been substantially performed. To search out such bond strength improvement, previous researchers had ever examined the bond strength of FRP plate through their experiment by setting up many variables. However, since the experiment for a research on the bond strength takes much of expenditure for setting up the equipment and is time‐consuming, also is difficult to be carried out, it is limitedly conducted. The purpose of this study was to develop the most suitable artificial neural network model by application of various neural network models and algorithm to the data of the bond strength experiment conducted by previous researchers. Many variables were used as input layers against bond strength: depth, width, modulus of elasticity, tensile strength of FRP plate and the compressive strength, tensile strength, and width of concrete. The developed artificial neural network model has been applied back‐propagation, and its error was learned to be converged within the range of 0.001. Besides, the process for the over‐fitting problem has been dissolved by Bayesian technique. The verification on the developed model was executed by comparison with the test results of bond strength made by other previous researchers, which was never been utilized to the learning as yet. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 5119–5127, 2006 相似文献
995.
针对60-GHz无线个域网,提出了一种吉比特里所码和卷积码级联译码器架构。在推荐级联架构中,里所码和卷积码分别作为外码和内码,通过交织器级联。采用基于Viterbi算法的8路并行卷积译码结构和基于RiBM算法的里所码译码结构,实现了超高速级联译码器。在TSMC 0.13μm CMOS工艺下,该译码器以0.135 nJ/bit和5.19 mm2的能耗资源开销实现了高达2 Gb/s的吞吐率。 相似文献
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997.
基于马尔可夫链的自适应性神经网络训练算法 总被引:1,自引:1,他引:0
提出一种基于马尔可夫链的自适应性神经网络训练方法,对传统的S型激励函数进行了改进,建立了自适应性的神经网络分类器。在假设样本中噪声服从于正态分布的情况下建立最大似然估计,通过后验概率建立马尔科夫链对样本进行训练,提高了神经网络训练速度。在轴承故障诊断中的测试结果表明,该算法可以迅速稳定地训练出神经网络,有效提高诊断的分类结果。 相似文献
998.
改进的无线传感网混沌Hopfield盲检测算法 总被引:2,自引:0,他引:2
在密集部署的无线传感器网络中,相邻传感器的信号可能高度相关。在无线传感网传输模型分簇的基础上,针对Hopfield神经网络极易陷入局部最优解、收敛速度慢等缺陷,利用混沌序列的遍历性和类随机性,提出一种改进的混沌Hopfield盲检测算法解决无线传感网簇内传感器信号盲检测问题。算法的思想是:利用混沌映射产生初始发送序列,并且在算法出现早熟收敛时进行小幅度的混沌扰动,借此降低算法的误码率。仿真结果表明,改进的混沌Hopfield神经网络算法所需数据量极短,从而成功实现簇内簇首传感器信号盲检测。 相似文献
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1000.
Members of health social networks may be susceptible to privacy leaks by the amount of information they leave behind. The threat to privacy increases when members of these networks reuse their pseudonyms in other social networks. The risk of re‐identifying users from such networks requires quantitative estimates to evaluate its magnitude. The estimates will enable managers and members of health social communities to take corrective measures. We introduce a new re‐identification attack, the social network attack, that takes advantage of the fact that users reuse their pseudonyms. To demonstrate the attack, we establish links between MedHelp and Twitter (two popular social networks) based on matching pseudonyms. We used Bayesian networks to model the re‐identification risk and used stylometric techniques to identify the strength of the links. On the basis of our model 7‐11. 8% of the MedHelp members in the sample population who reused their pseudonyms in Twitter were re‐identifiable compared with 1% who did not. The risk estimates were measured at the 5% risk threshold. Our model was able to re‐identify users with a sensitivity of 41% and specificity of 96%. The potential for re‐identification increases as more data is accumulated from these profiles, which makes the threat of re‐identification more serious. 相似文献