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111.
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113.
为了准确、快速地进行缺陷识别,介绍了一种新型的前馈神经网络模型,即径向基概率神经网络。与以往的算法相比,该方法具有分类识别精度高且速度快的优点。仿真获得了很好的结果。 相似文献
114.
Experimental analysis, modeling, and theoretical design of McMaster pore-filled nanofiltration membranes 总被引:1,自引:0,他引:1
A side-by-side comparison of the performance of McMaster pore-filled (MacPF) and commercial nanofiltration (NF) membranes is presented here. The single-salt and multi-component performance of these membranes is studied using experimental data and using a mathematical model. The pseudo two-dimensional model is based on the extended Nernst–Planck equation, a modified Poisson–Boltzmann equation, and hydrodynamic calculations. The model includes four structural properties of the membrane: pore radius, pure water permeability, surface charge density and the ratio of effective membrane thickness to water content. The analysis demonstrates that the rejection and transport mechanisms are the same in the commercial and MacPF membranes with different contributions from each type of mechanism (convection, diffusion and electromigration). Solute rejection in NF membranes is determined primarily by a combination of steric and electrostatic effects. The selectivity of MacPF membranes is primarily determined by electrostatic effects with a significantly smaller contribution of steric effects compared to commercial membranes. Hence, these membranes have the ability to reject ions while remaining highly permeable to low molecular weight organics. Additionally, a new theoretical membrane design approach is presented. This design procedure potentially offers the optimization of NF membrane performance by tailoring the membrane structure and operating variables to the specific process, simultaneously. The procedure is validated at the laboratory scale. 相似文献
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116.
A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series. 相似文献
117.
On effectiveness of wiretap programs in mapping social networks 总被引:1,自引:0,他引:1
Maksim Tsvetovat Kathleen M. Carley 《Computational & Mathematical Organization Theory》2007,13(1):63-87
Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost.
Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on
Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php 相似文献
118.
《Fiber and Integrated Optics》2007,26(6):335-342
A fiber-optic chemical sensor (FOCS) for detection of nitrogen dioxide (NO2) molecules is reported. The FOCS presents an optropode structure because of the transmission properties of the sensitive material. The NO2 FOCS is activated by using the semiconductor polymer: regioregular head-to-tail poly(3-octylthiophene-2,5-diyl). The operation wavelength of the sensor is 543.5 nm such that a simple LED and detector can be used for the design of this device. The sensor response decreases after each exposure, demonstrating the reduction in sensitivity as well as irreversibility lower than 5%. However, its properties such as rapid response, high selectivity, high sensitivity (0.43 ± 0.01 muW/ppm), hygroscopic properties, and its operation at room temperature make this kind of FOCS a good alternative for NO2 toxic gas detection. 相似文献
119.
Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions
in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions.
Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such
applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we
used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their
discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless
sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show
that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for
on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to
schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes
and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results
show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared
to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput.
As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery
model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless
networks as well. 相似文献
120.
简要介绍了ASON技术的特点、ASON的引入为新一代光网络带来的好处及衡量网络质量的一些参数和注意事项。 相似文献