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71.
72.
针对目前维修过程中经常遇到的有关机床工作台抖动的现象,进一步分析阐述了引起数控机床工作台抖动的原因及处理方法,有效避免在加工过程中由于工作台抖动而引起零件质量问题。 相似文献
73.
针对标准中心性测度不适用于非联通网络这一问题,提出了基于资源分配策略的复杂网络中心性测度。节点的资源分配中心性测度定义为节点从其它节点接受的资源量,如果一个节点从其他节点接受的资源量越多,则该节点越重要。通过人工网络和现实网络实验表明,该度量不仅适用于联通网络,也适用于非联通网络,相较于标准测度,可以检测桥节点,而且具有良好的稳定性。 相似文献
74.
75.
面向P2P特定信息的传播动力学模型研究 总被引:1,自引:1,他引:0
鉴于P2P特定信息传播与传染病传播的相似性,传播动力学是P2P特定信息传播的最新研究方向。针对现有传播动力学模型都不能准确模拟P2P特定信息传播过程的问题,对现有的SEIR传播动力学模型进行改进,建立了SEInR模型。该模型的主要特点包括:将传统的感染者(I)分为n个子类,每个子类赋予不同的模型参数;建立潜伏者(E)与移除者(R)之间的转换关系等。应用现代数学中的矩阵理论,得到SEInR模型的基本再生数计算公式,并对其进行分析。仿真结果表明,所提出的SEInR模型比传统SEIR模型能够更准确地模拟P2P特定信息传播过程,得到的基本再生数计算公式能够准确反映P2P特定信息的传播阂值。 相似文献
76.
The essence of intelligence is to use certain abilities to obtain knowledge, to use that knowledge, and to operate with that knowledge. New knowledge learned by a human is often related to old existing knowledge, and sometimes we could have more conceptual knowledge based on old knowledge. So, the knowledge in the brain exists in a related structural form, and this structure is dynamic, and therefore is evolvable. Based on the understanding of the real process of learning by a human being, we discuss how to make a model to describe the dynamic structure of knowledge. This model is also a principle of artificial brain design. Most of the knowledge a child learns is from natural language and perception information, and we define this as semantic knowledge. The model to describe the process and structure of knowledge growing in a network form is called a K-net. It is a dynamic network with two main dynamics: one is new knowledge added, and the other is aggregating knowledge existing in the network with some probability. Under these very natural conditions, we found that the network is originally a simple random net, and then some characteristics of a complex network gradually appear when more new knowledge is added and aggregated. A more interesting phenomenon is the appearance of a random hierarchical structure. Does this mean emergence? 相似文献
77.
Towards a continuous microfluidic rheometer 总被引:1,自引:1,他引:0
Pierre Guillot Thomas Moulin Roman Kötitz Matthieu Guirardel Arash Dodge Mathieu Joanicot Annie Colin Charles-Henri Bruneau Thierry Colin 《Microfluidics and nanofluidics》2008,5(5):619-630
In a previous paper we presented a way to measure the rheological properties of complex fluids on a microfluidic chip (Guillot
et al., Langmuir 22:6438, 2006). The principle of our method is to use parallel flows between two immiscible fluids as a pressure
sensor. In fact, in a such flow, both fluids flow side by side and the size occupied by each fluid stream depends only on
both flow rates and on both viscosities. We use this property to measure the viscosity of one fluid knowing the viscosity
of the other one, both flow rates and the relative size of both streams in a cross-section. We showed that using a less viscous
fluid as a reference fluid allows to define a mean shear rate with a low standard deviation in the other fluid. This method
allows us to measure the flow curve of a fluid with less than 250 μL of fluid. In this paper we implement this principle in
a fully automated set up which controls the flow rate, analyzes the picture and calculates the mean shear rate and the viscosity
of the studied fluid. We present results obtained for Newtonian fluids and complex fluids using this set up and we compare
our data with cone and plate rheometer measurements. By adding a mixing stage in the fluidic network we show how this set
up can be used to characterize in a continuous way the evolution of the rheological properties as a function of the formulation
composition. We illustrate this by measuring the rheological curve of four formulations of polyethylene oxide solution with
only 1.3 mL of concentrated polyethylene oxide solution. This method could be very useful in screening processes where the
viscosity range and the behavior of the fluid to an applied stress must be evaluated. 相似文献
78.
基于多Agent计算机仿真实验平台Swarm的综述* 总被引:1,自引:0,他引:1
结合具体实例“热虫实验”,解释了Swarm在人工世界环境下进行计算机仿真模拟的基本思想,探讨了Swarm的主要概念和系统结构;进而以Java语言为例介绍了面向对象思想用于Swarm编程的基本情况,并对Swarm所提供的软件工具包给出了Java形式的详细说明。 相似文献
79.
In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural
networks are used for identification of systems, all of their weights are trained independently, without considering interrelated
weights values. Thus, the training results are usually not good. The reason for this in that each parameter has its influence
on others during learning. To overcome this problem, we first give an exact mathematical equation that describes the relation
between weight values given a set of data conveying prior information. The we present a new learning method that trains part
of the weights and calculates the others using these exact mathematical equations. This method often a priori keeps the given
mathematical structure exactly the same during learning; in other words, training is done so that the network follows a predetermined
trajectory. Numerical computer simulation results are provided to support this approach.
This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January
19–22, 1999. 相似文献
80.