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151.
Ting-Ting Gao Dan Wu Ying-Ling Huang De-Zhong Yao 《中国电子科技》2007,5(3):272-277
A nonlinear method named detrended fluctuation analysis (DFA) was utilized to investigate the scaling behavior of the human electroencephalogram (EEG) in three emotional music conditions (fear, happiness, sadness) and a rest condition (eyes-closed). The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the 'approach-withdrawal' model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion. 相似文献
152.
稳定同位素井间示踪技术在深层块状稠油油藏水驱特征研究的应用 总被引:4,自引:0,他引:4
经对冷43块深层块状普通稠油水驱注水5个试验井组开展的稳定同位素示踪测试,利用中子活化技术分析样品,采用先进的示踪剂定量分析与流动模拟相互结合的方法,以示踪数值模拟为手段,评价了油井连通、油层平面及纵向上的非均质性、隔层的封隔性等因素对水驱控制程度及动用状况的影响,通过优化注水推进方向及速度,强化动态调整措施,提高了稳产基础。 相似文献
153.
钢铁企业的氧气厂要深入了解本企业的用氧情况及周边市场的需要,据此确定拟新增空分装置的设计参数,选定最适宜的空分流程。探讨了空分流程选择时要考虑的几个方面。介绍了一种对空分装置的投资和长期运行费用之间进行经济性分析的定量计算公式,并得出了四点结论。 相似文献
154.
Michael Fox Richard Hastings Scott Lovald Juan Heinrich 《Journal of Failure Analysis and Prevention》2007,7(3):165-174
A failure analysis case study is presented for a two-piece aerosol containing tetrafluoroethane, commonly referred to as Refrigerant
134a. A gentleman was preparing to recharge the air conditioning system of an automobile when the bottom exploded off the
aerosol container, propelling the body of the aerosol container like a rocket, which hit the man in the eye and blinded him
in that eye. The aerosol was never connected to the air conditioner, therefore backpressure from the air conditioner (AC)
compressor was ruled out as a cause for the explosion. The objective of the study was to determine why the aerosol exploded.
Several recently developed test methods were used, including two types of heat-to-burst tests and a puncture chamber to measure
the pressure-versus-temperature behavior of aerosols. More common test methods were also used, such as water bath pressure
tests, hydro pressure burst tests, pneumatic pressure burst tests, hardness measurements, weight measurements, metallography,
scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and an accident scenario recreation. A semi-empirical
correlation between the hardness and weights of the container bottoms was used to determine the explosion temperature and/or
pressure. This semi-empirical correlation agrees in principle with an analysis of the explosion pressures using finite-element
analysis (FEA). The root cause for the explosion was determined to be a lack of strength of the bottom of the two-piece aerosol
coupled with heating the aerosol to temperatures significantly above room temperature. 相似文献
155.
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157.
基于物元分析和层次分析的选厂综合性能评估 总被引:3,自引:2,他引:1
利用物元分析法和层次分析法的特点,将物元分析和层次分析法有机的结合起来建立选厂性能综合评估模型,建立的评估模型表明该评估方法能够克服传统方法中参评人员的主观不确定性和认识上的模糊性,可用于选厂有关指标的综合评估。 相似文献
158.
159.
Hyun-Chul Kim Author VitaeDaijin KimAuthor Vitae Sung Yang Bang Author Vitae 《Pattern recognition》2003,36(5):1095-1105
Linear discriminant analysis (LDA) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter. While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner. Second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modelling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for synthetic data classification, hand-written digit recognition, and alphabet recognition. 相似文献
160.