An oscillatory neural network and dynamical associative memory

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灰色残差修正模型在中长期负荷预测中的应用

电力系统负荷预测是电力系统发电计划的重要组成部分,也是电力系统经济运行的基础。本文在应用GM（1,1）模型的基础上,建立了两种应用残差对预测数据进修正的模型。实例表明本文提出的方法较传统GM（1,1）模型有更高的精度。     

方差分量估计和多余观测分量计算方法探讨

本文根据矩阵迹运算规则,对消去定向角未知数前后赫尔默特方差分量估计公式作了简化,简化后公式仍精确.本文还提出一种方差分量估计的近似方法.利用该法进行方差分量估计,其估值无偏.此外,本文还导出消去定向角未知数后多余观测分量计算公式.利用上述有关公式进行方差分量估计和多余观测分量计算,既能节省计算机内存,又能提高运算速度.     

沥青混合料水稳定性分析

根据沥青混合料的强度形成原理和破坏机理以及沥青混合料与水相互作用机理，着重从集料性质、沥青性质、混合料类型、集料粒径和面层压实层厚度等方面分析了影响沥青混合料水稳定性的因素，提出了应在集料选择、沥青选择、沥青混合料设计、加热与拌和、碾压工艺、环境等方面采取措施来改善沥青混合料的水稳定性。     

Fluid inclusions evidence for differential exhumation of ultrahigh pressure metamorphic rocks in the Sulu terrane

Since the discovery of coesite and diamond inclusions in eclogites from the Dabie-Sulu orogen, east-central China[1―3], this largest ultrahigh pressure (UHP) metamor- phic terrane in the world has attracted extensive scientific interests. A number of hydrous minerals such as zoisite, phengite, magnesite and talc have been found in the UHP rocks, showing that fluids have played an important role in this type of extreme metamorphic evolution[4―8]. Sev-eral techniques have been applied to th…     

水斗数对转轮性能影响的实验研究

研究了水斗数对冲击式水轮机性能的影响．对水斗数不等的几个转轮进行了能量试验，发现水斗数与转轮性能密切相关，进而探讨了水斗数对转轮性能影响的原因，为选型时正确确定水斗数提供帮助．     

某天气雷达站塔楼结构时程分析

运用通用有限元程序ANSYS对某天气雷达站塔楼结构进行了自振特性分析及地震载荷作用下的时程分析,通过计算得出了雷达站塔楼的前20阶频率、振型,并进一步计算了在地震作用下时程分析的位移及内力,结果表明该结构具有很大的抗侧刚度,且沿高度分布均匀,自振频率满足结构使用要求,验证了结构设计的可靠性,为结构设计提供了可靠的参考数据．     

油菜花序轴组织培养中愈伤组织同工酶和可溶性蛋白质变化的研究

本文报道了在愈伤组织形成过程中,酯酶、过氧化物酶同工酶及可溶性蛋白质含量的变化,并讨论了愈伤组织形成与这些大分子变化之间的关系。     

降雨径流系列的一致性分析

以吉林省平原区十屋水文站降雨径流系列为例,进行了降雨径流特性的分析,内容包括降雨径流双累积曲线的特性分析、年径流系数的变化过程分析、秩和检验等.     

Experimental and numerical study on plasma nitriding of AISI P20 mold steel

In this study, plasma nitriding was used to fabricate a hard protective layer on AISI P20 steel, at three process temperatures (450°C, 500°C, and 550°C) and over a range of time periods (2.5, 5, 7.5, and 10 h), and at a fixed gas N₂:H₂ ratio of 75vol%:25vol%. The morphology of samples was studied using optical microscopy and scanning electron microscopy, and the formed phase of each sample was determined by X-ray diffraction. The elemental depth profile was measured by energy dispersive X-ray spectroscopy, wavelength dispersive spectroscopy, and glow dispersive spectroscopy. The hardness profile of the samples was identified, and the microhardness profile from the surface to the sample center was recorded. The results show that ε-nitride is the dominant species after carrying out plasma nitriding in all strategies and that the plasma nitriding process improves the hardness up to more than three times. It is found that as the time and temperature of the process increase, the hardness and hardness depth of the diffusion zone considerably increase. Furthermore, artificial neural networks were used to predict the effects of operational parameters on the mechanical properties of plastic mold steel. The plasma temperature, running time of imposition, and target distance to the sample surface were all used as network inputs; Vickers hardness measurements were given as the output of the model. The model accurately reproduced the experimental outcomes under different operational conditions; therefore, it can be used in the effective simulation of the plasma nitriding process in AISI P20 steel.