Electrification of volcanic plumes

Volcanic lightning, perhaps the most spectacular consequence of the electrification of volcanic plumes, has been implicated in the origin of life on Earth, and may also exist in other planetary atmospheres. Recent years have seen volcanic lightning detection used as part of a portfolio of developing techniques to monitor volcanic eruptions. Remote sensing measurement techniques have been used to monitor volcanic lightning, but surface observations of the atmospheric electric Potential Gradient (PG) and the charge carried on volcanic ash also show that many volcanic plumes, whilst not sufficiently electrified to produce lightning, have detectable electrification exceeding that of their surrounding environment. Electrification has only been observed associated with ash-rich explosive plumes, but there is little evidence that the composition of the ash is critical to its occurrence. Different conceptual theories for charge generation and separation in volcanic plumes have been developed to explain the disparate observations obtained, but the ash fragmentation mechanism appears to be a key parameter. It is unclear which mechanisms or combinations of electrification mechanisms dominate in different circumstances. Electrostatic forces play an important role in modulating the dry fall-out of ash from a volcanic plume. Beyond the local electrification of plumes, the higher stratospheric particle concentrations following a large explosive eruption may affect the global atmospheric electrical circuit. It is possible that this might present another, if minor, way by which large volcanic eruptions affect global climate. The direct hazard of volcanic lightning to communities is generally low compared to other aspects of volcanic activity.     

动量BP算法在路基沉降预测中的应用

提出一种采用动量BP算法来预测路基沉降的方法,结合具体的工程实例,构建了预测路基沉降的具体BP神经网络模型。预测结果表明,该模型有较高的预测精度,可作为预测路基沉降的一种新方法。     

A Comparison between a Modified Counter Propagation Network and an Extended Self-Organizing Map in Remotely Sensed Data Classification

A modified counter propagation network model and an extended self-organizing map model have the same three-layer network architecture while employing slightly different learning rules. Their network architecture comprises an input layer, a Kohonen layer and an output layer. The neurons between two neighboring layers are fully connected and the neighboring neurons within the Kohonen layer also have neighborhood connections. The modified counter propagation network model employs the Kohonen algorithm to train the Kohonen layer while using the Widrow–Hoff rule to train the output layer. However, the extended self-organizing map model applies a modified Kohonen’s learning rule to train both the Kohonen layer and the output layer. This paper compares the performances of these two models in supervised classification of remotely sensed data. The training results show that compared to the extended self-organizing map model, the modified counter propagation model has faster learning speed but larger output errors. The classification results indicate that the extended self-organizing map model has a faster classification speed and a much higher classification precision than the modified counter propagation model.     

Irrigated area determination: a case study in the Province of San Juan,Argentina

Much of the central-western region of Argentina, where San Juan Province is located, experiences arid to semi-arid climatic conditions with low average annual rainfall accompanied by substantial evapotranspiration. Consequently, a viable crop industry depends to a large extent upon irrigation from major river systems. Increasing demand for water in the lower basin of the San Juan River is emphasizing the need for more accurate estimates of water used for irrigation. Since the water demand for a particular crop is very closely related to crop area, monitoring the area of crop under irrigation is considered a proxy for the amount of water used. Landsat 5 imagery for the growing season, field data and aerial photographs were used to evaluate crop area.     

Texture classification of Mediterranean land cover

Maximum likelihood (ML) and artificial neural network (ANN) classifiers were applied to three Landsat Thematic Mapper (TM) image sub-scenes (termed urban, agricultural and semi-natural) of Cukurova, Turkey. Inputs to the classifications comprised (i) spectral data and (ii) spectral data in combination with texture measures derived on a per-pixel basis. The texture measures used were: the standard deviation and variance and statistics derived from the co-occurrence matrix and the variogram. The addition of texture measures increased classification accuracy for the urban sub-scene but decreased classification accuracy for agricultural and semi-natural sub-scenes. Classification accuracy was dependent on the nature of the spatial variation in the image sub-scene and, in particular, the relation between the frequency of spatial variation and the spatial resolution of the imagery. For Mediterranean land, texture classification applied to Landsat TM imagery may be appropriate for the classification of urban areas only.     

人造卫星数据库管理系统中ORACLE FORMS的应用

ＯＲＡＣＬＥ ＦＯＲＭＳ是ＯＲＡＣＬＥ数据库系统应用程序重要的开发工具之一,它具有良好的窗口,模拟块以及触发器等功能,同时它作为ＯＲＡＣＬＥ大家族中的一员,与ＯＲＡＣＬＥ的其他应用工具又是分不开的。本文结合实例详细地介绍了ＯＲＡＣＬＥ ＦＯＲＭＳ在开发人造卫星数据库管理系统中的应用。     

参数调整对气象预测的B-P网络模型性能的影响

为了探索ＢＰ网络的参数调整特性,进行了参数α、β的选取对ＢＰ算法的收敛速度和模型的稳定性的影响研究。通过ＢＰ网络用于气象预测建模的参数调整个例分析表明：参数α、β的取值对ＢＰ模型的稳定性无显著影响,但参数值的调整尤其是β值的调整对建模的收敛速度有明显的影响。     

广州莲花山的人工丹霞地貌和旅游开发

通过实地考察,概述广州番禺莲花山与粤北丹霞山地质地貌的异同,从丹霞地貌角度阐述这座古石矿场“人工无意夺天工”形成的陡崖,岩洞,石墙,石巷,岩湖,石柱,石门,石桥等人工丹霞奇观,并对这座以全国罕有古石矿场为主景的莲花山旅游区的进一步开发,提出了讨论意见。     

同时满足多个阻尼反应谱的人造地震动时程合成

应用Ｍ．Ｈｉｒａｓａｗａ和Ｍ．Ｗａｔａｂｅ（１９９２）提出的方法,进行了相应的数学推导：即以满足某个阻尼比的反应谱的人造地震动作为初始时程,采用奇异值分解法,通过解线性方程组求出校正时程,并多次叠以逐步辐近     

陕西省人工神经元网络降水年,季度预报系统

利用Ｂ－Ｐ人工神经元网络进行了陕西省年度,季度降水预报试验,提出了利用０－１模型解决多等级预报问题的方法,并建立了年度,季度等级预报模型,经过试验,表明该方法预报效果良好,最后对模式在应用中的一些问题及目前其它预报模型的差异等进行了讨论。