人工神经网络方法在鹤壁汛期降水预报中的应用

在用经验统计方法和降水判别函数进行24h和12h晴雨预报的基础上，再用BP人工神经网络建立降水量级预报模型。经2003年汛期试用，预报准确率高于上级指导预报准确率，12h预报准确率高于24h预报准确率。     

神经网络在判别煤矿突水水源中的应用

矿井突水水源的识别是矿井防治水工作的基础，快速准确地判别突水的来源对整个矿井的安全生产起着十分重要的作用。以峰峰矿区梧桐庄矿为例，应用神经网络的方法，对矿井突水水源进行了系统研究。结果表明，运用该方法取得了较好的效果，并可以用此模型来解决类似的评价和判别问题。     

PNN网络在预测MCS移动中的应用

神经网络是空间数据挖掘的一种重要手段。本文运用概率神经网络(PNN网络)对1998年夏季影响青藏高原上中尺度对流系统(MCS)东移的环境物理量场的空间分布特征进行了研究，得到了高原上MCS移动方向与环境物理量场空间分布之间的关系。研究表明，使用概率神经网络预测中尺度对流系统的移动具有较好的效果，从而为研究高原上MCS东移与环境场之间的关系提供了一种新的方法和思路。     

A GIS Analysis to Evaluate Areas Suitable for Crushed Stone Aggregate Quarries in New England,USA

Aggregate is a low unit-value mineral commodity. Costs to move aggregate from the mine site to the point of use is a large fraction of the resource cost to users. Production sites for aggregate occur where suitable source materials exist and where transportation and market conditions are favorable. The increasing demand for aggregate and the difficulty of developing and permitting new sites and of renewal of permits on existing sites of aggregate production indicates that aggregate will be supplied from sources yet to be developed or delineated in many areas. Site development and permitting for aggregate production is difficult because many land management plans and zoning actions fail to anticipate prospective source areas for aggregate in a way that is consistent with both the source rock quality and the transportation and socioeconomic factors that define the economic viability of the industry. Spatial analysis provides a method to integrate both geology and economic (transportation and marketplace) parameters in a regional model. Weights of evidence (WofE) analysis has been used to measure the spatial correlation of geologic map, transportation network, and population data with current production sites for crushed stone aggregate in the New England region of the northeastern United States. Weighted logistic regression (WLR) is used with the WofE results to rank areas in terms of their relative suitability for production of crushed stone. Spatial analysis indicates that 85% of the 106 crushed stone aggregate quarries in New England are sited within 1.6 km (1 mile) of either a principal highway or rail line in the region. Seventy-eight percent of crushed stone aggregate quarries are sited in census tracts with population densities exceeding 100 people/mile². These relations illustrate the importance of proximity to both transportation corridors and developing areas where aggregate is predominately used. Only one active crushed stone quarry is located in a census tract with a population density less than 15 people/mile², reflecting the lack of sufficient market demand in many rural areas to develop an operation there. However, since 1990, almost all new quarries have been developed in census tracts with population densities less than 200 people/mile², indicating the difficulty of permitting new quarry sites in highly populated areas. Crushed stone aggregate is produced predominately from three hard rock types that are distributed widely in New England; 28% of sites use granitic rock, 25% use carbonate rocks, and 25% use mafic rock types that are categorized as trap rock by the aggregate industry. The other crushed stone aggregate sources include a variety of fine-grained metamorphic rock types. Carbonate rocks and Jurassic basalt (the primary trap rock source) are the most prevalent source rocks on an area-weighted basis. Spatial analysis can be used on a regional scale to rank areas by their relative suitabilityfor crushed stone aggregate production based on geology, transportation, and population parameters. The results of this regional analysis can identify areas for more detailed evaluation. As transportation or population features change, the model can be revised easily to reflect these changes.     

幂函数对建筑物沉降的回归分析

通过实例推导幂函数对建筑物沉降进行回归分析，并且得出较为精确的结果，同时也说明在选择回归函数的时候，还会受到很多条件影响。     

基于神经网络的建筑物沉降原因分析

在我们应用回归方法对建筑物沉降原因分析及沉降趋势的预测中，由于实际情况的复杂性及主观认识的局限性，这样所得的结果含有较多的人为因素，可能会与实际情况有所差异。针对这种情况，讨论了应用神经网络方法来发现和验证引起建筑物沉降的因素及对沉降趋势的预测。实例表明，该方法能取得较好的效果。     

应用Netfilter／iptables构建甘肃地震信息网络安全防火墙系统

介绍了应用netfilter/iptables技术构建甘肃地震信息网络安全防火墙的技术方案和设计思想。较完整地给出了甘肃地震信息网的防火墙配置脚本,并对每个功能的实现策略作了详尽的解释。客观地分析了netfilter/iptables技术的优点和不足。     

Computation of response spectra from mining tremors using neural networks

Underground coal and copper ore exploitation in two Polish mining regions causes mining tremors and a series of other negative phenomena in the environment. Although these tremors are strictly connected with human activity, they differ considerably from other paraseismic vibrations. The moment of their occurrence is not to be foreseen likewise for earthquakes. The main problem discussed in the paper was formulated as the neural network evaluation of a relation between mining tremor energies, epicentral distances and acceleration response spectra. Back-propagation neural networks with Resilient back-propagation learning method were used. Each input vector included information about the mining tremor energy and the epicentral distance. Values of acceleration response spectrum were expected as the outputs of neural networks. Neurally evaluated spectra were compared with spectra computed on the basis of experimental data. After the network is trained and tested, it can be used for mapping of new data of mining tremor energies and epicentral distances into the spectra. Then, what is the substantial advantage of neural approach, the prediction of acceleration response spectra can be performed without recording of surface vibrations. In the light of the results, it is visible that the presented way of computation of acceleration response spectra can be peculiarly applied to prognosis of mining tremors influences on structures.     

交通设计院所网络系统建设与思考

本文主要介绍了交通设计院所计算机网络系统建设的设计方案,并就计算机网络建设中应注意的问题提出几点思考.     

Slope stability evaluation using Back Propagation Neural Networks

The Yudonghe landslide, located in western Hubei Province of China, consists of eastern and western subunits as well as a main landslide mass with upper and lower slip surfaces. As an important landslide close to Shuibuya Dam on the Qing River, its stability is crucial, as the slide might reactivate because of a change in ground-water level caused by filling of the Shuibuya Reservoir. Existing weakness zones, growth of ruptures, the downslope attitude of geologic strata, and water infiltration, which reduced the strength of rocks and soils, have been found to be the most important factors contributing to the Yudonghe landslide. With regard to the landslide processes, it can be noted that the original large-scale slide activity was due to erosion by the Qing River, the second sliding resulted from the fall of blocks from the head scarp, and the final activity was the growth of the eastern and western secondary slides. A base failure was the main type of slope movement, however, it was obvious that more than one sliding event occurred, as inferred from striations and fractures detected by microstructure analysis of soils along the failure surfaces. Slope instability was evaluated by the method of Back Propagation Neural Networks (BPNN), in which a four-layer BPNN model with five input nodes, two hidden layers, and two output nodes was constructed using a training data set of landslide samples throughout the Qing River area. The predicted results of this analysis showed that the factor of safety was 1.10, which indicates that the Yudonghe landslide is currently in a marginally stable condition.