矿区控制网布网方案的选择

用计算机模拟法,讨论了控制网不同观测量的平差结果,提出了选择矿区控制网方案的原则.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.     

Automatic classification of volcanic earthquakes by using Multi-Layered neural networks

The application of neural networks as classifiers of seismic events is described with the aim of developing an automatic system for the classification of explosion quakes at the Stromboli volcano. The architecture of the network that we trained to identify four different classes of shocks was a Multi-Layer Perceptron, using the Back Error Propagation algorithm. Five different approaches for representing the information embedded in the seismograms, both in the time and in the frequency domain, were considered, and the results compared. The direct use of the time series of the shocks was not satisfactory. The auto-correlation function worked well, but in some cases it was misleading. A better performance was obtained with a frequency domain representation. Finally, the use of the envelope function did not work well. Combining parameters such as the auto-correlation and envelope functions can improve one source of error, but it may introduce new ones. The performance obtained highlights the importance of the data attributes used for the training of the network. Topologies with eight neurons in a single hidden layer gave, on average, the best results among the considered neural network structures. The overall results provide a large number of events (89% with the best performance) correctly classified, indicating that this automatic technique is reliable, and encouraging further applications in the field of volcanic seismology.     

基于网络药理学与分子对接技术探讨青蛇方外用治疗湿疹的作用机制

目的：基于网络药理学与分子对接技术探讨青蛇方外用治疗湿疹的作用机制。方法：应用TCMSP和UniProt数据库平台查询青蛇方组方的有效活性成分及靶基因,与疾病靶基因取交集后构建蛋白质-蛋白质相互作用(PPI)网络;应用DAVID数据库,对潜在核心作用靶点基因进行基因本体（GO）功能、京都基因与基因组百科全书（KEGG）通路富集分析。利用分子对接原理论证青蛇方生物活性成分与核心靶点的分子结合能力。结果：筛选出青蛇方有效成分63种,作用靶点99个;主要有β-谷甾醇、2,4,7-三羟基-9,10-二氢菲、靛蓝素、豆甾醇、色氨酸等关键成分;转录因子AP-1（JUN）、肿瘤坏死因子（TNF）、原癌基因（RELA）、干扰素γ（IFNG）、转化生长因子-β1（TGF-β1）等核心基因。关键信号通路有癌症信号通路、恰加斯病通路、弓形体病通路、脂质和动脉粥样硬化通路等,分子对接结果显示活性成分与核心靶点基因结合能力较强。结论：青蛇方外用可以通过多种活性成分、多种通路有效抑制免疫炎症反应,促进免疫系统正常运行,从而达到治疗湿疹的目的。     

Hyper Redundancy for Accuracy Enhancement in Automated Close Range Photogrammetry

A generic network design in close range photogrammetry is one where optimal multi-ray intersection geometry is obtained with as few camera stations as practicable. Hyper redundancy is a concept whereby, once the generic network is in place, many additional images are recorded, with the beneficial impact upon object point precision being equivalent to the presence of multiple exposures at each camera position within the generic network. The effective number of images per station within a hyper redundant network might well be in the range of 10 to 20 or more. As is apparent when it is considered that a hyper redundant network may comprise hundreds of images, the concept is only applicable in practice to fully automatic vision metrology systems, where it proves to be a very effective means of enhancing measurement accuracy at the cost of minimal additional work in the image recording phase. This paper briefly reviews the network design and accuracy aspects of hyper redundancy and illustrates the technique by way of the photogrammetric measurement of surface deformation of a radio telescope of 26 m diameter. This project required an object point measurement accuracy of σ  = 0·065 mm, or 1/400 000 of the diameter of the reflector.     

对江西农经网如何更好地提供信息服务的探讨

分析了江西农村经济信息网(www.jxagriec.gov.cn)的现状,指出农经网信息服务正面临着挑战,并在此基础上阐述了农经网信息服务的发展思路。     

广西新一代"区-地-县"三级气象信息网络系统

介绍广西新一代“区 -地 -县”三级气象信息网络系统在拓扑结构、业务软件和运行管理等方面的基本情况 ,总结了建设、运行管理过程中成功经验以及存在的问题 ,指出 :充分利用现有通讯网络资源 ,强化业务软件的开发应用 ,提高网络传输监控管理水平 ,是经济欠发达地区扩展气象信息网络业务能力的重要途径     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

Fuzzy neural network models for liquefaction prediction

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.