如何加强数字化区域前兆网络的安全性

为保证散字化区域前兆网络的正常运行，从网络系统设置以及网络服务两个方面详述了如何加强数字他区域前兆网路安全性的问题，给出了前兆网络安全防范的若干措施，提出了增强网络安全性的几点建议。     

遗传神经网络测井解释技术在宝北区块的应用初探

将遗传算法与人工神经网络(BP网络)的原理结合,对BP网络进行了改进,提高了收敛速度,并防止陷入局部极小。应用改进后的BP算法,建立了针对测井解释的神经网络模型,并应用此模型定量计算了宝北区块的多口井的渗透率值,其解释结果及精度均令人满意。     

关于知识经济与网络的理论思考

２１世纪是知识经济的世纪。知识经济的到来有其历史的必然性。它的本质特征是劳动资料取得了网络的形式,从而与机器经济相区分。知识经济的产业结构的核心是信息产业,而信息产业的核心是网络技术。网络已经深入到社会生活的方方面面。强化人们的信息观念与网络观念,加快信息网络化的建设,将对我国经济发展具有重大意义     

谈信息工作在旅游业中的作用

本文阐述了信息对发展旅游业的重要性，并着重对信息工作在旅游业中的作用问题进行探讨．同时介绍了我国目前旅游预订网络系统的发展概况，提出完善网络系统的建议及其队伍的建设。     

Intelligent determination of peak ground motion parameters based on intensity assessed by artificial neural network

Artificial Neural Network (ANN) model of computation based on mathematical model of neural processes is applied to establish an intelligent computing network from seismic intensity to peak ground parameter instead of the conventional statistical relationship in this paper. For a give seismic intensity rating, the network formed with actual strong ground motion records directly produces the corresponding peak ground parameters and the effects of earthquake magnitude and epicentral distance are included. The computed results of the network trained with a number of strong motion records in the West America show that such networks have obtained good conversion relationship from seismic intensity to peak ground parameters. The Chinese version of this paper appeared in the Chinese edition ofActa Seismologica Sinica,15, 208–216, 1993.     

“华北联网”项目中利用短波电台进行数据传送

文中介绍了1991年10月—1992年10月北京联网中心与参加华北联网的各台网中心之间进行的一系列短波数据传输试验和系统试运行的情况。     

北京遥测地震台网中心监测台

为适应华北联网后的北京遥测地震台网中心监测工作的扩展，研制了ＢＪＣ－２型北京遥测台网中心监测台，本监测台可溶纳７５路地震信息，可对台网的运行情况一目了然。维护台网的同时可方便与外台维修人员对话，设有故障告警，地震造警及定时告警，本监测台具有１８项功能，为地震监测值班人员提供了监控和检测的方便条件。     

岩石物理岩石孔喉结构特征对核磁T2谱影响的数值模拟

We built a three-dimensional irregular network model which can adequately describe reservoir rock pore-throat structures. We carried out numerical simulations to study the NMR T2 distribution of water-saturated rocks. The results indicate that there is a good correlation between T2 distribution and the pore radius frequency histogram. The total T2 distribution can be partitioned into pore body and pore throat parts. The effect of parameters including throat radius, pore-throat ratio, and coordination number of the micro- pore structure on the T2 distribution can be evaluated individually. The result indicates that： 1 ） with the increase of the pore throat radius, the T2 distribution moves toward longer relaxation times and its peak intensity increases; 2） with the increase of the pore-throat ratio, the T2 distribution moves towards longer T2 with the peak intensity increasing and the overlap between pore body T2 and pore throat T2 decreasing; 3） With the increase of connectivity, the short T2 component increases and peak signal intensity decreases slightly.     

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area,India

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function (RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area (India). The proposed approach is an integration of the Radial Basis Function (RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters (slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall, distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression (LR), Multi-layer Perceptron Neural Networks (MLP Neural Nets), Naïve Bayes (NB), and the hybrid model of Rotation Forest and Decision Trees (RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.     

Predicting the ecological status of rivers and streams under different climatic and socioeconomic scenarios using Bayesian Belief Networks

Freshwater systems have increasingly been subjected to a multitude of human pressures and the re-establishment of their ecological integrity is currently a major worldwide challenge. Expected future climate and socioeconomic changes will most probably further exacerbate such challenges. Modelling techniques may provide useful tools to help facing these demands, but their use is still limited within ecological quality assessment of water resources due to its technical complexity.We developed a Bayesian Belief Network (BBN) framework for modelling the ecological quality of rivers and streams in two European river basins located in two distinct European climatic regions: the Odense Fjord basin (Denmark) and the Sorraia basin (Portugal). This method enabled us to integrate different data sources into a single framework to model the effect of multiple stressors on several biological indicators of river water quality and, subsequently, on their ecological status. The BBN provided a simple interactive user interface with which we simulated combined climate and socioeconomic changes scenarios to assess their impacts on river ecological status.According to the resulting BBNs the scenarios demonstrated small impacts of climate and socioeconomic changes on the biological quality elements analysed. This yield a final ecological status similar to the baseline in the Odense case, and slightly worse in Sorraia. Since the present situation already depicts a high percentage of rivers and streams with moderate or worse ecological status in both basins, this means that many of them would not fulfil the Water Framework Directive target in the future. Results also showed that macrophytes and fish indices were mainly responsible for a non-desirable overall ecological status in Odense and Sorraia, respectively. The approach followed in this study is novel, since BBN modelling is used for the first time for assessing the ecological status of rivers and streams under future scenarios, using an ensemble of biological quality elements. An important advantage of this tool is that it may easily be updated with new knowledge on the nature of relationships already established in the BBN or even by introducing new causal links. By encompassing two case studies of very different characteristics, these BBN may be more easily adapted as decision-making tools for water management of other river basins.